{"meta":{"query_hash":"6c797537d611","filters":{"topic":"Spectroscopy and Chemometric Analyses"},"cohort_total":1277,"direct_labels_cover":1,"predictions_cover":1277,"exported":1277,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/6c797537d611","api":"https://metacan.xera.ac/api/v1/cohort?topic=Spectroscopy+and+Chemometric+Analyses"},"results":[{"id":"W110639278","doi":"","title":"Applying new spectral unmixing technique to Martian hyperspectral data","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"","keywords":"Hyperspectral imaging; Pixel; Remote sensing; Mars Exploration Program; Martian surface; Martian; Image resolution; Computer science; Environmental science; Artificial intelligence; Geology; Astrobiology; Physics","score_opus":0.028428519412090143,"score_gpt":0.2775654254698046,"score_spread":0.24913690605771446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W110639278","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05857988,0.0019816686,0.6397221,0.01773564,0.00037201107,0.001340301,0.00043847758,0.0014227421,0.27840716],"genre_scores_gemma":[0.3863883,0.00034599434,0.5762416,0.00026801878,0.0003791061,0.0003839872,0.0021282497,0.00018651277,0.033678245],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99588597,0.00051947526,0.0006661102,0.001628101,0.0005782183,0.00072215014],"domain_scores_gemma":[0.9917094,0.0006838996,0.00046925634,0.005946697,0.0006372636,0.0005534698],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026659793,0.00057003344,0.00064045354,0.00040554264,0.00037806126,0.0006442182,0.0043084016,0.0006874782,0.0024614956],"category_scores_gemma":[0.0020268417,0.00064312684,0.0002558059,0.0008534013,0.00016468398,0.00023220606,0.0040118187,0.002194896,0.00009768482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041900104,0.00088158774,0.0025438257,0.0005353393,0.00048709125,0.00003505051,0.003958328,0.00005531754,0.91662073,0.025409846,0.01076811,0.038662888],"study_design_scores_gemma":[0.00030665164,2.819155e-7,0.00016908262,0.0007328084,0.00023212568,0.000030375872,0.00021623203,0.002010517,0.9607876,0.0041591986,0.030540276,0.0008148778],"about_ca_topic_score_codex":0.0035392782,"about_ca_topic_score_gemma":0.0023058506,"teacher_disagreement_score":0.3278084,"about_ca_system_score_codex":0.00026268858,"about_ca_system_score_gemma":0.00061191997,"threshold_uncertainty_score":0.999602},"labels":[],"label_agreement":null},{"id":"W111433962","doi":"10.1007/978-0-387-25789-1_4","title":"Integrated Methodology for Characterization of Petroleum Samples and Its Application for Refinery Product Quality Modeling","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Devon Energy (Canada)","funders":"","keywords":"Refinery; Characterization (materials science); Oil refinery; Quality (philosophy); Product (mathematics); Process engineering; Petroleum engineering; Environmental science; Engineering; Materials science; Waste management; Nanotechnology; Mathematics","score_opus":0.26772818227263573,"score_gpt":0.39242176746541285,"score_spread":0.12469358519277712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W111433962","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011710717,0.000763584,0.96270096,0.000102284204,0.00003815802,0.00046183035,0.0007298311,0.000088719295,0.023403918],"genre_scores_gemma":[0.20333074,0.0018494325,0.13390973,0.0002644021,0.0015363378,0.00068460085,0.021849995,0.000329259,0.6362455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846756,0.000009319823,0.00068151834,0.0005382418,0.00012125543,0.00018212976],"domain_scores_gemma":[0.9982209,0.00043983484,0.0005424028,0.00027871426,0.00047460975,0.00004351316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006585038,0.0002701962,0.00070382596,0.00024139394,0.000055597855,0.000009468611,0.00012840828,0.0004002628,0.00015672769],"category_scores_gemma":[0.0004121687,0.00024515725,0.00015481064,0.00005760998,0.000037481674,0.000051944284,0.000029183786,0.00015401405,7.3710163e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000300178,0.000025437685,0.000017094639,0.0011869915,0.00022647016,3.92807e-8,0.000020966225,0.000013064812,0.89877754,0.09351535,0.000013389341,0.005903468],"study_design_scores_gemma":[0.0005585752,0.000056114863,0.0000072710304,0.000058643163,0.0006877342,0.0000029938271,0.00006097754,0.013350233,0.9574224,0.011247193,0.016079368,0.0004685143],"about_ca_topic_score_codex":0.00004311809,"about_ca_topic_score_gemma":0.000020888456,"teacher_disagreement_score":0.82879126,"about_ca_system_score_codex":0.00006321818,"about_ca_system_score_gemma":0.00006054322,"threshold_uncertainty_score":0.9997223},"labels":[],"label_agreement":null},{"id":"W1160274881","doi":"10.1117/12.2197139","title":"A new, fast and accurate spectrophotometric method for the determination of the optical constants of arbitrary absorptance thin films from a single transmittance curve: application to dielectric materials","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Transmittance; Refractive index; Materials science; Dielectric; Absorptance; Optics; Thin film; Absorption (acoustics); Range (aeronautics); Optoelectronics; Reflectivity; Physics; Nanotechnology","score_opus":0.015327428669071021,"score_gpt":0.2646244028608496,"score_spread":0.24929697419177857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1160274881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97694844,0.00041823753,0.020326814,0.0008634359,0.000074157455,0.000565828,0.00019258755,0.00002664873,0.0005838581],"genre_scores_gemma":[0.7095632,0.00008269079,0.28986907,0.000050852825,0.00015604345,0.00014693847,0.0000060303178,0.00003431182,0.000090876834],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805784,5.962925e-8,0.000712283,0.00035136662,0.0005893237,0.0002891351],"domain_scores_gemma":[0.9975838,0.0005996273,0.0005891519,0.00009626694,0.0010245789,0.00010659425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057615,0.0002511705,0.0004845277,0.000119622455,0.000062239225,0.00006648779,0.0008744744,0.00016595282,0.000016492582],"category_scores_gemma":[0.0012150522,0.00016999914,0.00032353197,0.0010071972,0.00016726687,0.00023339418,0.00010516482,0.0001959038,2.2350395e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024917247,0.00009876771,0.0001602912,0.00030894775,0.0002684373,1.968669e-8,0.00026561925,0.000034904333,0.94834614,0.048959512,0.00027528589,0.0010329238],"study_design_scores_gemma":[0.00079999794,0.00016348452,0.00043097715,0.00011823358,0.00033075208,0.000005265577,0.00077180815,0.026282223,0.9680253,0.0027762707,0.0001255039,0.00017017199],"about_ca_topic_score_codex":0.000048679038,"about_ca_topic_score_gemma":5.7765004e-7,"teacher_disagreement_score":0.26954225,"about_ca_system_score_codex":0.00011802495,"about_ca_system_score_gemma":0.00006109026,"threshold_uncertainty_score":0.6932364},"labels":[],"label_agreement":null},{"id":"W116711641","doi":"","title":"Data Evaluation in Environmental Research","year":2002,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Principal component analysis; Presentation (obstetrics); Computer science; Principal (computer security); Component (thermodynamics); Data science; Data mining; Information retrieval; Management science; Engineering; Artificial intelligence","score_opus":0.11504181653170638,"score_gpt":0.37955821688796687,"score_spread":0.2645164003562605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W116711641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9406441,0.031119341,0.00013060145,0.00061811187,0.000037168422,0.000066631845,0.000017631917,0.000018027507,0.02734838],"genre_scores_gemma":[0.9844611,0.010213497,0.00002493609,0.000015148583,0.00024445847,0.0000050238277,0.000040088064,0.000015503909,0.004980186],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99674094,0.00009126777,0.00024727656,0.0002925981,0.0008287145,0.0017992038],"domain_scores_gemma":[0.999282,0.00008595273,0.00006911014,0.0004760866,0.000024717749,0.00006213315],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038593358,0.00010195677,0.00013140065,0.0002759295,0.00017224142,0.000048681064,0.00071547163,0.000088846515,0.009687363],"category_scores_gemma":[0.00023310872,0.00009696721,0.000038092825,0.00045433454,0.000064582,0.0002563118,0.00012628567,0.0026255955,0.00019553155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025030065,0.0045835255,0.15990433,0.00006475675,0.0015754158,0.0000941177,0.0013752065,0.0002637745,0.19821955,0.016183136,0.012012577,0.6054733],"study_design_scores_gemma":[0.016250225,0.0010473343,0.007408075,0.00014922261,0.0013036749,0.0038171466,0.064674586,0.12596376,0.06722176,0.65103155,0.058437046,0.0026956159],"about_ca_topic_score_codex":0.000033660017,"about_ca_topic_score_gemma":0.00030935928,"teacher_disagreement_score":0.6348484,"about_ca_system_score_codex":0.0023976967,"about_ca_system_score_gemma":0.00032724836,"threshold_uncertainty_score":0.9996754},"labels":[],"label_agreement":null},{"id":"W1205568350","doi":"10.1021/acs.jafc.5b02738","title":"Fourier Transform Infrared (FTIR) Spectroscopy as a Utilitarian Tool for the Routine Determination of Acidity in Ester-Based Oils","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Fourier transform infrared spectroscopy; Chemistry; Oleic acid; Infrared; Acid value; Infrared spectroscopy; Analytical Chemistry (journal); Chromatography; Organic chemistry; Biochemistry","score_opus":0.02007171771447789,"score_gpt":0.26106274277812286,"score_spread":0.24099102506364498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1205568350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962507,0.000780345,0.00041255724,0.00076022965,0.000035190867,0.00007176273,0.000032573138,0.0000076241217,0.0016490355],"genre_scores_gemma":[0.9972404,0.000044516077,0.0019116466,0.000027045016,0.0002234134,0.000009488139,0.000015021335,0.0000068909408,0.00052157836],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878573,0.000008159452,0.00055640814,0.00014196338,0.000298503,0.00020925511],"domain_scores_gemma":[0.9989116,0.00017474755,0.000400379,0.00012520648,0.00027253598,0.00011553934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477734,0.00018527496,0.0003665141,0.00003730468,0.000055667606,0.000040176936,0.00022832528,0.00015437015,0.00007964533],"category_scores_gemma":[0.00026918386,0.0001024358,0.00020624342,0.00025457857,0.00007488759,0.0001787271,0.000017867653,0.0002740464,2.4273987e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005050352,0.00030581577,0.0023223355,0.0004067157,0.00016073328,0.000004890676,0.0005173533,0.000018224875,0.9938977,0.000011277416,0.00046009803,0.0013898724],"study_design_scores_gemma":[0.0023353328,0.00029769004,0.0024478037,0.00006967756,0.00023615081,0.000102220445,0.0017620401,0.0001045877,0.9917887,0.00048801315,0.00021447892,0.00015332244],"about_ca_topic_score_codex":0.000010526061,"about_ca_topic_score_gemma":0.0000068322825,"teacher_disagreement_score":0.0021089655,"about_ca_system_score_codex":0.000117680676,"about_ca_system_score_gemma":0.00011085727,"threshold_uncertainty_score":0.41772106},"labels":[],"label_agreement":null},{"id":"W146732526","doi":"","title":"Utilization of Near Infrared Reflectance Spectroscopy for the Evaluation and Characterization of Barley in Western Canada","year":2014,"lang":"en","type":"article","venue":"SHAREOK (University of Oklahoma)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Oklahoma State University","keywords":"Near infrared reflectance spectroscopy; Reflectivity; Characterization (materials science); Remote sensing; Infrared; Infrared spectroscopy; Spectroscopy; Environmental science; Diffuse reflectance infrared fourier transform; Near-infrared spectroscopy; Materials science; Geography; Optics; Chemistry; Nanotechnology; Physics; Astronomy","score_opus":0.0302658455620561,"score_gpt":0.2700348028614587,"score_spread":0.2397689572994026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W146732526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954653,0.000104332576,0.0030290033,0.000108162094,0.000016525311,0.0001131042,0.00008831132,0.000005112547,0.0010701637],"genre_scores_gemma":[0.9992651,0.000061008646,0.0002918601,0.000010067193,0.000010777568,7.4126484e-7,0.00010862105,0.0000050900326,0.00024674134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942654,0.000018617056,0.00013145467,0.00013342763,0.0002017627,0.00008818241],"domain_scores_gemma":[0.99927866,0.000099955345,0.00025524423,0.00016932028,0.00017615355,0.000020672274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015518825,0.000066821565,0.0001865007,0.00006334776,0.000065995475,0.0000054917746,0.0001483187,0.00005130586,0.00028945075],"category_scores_gemma":[0.0001004987,0.00007110056,0.000030323796,0.00029962393,0.000071723865,0.000108376335,0.000029262432,0.000045113586,1.4052566e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027705915,0.00007679542,0.10946371,0.0005256225,0.0000980589,4.4090916e-7,0.001246109,0.00008077346,0.8834739,0.00029906945,0.00018683053,0.004271675],"study_design_scores_gemma":[0.0018948658,0.00009604808,0.37793675,0.00012824664,0.00031391397,0.0000010054508,0.0015257965,0.047417328,0.5690059,0.00034610063,0.00115137,0.00018266535],"about_ca_topic_score_codex":0.011136453,"about_ca_topic_score_gemma":0.048131123,"teacher_disagreement_score":0.31446797,"about_ca_system_score_codex":0.00008388964,"about_ca_system_score_gemma":0.00016584463,"threshold_uncertainty_score":0.99544847},"labels":[],"label_agreement":null},{"id":"W1483360850","doi":"10.1533/9780857090973.5.401","title":"Spectroscopic techniques for fungi and mycotoxins detection","year":2011,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mycotoxin; Biology; Biotechnology","score_opus":0.01854224370872129,"score_gpt":0.2543679948012419,"score_spread":0.2358257510925206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483360850","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012570982,0.00503286,0.0002556894,0.0000074125214,0.00007505935,0.00031935953,0.000055321154,0.00026637682,0.9938622],"genre_scores_gemma":[0.007687293,0.0009902605,0.0010890756,0.00008658463,0.0005903721,0.00015438405,0.000027646349,0.00012965225,0.98924476],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849164,0.0000036347258,0.0003867287,0.00060192303,0.00017390678,0.00034217318],"domain_scores_gemma":[0.9989412,0.00007090376,0.0002887289,0.00049215724,0.000082325256,0.00012468405],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000091618225,0.0004958998,0.00063004275,0.0002603067,0.00015465879,0.00005062323,0.00022159,0.00064787775,0.0016249606],"category_scores_gemma":[0.000021566322,0.00048583548,0.000275692,0.000020200961,0.00014419011,0.000039711013,0.00008560519,0.00045706792,0.000035830817],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003911186,0.000011583871,0.000010096576,0.00042268055,0.00026405137,0.000005198748,0.000054217086,2.1606437e-9,0.04919048,0.0028203863,0.00006370984,0.94711846],"study_design_scores_gemma":[0.00013609849,0.00007902205,0.0000019116662,0.00010332712,0.00049895846,0.000013027635,0.0000055952432,7.846282e-7,0.44939786,0.019923853,0.52948546,0.00035408206],"about_ca_topic_score_codex":0.0000016515781,"about_ca_topic_score_gemma":0.000021359214,"teacher_disagreement_score":0.9467644,"about_ca_system_score_codex":0.00014541246,"about_ca_system_score_gemma":0.000051685212,"threshold_uncertainty_score":0.9997593},"labels":[],"label_agreement":null},{"id":"W1490817772","doi":"10.1007/978-3-642-13681-8_69","title":"Classification of High-Resolution NMR Spectra Based on Complex Wavelet Domain Feature Selection and Kernel-Induced Random Forest","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Random forest; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Wavelet; Feature selection; Kernel (algebra); Wavelet transform; Discrete wavelet transform; Computer science; Mathematics; Nuclear magnetic resonance; Physics","score_opus":0.018991798550626476,"score_gpt":0.2548564452438629,"score_spread":0.23586464669323645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490817772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06209857,0.0001045274,0.9325883,0.00075004884,0.0002273369,0.00026640296,0.000030004376,0.00009273441,0.0038421245],"genre_scores_gemma":[0.9408863,0.000015435133,0.05824392,0.00016690757,0.00041720783,0.0000070237506,0.0000733302,0.000031837404,0.00015803282],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997614,0.000018230312,0.00037735808,0.0009426442,0.00067506614,0.0003726552],"domain_scores_gemma":[0.9982855,0.0004396651,0.00042887984,0.0005600426,0.00017930094,0.00010664567],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034880496,0.00043169298,0.00059349096,0.0007081997,0.00022570392,0.0001115505,0.0005116151,0.00067971204,0.00019532331],"category_scores_gemma":[0.000119480086,0.00038681977,0.00011934692,0.0005791099,0.00046881018,0.00010234859,0.00008366201,0.0011663032,0.0000040001596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005473525,0.00021672585,0.0013513672,0.0003610609,0.000087107015,0.00001750473,0.0002444829,0.011360071,0.9461817,0.007279391,0.00007037868,0.032282826],"study_design_scores_gemma":[0.0033448315,0.00041036983,0.009873938,0.00040539665,0.00018067232,0.000055989258,0.0000019993156,0.54008406,0.39676875,0.04722807,0.00052081817,0.0011250876],"about_ca_topic_score_codex":0.000072350515,"about_ca_topic_score_gemma":0.00035058951,"teacher_disagreement_score":0.87878776,"about_ca_system_score_codex":0.00029529855,"about_ca_system_score_gemma":0.00020531255,"threshold_uncertainty_score":0.9998584},"labels":[],"label_agreement":null},{"id":"W1493072409","doi":"10.1111/1467-9868.00372","title":"Adaptive Varying-Coefficient Linear Models","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; University of North Carolina at Charlotte; National Science Foundation","keywords":"Akaike information criterion; Mathematics; Linear model; Multivariate statistics; Nonparametric regression; Smoothing; Univariate; Nonparametric statistics; Mathematical optimization; Information Criteria; Applied mathematics; Computer science; Model selection; Statistics","score_opus":0.0774691872717972,"score_gpt":0.3342223353056426,"score_spread":0.2567531480338454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493072409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001936663,0.0005194626,0.9895844,0.00040559412,0.00040533338,0.00007357308,0.00029017398,0.000030974425,0.0067538107],"genre_scores_gemma":[0.1849965,0.000064889966,0.81264263,0.00049504935,0.0002290686,0.0000061861288,0.0000059278277,0.000043228196,0.0015165369],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964255,0.0006829272,0.0010515016,0.00038516326,0.00076208205,0.0006928113],"domain_scores_gemma":[0.992357,0.0059269313,0.0005800095,0.0003636123,0.00038037033,0.00039206413],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016036954,0.0003792013,0.0009592989,0.000043509703,0.00039722733,0.00005580895,0.00060233974,0.00033039367,0.003978982],"category_scores_gemma":[0.0084602265,0.00025083288,0.0005129578,0.00046848785,0.0010523754,0.00011860054,0.00014875435,0.0013175537,0.000015990738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012610288,0.0009902318,0.00059565355,0.00030124057,0.0021451195,0.00017413955,0.0010879514,0.03302195,0.0035491486,0.92384785,0.02966502,0.0033606861],"study_design_scores_gemma":[0.0040273727,0.0016143712,0.00082548236,0.0001442881,0.004745329,0.0008538419,0.004641177,0.1376076,0.06305139,0.7644962,0.016289506,0.0017034131],"about_ca_topic_score_codex":0.00003423776,"about_ca_topic_score_gemma":0.000001932948,"teacher_disagreement_score":0.18305983,"about_ca_system_score_codex":0.0002704841,"about_ca_system_score_gemma":0.0002579638,"threshold_uncertainty_score":0.9999944},"labels":[],"label_agreement":null},{"id":"W1493909250","doi":"10.1006/bioe.2002.0123","title":"AE—Automation and Emerging Technologies","year":2002,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Airflow; Artificial neural network; Approximation error; Backpropagation; Air temperature; Volumetric flow rate; Water content; Energy consumption; Moisture; Process engineering; Environmental science; Biological system; Simulation; Materials science; Mathematics; Meteorology; Engineering; Computer science; Statistics; Machine learning; Composite material; Mechanical engineering; Geotechnical engineering; Mechanics","score_opus":0.009634405340607806,"score_gpt":0.2024086528939183,"score_spread":0.1927742475533105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493909250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97859335,0.011108887,0.0016269592,0.00017240748,0.000106955944,0.000043354994,0.0000045784113,0.0019897767,0.0063537024],"genre_scores_gemma":[0.99891263,0.00014149617,0.00042690363,0.00000208351,0.000055115583,0.00000953919,0.0000014116592,0.000013094167,0.00043770255],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949,0.0000011101602,0.00013316747,0.00014026687,0.00008202306,0.00015340366],"domain_scores_gemma":[0.99976313,0.000024286446,0.000035835063,0.00014205209,0.000011775208,0.000022935192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031544798,0.00010151523,0.00012944671,0.00014487401,0.00004015185,0.000032841763,0.000077318175,0.000088388864,0.00012533632],"category_scores_gemma":[0.00007008715,0.000097661534,0.000025750644,0.00027031935,0.00000957792,0.00007422981,0.000027960394,0.00007944718,0.000016079422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.1308637e-7,0.000019010933,0.003202442,0.0006420243,0.00008118441,0.000008504385,0.00018187914,0.00018189766,0.9900555,0.000880632,0.0004721796,0.0042740605],"study_design_scores_gemma":[0.00022064835,0.0000096194035,0.00016490003,0.00010320488,0.000056663037,0.000051972238,0.00085517607,0.0894578,0.89675146,0.000008505933,0.011989446,0.00033059684],"about_ca_topic_score_codex":0.000008304785,"about_ca_topic_score_gemma":3.0470218e-7,"teacher_disagreement_score":0.09330401,"about_ca_system_score_codex":0.00003814929,"about_ca_system_score_gemma":9.976956e-7,"threshold_uncertainty_score":0.3982522},"labels":[],"label_agreement":null},{"id":"W149700751","doi":"","title":"Pairwise Rayleigh quotient classifier with application to the analysis of breast tumors","year":2007,"lang":"en","type":"article","venue":"International Conference on Signal Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Pairwise comparison; Rayleigh quotient; Linear discriminant analysis; Quadratic classifier; Linear classifier; Feature vector; Computer science; Mathematics; Binary classification; Quotient; Support vector machine; Eigenvalues and eigenvectors","score_opus":0.025800800206567647,"score_gpt":0.3074879482745245,"score_spread":0.28168714806795686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W149700751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73427373,0.00005541429,0.19094647,0.0016084486,0.000026323314,0.00011463002,0.0000660019,0.00007012518,0.07283883],"genre_scores_gemma":[0.9987638,0.0000018918072,0.00025305597,0.00016604307,0.000113267786,0.000016877068,0.000044908287,0.000010153934,0.00062999036],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859726,0.000007532475,0.00031501544,0.00029669586,0.0006031446,0.0001803394],"domain_scores_gemma":[0.9989417,0.000079373225,0.00026243398,0.00017803273,0.00045944637,0.00007899579],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022202483,0.00014354293,0.00020948597,0.0004020759,0.000091522634,0.00007383791,0.000419676,0.000050276536,0.0010401303],"category_scores_gemma":[0.00001927149,0.0000952918,0.00008530181,0.0012244411,0.00008169374,0.00009006906,0.00003518399,0.00016322015,0.000013280711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00296294,0.0014032312,0.12102096,0.00017885723,0.0044503473,0.0000314398,0.0025709716,0.012714078,0.5756171,0.024770528,0.00035288083,0.25392666],"study_design_scores_gemma":[0.0011650091,0.00022535131,0.08910854,0.00043979494,0.0025751109,0.00004577454,0.008006132,0.1308025,0.763539,0.0010154692,0.0020552748,0.0010220191],"about_ca_topic_score_codex":0.00006531577,"about_ca_topic_score_gemma":0.000065440094,"teacher_disagreement_score":0.26449007,"about_ca_system_score_codex":0.000112635236,"about_ca_system_score_gemma":0.00008922374,"threshold_uncertainty_score":0.99987304},"labels":[],"label_agreement":null},{"id":"W1499360294","doi":"10.1002/0471220450.ch7","title":"Industrial Applications and Processes","year":2002,"lang":"en","type":"other","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Queen's University","funders":"","keywords":"Flexibility (engineering); Homogeneous; Process engineering; Industrial production; Scale (ratio); Biochemical engineering; Process (computing); Production (economics); Viscosity; Computer science; Manufacturing engineering; Systems engineering; Industrial engineering; Materials science; Engineering; Thermodynamics; Physics","score_opus":0.03227150169522243,"score_gpt":0.2710439501653724,"score_spread":0.23877244847015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499360294","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000034940206,0.004594752,0.00022707881,0.00003850519,0.0000073846422,0.00004803756,0.00003393907,0.00023008072,0.9948167],"genre_scores_gemma":[0.0003178748,0.0012695108,0.0002428519,0.00002902483,0.00066817936,0.0000630693,0.000032130247,0.00012469233,0.99725264],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995076,0.0000010625592,0.00008268312,0.00021881817,0.000085096486,0.00010472534],"domain_scores_gemma":[0.99966115,0.00002695345,0.00007355136,0.00017748661,0.000012205896,0.000048642847],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000007534491,0.00013438071,0.00017614971,0.00011765308,0.000024316903,0.00002526529,0.00010741972,0.0003313077,0.10874058],"category_scores_gemma":[0.000028380793,0.0001141916,0.000020472131,0.00030180914,0.000045987494,0.000010514469,0.00002498858,0.00014033641,0.00013948602],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.9509474e-7,0.000040795334,0.00027422575,0.00022531798,0.0000777927,5.7051e-7,0.000003734702,1.8856163e-8,0.00006741358,0.00012210409,0.9970002,0.0021871263],"study_design_scores_gemma":[0.00013611442,0.000002309276,2.0422702e-7,0.000016689206,0.00013109359,0.0000024124267,0.00003133919,7.6514357e-7,0.0044595,0.000035022753,0.99504304,0.00014151381],"about_ca_topic_score_codex":0.00006910201,"about_ca_topic_score_gemma":0.000017264341,"teacher_disagreement_score":0.1086011,"about_ca_system_score_codex":0.00001150798,"about_ca_system_score_gemma":0.000023911964,"threshold_uncertainty_score":0.89207417},"labels":[],"label_agreement":null},{"id":"W1501808462","doi":"10.1109/icnnb.2005.1614966","title":"Modelling and Analysis of Salmonella Typhimurium Infections using Logistic Regression and Neural Network Models","year":2006,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada; University of Guelph","funders":"Public Health Agency","keywords":"Logistic regression; Artificial neural network; Statistics; Binomial regression; Regression analysis; Standard deviation; Negative binomial distribution; Salmonella; Computer science; Mathematics; Artificial intelligence; Biology","score_opus":0.05555417908130067,"score_gpt":0.2988534303371272,"score_spread":0.24329925125582652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501808462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8458983,0.00172561,0.14690349,0.000009307077,0.000010486859,0.000015882708,0.0000060263365,0.00003398885,0.005396931],"genre_scores_gemma":[0.9962072,0.0001368708,0.0030461098,0.000008621654,0.000063774205,0.0000010247477,0.000018306715,0.00000930827,0.0005088316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924994,0.000009246691,0.0002371727,0.00022639557,0.00010310393,0.00017415611],"domain_scores_gemma":[0.9995314,0.00010925883,0.00011063203,0.00015567744,0.000047825157,0.000045199893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056724926,0.00012565474,0.00031329523,0.00021980837,0.00011892602,0.000027384645,0.000043116277,0.00009334399,0.00015215293],"category_scores_gemma":[0.000008102999,0.00010402611,0.00007831648,0.0010284234,0.00008322816,0.00008201749,0.000045718563,0.000093363116,1.5640308e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008311979,0.000034147466,0.030729244,0.000024537714,0.0001977648,0.0000011619406,0.000010131197,0.9643161,0.0041359705,0.0004908724,0.000030316369,0.000021428794],"study_design_scores_gemma":[0.000105907195,0.000007313874,0.00021957532,0.000009978019,0.0019006181,0.0000043763125,0.000041104107,0.9903362,0.0042405715,0.003019347,0.0000032360072,0.000111815345],"about_ca_topic_score_codex":0.0014185106,"about_ca_topic_score_gemma":0.000030673644,"teacher_disagreement_score":0.15030888,"about_ca_system_score_codex":0.00002115148,"about_ca_system_score_gemma":0.000008613782,"threshold_uncertainty_score":0.42420617},"labels":[],"label_agreement":null},{"id":"W1502097978","doi":"10.1214/lnms/1215091950","title":"Tests for non-correlation of two multivariate time series: a nonparametric approach","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes-monograph series","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Nonparametric statistics; Multivariate statistics; Series (stratigraphy); Correlation; Statistics; Mathematics; Econometrics; Geology; Geometry","score_opus":0.015047465474297179,"score_gpt":0.2619217313087677,"score_spread":0.24687426583447056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502097978","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021541582,0.0102021545,0.2277561,0.0002752184,0.00056151167,0.0023664522,0.0016582551,0.0007612919,0.75426483],"genre_scores_gemma":[0.23204899,0.0020438442,0.33410832,0.00072256476,0.0019568142,0.00088048493,0.006659968,0.001568696,0.4200103],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99694484,0.000017430088,0.0009271795,0.0010080315,0.00048116117,0.00062135904],"domain_scores_gemma":[0.996769,0.00066721276,0.0010539072,0.00095983996,0.0003901534,0.0001598832],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018562427,0.0010550357,0.0015727814,0.0012904443,0.0002416193,0.00009340515,0.00048193408,0.0012284833,0.001147056],"category_scores_gemma":[0.0004407017,0.0009939328,0.00092801533,0.0011153372,0.00037120763,0.00027883545,0.00007184662,0.0008262347,0.000026286743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019591229,0.0064415094,0.020705655,0.04360064,0.06152694,0.00024183794,0.013258502,0.035871204,0.6085436,0.102862395,0.027133027,0.060223483],"study_design_scores_gemma":[0.009441735,0.0024197006,0.0002570401,0.0012596595,0.013384661,0.00040526484,0.00017446242,0.00405653,0.701088,0.16346152,0.09514572,0.008905674],"about_ca_topic_score_codex":0.000049459417,"about_ca_topic_score_gemma":0.000015199681,"teacher_disagreement_score":0.33425456,"about_ca_system_score_codex":0.0001253568,"about_ca_system_score_gemma":0.0001261627,"threshold_uncertainty_score":0.99976605},"labels":[],"label_agreement":null},{"id":"W1502399419","doi":"","title":"Minimum description length methods of medium-scale simultaneous inference","year":2010,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Minimum description length; Inference; Feature selection; Bayes' theorem; Computer science; Statistic; Scale (ratio); Parametric statistics; Feature (linguistics); Selection (genetic algorithm); Algorithm; Data mining; Mathematics; Pattern recognition (psychology); Artificial intelligence; Statistics; Bayesian probability","score_opus":0.022565634706350573,"score_gpt":0.34728207845412484,"score_spread":0.32471644374777425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502399419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8693564,0.00008674521,0.028734025,0.00008165123,0.00015111451,0.000028775132,0.0000074105574,0.00010977957,0.1014441],"genre_scores_gemma":[0.92459416,0.000028653296,0.067175545,0.000041748637,0.00009632651,0.0000044836415,0.000007983995,0.000011457329,0.008039648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991066,0.00001401846,0.00028227636,0.0002197906,0.0001770776,0.00020027],"domain_scores_gemma":[0.9986229,0.00069308246,0.00011489384,0.00035502115,0.00012424219,0.000089895104],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001724179,0.0001333944,0.00025271336,0.000095108444,0.00004278506,0.000019163223,0.00025703476,0.00019079733,0.009012377],"category_scores_gemma":[0.00094785774,0.00011203909,0.00008882508,0.00033403255,0.00013225854,0.0000842924,0.000051245966,0.00030166595,0.000026155689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012836949,0.00008922273,0.0038971533,0.000039346294,0.00003551065,0.0000015523087,0.00010789775,0.000006931356,0.9914784,0.00016094647,0.00017941024,0.0039907927],"study_design_scores_gemma":[0.0001795641,0.000020788544,0.00017355755,0.0000042293295,0.00009185079,0.0000058999467,0.00038330065,0.0018135922,0.9946896,0.000598661,0.0019046096,0.0001343358],"about_ca_topic_score_codex":0.00012944861,"about_ca_topic_score_gemma":0.000068414534,"teacher_disagreement_score":0.09340446,"about_ca_system_score_codex":0.000018138264,"about_ca_system_score_gemma":0.00005627332,"threshold_uncertainty_score":0.99189353},"labels":[],"label_agreement":null},{"id":"W1502876942","doi":"10.5772/26329","title":"Analytical Methods and Phytochemistry of the Typical Italian Liquor \"Limoncello\": A Review","year":2012,"lang":"en","type":"review","venue":"InTech eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mediterranean climate; Mediterranean area; Scale (ratio); Geography; Agricultural economics; Economy; Economics; Archaeology; Cartography","score_opus":0.08326565039682789,"score_gpt":0.4299934139336255,"score_spread":0.34672776353679763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502876942","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000071703607,0.9578646,0.00011495023,0.000026823267,0.000049269245,0.00018918482,0.00003694606,0.00004090234,0.04167017],"genre_scores_gemma":[0.00024611122,0.9887269,0.0010454997,0.00007293713,0.00020700092,0.00009009017,0.000011914362,0.00005293075,0.009546615],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978953,0.00010523271,0.00088825065,0.0004637059,0.00027215347,0.0003753653],"domain_scores_gemma":[0.99776965,0.00038374914,0.0005716314,0.0010201609,0.000080865175,0.00017395876],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041708138,0.00050525356,0.0022051944,0.000076400436,0.00005806046,0.000022160759,0.0007790051,0.0006073903,0.0010271947],"category_scores_gemma":[0.0004811143,0.00031092257,0.0010194072,0.0003246853,0.00045795122,0.000019505847,0.0004070025,0.0010444844,0.000014343048],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074733452,0.000079403995,0.000009900562,0.14567594,0.0008064792,0.000004150281,0.000018032359,9.597821e-10,0.0023423694,0.00012768018,0.0005121835,0.85041636],"study_design_scores_gemma":[0.00005456371,0.000009344028,1.7470317e-7,0.022626715,0.007204156,0.000096375596,0.000014142183,7.105229e-7,0.08845269,0.00006716777,0.881159,0.00031493182],"about_ca_topic_score_codex":0.0000059639965,"about_ca_topic_score_gemma":5.368393e-7,"teacher_disagreement_score":0.8806468,"about_ca_system_score_codex":0.000109694774,"about_ca_system_score_gemma":0.00018375306,"threshold_uncertainty_score":0.9999343},"labels":[],"label_agreement":null},{"id":"W1509842302","doi":"10.1002/0470027320.s8303","title":"Guidelines for the Development and Validation of Near‐Infrared Spectroscopic Methods in the Pharmaceutical Industry","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's Health Research Institute","funders":"","keywords":"Calibration; Computer science; Sample (material); Software; Data collection; Robustness (evolution); Repeatability; Data mining; Systems engineering; Reliability engineering; Engineering; Statistics","score_opus":0.10636195436020188,"score_gpt":0.4409858678831364,"score_spread":0.3346239135229345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509842302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016952164,0.03438302,0.77632576,0.0017997667,0.00023352094,0.0012700433,0.00018074298,0.000097927856,0.18401399],"genre_scores_gemma":[0.00026787832,0.0018336996,0.9691091,0.00036728778,0.0007469606,0.00025596484,0.00021111908,0.00018129688,0.02702665],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980423,0.00006117643,0.00079132634,0.0003618345,0.0004551852,0.00028820935],"domain_scores_gemma":[0.9981896,0.00075202103,0.0005289846,0.00035419693,0.00012614632,0.00004901253],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056686415,0.0003294163,0.00054566667,0.00019972998,0.00012088804,0.00005901379,0.00044387288,0.0004596428,0.006461336],"category_scores_gemma":[0.0003314969,0.00021645073,0.00012421372,0.0004134297,0.0002886003,0.00005132686,0.00005868853,0.00047594117,0.000002922053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024751143,0.00049424224,0.0073810173,0.0010325717,0.0010753105,0.0000033928204,0.00084965787,0.00007528104,0.83541983,0.0037095633,0.14728983,0.0024218124],"study_design_scores_gemma":[0.0007118799,0.000041498573,0.000097830896,0.00024267091,0.00026169786,0.0000049161126,0.000295463,0.00030403316,0.95367557,0.00077090116,0.04339324,0.00020033034],"about_ca_topic_score_codex":0.00006487442,"about_ca_topic_score_gemma":0.000031236956,"teacher_disagreement_score":0.19278339,"about_ca_system_score_codex":0.00007437722,"about_ca_system_score_gemma":0.00033443884,"threshold_uncertainty_score":0.9944469},"labels":[],"label_agreement":null},{"id":"W1516419552","doi":"10.1002/9781118488553.ch5","title":"Recent developments in infrared spectroscopy for the detection of food chemical hazards","year":2014,"lang":"en","type":"other","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Infrared spectroscopy; Infrared; Spectroscopy; Environmental science; Chemistry; Materials science; Environmental chemistry; Physics; Organic chemistry; Optics","score_opus":0.014548383147223239,"score_gpt":0.2688629366869807,"score_spread":0.25431455353975746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516419552","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008300169,0.003240681,0.00919034,0.000048644055,0.00015773455,0.00027151115,0.00004685717,0.00013069932,0.9860835],"genre_scores_gemma":[0.08450274,0.0035931808,0.0076280916,0.00019010839,0.0007729484,0.0003681527,0.0001484708,0.0006016165,0.9021947],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881834,0.000007047039,0.0003515891,0.0003188562,0.00023062527,0.0002735367],"domain_scores_gemma":[0.9991559,0.0001396766,0.00026107786,0.00036041753,0.000041826883,0.000041151852],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010543997,0.0002566416,0.00044407963,0.00022837764,0.00002417843,0.00001487307,0.00033442775,0.0004107608,0.005688705],"category_scores_gemma":[0.00018600756,0.00018682373,0.00012193792,0.00041754756,0.00006027436,0.000012843784,0.000054151573,0.00022598475,0.000015050061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026199516,0.00038798075,0.0006737734,0.001061064,0.0016071033,8.3691765e-7,0.00009420415,0.0000014948704,0.72086877,0.000384744,0.24612956,0.028528485],"study_design_scores_gemma":[0.00042873822,0.000021083602,0.00001198216,0.000037823273,0.0000867928,7.821768e-7,0.000036121895,0.00002392163,0.65746486,0.0001231184,0.34161425,0.00015050604],"about_ca_topic_score_codex":0.00005040616,"about_ca_topic_score_gemma":0.00018620542,"teacher_disagreement_score":0.0954847,"about_ca_system_score_codex":0.000170708,"about_ca_system_score_gemma":0.000093217626,"threshold_uncertainty_score":0.99522024},"labels":[],"label_agreement":null},{"id":"W1520209841","doi":"10.1002/0470027320.s4101","title":"Spectra– Structure Correlations in the Mid‐ and Far‐Infrared","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Infrared spectroscopy; Infrared; Spectral line; Molecule; Group (periodic table); Spectrum (functional analysis); Chemistry; Absorption spectroscopy; Computational chemistry; Physics; Optics; Organic chemistry; Quantum mechanics","score_opus":0.009894871180665583,"score_gpt":0.2581207424980683,"score_spread":0.24822587131740273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520209841","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007863563,0.02386599,0.024332294,0.00032114057,0.00013137076,0.00033839894,0.00069196755,0.00013131433,0.94940114],"genre_scores_gemma":[0.01619813,0.02991653,0.3086719,0.0018515945,0.0051299636,0.0002645223,0.0049338033,0.0017224834,0.6313111],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838066,0.0000285836,0.0004043919,0.00042339906,0.00046477516,0.00029816382],"domain_scores_gemma":[0.9989787,0.00016442312,0.0003480055,0.0004196694,0.000029071598,0.000060158385],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000062313295,0.00037005576,0.0004970615,0.00039231393,0.00008900143,0.000066040084,0.00036617264,0.00040286192,0.034517393],"category_scores_gemma":[0.00005472858,0.00029330706,0.000113692295,0.000526753,0.00022550223,0.0000705596,0.000038661652,0.0004960372,0.000018804429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009067401,0.00036203596,0.016920526,0.0003424501,0.0005109657,0.000038165006,0.0005747831,0.00003985741,0.68237036,0.010789143,0.28779528,0.00016576958],"study_design_scores_gemma":[0.0015045729,0.000108380584,0.00152521,0.0004966606,0.00046468072,0.00008461185,0.00057663285,0.00011292841,0.94888425,0.012549307,0.03290437,0.0007884089],"about_ca_topic_score_codex":0.00030199828,"about_ca_topic_score_gemma":0.00062121346,"teacher_disagreement_score":0.31809008,"about_ca_system_score_codex":0.00007789965,"about_ca_system_score_gemma":0.00012356827,"threshold_uncertainty_score":0.9999519},"labels":[],"label_agreement":null},{"id":"W1522203849","doi":"10.1002/cjs.11202","title":"A statistical test for mixture detection with application to component identification in multidimensional biomolecular NMR studies","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Test statistic; Mathematics; Statistics; Estimator; Statistical hypothesis testing; Component (thermodynamics); Regression analysis; Nonlinear regression; Applied mathematics","score_opus":0.013259488744065224,"score_gpt":0.27064778275353196,"score_spread":0.2573882940094667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522203849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12532516,0.00028113663,0.87342846,0.00026627243,0.000039244187,0.00017897344,0.0004589955,0.0000043226164,0.000017441085],"genre_scores_gemma":[0.9370568,0.000011372,0.06261678,0.00008696566,0.000055795223,0.000044312805,0.000052836567,0.000015107795,0.000060020233],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907094,0.000009899212,0.00039228375,0.00014715886,0.00016717266,0.00021252515],"domain_scores_gemma":[0.9983633,0.00036199554,0.0002068222,0.00010890178,0.00064726407,0.00031172612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011360816,0.000114283735,0.00021690955,0.0003112957,0.000094516596,0.000040208255,0.00009712103,0.00005415486,0.00008859466],"category_scores_gemma":[0.0007411272,0.00010016244,0.000023059825,0.00030308316,0.00007277341,0.000062678024,0.0000051009,0.00013317306,0.000011881802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001241624,0.00022093597,0.02618788,0.00039026202,0.00042181174,0.00012433648,0.0009656534,0.0012857907,0.9346519,0.003002363,0.009145565,0.023479316],"study_design_scores_gemma":[0.005891036,0.001707009,0.18584554,0.00047001956,0.001507738,0.00038715277,0.008575731,0.03757743,0.72883224,0.015415686,0.012050143,0.0017402434],"about_ca_topic_score_codex":0.002006874,"about_ca_topic_score_gemma":0.011624164,"teacher_disagreement_score":0.81173164,"about_ca_system_score_codex":0.00036136925,"about_ca_system_score_gemma":0.0002339205,"threshold_uncertainty_score":0.64865565},"labels":[],"label_agreement":null},{"id":"W1532965831","doi":"10.1002/0470027320.s8955","title":"Applications of Vibrational Spectroscopy to the Analysis of Fish and Other Aquatic Food Products","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"InStream Fisheries Research (Canada)","funders":"","keywords":"Fish <Actinopterygii>; Fish products; Product (mathematics); Quality (philosophy); Business; Food products; Fishery; Fish processing; Product line; Food science; Engineering; Chemistry; Biology; Mathematics; Manufacturing engineering","score_opus":0.01312893891705982,"score_gpt":0.2712916372084697,"score_spread":0.2581626982914099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532965831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001369696,0.008315759,0.80570054,0.0018290477,0.00009935985,0.0014151181,0.004460712,0.00015394298,0.17665583],"genre_scores_gemma":[0.025650365,0.0052727577,0.8990862,0.0030162681,0.0048321146,0.0016983467,0.0044202562,0.0016289785,0.054394722],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99712735,0.000040642863,0.0009195346,0.00069891184,0.00087401306,0.000339567],"domain_scores_gemma":[0.9974456,0.00024530906,0.0011285783,0.000899526,0.00015980018,0.00012121141],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017376673,0.0004710357,0.0011651627,0.0011244484,0.00010271161,0.000040319737,0.0005786877,0.00028509754,0.013787591],"category_scores_gemma":[0.00011674397,0.00039430766,0.0003190923,0.0027061715,0.0003787322,0.00006690461,0.00010123169,0.00024878114,0.000015763728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009934388,0.0005072581,0.004508826,0.0004366833,0.005046178,5.2542066e-7,0.00018934917,0.000118644864,0.9182534,0.0054617464,0.06528837,0.00008965126],"study_design_scores_gemma":[0.00035432196,0.00016330648,0.00021171982,0.00016311878,0.0026623986,0.0000029076937,0.000102491606,0.00015781858,0.98052865,0.0006099166,0.014702304,0.00034106444],"about_ca_topic_score_codex":0.00032204072,"about_ca_topic_score_gemma":0.0009937393,"teacher_disagreement_score":0.1222611,"about_ca_system_score_codex":0.00008338827,"about_ca_system_score_gemma":0.0002859102,"threshold_uncertainty_score":0.99985087},"labels":[],"label_agreement":null},{"id":"W1535420376","doi":"10.1016/b978-0-12-380221-7.50009-3","title":"10.1016/b978-0-12-380221-7.50009-3","year":2000,"lang":"en","type":"book-chapter","venue":"Time to knit","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deconvolution; Computer science; Mathematics; Environmental science; Algorithm","score_opus":0.01047784378404289,"score_gpt":0.1962143610853008,"score_spread":0.1857365173012579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535420376","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016113539,0.0011074605,7.542105e-7,0.00007758213,0.0000023439156,0.00009796158,0.00021636822,0.0003235355,0.9981724],"genre_scores_gemma":[0.00000409665,0.0000055118617,0.000061566054,0.00005687345,0.00079574366,0.000018999617,0.0003218711,0.00018449484,0.99855083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765885,0.0000055500764,0.000502903,0.00077493285,0.0005228322,0.00053494034],"domain_scores_gemma":[0.9983038,0.00010901677,0.00020377028,0.00097409287,0.000076697026,0.0003326314],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007253029,0.00069367175,0.000843403,0.0003902581,0.00012528624,0.000085308784,0.0007067519,0.0007286405,0.99981743],"category_scores_gemma":[0.000038836984,0.0007268661,0.00043018567,0.0001297811,0.00010016838,0.0000658758,0.0001496764,0.00068577926,0.994553],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019413244,0.000090193236,5.7825464e-8,0.00012926583,0.00077883113,0.00009089771,0.000012943719,0.000011291405,0.00048601063,0.00008785918,0.6697001,0.32841843],"study_design_scores_gemma":[0.00024366385,0.000066421606,1.3664099e-7,0.00009089258,0.0005802561,0.000024096818,8.61373e-7,0.0000073951223,0.0032304928,0.00029112786,0.9946633,0.0008013313],"about_ca_topic_score_codex":0.000023275306,"about_ca_topic_score_gemma":3.8894277e-7,"teacher_disagreement_score":0.3276171,"about_ca_system_score_codex":0.00022931452,"about_ca_system_score_gemma":0.00009953382,"threshold_uncertainty_score":0.9995182},"labels":[],"label_agreement":null},{"id":"W1535606068","doi":"10.1002/0470027320.s6501","title":"Vibrational Spectroscopy of Food and Food Products","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Raman spectroscopy; Chemometrics; Spectroscopy; Infrared spectroscopy; Analytical Chemistry (journal); Near-infrared spectroscopy; Chemistry; Spectrometer; Fourier transform infrared spectroscopy; Materials science; Optics; Chromatography; Physics; Organic chemistry","score_opus":0.013969707301831941,"score_gpt":0.2537252568856138,"score_spread":0.23975554958378187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535606068","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033193426,0.054652885,0.19884738,0.00057437667,0.0004102407,0.00090491143,0.002824477,0.0004990427,0.7379673],"genre_scores_gemma":[0.022599457,0.015007375,0.89241016,0.0003097697,0.005983434,0.0002537169,0.0030172272,0.0018213601,0.058597527],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967086,0.00003182029,0.00087959727,0.0009150851,0.00094775856,0.0005171495],"domain_scores_gemma":[0.9977421,0.00014042418,0.0010915248,0.00067273877,0.00018124774,0.000171974],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000111776826,0.000699085,0.0011908474,0.00067493785,0.00010617867,0.000058137295,0.00045022904,0.0005468401,0.025290562],"category_scores_gemma":[0.00013268458,0.0007031429,0.00023935697,0.0007082168,0.00050990115,0.0001566995,0.000126941,0.00042416985,0.000027675673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007969412,0.00035578644,0.0014746068,0.00073465117,0.0008333877,0.0000022877764,0.00004502036,0.0000042985744,0.9360938,0.0076730037,0.052678466,0.000024979325],"study_design_scores_gemma":[0.000948096,0.0006523614,0.000072831914,0.00044800094,0.00038253117,0.00002452262,0.00005164435,0.000022600252,0.9904825,0.0028414526,0.0035034919,0.0005699619],"about_ca_topic_score_codex":0.000073914096,"about_ca_topic_score_gemma":0.000116871444,"teacher_disagreement_score":0.69356275,"about_ca_system_score_codex":0.00011815726,"about_ca_system_score_gemma":0.00046342728,"threshold_uncertainty_score":0.999542},"labels":[],"label_agreement":null},{"id":"W1537375653","doi":"10.1002/cem.2428","title":"Exploratory data analysis with noisy measurements","year":2012,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visualization; Computer science; Projection (relational algebra); Data mining; Subspace topology; Preprocessor; Multivariate statistics; Variance (accounting); Principal component analysis; Chemometrics; Statistics; Algorithm; Artificial intelligence; Mathematics; Machine learning","score_opus":0.1549297124907503,"score_gpt":0.33370397274337454,"score_spread":0.17877426025262425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537375653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93654805,0.018183257,0.018173238,0.00021116891,0.00025075054,0.00004556543,0.00004847264,0.000054617518,0.026484858],"genre_scores_gemma":[0.99319285,0.00018528146,0.005377575,0.00007873887,0.0005197078,9.587625e-7,0.000023603992,0.000025768286,0.0005955163],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974468,0.000020746222,0.00061144686,0.00020517553,0.0012926549,0.0004231789],"domain_scores_gemma":[0.9972388,0.0001592117,0.00087661337,0.00085768104,0.0004997489,0.00036790813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010386326,0.00021783379,0.0006403437,0.0021536173,0.000079639096,0.00006933513,0.0010424082,0.00010062024,0.0009283716],"category_scores_gemma":[0.00077946385,0.00016109775,0.00021103774,0.010615568,0.000060779577,0.0009639158,0.00014421737,0.0004026039,0.000014408641],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012010311,0.0009075644,0.9611359,0.00009258566,0.00727122,0.000030174473,0.00018975612,0.00006868535,0.023724895,0.000011410973,0.0051632207,0.0012844721],"study_design_scores_gemma":[0.0025532262,0.00017252902,0.015599236,0.00004555291,0.02255917,0.0002973241,0.0022891895,0.00015149321,0.93356454,0.000031695028,0.02187111,0.00086494646],"about_ca_topic_score_codex":0.0000049681425,"about_ca_topic_score_gemma":0.0000015958083,"teacher_disagreement_score":0.9455367,"about_ca_system_score_codex":0.00020393233,"about_ca_system_score_gemma":0.00011954487,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W1543449741","doi":"10.1533/9780857095770.3.451","title":"Development of multispectral imaging systems for quality evaluation of cereal grains and grain products","year":2012,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multispectral image; Hyperspectral imaging; Grading (engineering); Quality assurance; Mildew; Grain quality; Agricultural engineering; Environmental science; Computer science; Remote sensing; Artificial intelligence; Engineering; Agronomy; Geography; Horticulture; Operations management; Biology; Civil engineering","score_opus":0.06844803062155849,"score_gpt":0.33642840815581465,"score_spread":0.2679803775342562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543449741","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017254928,0.022668198,0.000054880144,0.000016476592,0.00021110332,0.00095199735,0.00021399307,0.000044492866,0.95858395],"genre_scores_gemma":[0.3895162,0.00013328275,0.004518463,0.0000098236105,0.00047059273,0.0001891785,0.00024402996,0.00010953112,0.6048089],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774903,0.000024156994,0.0009030741,0.00041491832,0.0006338448,0.00027498978],"domain_scores_gemma":[0.99800223,0.00008335463,0.0008985313,0.00039105237,0.00054284633,0.00008196931],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013945212,0.0003361708,0.0007692495,0.00018425552,0.00007613408,0.000015484848,0.00016103958,0.00019422831,0.00009422008],"category_scores_gemma":[0.00014839236,0.00032093495,0.00014619186,0.00002595424,0.00014613125,0.00003968115,0.000062312196,0.00017983386,0.0000014247804],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005555436,0.000060614155,0.00041324756,0.0051177433,0.0007088564,4.4075256e-7,0.0018465446,7.597852e-7,0.04155423,0.0049939933,0.000047708832,0.9452003],"study_design_scores_gemma":[0.004734651,0.000061767285,0.0010953706,0.0021504895,0.0072233435,0.00003507777,0.0012709623,0.00040456414,0.55955994,0.006047308,0.4150056,0.0024109406],"about_ca_topic_score_codex":0.000005989018,"about_ca_topic_score_gemma":0.000018167915,"teacher_disagreement_score":0.9427894,"about_ca_system_score_codex":0.00021886437,"about_ca_system_score_gemma":0.00031317823,"threshold_uncertainty_score":0.99992424},"labels":[],"label_agreement":null},{"id":"W1544733054","doi":"10.5772/20679","title":"SR-IMS Molecular Spectroscopic Image of Functional Groups in Biopolymers in Feed Systems","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Northeast Agricultural University; Brookhaven National Laboratory; Case Western Reserve University; U.S. Department of Energy","keywords":"Image (mathematics); Materials science; Computational biology; Computer science; Biological system; Chemistry; Polymer science; Biology; Computer vision","score_opus":0.01842382016853936,"score_gpt":0.23820466518231176,"score_spread":0.2197808450137724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544733054","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02209449,0.0028166797,0.0009556309,0.000009441863,0.00019205762,0.00017889673,0.00004801142,0.00007946638,0.9736253],"genre_scores_gemma":[0.7785863,0.00006438985,0.00006961309,0.000022405022,0.000104578074,0.00003990825,0.000045748082,0.0000895343,0.22097747],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976905,0.0000118736225,0.00086824794,0.0006078706,0.00041388374,0.00040765328],"domain_scores_gemma":[0.99872404,0.000065575085,0.00043783127,0.00059766724,0.000085572065,0.00008930571],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013130478,0.00051585445,0.00092899683,0.0010297284,0.000023407825,0.000026986025,0.00042870842,0.00071501214,0.0031274462],"category_scores_gemma":[0.000034597513,0.0005385541,0.00029174774,0.00010328268,0.0003114207,0.000039888528,0.00012720287,0.00092547433,0.00007619563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099987046,0.000059919643,0.00023127116,0.00044288248,0.00024954317,0.00019223495,0.000102471655,4.759359e-7,0.98113585,0.017260253,0.00004649323,0.00017861395],"study_design_scores_gemma":[0.0006387358,0.00005908449,0.000026363017,0.0005935329,0.00015297307,0.000021552878,0.00016962798,0.000004616946,0.9889828,0.007981466,0.00088753796,0.00048169476],"about_ca_topic_score_codex":0.0005885974,"about_ca_topic_score_gemma":0.00005128503,"teacher_disagreement_score":0.75649184,"about_ca_system_score_codex":0.00039676734,"about_ca_system_score_gemma":0.00014147043,"threshold_uncertainty_score":0.9997066},"labels":[],"label_agreement":null},{"id":"W1546153531","doi":"10.1002/9780470027318.a2021","title":"Near‐Infrared Spectroscopy of Polymers and Rubbers","year":2000,"lang":"en","type":"other","venue":"Encyclopedia of Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Spectroscopy; Infrared spectroscopy; Polymer; Materials science; Polymerization; Near-infrared spectroscopy; Monomer; Infrared; Analytical Chemistry (journal); Chemistry; Organic chemistry; Optics; Composite material","score_opus":0.005742057661942908,"score_gpt":0.24498816644685592,"score_spread":0.239246108784913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546153531","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076462636,0.005154225,0.000008514234,0.000031641797,0.000024514638,0.000043415934,0.00025972072,0.00009404948,0.98673767],"genre_scores_gemma":[0.0036954253,0.004201998,0.00090253475,0.000020364496,0.0003266125,0.0000052858777,0.00014110615,0.00026932856,0.9904373],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978525,0.000008084072,0.0006301891,0.00060364726,0.00046699392,0.00043856804],"domain_scores_gemma":[0.9984408,0.000120337245,0.0004497536,0.00068773056,0.00002494651,0.00027643814],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000045788292,0.0005063346,0.0011522239,0.00013660674,0.00003237384,0.00001934402,0.0004307774,0.0007645125,0.10842356],"category_scores_gemma":[0.00010516766,0.00050586055,0.00030839833,0.0004974172,0.0008180234,0.00003075673,0.00008562904,0.0004968202,0.000016620203],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018390061,0.00051936076,0.0043795733,0.0044960435,0.0019740344,0.000050847262,0.000118421645,0.0000033706442,0.025813645,0.000050199207,0.961824,0.00058657414],"study_design_scores_gemma":[0.00090102875,0.000043537893,0.00004668151,0.00034102012,0.001840123,0.000016952865,0.00021106857,0.00010401395,0.20059153,0.00014776642,0.79486793,0.0008883198],"about_ca_topic_score_codex":0.00017500669,"about_ca_topic_score_gemma":0.000002794445,"teacher_disagreement_score":0.1747779,"about_ca_system_score_codex":0.000040607505,"about_ca_system_score_gemma":0.00019239497,"threshold_uncertainty_score":0.9997393},"labels":[],"label_agreement":null},{"id":"W1546360871","doi":"10.1007/3-540-45049-1_64","title":"Improving Peanut Maturity Prediction Using a Hybrid Artificial Neural Network and Fuzzy Inference System","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adaptive neuro fuzzy inference system; Computer science; Artificial neural network; Inference system; Robustness (evolution); Neuro-fuzzy; Artificial intelligence; Fuzzy inference system; Machine learning; Fuzzy logic; Data mining; Fuzzy control system","score_opus":0.019179345914209507,"score_gpt":0.24855094525379826,"score_spread":0.22937159933958876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546360871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11607095,0.002477576,0.87041795,0.0000594817,0.0010406917,0.00026969524,0.00008305672,0.0004016878,0.009178925],"genre_scores_gemma":[0.98618555,0.000032549535,0.011895533,0.000086520835,0.0016788251,0.0000026284144,0.000016089354,0.000028538585,0.000073756426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749976,0.000012273926,0.00045599812,0.0009861218,0.0005035027,0.0005423333],"domain_scores_gemma":[0.9988153,0.00021382685,0.0002711787,0.00048702204,0.00008269423,0.00012998552],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026114742,0.00042039633,0.00049459975,0.00031951736,0.0003619339,0.0003645949,0.00051928434,0.00027522372,0.00006590861],"category_scores_gemma":[0.000049732353,0.00040358864,0.00009209126,0.00036978285,0.00046102068,0.00022092437,0.0002999958,0.00086088484,0.0000036320369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016953479,0.00008290552,0.0024267621,0.0015372041,0.00013581454,0.00040147844,0.0005366126,0.32679692,0.015561259,0.0028461518,0.000027602287,0.6494778],"study_design_scores_gemma":[0.00014263384,0.0000467606,0.000047606314,0.00048030788,0.00012781125,0.00024232698,0.0000011655079,0.97248673,0.011740644,0.014104013,0.000045556193,0.0005344374],"about_ca_topic_score_codex":0.00010579433,"about_ca_topic_score_gemma":0.000024537287,"teacher_disagreement_score":0.8701146,"about_ca_system_score_codex":0.00036869803,"about_ca_system_score_gemma":0.00019105033,"threshold_uncertainty_score":0.9998416},"labels":[],"label_agreement":null},{"id":"W1546884344","doi":"10.1007/978-3-642-13923-9_15","title":"Computerized Pork Quality Evaluation System","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Principal component analysis; Hyperspectral imaging; Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Hypercube; Computer science; Quality (philosophy); Mathematics; Computer vision","score_opus":0.0356077043467755,"score_gpt":0.3207598237616105,"score_spread":0.285152119414835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546884344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041799764,0.0006452348,0.953742,0.00013692834,0.0012444989,0.00022782126,0.000021463957,0.00024377702,0.039558295],"genre_scores_gemma":[0.91201466,0.000017261022,0.08503967,0.000345217,0.0015179177,0.000016421021,0.00006660877,0.000057684905,0.00092457596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99618256,0.000025290523,0.00065867865,0.0012018246,0.0014510244,0.00048059723],"domain_scores_gemma":[0.9973566,0.00044415364,0.0004957086,0.0011587157,0.00039419442,0.0001506184],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013848653,0.00048733572,0.0007452215,0.0005278418,0.00024827677,0.00024964122,0.0013078075,0.0006589103,0.0008523028],"category_scores_gemma":[0.00017455244,0.00044647537,0.00019173247,0.00050485804,0.00058843696,0.00014012118,0.00038052638,0.0011772601,0.000054821354],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013859874,0.00025527796,0.0014531636,0.0021063297,0.00032824755,0.0001543424,0.0012641265,0.011573081,0.1895912,0.017865459,0.00006821597,0.775202],"study_design_scores_gemma":[0.0032112405,0.00017809916,0.00069847034,0.0017497845,0.0007809531,0.0002873265,0.0000056041563,0.42473647,0.4783683,0.081521615,0.0042240536,0.004238068],"about_ca_topic_score_codex":0.00005779319,"about_ca_topic_score_gemma":0.000047952235,"teacher_disagreement_score":0.90783465,"about_ca_system_score_codex":0.0006850652,"about_ca_system_score_gemma":0.0005000005,"threshold_uncertainty_score":0.9997987},"labels":[],"label_agreement":null},{"id":"W1549230915","doi":"10.1002/9780470027318.a5207","title":"Signal Processing in Analytical Chemistry","year":2000,"lang":"en","type":"other","venue":"Encyclopedia of Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Smoothing; Multidimensional signal processing; Signal processing; Computer science; Analog signal; Hadamard transform; Algorithm; SIGNAL (programming language); Digital signal processing; Decimation; Analog signal processing; Electronic engineering; Mathematics; Filter (signal processing); Engineering; Computer hardware; Computer vision","score_opus":0.009449228435207275,"score_gpt":0.2678083772669726,"score_spread":0.2583591488317653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549230915","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002916315,0.0031794917,0.000033084234,0.00006495012,0.000014513575,0.00006439019,0.0001605506,0.00022280647,0.9933439],"genre_scores_gemma":[0.01982728,0.0014204454,0.000240478,0.00003331798,0.0011566917,0.000023482204,0.00034253602,0.00048757315,0.9764682],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99578935,0.000014295013,0.0011690044,0.001204445,0.0008792701,0.000943632],"domain_scores_gemma":[0.99804395,0.00011930666,0.0004990237,0.00083635695,0.000050975486,0.0004504074],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012300613,0.0008980845,0.001675629,0.00023870997,0.00004322083,0.00004862737,0.00095955963,0.0016556954,0.19885579],"category_scores_gemma":[0.00014403468,0.0009165253,0.0005212687,0.001301128,0.00056636386,0.00006617885,0.00015434323,0.0014890321,0.00008867462],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033480904,0.0028133576,0.009957167,0.015227126,0.0011174802,0.0007700192,0.00012436936,0.00006794314,0.009500507,0.00003381156,0.9530464,0.0070070038],"study_design_scores_gemma":[0.0014035713,0.000019546495,0.000054042863,0.001126713,0.0015600646,0.00004961339,0.00022600057,0.003324538,0.025518347,0.0001333116,0.96471447,0.0018697773],"about_ca_topic_score_codex":0.00011097037,"about_ca_topic_score_gemma":0.000007848721,"teacher_disagreement_score":0.19876713,"about_ca_system_score_codex":0.0001950116,"about_ca_system_score_gemma":0.00047447727,"threshold_uncertainty_score":0.99964035},"labels":[],"label_agreement":null},{"id":"W1551408431","doi":"","title":"The objective measurement of alpha-amylase in wheat kernels using spectral imaging","year":2010,"lang":"en","type":"article","venue":"World Automation Congress","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Starch; Hyperspectral imaging; Kernel (algebra); Germination; Amylase; Multispectral image; Alpha-amylase; Biological system; Alpha (finance); Chemistry; Mathematics; Food science; Artificial intelligence; Computer science; Enzyme; Horticulture; Biology; Statistics; Biochemistry","score_opus":0.017851161596288585,"score_gpt":0.2875978696934591,"score_spread":0.2697467080971705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551408431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9840229,0.00040304547,0.00023316771,0.00036752564,0.00036422207,0.00009921744,0.000007773605,0.00008373564,0.014418372],"genre_scores_gemma":[0.9988837,0.000007077673,0.0003371885,0.000021281887,0.00009693798,0.000011341221,0.0000029970804,0.0000138261485,0.000625618],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987511,0.000024133598,0.0003619576,0.00020761817,0.0004058338,0.00024936613],"domain_scores_gemma":[0.9991285,0.00013489404,0.00022808186,0.00028789332,0.00017385816,0.000046755496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036929542,0.00014127638,0.00020240714,0.00021670312,0.00013835449,0.00007183704,0.0002187108,0.000038088827,0.0006540558],"category_scores_gemma":[0.00020123627,0.00011329097,0.00008031613,0.00060906576,0.00015740797,0.0001563474,0.00003751604,0.0002736148,0.0000063148227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031215466,0.00010547577,0.045166045,0.000053132113,0.00008484118,0.000007911022,0.00025519356,0.00014938033,0.9491946,0.0012943845,0.00026959836,0.003388201],"study_design_scores_gemma":[0.00047804142,0.0000031749344,0.012355478,0.00005547456,0.00006701864,0.000006570404,0.00035800878,0.020655826,0.964887,0.0004706396,0.00050864695,0.00015412296],"about_ca_topic_score_codex":0.00022798835,"about_ca_topic_score_gemma":0.0013734997,"teacher_disagreement_score":0.03281057,"about_ca_system_score_codex":0.00018670285,"about_ca_system_score_gemma":0.000102281134,"threshold_uncertainty_score":0.7161452},"labels":[],"label_agreement":null},{"id":"W1562060445","doi":"10.1002/cem.2535","title":"Process analytical chemistry and chemometrics, Bruce Kowalski's legacy at The Dow Chemical Company","year":2013,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Chemometrics; Chemistry; Process analytical technology; Industrial chemistry; Analytical Chemistry (journal); Graduate students; Process (computing); Engineering; Computer science; Chemical engineering; Biochemical engineering; Sociology; Organic chemistry; Chromatography; Bioprocess","score_opus":0.014295135653689053,"score_gpt":0.2765323366603345,"score_spread":0.26223720100664544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562060445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9717013,0.0078065223,0.0001684752,0.0028193519,0.0000925629,0.00009840756,0.000011922869,0.00004816836,0.017253272],"genre_scores_gemma":[0.99230736,0.000781056,0.0004930361,0.000333484,0.0007257775,0.000010052515,0.000011847205,0.000061366074,0.0052760234],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958861,0.000022247497,0.0013076527,0.00050053344,0.0015150969,0.00076839793],"domain_scores_gemma":[0.9954078,0.0010426481,0.001161787,0.00061820325,0.001119253,0.00065028213],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067332096,0.00052058813,0.0010857749,0.0007147348,0.00023374286,0.00041741494,0.001159827,0.00045222288,0.0062944745],"category_scores_gemma":[0.0042449245,0.00036646958,0.00045089383,0.0072972747,0.00053026405,0.0006510393,0.00035277335,0.0013556995,0.000066458866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043045438,0.00238952,0.08323014,0.0023490842,0.0027484612,0.00020237971,0.0007182426,0.000069612695,0.78540474,0.000157444,0.08693407,0.035365846],"study_design_scores_gemma":[0.0018943173,0.00006239208,0.0023157962,0.00005679869,0.00077931676,0.0012757414,0.0012258204,0.00080658594,0.9814201,0.00057446794,0.008923506,0.0006651783],"about_ca_topic_score_codex":0.000021878868,"about_ca_topic_score_gemma":4.012986e-7,"teacher_disagreement_score":0.19601533,"about_ca_system_score_codex":0.0005186704,"about_ca_system_score_gemma":0.00018855311,"threshold_uncertainty_score":0.9998787},"labels":[],"label_agreement":null},{"id":"W1564288303","doi":"10.1111/j.1755-0238.2005.tb00029.x","title":"Grape and wine analysis - enhancing the power of spectroscopy with chemometrics..","year":2005,"lang":"en","type":"article","venue":"Australian Journal of Grape and Wine Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National Du Cancer; Australian Government; Alberta Water Research Institute","keywords":"Wine; Chemometrics; White Wine; Aging of wine; Distillation; Chemistry; Food science; Oenology; Chromatography; Viticulture","score_opus":0.03740790012321086,"score_gpt":0.35831602028435394,"score_spread":0.32090812016114306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564288303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9904681,0.0020383692,0.00015340246,0.0060339924,0.0000094348425,0.00004690632,0.000007846235,0.000005864646,0.0012361088],"genre_scores_gemma":[0.995779,0.00078800815,0.0010159131,0.00003377813,0.00040857992,0.0000015044201,0.0000032300914,0.000016802349,0.001953184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767333,0.00006121102,0.0005862186,0.00024935056,0.0009342251,0.00049563544],"domain_scores_gemma":[0.9980085,0.0005914257,0.00032584407,0.0003191712,0.00049886695,0.00025622774],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00141054,0.0001976974,0.0005962607,0.0015486227,0.00021704091,0.000105554245,0.000371608,0.00010255694,0.0013228273],"category_scores_gemma":[0.00032485984,0.00012047944,0.00019426817,0.0053150826,0.00051960396,0.00023133168,0.00007136456,0.00080751715,0.0000022273675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083798775,0.00054264686,0.14112292,0.00020403569,0.0061495225,0.0001788576,0.0011321803,0.000051206465,0.840279,0.0001527571,0.0075724567,0.0017764161],"study_design_scores_gemma":[0.0011083345,0.0007390228,0.024990175,0.000104327315,0.0013297121,0.00028316453,0.002888638,0.000020080572,0.9655225,0.00021391311,0.002587949,0.00021217194],"about_ca_topic_score_codex":0.000076555785,"about_ca_topic_score_gemma":0.000017237739,"teacher_disagreement_score":0.1252435,"about_ca_system_score_codex":0.00005380778,"about_ca_system_score_gemma":0.00009283756,"threshold_uncertainty_score":0.9995901},"labels":[],"label_agreement":null},{"id":"W1566653439","doi":"","title":"DER_SNR: A Simple & General Spectroscopic Signal-to-Noise Measurement Algorithm","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algorithm; Outlier; Computer science; Context (archaeology); Noise (video); Signal-to-noise ratio (imaging); Simple (philosophy); SIGNAL (programming language); Process (computing); SIMPLE algorithm; Quality (philosophy); Artificial intelligence; Physics; Telecommunications","score_opus":0.03260598257513177,"score_gpt":0.2658658535532008,"score_spread":0.23325987097806905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566653439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7593304,0.0008768566,0.040513653,0.0007809218,0.00012202815,0.00019677267,0.000039711795,0.00043298726,0.19770667],"genre_scores_gemma":[0.96903914,0.0000710854,0.023058569,0.00057279033,0.000585833,0.000050513136,0.00001852246,0.000042403055,0.006561142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787414,0.000015782669,0.00030332714,0.00047351955,0.00075409905,0.0005791372],"domain_scores_gemma":[0.999014,0.000026836111,0.00007507865,0.00045935024,0.00013328237,0.0002914075],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000114511204,0.00026715102,0.00035410604,0.00015916933,0.00023252706,0.00003472778,0.0003477887,0.000105426494,0.016293213],"category_scores_gemma":[0.000057730365,0.00025994552,0.00015966526,0.0006044924,0.00006291602,0.00007113888,0.00008613717,0.00022537874,0.0004033696],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016385662,0.00018583072,0.0037561283,0.000024646512,0.00013825909,0.000059087655,0.00015543851,0.000015664347,0.9654106,0.000091032816,0.027348913,0.0027980083],"study_design_scores_gemma":[0.0005562927,0.000066473614,0.0009069621,0.000009436203,0.00010998505,0.000033822143,0.000061214014,0.0006619934,0.98264647,0.0013244704,0.013276291,0.00034659496],"about_ca_topic_score_codex":0.00007950504,"about_ca_topic_score_gemma":0.000008894918,"teacher_disagreement_score":0.20970875,"about_ca_system_score_codex":0.00031536492,"about_ca_system_score_gemma":0.0001371183,"threshold_uncertainty_score":0.9999853},"labels":[],"label_agreement":null},{"id":"W1567013193","doi":"10.3390/rs70608107","title":"Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Agriculture and Agri-Food Canada; University of Lethbridge","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale; Arabian Gulf University","keywords":"Environmental science; Crop residue; Hyperspectral imaging; Tillage; Cover crop; Soil quality; Residue (chemistry); Remote sensing; Soil science; Agriculture; Agronomy; Soil water; Agroforestry; Geography","score_opus":0.10503747686391422,"score_gpt":0.32075085463590025,"score_spread":0.21571337777198601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567013193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9341239,0.0003039125,0.053732015,0.00008447651,0.000121069424,0.000043586828,0.000020570702,0.000084043946,0.011486454],"genre_scores_gemma":[0.92567724,0.000012469837,0.073718354,0.000022582999,0.00038909126,1.8093235e-9,0.00004378037,0.00002830244,0.00010819653],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99825966,0.000060327446,0.00042434369,0.0005293905,0.00037205455,0.000354208],"domain_scores_gemma":[0.9981297,0.00012582049,0.00022902354,0.0011882109,0.00018213414,0.00014506512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058530597,0.00020226983,0.00042523193,0.00013599318,0.00010142241,0.000043572214,0.00032217806,0.00016858836,0.00006968394],"category_scores_gemma":[0.0011640693,0.00020802338,0.000080646845,0.0005415477,0.00013292831,0.00016118963,0.0002671405,0.0002641585,0.0000057988827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065786146,0.000039720057,0.0008094607,0.00011999342,0.000110258894,0.00003156499,0.00039044177,0.00035308898,0.9866644,0.000012787881,0.000100982565,0.0113015035],"study_design_scores_gemma":[0.0005772242,0.000017196175,0.0000923576,0.0001042234,0.00026045056,0.00013779437,0.0011247547,0.38184133,0.61421806,0.0009259228,0.00036264412,0.00033803383],"about_ca_topic_score_codex":0.003826349,"about_ca_topic_score_gemma":0.00003412622,"teacher_disagreement_score":0.38148823,"about_ca_system_score_codex":0.00027775357,"about_ca_system_score_gemma":0.00023261267,"threshold_uncertainty_score":0.84829473},"labels":[],"label_agreement":null},{"id":"W1586556109","doi":"10.1007/1-4020-5497-1_64","title":"NIR Spectroscopy as a Tool for Quality Screening","year":2007,"lang":"en","type":"book-chapter","venue":"Developments in plant breeding","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Spectroscopy; Quality (philosophy); Materials science; Environmental science; Physics","score_opus":0.0903343613912342,"score_gpt":0.3403913247779268,"score_spread":0.2500569633866926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586556109","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005365346,0.00047840216,0.003279425,0.00003257123,0.00027977754,0.00028087123,0.00041719957,0.00018339597,0.98968303],"genre_scores_gemma":[0.024476927,0.00066471024,0.09351642,0.0007675751,0.0013501268,0.00011508401,0.0028672111,0.00035188365,0.8758901],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964739,0.0000044021294,0.0011348482,0.0008750827,0.0006915184,0.00082024414],"domain_scores_gemma":[0.9984032,0.0004987659,0.000549241,0.0003206621,0.000091106376,0.0001370297],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007505971,0.00066754856,0.00096049154,0.0007691367,0.00019813165,0.00009882432,0.0005330691,0.00076128467,0.001781737],"category_scores_gemma":[0.0002575407,0.0007290917,0.00023250376,0.00017677115,0.00006958542,0.0001227622,0.00017974857,0.00080372987,0.00007366233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064479588,0.0009121409,0.09952606,0.009004603,0.011197689,0.0023053526,0.0041124104,0.00003680994,0.19395368,0.5330454,0.046994936,0.09246297],"study_design_scores_gemma":[0.0054685525,0.00014488568,0.0015795925,0.0035968425,0.00073001214,0.00027205216,0.00063105376,0.000069283444,0.3418442,0.015461314,0.62464,0.005562263],"about_ca_topic_score_codex":0.00006100183,"about_ca_topic_score_gemma":0.000050008595,"teacher_disagreement_score":0.577645,"about_ca_system_score_codex":0.00068267726,"about_ca_system_score_gemma":0.00021329711,"threshold_uncertainty_score":0.999516},"labels":[],"label_agreement":null},{"id":"W1588680921","doi":"10.1006/bioe.2002.0064","title":"AE—Automation and Emerging Technologies","year":2002,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Probabilistic neural network; Backpropagation; Computer science; Automation; Probabilistic logic; Standard deviation; Network architecture; Mathematics; Statistics; Engineering; Time delay neural network","score_opus":0.009634405340607806,"score_gpt":0.2024086528939183,"score_spread":0.1927742475533105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588680921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97859335,0.011108887,0.0016269592,0.00017240748,0.000106955944,0.000043354994,0.0000045784113,0.0019897767,0.0063537024],"genre_scores_gemma":[0.99891263,0.00014149617,0.00042690363,0.00000208351,0.000055115583,0.00000953919,0.0000014116592,0.000013094167,0.00043770255],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949,0.0000011101602,0.00013316747,0.00014026687,0.00008202306,0.00015340366],"domain_scores_gemma":[0.99976313,0.000024286446,0.000035835063,0.00014205209,0.000011775208,0.000022935192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031544798,0.00010151523,0.00012944671,0.00014487401,0.00004015185,0.000032841763,0.000077318175,0.000088388864,0.00012533632],"category_scores_gemma":[0.00007008715,0.000097661534,0.000025750644,0.00027031935,0.00000957792,0.00007422981,0.000027960394,0.00007944718,0.000016079422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.1308637e-7,0.000019010933,0.003202442,0.0006420243,0.00008118441,0.000008504385,0.00018187914,0.00018189766,0.9900555,0.000880632,0.0004721796,0.0042740605],"study_design_scores_gemma":[0.00022064835,0.0000096194035,0.00016490003,0.00010320488,0.000056663037,0.000051972238,0.00085517607,0.0894578,0.89675146,0.000008505933,0.011989446,0.00033059684],"about_ca_topic_score_codex":0.000008304785,"about_ca_topic_score_gemma":3.0470218e-7,"teacher_disagreement_score":0.09330401,"about_ca_system_score_codex":0.00003814929,"about_ca_system_score_gemma":9.976956e-7,"threshold_uncertainty_score":0.3982522},"labels":[],"label_agreement":null},{"id":"W1591507297","doi":"10.1533/9780857095763.1.111","title":"Optical sensors and online spectroscopy for automated quality and safety inspection of food products","year":2013,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Quality (philosophy); Food safety; Food quality; Instrumentation (computer programming); Visual inspection; Food processing; Food products; Computer science; Quality assurance; Food industry; Engineering; Systems engineering; Artificial intelligence; Operations management; Food science","score_opus":0.030258955493806274,"score_gpt":0.29996403414343636,"score_spread":0.2697050786496301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591507297","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078284115,0.002787215,0.000016911861,0.00018872373,0.00009224658,0.00064771425,0.0005626719,0.00046360478,0.9169568],"genre_scores_gemma":[0.07009268,0.00094311207,0.011066447,0.00008872397,0.0007455879,0.000050069208,0.0003241632,0.00018501694,0.9165042],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979597,0.000011720899,0.00075973384,0.00069703424,0.00026873255,0.00030311782],"domain_scores_gemma":[0.9984767,0.00018534795,0.00048160733,0.00046800662,0.00024725002,0.00014108869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017676939,0.0004687402,0.0009918432,0.00017927178,0.00011725117,0.000038149254,0.00012933527,0.000491789,0.00020004234],"category_scores_gemma":[0.00018213806,0.0004345995,0.00014897875,0.00003894211,0.00037677365,0.000046744983,0.000097625736,0.0004018198,0.00000477752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014791671,0.00054158986,0.00035636922,0.016099138,0.0055258293,0.000015309924,0.000856231,0.0000038257213,0.45973048,0.07363052,0.0013643766,0.44039714],"study_design_scores_gemma":[0.0036103681,0.0009860839,0.00088045554,0.00086890615,0.0030427002,0.0001108415,0.00022673678,0.00042786374,0.7971603,0.017528214,0.17288066,0.0022768339],"about_ca_topic_score_codex":0.0000026704022,"about_ca_topic_score_gemma":0.000012085198,"teacher_disagreement_score":0.4381203,"about_ca_system_score_codex":0.0001128745,"about_ca_system_score_gemma":0.00008482368,"threshold_uncertainty_score":0.9998106},"labels":[],"label_agreement":null},{"id":"W1595650229","doi":"10.5772/33960","title":"Application of Chemometrics to the Interpretation of Analytical Separations Data","year":2012,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Wildlife Fund Canada","funders":"","keywords":"Chemometrics; Interpretation (philosophy); Computer science; Machine learning","score_opus":0.05012095603688533,"score_gpt":0.33693525210696,"score_spread":0.2868142960700747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595650229","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017365343,0.00073813746,0.15410303,0.00010528024,0.000043277207,0.00020279193,0.00046446937,0.0000433321,0.84412605],"genre_scores_gemma":[0.8648475,0.000029390341,0.0005956685,0.00005311992,0.0001666039,0.000020747164,0.00032866508,0.000036475936,0.13392188],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855494,0.0000047485973,0.00059068785,0.00032481144,0.00037467404,0.00015013269],"domain_scores_gemma":[0.9974283,0.00025067612,0.00046518652,0.0015302617,0.00025402795,0.00007154745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021876517,0.00021470983,0.00042944064,0.00043810427,0.000035987876,0.000014271524,0.0010191508,0.00029695098,0.0005729652],"category_scores_gemma":[0.00025516693,0.00017201767,0.00014096183,0.00017738398,0.0001492889,0.000041087256,0.0003577943,0.00034763516,0.000061321225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044341627,0.00046456157,0.00062442076,0.0019122637,0.004603938,0.0000033541473,0.0021395723,0.000045491346,0.40533957,0.18735726,0.014854551,0.3822116],"study_design_scores_gemma":[0.00015988013,0.000056014538,0.000011892624,0.00014275871,0.0017889962,0.0000084103285,0.00013310261,0.003060896,0.78406745,0.003829228,0.20633443,0.00040692187],"about_ca_topic_score_codex":0.000035868037,"about_ca_topic_score_gemma":0.000018342058,"teacher_disagreement_score":0.8646738,"about_ca_system_score_codex":0.00008466209,"about_ca_system_score_gemma":0.000068876085,"threshold_uncertainty_score":0.7014677},"labels":[],"label_agreement":null},{"id":"W1596568583","doi":"10.1002/9780470988459.ch3","title":"Near‐Infrared Spectroscopy for Process Analytical Chemistry: Theory, Technology and Implementation","year":2005,"lang":"en","type":"other","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Process (computing); Infrared spectroscopy; Infrared; Spectroscopy; Chemistry; Analytical Chemistry (journal); Nanotechnology; Process engineering; Computer science; Materials science; Engineering; Physics; Environmental chemistry; Organic chemistry; Optics","score_opus":0.007720759890083904,"score_gpt":0.32980021023546535,"score_spread":0.32207945034538144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596568583","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015096744,0.0014899812,0.0019094427,0.0002818276,0.000016333064,0.00021217526,0.00017180457,0.0005311121,0.99387765],"genre_scores_gemma":[0.0040915073,0.00016594154,0.0053240433,0.0000731404,0.00047312834,0.00014887296,0.00031013388,0.00032111208,0.9890921],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985307,0.0000034054133,0.00027637935,0.00059262686,0.00017144986,0.00042546107],"domain_scores_gemma":[0.9992222,0.00005465868,0.0002142873,0.00036663815,0.000045589823,0.00009660128],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000071042596,0.00037235214,0.00050996547,0.00027072508,0.00008272987,0.000078796715,0.0002722437,0.00063775457,0.13359064],"category_scores_gemma":[0.00006765277,0.00034150959,0.00009713052,0.0005473279,0.00022597401,0.000032039527,0.000051796746,0.00024145687,0.000021152833],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019630144,0.00034585307,0.0025621087,0.0028996712,0.002243745,0.000009177705,0.00010415431,9.802849e-7,0.029940767,0.01904491,0.93213546,0.010516864],"study_design_scores_gemma":[0.0011937558,0.00003997881,0.0000036011272,0.000041023584,0.0008256112,0.000015765027,0.0010958861,0.00023140911,0.43676203,0.0046777753,0.55452025,0.0005929371],"about_ca_topic_score_codex":0.000014936126,"about_ca_topic_score_gemma":0.000016385677,"teacher_disagreement_score":0.40682125,"about_ca_system_score_codex":0.000085512955,"about_ca_system_score_gemma":0.0001116237,"threshold_uncertainty_score":0.9999037},"labels":[],"label_agreement":null},{"id":"W1596697568","doi":"","title":"A responsitivity-based criterion for accurate calibration of FTIR spectra: theoretical development and bandwidth estimation","year":2011,"lang":"en","type":"article","venue":"Sound Ideas (University of Puget Sound)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Eurostars; Office of Experimental Program to Stimulate Competitive Research; National Aeronautics and Space Administration; National Science Foundation","keywords":"Radiance; Spectrometer; Optics; Fourier transform; Radiometric calibration; Radiometry; Remote sensing; Interferometry; Physics; Bandwidth (computing); Calibration; Fourier transform spectroscopy; Fourier transform infrared spectroscopy; Geology; Telecommunications; Computer science","score_opus":0.02853209788942167,"score_gpt":0.253337065310798,"score_spread":0.22480496742137634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596697568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76021063,0.00003594278,0.23859237,0.000051504536,0.0000128482525,0.0000676592,0.000028874054,0.00002817628,0.0009720058],"genre_scores_gemma":[0.96464425,0.000004689384,0.035110608,0.000012782058,0.000011870623,8.361938e-7,0.00005678686,0.000009170656,0.00014902327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992533,0.000029083923,0.000166372,0.00022558981,0.00016288417,0.00016282265],"domain_scores_gemma":[0.9992848,0.00020262762,0.00018325518,0.00016091303,0.00009854579,0.00006984327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022640037,0.00012552971,0.00025277666,0.00017747177,0.00018631421,0.0000166354,0.00012329938,0.00011595439,0.0005904872],"category_scores_gemma":[0.00011819755,0.00014427645,0.000085126885,0.00022629913,0.0003525649,0.0002242707,0.00003835471,0.000076922515,0.0000016692128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020339312,0.0004651014,0.0060210614,0.0007997891,0.00032115434,0.000012034962,0.008945125,0.000058259593,0.016789239,0.96418566,0.00010039983,0.0002682391],"study_design_scores_gemma":[0.001086114,0.00011728159,0.0015948777,0.000038311384,0.0002458364,0.0000035357643,0.0025874847,0.009386966,0.05567027,0.92902803,0.000024364172,0.00021691657],"about_ca_topic_score_codex":0.00005496831,"about_ca_topic_score_gemma":0.000043263448,"teacher_disagreement_score":0.2044336,"about_ca_system_score_codex":0.00006877424,"about_ca_system_score_gemma":0.00010470997,"threshold_uncertainty_score":0.6465421},"labels":[],"label_agreement":null},{"id":"W1635080848","doi":"","title":"Segmentation and Detection of Extended Structures in Low Frequency Astronomical Surveys using Hybrid Wavelet Decomposition","year":2011,"lang":"en","type":"article","venue":"ASPC","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Segmentation; Wavelet; Brightness; Computer science; Computer vision; Galactic plane; Pattern recognition (psychology); Physics; Astronomy","score_opus":0.021920654168903167,"score_gpt":0.27938887430614007,"score_spread":0.2574682201372369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1635080848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905854,0.00008979017,0.008847952,0.0000013902586,0.00002150711,0.000027019363,0.000010133498,0.000011972527,0.00040482584],"genre_scores_gemma":[0.9954769,0.0000071981253,0.004452076,0.0000030138274,0.000023325732,0.000002202884,0.000022164431,0.000007226195,0.000005863025],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994633,0.000032404194,0.0001850302,0.0001476658,0.00006512077,0.00010645259],"domain_scores_gemma":[0.9997442,0.000025698328,0.0000960631,0.00008600005,0.000020238467,0.000027793947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009769462,0.000078918034,0.00012735811,0.00010616697,0.000027382615,0.000007794618,0.00004404991,0.000046729296,0.0003621795],"category_scores_gemma":[0.0000173852,0.000081961465,0.0000270689,0.00011336381,0.000038223912,0.00010179658,0.000013966506,0.00007893123,7.9961643e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020324487,0.000044164386,0.04425354,0.000031740874,0.000026605947,0.0000015088079,0.000081279555,0.000008884902,0.9515745,0.000023740655,5.934841e-7,0.00393313],"study_design_scores_gemma":[0.0002436891,0.000020099678,0.17556009,0.000008369656,0.000031233118,0.000006432444,0.00011843304,0.0007018227,0.8222424,0.0010041022,7.353247e-8,0.000063288775],"about_ca_topic_score_codex":0.0006242012,"about_ca_topic_score_gemma":0.000053960204,"teacher_disagreement_score":0.13130654,"about_ca_system_score_codex":0.00007190476,"about_ca_system_score_gemma":0.000012688451,"threshold_uncertainty_score":0.39656115},"labels":[],"label_agreement":null},{"id":"W1643611377","doi":"10.1017/s0074180900195087","title":"Advantages of the Broadening Function (BF) over the Cross-Correlation Function (CCF)","year":2004,"lang":"en","type":"article","venue":"Symposium - International Astronomical Union","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deconvolution; Function (biology); Simplicity; Correlation; Cross-correlation; Mathematics; Correlation function (quantum field theory); Applied mathematics; Statistics; Algorithm; Physics; Quantum mechanics","score_opus":0.0056420679754663455,"score_gpt":0.2509227497458954,"score_spread":0.24528068177042905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1643611377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96994287,0.000056068726,0.020689504,0.0009740554,0.0009101882,0.000069355745,0.00002203775,0.00004405658,0.0072918586],"genre_scores_gemma":[0.99744016,0.000008401014,0.00010689114,0.00006582977,0.00052333367,0.000013014516,0.00008355073,0.000017137583,0.0017416838],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998844,0.000022890863,0.00036461465,0.00026311647,0.00033166402,0.00017367597],"domain_scores_gemma":[0.9992262,0.000085472646,0.0002914723,0.00026028138,0.000099014476,0.00003754333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012673887,0.00015908208,0.00013169278,0.00008357612,0.00017746752,0.00007417216,0.00037984,0.00011581882,0.0008957313],"category_scores_gemma":[0.000049900682,0.00010719113,0.00020356501,0.00020467165,0.00014926965,0.00029703026,0.000118426564,0.00030872456,0.00002767495],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040275577,0.00023677433,0.3984643,0.000020593023,0.00037627137,2.462179e-7,0.00013893671,0.09862576,0.48767555,0.012550953,0.00020291208,0.0013049513],"study_design_scores_gemma":[0.0021598553,0.00011280647,0.69031984,0.00007536189,0.00035361474,0.00000988827,0.00033881378,0.00413785,0.29336998,0.002650238,0.0061591486,0.00031260643],"about_ca_topic_score_codex":0.00013103172,"about_ca_topic_score_gemma":0.00001108783,"teacher_disagreement_score":0.29185554,"about_ca_system_score_codex":0.00036716586,"about_ca_system_score_gemma":0.000050033017,"threshold_uncertainty_score":0.98076296},"labels":[],"label_agreement":null},{"id":"W1660868925","doi":"10.1109/ultsym.1988.49433","title":"Practical ultrasonic spectrometric measurement of solution concentrations by a tracking technique","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Tracking (education); Resonator; Ultrasonic sensor; Resonance (particle physics); Temperature measurement; Function (biology); Acoustics; Analytical Chemistry (journal); Methanol; Phase (matter); Materials science; Computer science; Chemistry; Physics; Optoelectronics; Chromatography; Thermodynamics; Organic chemistry","score_opus":0.04669278242212255,"score_gpt":0.3153679992945924,"score_spread":0.26867521687246987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1660868925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025580406,0.0017104488,0.75671774,0.00037109826,0.000037792448,0.00020923231,0.000015382959,0.00018741647,0.21517047],"genre_scores_gemma":[0.98851573,0.00011223498,0.01080428,0.000021316808,0.000016528913,0.000027071737,0.0000069265757,0.000011045661,0.00048484476],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872774,0.000027872202,0.0003085388,0.00022044792,0.00044901393,0.00026639717],"domain_scores_gemma":[0.9992876,0.00013308795,0.00014612272,0.00020754652,0.00015111522,0.00007451651],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035461187,0.00012555458,0.0002055794,0.00014998656,0.000072269584,0.000022973489,0.00008441098,0.00011294994,0.004719933],"category_scores_gemma":[0.0012629653,0.00011758407,0.000095193594,0.0011815286,0.000058712725,0.0001194708,0.000006581117,0.00019453526,0.000009705419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000477115,0.0003122717,0.0016991657,0.000027655118,0.0000762018,8.75069e-7,0.000012387312,7.3841267e-7,0.9891393,0.0059043937,0.0027598382,0.000062356914],"study_design_scores_gemma":[0.00023148804,0.000034898825,0.00004769563,0.000009234025,0.00013638347,0.000025113812,0.00020159925,0.000023342387,0.99736464,0.00022277207,0.0015762583,0.00012659424],"about_ca_topic_score_codex":0.00003351565,"about_ca_topic_score_gemma":0.0000058361497,"teacher_disagreement_score":0.9629353,"about_ca_system_score_codex":0.00028550875,"about_ca_system_score_gemma":0.0001503058,"threshold_uncertainty_score":0.9961899},"labels":[],"label_agreement":null},{"id":"W1681713269","doi":"10.1016/b978-012763560-6/50021-0","title":"NIR Band Assignments for Organic Compounds, Polymers, and Rubbers","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Near-infrared spectroscopy; Polymer; Materials science; Absorption (acoustics); Absorption band; Wavelength; Infrared; Polymer science; Chemistry; Analytical Chemistry (journal); Optoelectronics; Organic chemistry; Composite material; Optics; Physics","score_opus":0.015554846037453808,"score_gpt":0.2478558409538765,"score_spread":0.2323009949164227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1681713269","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026338495,0.0064288126,0.000017995504,0.00005319666,0.00010584923,0.0002013143,0.0001269645,0.0000867999,0.99271566],"genre_scores_gemma":[0.0061537493,0.00063221855,0.0001390362,0.00026198078,0.0004388402,0.00003116538,0.00014384664,0.00014848614,0.99205065],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981501,0.0000049803575,0.0004368739,0.00065240194,0.00031853808,0.0004371093],"domain_scores_gemma":[0.9987786,0.00014185079,0.0003152432,0.0004927704,0.000057268157,0.00021430138],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008604642,0.0005712352,0.0007661445,0.00018552114,0.00020530963,0.00008979137,0.00029823283,0.0005385516,0.0057873195],"category_scores_gemma":[0.000020090434,0.00056301226,0.00025824306,0.000028697532,0.00019532195,0.00003810153,0.0000853868,0.0004101038,0.000042043877],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019715511,0.00008530969,0.00040992675,0.0011609739,0.0035778894,0.00008228119,0.000279229,1.2181333e-7,0.04115929,0.0027532692,0.0070026373,0.9432919],"study_design_scores_gemma":[0.00087996904,0.000047337493,0.000003364446,0.00015338231,0.001103622,0.00004253334,0.000047451118,0.0000017732674,0.022553297,0.0041284645,0.9703686,0.000670197],"about_ca_topic_score_codex":0.0000015724903,"about_ca_topic_score_gemma":0.000009142711,"teacher_disagreement_score":0.963366,"about_ca_system_score_codex":0.00019380625,"about_ca_system_score_gemma":0.00009484758,"threshold_uncertainty_score":0.9996821},"labels":[],"label_agreement":null},{"id":"W172083801","doi":"","title":"Chemometric and signal processing methods for real time monitoring and modeling : applications in the pulp and paper industry","year":2007,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pulp (tooth); Signal processing; Process engineering; Computer science; Pulp and paper industry; Quality (philosophy); Agricultural engineering; Engineering; Digital signal processing; Computer hardware","score_opus":0.03989684984512271,"score_gpt":0.36318882042322903,"score_spread":0.3232919705781063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W172083801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8277785,0.004079844,0.16223353,0.0016461213,0.00001787682,0.00053272425,0.0001125397,0.000223747,0.0033751195],"genre_scores_gemma":[0.91297144,0.00021969125,0.086203724,0.000025240635,0.00007103754,0.00019432504,0.00018025909,0.000016073674,0.0001182102],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871254,0.000008708175,0.000415838,0.00044031852,0.00014172036,0.00028089774],"domain_scores_gemma":[0.9989881,0.00015539417,0.00021047704,0.00038749038,0.00018469259,0.00007386645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008487601,0.00018028753,0.0002568979,0.00092098536,0.00023586786,0.000078167985,0.00034511526,0.00039049538,0.000018156446],"category_scores_gemma":[0.0004841414,0.00015247421,0.000024581717,0.0020795015,0.00027719693,0.0004447386,0.0001966841,0.00047365483,2.8110955e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009422644,0.00062542106,0.11256196,0.0011983444,0.00019453584,0.0000033185004,0.00034594155,0.0001282146,0.27906647,0.013376429,0.00020080322,0.59220433],"study_design_scores_gemma":[0.0041418513,0.00016059991,0.012346135,0.0004053753,0.0011387002,0.00010562026,0.008691154,0.151191,0.7730046,0.0039740964,0.043027237,0.001813667],"about_ca_topic_score_codex":0.00007844904,"about_ca_topic_score_gemma":0.0000038481367,"teacher_disagreement_score":0.5903907,"about_ca_system_score_codex":0.00004786709,"about_ca_system_score_gemma":0.00005782578,"threshold_uncertainty_score":0.62177175},"labels":[],"label_agreement":null},{"id":"W1730608175","doi":"10.1093/mnras/stu205","title":"Artificial neural network based calibrations for the prediction of galactic [N ii] λ6584 and Hα line luminosities","year":2014,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Physics; Astrophysics; Galaxy; Metallicity; Balmer series; Active galactic nucleus; Line (geometry); Emission spectrum; Luminosity; Sky; Artificial neural network; Calibration; Stellar mass; Spectral line; Star formation; Astronomy; Artificial intelligence; Computer science; Mathematics; Geometry","score_opus":0.014200856991831738,"score_gpt":0.22284959457129044,"score_spread":0.2086487375794587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1730608175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967898,0.000162531,0.0016234769,0.0009214171,0.00009260757,0.00010174523,0.00015126774,0.000018486096,0.00013863663],"genre_scores_gemma":[0.997937,2.0795514e-7,0.0013111349,0.000045282108,0.0004848277,0.000019158408,0.000019439309,0.000010646736,0.00017229405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991742,0.000022405044,0.00030708028,0.00016092788,0.0001450373,0.00019035893],"domain_scores_gemma":[0.998762,0.0006791112,0.00023838329,0.00022807428,0.000052089898,0.00004033388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018692632,0.00011956109,0.00022001233,0.000007550546,0.00032922014,0.000026052445,0.00022499794,0.00009081779,0.000086676424],"category_scores_gemma":[0.000115902156,0.000076823926,0.00030158766,0.000087268774,0.00029461496,0.00005093794,0.00009016656,0.00015251555,1.4719195e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008097933,0.000093392395,0.029481698,0.00006942081,0.0001558389,3.2118106e-9,0.000111440386,0.9673499,0.00094077794,0.00007659406,0.0013854171,0.00025458095],"study_design_scores_gemma":[0.00022063007,0.00007339782,0.014524418,0.000011507698,0.00044372035,5.390077e-9,0.00021747367,0.9468912,0.03729666,0.000036449015,0.0002240261,0.000060526032],"about_ca_topic_score_codex":0.000113134556,"about_ca_topic_score_gemma":0.000018682444,"teacher_disagreement_score":0.036355883,"about_ca_system_score_codex":0.000038809463,"about_ca_system_score_gemma":0.000032480264,"threshold_uncertainty_score":0.31327888},"labels":[],"label_agreement":null},{"id":"W1745028821","doi":"10.4141/cjas-2014-024","title":"Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta; Alberta Livestock and Meat Agency; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Beef Cattle Research Council","keywords":"Near infrared reflectance spectroscopy; Reflectivity; Infrared; Spectroscopy; Infrared spectroscopy; Diffuse reflectance infrared fourier transform; Materials science; Remote sensing; Near-infrared spectroscopy; Optics; Chemistry; Physics; Astronomy; Geology","score_opus":0.02050082484740841,"score_gpt":0.29219383753205147,"score_spread":0.2716930126846431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1745028821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898954,0.00031757876,0.0006243731,0.00013601835,0.000045117195,0.000012389506,0.0000040358973,0.0000026306284,0.008962467],"genre_scores_gemma":[0.9941229,0.000014791704,0.0057021095,0.00006184199,0.00006428262,1.2022424e-7,2.699492e-7,0.000005889486,0.00002774653],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902064,0.000009137609,0.00025582424,0.00015217671,0.00026769924,0.00029450125],"domain_scores_gemma":[0.9989907,0.000033710545,0.00027388794,0.00011879117,0.00018511293,0.00039775635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035028628,0.00008764169,0.00019058549,0.00030103524,0.0002567554,0.0001228148,0.00033222765,0.00003775113,0.00013394398],"category_scores_gemma":[0.00036667963,0.00008083477,0.000041959767,0.0008730477,0.00067132025,0.00041306476,0.000015986288,0.00014218928,6.53412e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016465536,0.0000055852674,0.009029416,0.000021242853,0.000007184043,0.0000054367624,0.0002637452,0.000016787764,0.990056,0.0003579779,0.00008353711,0.0001366488],"study_design_scores_gemma":[0.0002517754,0.00030533582,0.020786032,0.00010286662,0.00007988743,0.00010514531,0.0005406348,0.0023465923,0.97392255,0.0010948108,0.00030362076,0.00016072618],"about_ca_topic_score_codex":0.0010346054,"about_ca_topic_score_gemma":0.00051083684,"teacher_disagreement_score":0.0161334,"about_ca_system_score_codex":0.00023189808,"about_ca_system_score_gemma":0.00090195256,"threshold_uncertainty_score":0.32963464},"labels":[],"label_agreement":null},{"id":"W174923934","doi":"10.1016/b978-012763560-6/50032-5","title":"Review of Chemometrics Applied to SpectroscopyData Preprocessing","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Preprocessor; Sample (material); Chemometrics; Data pre-processing; Data set; Computer science; Set (abstract data type); Calibration; Data mining; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Machine learning; Chemistry; Chromatography","score_opus":0.02422436136850505,"score_gpt":0.2892363286640501,"score_spread":0.26501196729554505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W174923934","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000103230495,0.09330827,0.000051898944,0.000056349047,0.00005440876,0.0003097954,0.000101212834,0.00011618471,0.90599155],"genre_scores_gemma":[0.00020722837,0.030548835,0.0013985068,0.0014578444,0.0004593445,0.00005091886,0.00015779225,0.00018237156,0.96553713],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646693,0.0000051823604,0.0011668986,0.0010630216,0.0007600872,0.0005378825],"domain_scores_gemma":[0.9966846,0.00012150513,0.0009116414,0.0017392571,0.0002509972,0.00029197845],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032872477,0.0007629257,0.0017875782,0.0006983589,0.000094703995,0.0000471066,0.0010635994,0.00050184666,0.008484949],"category_scores_gemma":[0.0002263439,0.00075676176,0.00044191146,0.0003823568,0.00014421283,0.00004069013,0.00039523994,0.0007623896,0.00026427547],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045023717,0.00005861761,0.000012513378,0.03622478,0.00057647156,0.000023185217,0.000057460482,4.1899366e-7,0.008570977,0.0016320035,0.0048509245,0.9479476],"study_design_scores_gemma":[0.0001973842,0.0000275329,6.0766166e-7,0.010644494,0.0011541689,0.000021143851,0.000009706123,4.5860492e-7,0.06690181,0.0016535543,0.918675,0.0007141287],"about_ca_topic_score_codex":8.55829e-7,"about_ca_topic_score_gemma":0.0000017115798,"teacher_disagreement_score":0.9472335,"about_ca_system_score_codex":0.00028693,"about_ca_system_score_gemma":0.00022841433,"threshold_uncertainty_score":0.99948835},"labels":[],"label_agreement":null},{"id":"W1774056419","doi":"10.1016/b978-012763560-6/50022-2","title":"Review of Interpretive Spectroscopy For Raman and Infrared","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Infrared; Fingerprint (computing); Infrared spectroscopy; Molecule; Raman spectroscopy; Vibration; Group (periodic table); Coupling (piping); Chemistry; Molecular vibration; Spectroscopy; Molecular physics; Computational chemistry; Physics; Materials science; Quantum mechanics; Computer science; Artificial intelligence","score_opus":0.014296863748274378,"score_gpt":0.28222057692226793,"score_spread":0.26792371317399355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1774056419","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013253766,0.1239452,0.000033771343,0.000034786623,0.00004381864,0.00029885708,0.00016704481,0.000042315747,0.8754209],"genre_scores_gemma":[0.00011735908,0.054359138,0.0007820966,0.00063945726,0.00023019416,0.000065119726,0.00007398685,0.00009196991,0.9436407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983089,0.000006548211,0.0006687018,0.00051951164,0.00021134075,0.00028497205],"domain_scores_gemma":[0.9983413,0.00020796443,0.0005872969,0.0005709106,0.00017367094,0.00011883087],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014277418,0.00048357283,0.0012112731,0.00017377829,0.000057135407,0.000021184098,0.00030840942,0.00035834647,0.0023850987],"category_scores_gemma":[0.00015438958,0.00045506115,0.0004311243,0.00003232693,0.00023466743,0.000029713136,0.00012118766,0.0003953492,0.000012731146],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030054714,0.00019234866,0.000031413863,0.08205414,0.0029513887,0.000033537028,0.0004564813,2.4838691e-8,0.0076128854,0.013400822,0.013702993,0.8792634],"study_design_scores_gemma":[0.0005748113,0.00009094455,3.7185774e-7,0.017735088,0.001334851,0.000019508632,0.000019628758,0.0000013326967,0.020704307,0.01175493,0.94730514,0.00045906616],"about_ca_topic_score_codex":5.2742047e-7,"about_ca_topic_score_gemma":0.0000019338847,"teacher_disagreement_score":0.93360215,"about_ca_system_score_codex":0.00012404575,"about_ca_system_score_gemma":0.000095265226,"threshold_uncertainty_score":0.99979013},"labels":[],"label_agreement":null},{"id":"W178902996","doi":"10.1016/b978-012763560-6/50031-3","title":"Review of Chemometrics Applied to SpectroscopyQuantitative and Qualitative Analysis","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Chemometrics; Computer science; Analytical Chemistry (journal); Chemistry; Chromatography; Machine learning","score_opus":0.031712125155389195,"score_gpt":0.34829763552218695,"score_spread":0.3165855103667978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W178902996","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000091814945,0.07185322,0.00011902738,0.0000727766,0.000020960157,0.000295063,0.00020937469,0.0000525187,0.92728525],"genre_scores_gemma":[0.0014816067,0.042917654,0.0022094524,0.00095876947,0.0001263422,0.000057693287,0.00016277585,0.000110852816,0.95197487],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99691373,0.00002317699,0.0010841147,0.00092153135,0.00064348866,0.000413935],"domain_scores_gemma":[0.99707496,0.0005234742,0.0009272761,0.0008507924,0.00034068234,0.0002828149],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00052865,0.0007147327,0.0025039967,0.0016869525,0.000082597326,0.000032193864,0.00044226355,0.00037989617,0.003970512],"category_scores_gemma":[0.00027648918,0.00068618485,0.0006558743,0.0010043974,0.00027402453,0.00003065093,0.00021044073,0.00057470443,0.000072811956],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032050494,0.00023291145,0.000068470115,0.060654286,0.036435105,0.00010421723,0.010983111,0.0000031697355,0.008643411,0.07316684,0.0038861018,0.8055019],"study_design_scores_gemma":[0.00063419546,0.00019296311,0.0000072527173,0.0071945293,0.02536129,0.00001531701,0.0014244223,0.0000023652376,0.032276973,0.012331118,0.9184893,0.0020702945],"about_ca_topic_score_codex":0.0000031578747,"about_ca_topic_score_gemma":0.000010071555,"teacher_disagreement_score":0.9146032,"about_ca_system_score_codex":0.00020415506,"about_ca_system_score_gemma":0.00009848338,"threshold_uncertainty_score":0.9995589},"labels":[],"label_agreement":null},{"id":"W1838682225","doi":"10.1002/cem.2700","title":"Simple methods for the optimization of complex‐valued kurtosis as a projection index","year":2015,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"Innovation PEI; National Aeronautics and Space Administration","keywords":"Projection pursuit; Kurtosis; Projection (relational algebra); Computer science; Maxima and minima; Algorithm; Principal component analysis; Simple (philosophy); Transformation (genetics); Multivariate statistics; Orthographic projection; Data mining; Index (typography); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Statistics","score_opus":0.1073682465068941,"score_gpt":0.42299255639326866,"score_spread":0.3156243098863746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1838682225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01924789,0.0019480846,0.9741998,0.0003621329,0.00016796748,0.00012770902,0.0000093960025,0.000017350836,0.0039197104],"genre_scores_gemma":[0.81234586,0.00028090295,0.1858897,0.00013437703,0.00047027928,0.000011801492,0.00001219674,0.000038855804,0.0008160406],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99837613,0.000039978997,0.00072413846,0.00013207745,0.0005256304,0.0002020701],"domain_scores_gemma":[0.9961593,0.000898684,0.0012021023,0.00023559759,0.0013763506,0.00012794156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015012732,0.0001531459,0.000472304,0.0009353579,0.00008014277,0.00005037238,0.0003973096,0.00015411146,0.00032359283],"category_scores_gemma":[0.006698926,0.000107138054,0.00029395716,0.0040557627,0.000068973284,0.00017472713,0.000049895276,0.00025438314,9.434952e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00620602,0.004880599,0.06442147,0.0022545024,0.009055616,0.000018249824,0.0043548155,0.22556733,0.37942567,0.0015368946,0.09617277,0.20610607],"study_design_scores_gemma":[0.0034496803,0.0005549119,0.00034512285,0.000025951305,0.0014368378,0.00013530951,0.0034147855,0.14274758,0.8260404,0.0021279852,0.019412784,0.00030863713],"about_ca_topic_score_codex":0.00003334824,"about_ca_topic_score_gemma":5.3523803e-7,"teacher_disagreement_score":0.793098,"about_ca_system_score_codex":0.00022285855,"about_ca_system_score_gemma":0.0002359519,"threshold_uncertainty_score":0.8019724},"labels":[],"label_agreement":null},{"id":"W1846001532","doi":"10.1002/0470027320.s8206","title":"Vibrational Spectroscopy of Membranes","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Membrane; Infrared spectroscopy; Chemistry; Infrared; Spectroscopy; Analytical Chemistry (journal); Materials science; Chromatography; Biochemistry; Organic chemistry; Optics; Physics","score_opus":0.012522475753760736,"score_gpt":0.2696507426927479,"score_spread":0.25712826693898716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1846001532","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019621475,0.01593054,0.18336993,0.00016168879,0.00023747052,0.00026986992,0.0009356028,0.00028825933,0.79861045],"genre_scores_gemma":[0.0022465624,0.01861081,0.78706443,0.00032758553,0.0054864055,0.00018939195,0.0038395748,0.0018321334,0.1804031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963006,0.00003525883,0.0010848925,0.00080634333,0.0012049054,0.000567993],"domain_scores_gemma":[0.99730057,0.00020566389,0.0013601419,0.0007842613,0.00016343786,0.0001859072],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000118238524,0.0007555642,0.0014162501,0.00082404196,0.00010159774,0.000050055358,0.000714327,0.0006884378,0.17794932],"category_scores_gemma":[0.00009582762,0.0007558257,0.0005195918,0.00076635706,0.000504511,0.00015100882,0.00010751193,0.00045846772,0.00014094336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011438157,0.00038264474,0.0014637264,0.0006504238,0.00063424854,0.000008323708,0.00003129281,0.000021861857,0.896893,0.0032375657,0.09654048,0.00002200632],"study_design_scores_gemma":[0.00088057196,0.0001374025,0.000035631285,0.0005199238,0.00043158146,0.000019648354,0.000056933277,0.000060291877,0.9859074,0.0016089468,0.009739379,0.0006023116],"about_ca_topic_score_codex":0.00036942607,"about_ca_topic_score_gemma":0.00013766767,"teacher_disagreement_score":0.61820734,"about_ca_system_score_codex":0.00015779695,"about_ca_system_score_gemma":0.00049245264,"threshold_uncertainty_score":0.99948925},"labels":[],"label_agreement":null},{"id":"W1850501806","doi":"10.1007/3-540-45014-9_5","title":"Multiple Classifier Combination Methodologies for Different Output Levels","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":122,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Machine learning; Artificial intelligence; Scope (computer science); Data mining; Data science","score_opus":0.10073118306966654,"score_gpt":0.32968489645825944,"score_spread":0.22895371338859288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1850501806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001258313,0.000532369,0.98841417,0.00027454615,0.00037514354,0.00018542778,0.00005015694,0.00011808369,0.008791788],"genre_scores_gemma":[0.823624,0.00011849082,0.1666314,0.0005845693,0.00063825405,0.00004353953,0.00006889997,0.00007138104,0.008219424],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997631,0.000013240939,0.00040938132,0.0009704452,0.0004893425,0.00048659134],"domain_scores_gemma":[0.99695796,0.0019889278,0.0002280924,0.0006030434,0.0001360652,0.00008593758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000300456,0.0004580741,0.0006411149,0.0005298655,0.00020189704,0.00016558536,0.0009700804,0.00047605915,0.00044957706],"category_scores_gemma":[0.000396861,0.00038060383,0.00021531215,0.0002484687,0.0005091462,0.00012014965,0.00021139724,0.0005529,0.0000110812025],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081471415,0.00012690519,0.00058276026,0.00027416428,0.00013227953,0.00001474996,0.00041805912,0.004970399,0.010089167,0.0037000864,0.00012838478,0.9794816],"study_design_scores_gemma":[0.0015149852,0.00022319065,0.0006625305,0.00030127657,0.00021892368,0.000021446065,0.000002426868,0.08257046,0.42280158,0.48503628,0.005146941,0.0014999302],"about_ca_topic_score_codex":0.000010676161,"about_ca_topic_score_gemma":0.000026354957,"teacher_disagreement_score":0.9779816,"about_ca_system_score_codex":0.0003429708,"about_ca_system_score_gemma":0.00012448124,"threshold_uncertainty_score":0.9998646},"labels":[],"label_agreement":null},{"id":"W1861841290","doi":"","title":"Identification of Log Characteristics in Computed Tomography Images Using Back-Propagation Neural Networks with the Resilient Back-Propagation Training Algorithm and Textural Analysis: Preliminary Results","year":2008,"lang":"en","type":"article","venue":"Wood and Fiber Science (Society of Wood Science and Technology)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; FPInnovations; New Brunswick Innovation Foundation","keywords":"Backpropagation; Artificial intelligence; Maple; Artificial neural network; Classifier (UML); Black spruce; Gradient descent; Algorithm; Pattern recognition (psychology); Mathematics; Computer science; Mean squared error; Machine learning; Statistics; Geography; Botany","score_opus":0.014514256087791767,"score_gpt":0.24301002464342575,"score_spread":0.22849576855563397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1861841290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944516,0.00041318015,0.0045256554,0.0004080237,0.000013086862,0.000108923035,0.000013085234,0.000021152173,0.00004531816],"genre_scores_gemma":[0.99398965,0.00015359162,0.005791146,0.000016881559,0.000012675654,0.000003550398,0.000005910065,0.0000040220966,0.000022593045],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982529,0.000011450427,0.00040858728,0.0005579302,0.0004553082,0.00031380032],"domain_scores_gemma":[0.9988012,0.000053441065,0.00044386266,0.00028186952,0.00036045554,0.000059185626],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00083268725,0.00015031635,0.00029838143,0.0005685424,0.0005788925,0.00007612506,0.0003667373,0.00011060647,0.0000029195396],"category_scores_gemma":[0.00006811939,0.0001070897,0.000044160897,0.008224123,0.006184194,0.00050565327,0.00015382365,0.00021811467,1.1163974e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013416469,0.00023619193,0.059786286,0.00015954865,0.00019787262,0.000005540046,0.0061382293,0.0010523308,0.88310003,0.00010758468,0.000043270822,0.049038973],"study_design_scores_gemma":[0.0005979742,0.00026676632,0.08642705,0.000068316745,0.00022638451,0.000067065695,0.00596379,0.66255605,0.24353068,0.000049892322,0.0000031533125,0.00024287263],"about_ca_topic_score_codex":0.000027768983,"about_ca_topic_score_gemma":0.0000022299444,"teacher_disagreement_score":0.66150373,"about_ca_system_score_codex":0.000035004567,"about_ca_system_score_gemma":0.00011055465,"threshold_uncertainty_score":0.9965204},"labels":[],"label_agreement":null},{"id":"W187159140","doi":"10.1007/978-3-642-03882-2_535","title":"Estimation of Betamethasone Release Profiles from an in Situ Forming System Based on the Biodegradable Polymer Using Artificial Neural Networks","year":2009,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Multilayer perceptron; Perceptron; Artificial intelligence; Principal component analysis; Computer science; Pattern recognition (psychology); Biological system; Biology","score_opus":0.01631426020408913,"score_gpt":0.25158153582863857,"score_spread":0.23526727562454944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W187159140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59370005,0.038045835,0.07466074,0.0065829474,0.0074282065,0.0057376283,0.00425835,0.0032289878,0.2663572],"genre_scores_gemma":[0.99383855,0.00006167588,0.00023910895,0.00040593432,0.000984258,0.00002175458,0.0006503986,0.00010970821,0.0036886395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964199,0.000044167544,0.0009621183,0.00068118854,0.0013270808,0.0005655249],"domain_scores_gemma":[0.997623,0.0006164621,0.00048463937,0.00073281926,0.0000522489,0.000490794],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036090182,0.000696654,0.0010943182,0.00039459323,0.00012444743,0.00006980541,0.0005473583,0.0006835529,0.0007171907],"category_scores_gemma":[0.00008165268,0.0005320324,0.00025882083,0.0003866398,0.00039931678,0.00010305349,0.00009936768,0.0016001428,0.000005994614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025177805,0.00682322,0.00021265655,0.0060452553,0.004118229,0.0023883653,0.00045444578,0.16931778,0.03905774,0.12133211,0.009832672,0.63789976],"study_design_scores_gemma":[0.0006606903,0.000105579806,0.000008077896,0.002037724,0.00035803617,0.00000491204,0.00002070328,0.9781182,0.016222248,0.00025701657,0.0016204432,0.00058640505],"about_ca_topic_score_codex":0.00015156892,"about_ca_topic_score_gemma":0.000049458107,"teacher_disagreement_score":0.8088004,"about_ca_system_score_codex":0.00016464236,"about_ca_system_score_gemma":0.00012244313,"threshold_uncertainty_score":0.9997131},"labels":[],"label_agreement":null},{"id":"W1886065653","doi":"10.1039/b102901k","title":"Near infrared reflectance spectroscopy for the fast identification of PVC-based films","year":2001,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Vinyl chloride; Polyvinyl chloride; Partial least squares regression; Materials science; Calibration; Near-infrared spectroscopy; Reflectivity; Spectroscopy; Analytical Chemistry (journal); Near infrared reflectance spectroscopy; Infrared spectroscopy; Chromatography; Composite material; Chemistry; Mathematics; Optics; Organic chemistry; Polymer","score_opus":0.021445390959424635,"score_gpt":0.3068511403721137,"score_spread":0.28540574941268904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1886065653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8839402,0.0034754637,0.085234754,0.0029314205,0.00011231234,0.00035133102,0.0001206006,0.00014990219,0.023684014],"genre_scores_gemma":[0.9919432,0.00013582435,0.00062816666,0.00017137987,0.00011777517,0.000046566995,0.000040852003,0.00001803445,0.0068982164],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989063,0.000017948503,0.0003760349,0.00022043228,0.00024180133,0.00023747503],"domain_scores_gemma":[0.9983561,0.00035747013,0.00032307164,0.000802081,0.00012835096,0.000032907705],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028538003,0.00014063339,0.00023516141,0.000063505955,0.00033609083,0.00007414684,0.00059911975,0.00006441952,0.0010259851],"category_scores_gemma":[0.00014433578,0.00008481439,0.00022588963,0.0010399715,0.00019083625,0.000057325833,0.000023830058,0.00013781998,0.000023973855],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018931204,0.00009959148,0.0034351638,0.000053954154,0.0003427787,0.0000010505356,0.00017820662,0.0014530407,0.9885445,0.0004585605,0.0050277496,0.00021606935],"study_design_scores_gemma":[0.00037292554,0.00002819398,0.0007295186,0.000011092503,0.000645767,0.0000023221519,0.0005552389,0.02225966,0.9697659,0.000745186,0.004754966,0.00012921348],"about_ca_topic_score_codex":0.000078491554,"about_ca_topic_score_gemma":0.00003544683,"teacher_disagreement_score":0.10800298,"about_ca_system_score_codex":0.000044122433,"about_ca_system_score_gemma":0.00005775006,"threshold_uncertainty_score":0.9998872},"labels":[],"label_agreement":null},{"id":"W1906343663","doi":"10.1002/0470027320.s8934","title":"Introduction to Vibrational Spectroscopy in Food Science","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Food quality; Sophistication; Food safety; Quality (philosophy); Process (computing); Data science; Computer science; Nanotechnology; Biochemical engineering; Chemistry; Engineering; Food science; Materials science; Physics","score_opus":0.010808339088695846,"score_gpt":0.27431286905154634,"score_spread":0.2635045299628505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906343663","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010280848,0.0038298168,0.3349871,0.0019263099,0.0006761448,0.00062342605,0.0004452746,0.00040028212,0.6560836],"genre_scores_gemma":[0.0035320055,0.0023154956,0.8879194,0.0007239097,0.017321397,0.0003899731,0.0012928239,0.0012073528,0.08529763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995662,0.000024201807,0.000820272,0.0013009747,0.0013977749,0.0007948241],"domain_scores_gemma":[0.9981606,0.00007707734,0.0005387065,0.00078752876,0.00015787168,0.00027819176],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025717757,0.0006312585,0.00088469475,0.0021586844,0.00016329919,0.00013947321,0.0008304477,0.00039484332,0.07228818],"category_scores_gemma":[0.0002301843,0.0006729286,0.00019537579,0.0026613683,0.0005209556,0.0002876805,0.0001585604,0.00052475434,0.00030450078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008458214,0.00027748977,0.00095156766,0.00009085178,0.00009555395,0.000004343209,0.000043731117,0.000073017756,0.850433,0.005042221,0.14287335,0.000030317755],"study_design_scores_gemma":[0.0006151819,0.0002578682,0.00012998118,0.00024112637,0.00007176296,0.000016464353,0.000058968664,0.000055678025,0.9784339,0.0011966818,0.018330371,0.00059201644],"about_ca_topic_score_codex":0.00021821554,"about_ca_topic_score_gemma":0.00050289516,"teacher_disagreement_score":0.57078594,"about_ca_system_score_codex":0.00072230113,"about_ca_system_score_gemma":0.00084335514,"threshold_uncertainty_score":0.9995722},"labels":[],"label_agreement":null},{"id":"W1910121819","doi":"10.1109/imtc.2000.848827","title":"Algorithms for interpretation of spectrometric data-a comparative study","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Tikhonov regularization; Algorithm; Kalman filter; Interpretation (philosophy); Computer science; Bandwidth (computing); Regularization (linguistics); Blind deconvolution; Mathematical optimization; Mathematics; Inverse problem; Artificial intelligence","score_opus":0.16688190590979243,"score_gpt":0.39104454223676055,"score_spread":0.22416263632696812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910121819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71623385,0.00157861,0.095071025,0.000089694266,0.00009327151,0.00051351334,0.00027732414,0.0001706763,0.18597205],"genre_scores_gemma":[0.9912995,0.000014077761,0.0045704804,0.000011897404,0.00004196314,0.000016412774,0.000059513815,0.000007692001,0.0039784634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911606,0.000007928074,0.00027782298,0.00028236484,0.00017605512,0.00013975136],"domain_scores_gemma":[0.99902266,0.00026093866,0.00012816614,0.00047025736,0.00008247144,0.000035492296],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009533354,0.00010785161,0.00031079643,0.00024988505,0.000037601603,0.000018095174,0.00037962812,0.000035308618,0.0057190666],"category_scores_gemma":[0.00011680634,0.00009128669,0.000056678233,0.00093537784,0.000031487147,0.0001329436,0.00006986286,0.00006905736,0.000023760971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015341311,0.06048752,0.13633038,0.0020895062,0.02339665,0.00004131815,0.057698056,0.00063498033,0.31620115,0.0059162593,0.25118953,0.14448051],"study_design_scores_gemma":[0.0035914453,0.0010796167,0.001083803,0.000014113532,0.0015361664,0.0000068398817,0.03139395,0.38271064,0.5761869,0.00044600727,0.0013333397,0.00061720953],"about_ca_topic_score_codex":0.000043956585,"about_ca_topic_score_gemma":0.000013204256,"teacher_disagreement_score":0.38207564,"about_ca_system_score_codex":0.000035191246,"about_ca_system_score_gemma":0.0000055267706,"threshold_uncertainty_score":0.99518985},"labels":[],"label_agreement":null},{"id":"W1912022245","doi":"10.1139/cjc-2012-0372","title":"Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backpropagation; Conjugate gradient method; Artificial neural network; Gradient descent; Levenberg–Marquardt algorithm; Chemistry; Artificial intelligence; Furan; Algorithm; Biological system; Computer science; Organic chemistry","score_opus":0.017786928018606544,"score_gpt":0.21813789303434125,"score_spread":0.2003509650157347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1912022245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99594325,0.00198421,0.00018316749,0.00027444525,0.00001886596,0.000021923925,0.000007111404,0.0000018697152,0.0015651649],"genre_scores_gemma":[0.9991792,0.000025772717,0.0006214759,0.000016677359,0.00011161639,0.0000015467956,0.00000521539,0.000008722175,0.000029817265],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899757,0.000013609295,0.00059544545,0.00008943278,0.00011970235,0.00018426821],"domain_scores_gemma":[0.99899715,0.00006186676,0.0004976748,0.00010903781,0.00020900898,0.00012528092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014111631,0.000099924095,0.00026518904,0.00007673474,0.00007117879,0.000017677608,0.0001659313,0.00008077768,0.00040455852],"category_scores_gemma":[0.00017190646,0.00008376481,0.00006978025,0.00036457056,0.00017684608,0.00012232734,0.000015385336,0.0002262073,2.0056747e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011920305,0.000013322945,0.059077878,0.00024525443,0.00017045907,0.0000021626347,0.0007535378,0.009187747,0.9286522,0.00016326999,0.00012313697,0.0015990983],"study_design_scores_gemma":[0.00018474691,0.000009095764,0.000794835,0.00027074863,0.00008293947,0.000024233124,0.004938277,0.018042423,0.9696155,0.005890991,0.000028851482,0.000117356765],"about_ca_topic_score_codex":0.0010072376,"about_ca_topic_score_gemma":0.0002852295,"teacher_disagreement_score":0.058283042,"about_ca_system_score_codex":0.000052810807,"about_ca_system_score_gemma":0.00035919598,"threshold_uncertainty_score":0.4429632},"labels":[],"label_agreement":null},{"id":"W1927916289","doi":"10.1109/imtc.2000.848694","title":"Signal processing support for interpretation of circular dichroic data in the study of protein","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Dichroic glass; Interpretation (philosophy); Computer science; SIGNAL (programming language); Signal processing; Experimental data; Artificial intelligence; Quality (philosophy); Data mining; Computer vision; Digital signal processing; Optics; Mathematics; Physics; Computer hardware; Statistics","score_opus":0.05393793540883196,"score_gpt":0.3259311681658643,"score_spread":0.27199323275703235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1927916289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852269,0.00027462386,0.008239851,0.000033671884,0.000002296433,0.00030697175,0.000013311856,0.00001110692,0.0058912463],"genre_scores_gemma":[0.9993258,0.0000015796828,0.00028581885,0.00001274205,0.000014024966,0.000039250874,0.000023825454,0.000005818216,0.00029113164],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992594,0.0000117992795,0.00027915832,0.00016546295,0.00019333526,0.00009082133],"domain_scores_gemma":[0.9994208,0.000059555474,0.00013521005,0.00032912093,0.000045281588,0.000009990341],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002046064,0.00006478751,0.00015786669,0.00007264784,0.00002109898,0.000011047601,0.0004103055,0.000031210875,0.0009862973],"category_scores_gemma":[0.000075458134,0.000044725217,0.00002603742,0.00035161455,0.000028078503,0.00010585081,0.000039787865,0.000061742016,7.526293e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024701923,0.010825664,0.06918378,0.0040231966,0.00052919803,0.000013771081,0.023054492,0.00013027099,0.8282686,0.00012498429,0.00087082357,0.06272821],"study_design_scores_gemma":[0.0029097118,0.0007801238,0.0011546956,0.00012835633,0.0005863091,0.000006755854,0.046648104,0.15549935,0.7914241,0.00031621047,0.0002252429,0.00032104083],"about_ca_topic_score_codex":0.00005115587,"about_ca_topic_score_gemma":0.000020618148,"teacher_disagreement_score":0.15536909,"about_ca_system_score_codex":0.000012863236,"about_ca_system_score_gemma":0.000016090155,"threshold_uncertainty_score":0.9999269},"labels":[],"label_agreement":null},{"id":"W1932774954","doi":"10.5539/apr.v7n6p34","title":"Optical Imaging Method for Determining Symptoms Severity of Cassava Mosaic Disease","year":2015,"lang":"en","type":"article","venue":"Applied Physics Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Abdus Salam International Centre for Theoretical Physics","keywords":"Mosaic; Constraint (computer-aided design); Scale (ratio); Computer science; Mathematics; Cartography; Geography; Geometry","score_opus":0.10984591794632857,"score_gpt":0.4374364115334293,"score_spread":0.32759049358710074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1932774954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6105161,0.00065388356,0.24879462,0.00030496725,0.00007919711,0.0007376139,0.00031133182,0.0002215991,0.1383807],"genre_scores_gemma":[0.98661715,0.0000045594097,0.012510266,0.000028009923,0.00025157546,0.00014035462,0.0000620428,0.00003664474,0.00034939838],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980501,0.000027958326,0.00023236069,0.0004008995,0.0007371137,0.00055156526],"domain_scores_gemma":[0.9979199,0.00082464074,0.000066882734,0.00047807264,0.0003412157,0.0003692485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077723723,0.00015248332,0.0003100718,0.000098563556,0.000122056284,0.000051028797,0.00038785744,0.000060301387,0.00006125573],"category_scores_gemma":[0.0002945977,0.0001506423,0.00011133346,0.00068872084,0.00020027783,0.00007371173,0.00022521164,0.00035877776,0.000016374088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010925998,0.001135645,0.014079532,0.0012586465,0.00050526555,0.00003355753,0.0010875097,0.00069822976,0.80919373,0.052999258,0.006423454,0.1114926],"study_design_scores_gemma":[0.0009927782,0.000024943578,0.000102229635,0.000018478979,0.00013971233,0.000001799868,0.00090060034,0.033634,0.9335859,0.029417349,0.0009216982,0.00026055268],"about_ca_topic_score_codex":0.00004458671,"about_ca_topic_score_gemma":7.4835447e-7,"teacher_disagreement_score":0.37610105,"about_ca_system_score_codex":0.0001510196,"about_ca_system_score_gemma":0.00028317084,"threshold_uncertainty_score":0.6143015},"labels":[],"label_agreement":null},{"id":"W1934588630","doi":"10.1007/978-3-642-54888-8_6","title":"Near-infrared Imaging and Spectroscopy","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Near-infrared spectroscopy; Chemical imaging; Spectroscopy; Electromagnetic spectrum; Spectral imaging; Materials science; Imaging spectroscopy; Multispectral image; Infrared; Optics; Fourier transform infrared spectroscopy; Remote sensing; Physics; Geography","score_opus":0.009584131315813142,"score_gpt":0.2410423286444237,"score_spread":0.23145819732861056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1934588630","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000139452,0.0029245112,0.000989037,0.00013861172,0.00004688149,0.000034687277,0.000018605007,0.00021278363,0.99549544],"genre_scores_gemma":[0.004776335,0.0004861927,0.0025990312,0.00037591238,0.00041050336,0.0000033109436,0.000058589165,0.00009366943,0.99119645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985692,0.00000207275,0.00030129135,0.0005614676,0.00024782517,0.00031816622],"domain_scores_gemma":[0.99900204,0.0000889981,0.00017758942,0.0005255198,0.000049111077,0.000156723],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000056266483,0.00044114317,0.00056819594,0.00012846306,0.00013478208,0.00016983064,0.00022739587,0.00028228443,0.036360867],"category_scores_gemma":[0.000028603637,0.00041308076,0.00016317268,0.00003905395,0.00021083711,0.000042439588,0.00012542143,0.00046414754,0.0002980847],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016207746,0.00008635677,0.004786587,0.0017426062,0.0022815794,0.0002648971,0.0002338231,0.0000026172575,0.08351117,0.52104264,0.36814854,0.0177371],"study_design_scores_gemma":[0.0005598867,0.00002504613,0.000017957476,0.00010806972,0.0007956433,0.000070620415,0.000042186562,0.0003083282,0.09495606,0.040753644,0.8612301,0.0011324624],"about_ca_topic_score_codex":0.000025178882,"about_ca_topic_score_gemma":0.0000038510834,"teacher_disagreement_score":0.49308154,"about_ca_system_score_codex":0.00008750196,"about_ca_system_score_gemma":0.000050933326,"threshold_uncertainty_score":0.9998321},"labels":[],"label_agreement":null},{"id":"W1939859687","doi":"10.1163/9789004683846_008","title":"Near infrared reflectance spectroscopy and related technologies for the analysis of feed ingredients","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Near infrared reflectance spectroscopy; Reflectivity; Spectroscopy; Infrared; Materials science; Infrared spectroscopy; Near-infrared spectroscopy; Remote sensing; Optics; Chemistry; Geography; Physics; Astronomy","score_opus":0.014507630067699503,"score_gpt":0.2700663742407837,"score_spread":0.2555587441730842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939859687","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004905037,0.0090955,0.00012961766,0.00019323551,0.000033632365,0.00019405554,0.00018092646,0.00033548023,0.98493254],"genre_scores_gemma":[0.123442434,0.008737724,0.002267233,0.000049143167,0.000039811006,0.000040616975,0.00019366444,0.00008683247,0.8651425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99834806,0.00000259096,0.000566819,0.0005301643,0.000266074,0.00028631662],"domain_scores_gemma":[0.9982821,0.00035598993,0.0004476621,0.0007758809,0.00010288766,0.000035486057],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008842312,0.0003869606,0.00092703145,0.0005200588,0.00018464598,0.000054790024,0.0004762645,0.0006818193,0.0048332615],"category_scores_gemma":[0.0001178663,0.00027633517,0.00042934934,0.0006677677,0.0004988436,0.000048089263,0.00009618027,0.0004371232,0.0000072565126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045247474,0.0008342731,0.0072668013,0.0029247152,0.29619923,0.000103987484,0.0028090673,0.0009335447,0.26602983,0.32001683,0.04485467,0.053502277],"study_design_scores_gemma":[0.0028043215,0.00038813942,0.00032234754,0.00037445736,0.07283247,0.000022334554,0.0011660384,0.0073374603,0.5876818,0.2011755,0.12323665,0.0026585432],"about_ca_topic_score_codex":0.000021744134,"about_ca_topic_score_gemma":0.00002113164,"teacher_disagreement_score":0.3216519,"about_ca_system_score_codex":0.00009086189,"about_ca_system_score_gemma":0.000049884693,"threshold_uncertainty_score":0.9999689},"labels":[],"label_agreement":null},{"id":"W1952785290","doi":"10.4141/cjas2010-001","title":"Short Communication: An investigation of the use of near infrared reflectance spectroscopy to predict the energy value of meat and bone meal for swine","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Near infrared reflectance spectroscopy; Meat and bone meal; Reflectivity; Meal; Bone meal; Near-infrared spectroscopy; Food science; Animal science; Chemistry; Biology; Fish meal; Optics; Physics","score_opus":0.06752269398508615,"score_gpt":0.28353982578796794,"score_spread":0.2160171318028818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1952785290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987077,0.00033040857,0.00016111821,0.00024234738,0.000018659077,0.00004137583,0.000021658017,0.0000013734299,0.00047535094],"genre_scores_gemma":[0.9922326,0.000026477815,0.007599098,0.00008719014,0.000024711813,0.0000012291825,5.569807e-7,0.0000048083034,0.000023328961],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990926,0.000024940373,0.00035377697,0.00010394117,0.00025746654,0.00016730312],"domain_scores_gemma":[0.9986309,0.00007307008,0.0003093006,0.00033002804,0.00039284807,0.00026388787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038244773,0.00007250352,0.00018520467,0.000121363286,0.00020066755,0.000029681722,0.00069219666,0.00003691861,0.000031862433],"category_scores_gemma":[0.0003518823,0.000047863512,0.00005220707,0.0008615132,0.0011285618,0.0002934844,0.00003411671,0.00009807825,2.7271179e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080866565,0.000010760927,0.03505717,0.000014216673,0.000019202622,5.577344e-7,0.0014910105,0.000031286305,0.9607262,0.0023144726,0.00018435926,0.0000698809],"study_design_scores_gemma":[0.00009942724,0.0003247117,0.090030454,0.00005417113,0.000062700004,0.000019276762,0.0003132651,0.00043615262,0.90785396,0.00062258274,0.00013250105,0.00005078415],"about_ca_topic_score_codex":0.0033631208,"about_ca_topic_score_gemma":0.0033239769,"teacher_disagreement_score":0.054973286,"about_ca_system_score_codex":0.00007525966,"about_ca_system_score_gemma":0.00088642124,"threshold_uncertainty_score":0.5084055},"labels":[],"label_agreement":null},{"id":"W1962191845","doi":"10.1002/cem.2568","title":"Re‐centered kurtosis as a projection pursuit index for multivariate data analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"University of California, Irvine; Innovation PEI","keywords":"Projection pursuit; Kurtosis; Principal component analysis; Projection (relational algebra); Index (typography); Computer science; Pattern recognition (psychology); Multivariate statistics; Artificial intelligence; Data set; Mathematics; Statistics; Algorithm; Data mining","score_opus":0.0747765203550032,"score_gpt":0.3559334119998017,"score_spread":0.2811568916447985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1962191845","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82875466,0.0033638459,0.1491426,0.0034240547,0.0006064565,0.0005717089,0.00025787525,0.00012380624,0.01375501],"genre_scores_gemma":[0.98773235,0.0002670418,0.008125868,0.00016200921,0.00049466034,0.00001597316,0.000091071866,0.000036124296,0.003074884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973479,0.000020274843,0.0009805856,0.00040617815,0.0008315385,0.000413532],"domain_scores_gemma":[0.99617016,0.00048328185,0.0013373872,0.0008319444,0.00093785604,0.00023937432],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000690241,0.00026462896,0.00079656247,0.0033570565,0.00011650899,0.00028168366,0.0013081438,0.00025839833,0.0030204926],"category_scores_gemma":[0.0040294114,0.00022422771,0.0005309806,0.009493505,0.000045818,0.0007707682,0.00020868672,0.00043098192,0.000025778361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016878466,0.005612278,0.32280073,0.0013943905,0.046576556,0.00006910339,0.0011824153,0.0005582311,0.36377358,0.000106953965,0.18551192,0.070726],"study_design_scores_gemma":[0.011389237,0.0008783889,0.023923721,0.00014244395,0.027656596,0.0001768871,0.006903433,0.050344396,0.8321842,0.002592559,0.0418286,0.0019795715],"about_ca_topic_score_codex":0.00023524523,"about_ca_topic_score_gemma":0.000011599092,"teacher_disagreement_score":0.46841058,"about_ca_system_score_codex":0.00027269108,"about_ca_system_score_gemma":0.00014064767,"threshold_uncertainty_score":0.9978909},"labels":[],"label_agreement":null},{"id":"W1963443146","doi":"10.1109/aim.1997.652867","title":"Industrial automation for a fish processing quality assurance workcell","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Workcell; Automation; Quality assurance; Context (archaeology); sort; Computer science; Identification (biology); Fish processing; Quality (philosophy); Fish <Actinopterygii>; Manufacturing engineering; Engineering; Artificial intelligence; Operations management; Database; Robot; Mechanical engineering; Fishery; Biology","score_opus":0.10161324624595335,"score_gpt":0.33390126403566567,"score_spread":0.2322880177897123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963443146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62388104,0.0005846754,0.027469417,0.0021229174,0.00012411912,0.00022168522,0.00004725915,0.0008143598,0.34473455],"genre_scores_gemma":[0.97537994,0.0000096140375,0.0020960511,0.00017572618,0.00032247542,0.00003390772,0.000016735916,0.000011298966,0.021954227],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919426,0.000007003402,0.00024592664,0.00020532803,0.00015220193,0.00019527996],"domain_scores_gemma":[0.9994669,0.00015709022,0.00012662119,0.00014466779,0.00005839103,0.00004631347],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011294151,0.00010085931,0.0001712897,0.000044967008,0.00010517126,0.00006883232,0.00011798871,0.00014811388,0.0048641507],"category_scores_gemma":[0.00032673287,0.00009236787,0.00007718752,0.00036181865,0.000022511007,0.00013157967,0.00001149768,0.00010866199,0.00002018611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002547805,0.0016639268,0.0561606,0.0018587919,0.0004270796,0.0000048273528,0.0011673277,0.0001594544,0.1813148,0.0013092501,0.47671336,0.27896583],"study_design_scores_gemma":[0.0027409054,0.000035117835,0.0007019936,0.00006858183,0.00018887257,0.000003605576,0.00059066905,0.021604327,0.93236023,0.0005346185,0.04057537,0.000595691],"about_ca_topic_score_codex":0.000019328832,"about_ca_topic_score_gemma":0.000011596132,"teacher_disagreement_score":0.75104547,"about_ca_system_score_codex":0.00005767146,"about_ca_system_score_gemma":0.000018212546,"threshold_uncertainty_score":0.99604553},"labels":[],"label_agreement":null},{"id":"W1963967323","doi":"10.1139/v01-198","title":"1-Propanol hydrate by IR spectroscopy","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chemistry; Hydrate; Hydrogen bond; Infrared spectroscopy; Propanol; Intermolecular force; Aqueous solution; Molecule; Spectroscopy; Physical chemistry; Alcohol; Crystallography; Analytical Chemistry (journal); Ethanol; Organic chemistry","score_opus":0.010503773822054948,"score_gpt":0.21076412234280084,"score_spread":0.2002603485207459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963967323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8087066,0.008647714,0.000099571545,0.001284396,0.00010960443,0.000019761472,0.00007664833,0.000025941194,0.1810298],"genre_scores_gemma":[0.9817251,0.00017600993,0.0003002951,0.00020068344,0.0004677049,0.0000015385872,0.000009131842,0.000034747674,0.017084792],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853194,0.000006363127,0.0004615899,0.00020417047,0.00025022827,0.0005457215],"domain_scores_gemma":[0.9981889,0.000035921592,0.00033622963,0.0003046724,0.00013886693,0.0009953711],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000082417566,0.00023401697,0.0003703589,0.000102905346,0.00013890884,0.0001015286,0.00045940344,0.00019897649,0.040674407],"category_scores_gemma":[0.00016247583,0.0002302827,0.00020378208,0.00038160334,0.00015484089,0.00013868639,0.000011191519,0.0005744523,0.00007511647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005188956,0.00003010076,0.0017432597,0.00005918345,0.000114971706,0.00024346347,0.00008311008,0.0000030203353,0.8551347,0.0000032420423,0.14242277,0.00015698363],"study_design_scores_gemma":[0.0003955248,0.000017176206,0.000012589504,0.000031665735,0.00009528373,0.00043293473,0.00023635596,0.000033208307,0.9557584,0.00010585231,0.04264297,0.00023804007],"about_ca_topic_score_codex":0.0003353434,"about_ca_topic_score_gemma":0.000094294286,"teacher_disagreement_score":0.17301853,"about_ca_system_score_codex":0.00037505545,"about_ca_system_score_gemma":0.00039467937,"threshold_uncertainty_score":0.9602026},"labels":[],"label_agreement":null},{"id":"W1964019662","doi":"10.1081/sta-100104350","title":"MULTI-FREQUENTIAL PERIODOGRAM ANALYSIS AND THE DETECTION OF PERIODIC COMPONENTS IN TIME SERIES","year":2001,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Estimator; Series (stratigraphy); Least-squares function approximation; Autoregressive model; Statistics; Trigonometric functions; Applied mathematics; Mathematical analysis","score_opus":0.02996039654467612,"score_gpt":0.3732688548245003,"score_spread":0.34330845827982415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964019662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4486207,0.005133478,0.54557323,0.000046378194,0.000011341925,0.00008671886,0.000031982014,0.00001666081,0.00047952033],"genre_scores_gemma":[0.87585,0.0029002419,0.12084345,0.000014754553,0.000004480522,0.000026008824,0.000035048935,0.0000066867537,0.00031932333],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99812305,0.0011705965,0.00035877,0.00015034899,0.000083566025,0.000113677066],"domain_scores_gemma":[0.99776006,0.0015655095,0.00017006253,0.0004280472,0.00004556141,0.000030778214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002187584,0.000107562824,0.0003574736,0.00024606666,0.0001408925,0.00004308101,0.00023569861,0.00007446161,0.0002542278],"category_scores_gemma":[0.0006914434,0.000088031935,0.00004441851,0.00072389375,0.0007664397,0.000071448296,0.00012011945,0.0002111057,6.4621315e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004717648,0.00083318236,0.2555857,0.0003643142,0.0016599102,0.000008336718,0.016482156,0.00016509663,0.49823675,0.050269708,0.0000047700305,0.17167245],"study_design_scores_gemma":[0.014435648,0.00014638536,0.5555805,0.0002084097,0.0053807194,0.00008440496,0.01412122,0.06458883,0.19174013,0.15074241,0.0017368576,0.0012344874],"about_ca_topic_score_codex":0.00034700963,"about_ca_topic_score_gemma":0.00017647636,"teacher_disagreement_score":0.42722932,"about_ca_system_score_codex":0.000027817394,"about_ca_system_score_gemma":0.000009729137,"threshold_uncertainty_score":0.3589838},"labels":[],"label_agreement":null},{"id":"W1964057864","doi":"10.1117/12.417168","title":"Multivariate image analysis for process monitoring and control","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Multispectral image; Principal component analysis; Artificial intelligence; Pixel; Computer vision; Process (computing); Remote sensing; Pattern recognition (psychology); Image processing; Data mining; Image (mathematics); Geology","score_opus":0.012023561229232316,"score_gpt":0.26680392247703977,"score_spread":0.25478036124780745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964057864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965142,0.00020396657,0.00042288686,0.0007981597,0.000059076407,0.00026970022,0.000079535996,0.00008420976,0.0015682753],"genre_scores_gemma":[0.9549724,0.00012195135,0.043809686,0.000027312966,0.00044634772,0.0002213371,0.000008867853,0.00004915447,0.00034295296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803257,5.6279066e-9,0.0006061982,0.00045099805,0.00048466746,0.00042553485],"domain_scores_gemma":[0.9975503,0.00025110663,0.00040755453,0.000057830828,0.0015975733,0.00013561019],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037343602,0.00032233275,0.0005983189,0.00018362334,0.00011970085,0.00014223845,0.0006486622,0.00020004029,0.000047117257],"category_scores_gemma":[0.0007772467,0.00027320758,0.00078469183,0.00074670155,0.00017985742,0.0004530423,0.00007510315,0.0002440462,4.9841196e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021649466,0.00014496302,0.014963259,0.0007517349,0.0034647489,1.0992429e-7,0.00018165864,0.000101233636,0.9566645,0.02322738,0.00014867993,0.00013523661],"study_design_scores_gemma":[0.0028328358,0.00016209944,0.0033263655,0.00015325645,0.0034815883,0.000012897701,0.0031129809,0.052198246,0.9322716,0.0011706586,0.0007330335,0.0005444261],"about_ca_topic_score_codex":0.00001559567,"about_ca_topic_score_gemma":7.898425e-8,"teacher_disagreement_score":0.05209701,"about_ca_system_score_codex":0.00011929123,"about_ca_system_score_gemma":0.000020736072,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W1964384910","doi":"10.1002/aic.10164","title":"Monitoring flames in an industrial boiler using multivariate image analysis","year":2004,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Boiler (water heating); Combustion; Process engineering; RGB color model; Partial least squares regression; Multivariate statistics; Engineering; Environmental science; Computer science; Waste management; Chemistry; Artificial intelligence; Machine learning","score_opus":0.07102780569309602,"score_gpt":0.3586727671171978,"score_spread":0.2876449614241018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964384910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965405,0.00027494223,0.0022623024,0.00007343561,0.00011425837,0.000016439015,0.0000033463257,0.000028679156,0.0006860932],"genre_scores_gemma":[0.9925408,0.000049996976,0.0060313772,0.000013942825,0.0011990899,0.0000013126717,0.000003321753,0.000020812258,0.00013935089],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985105,0.000041803214,0.0004932207,0.00023926576,0.00031638533,0.00039885344],"domain_scores_gemma":[0.99918365,0.0000461042,0.0002640175,0.00023106112,0.000086845816,0.00018831358],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039883595,0.0001904006,0.00039548732,0.0006252554,0.0001724976,0.00020549899,0.00027799554,0.00020630643,0.0012243978],"category_scores_gemma":[0.000181625,0.00017541314,0.00022053246,0.0016754551,0.00004074206,0.000506681,0.000045931964,0.0008731046,0.000010522361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046954396,0.00021111901,0.37590653,0.000006027702,0.0007746604,0.00015451855,0.0004846649,0.0068201115,0.614648,0.000004053354,0.000010501022,0.0009328789],"study_design_scores_gemma":[0.0032065804,0.000042399097,0.026874758,0.00007882343,0.00220292,0.00009121759,0.0023097303,0.001324164,0.9629255,0.00042158933,0.00005746472,0.0004648827],"about_ca_topic_score_codex":0.00058872235,"about_ca_topic_score_gemma":0.000027710423,"teacher_disagreement_score":0.34903178,"about_ca_system_score_codex":0.0004532623,"about_ca_system_score_gemma":0.00013360879,"threshold_uncertainty_score":0.9996886},"labels":[],"label_agreement":null},{"id":"W1966223536","doi":"10.1080/10601325.2013.802143","title":"Nitrile Rubber Reactor Operation Troubleshooting with Principal Component Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Macromolecular Science Part A","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Troubleshooting; Mooney viscosity; Principal component analysis; Process (computing); Statistical process control; Nitrile rubber; Computer science; Synthetic rubber; Natural rubber; Ranking (information retrieval); Process engineering; Acrylonitrile; Component (thermodynamics); Materials science; Artificial intelligence; Engineering; Copolymer","score_opus":0.01064874802136352,"score_gpt":0.25467266676122485,"score_spread":0.24402391873986135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966223536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915977,0.0001446924,0.00508742,0.00020250755,0.00003893172,0.000041494717,0.0000016135145,0.000012489691,0.0028731471],"genre_scores_gemma":[0.9964261,0.000010317543,0.0031258934,0.00008971501,0.0001103428,0.0000044079975,0.0000030873407,0.000009374597,0.00022078179],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979836,0.000019774616,0.00046912424,0.0002474549,0.00093276694,0.0003473158],"domain_scores_gemma":[0.9984787,0.000038149745,0.00046695306,0.00028076163,0.0004932292,0.00024220283],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005220049,0.00015127433,0.00036574632,0.00050116505,0.00019089038,0.0003106241,0.00049904507,0.000046710953,0.0018611298],"category_scores_gemma":[0.00011006763,0.00010655878,0.00019552828,0.0021345273,0.00020612289,0.0005510306,0.00006350856,0.00024679207,0.000028369577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002153065,0.0001794448,0.020129293,0.000011501254,0.00043453314,0.000054139124,0.00016417717,0.00071261293,0.97792006,0.000058308735,0.00014345761,0.00017096901],"study_design_scores_gemma":[0.0005662594,0.00010934519,0.0037384848,0.000026303189,0.0009917282,0.00018422774,0.00053892314,0.004103219,0.9889565,0.000032310425,0.000523807,0.00022888616],"about_ca_topic_score_codex":0.00005714715,"about_ca_topic_score_gemma":0.000005804684,"teacher_disagreement_score":0.016390808,"about_ca_system_score_codex":0.0001628937,"about_ca_system_score_gemma":0.00021899691,"threshold_uncertainty_score":0.99905133},"labels":[],"label_agreement":null},{"id":"W1966985101","doi":"10.1016/j.ejpb.2013.12.013","title":"Development of a multivariate light-induced fluorescence (LIF) PAT tool for in-line quantitative analysis of pharmaceutical granules in a V-blender","year":2013,"lang":"en","type":"article","venue":"European Journal of Pharmaceutics and Biopharmaceutics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"Université de Sherbrooke; Pfizer","keywords":"Process analytical technology; Multivariate statistics; Fluorescence; Process engineering; Critical quality attributes; Biological system; Process (computing); Fluorescence spectroscopy; Spectrum analyzer; Process control; Materials science; Particle size; Computer science; Analytical Chemistry (journal); Chemistry; Chromatography; Optics; Chemical engineering; Engineering; Machine learning; Physics; Bioprocess","score_opus":0.09651017128817813,"score_gpt":0.3826847537102118,"score_spread":0.2861745824220337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966985101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99623907,0.0016732435,0.0013024115,0.00018871982,0.00009661748,0.0002180692,0.00004078909,0.000009264061,0.00023180821],"genre_scores_gemma":[0.97756815,0.0013193424,0.020887624,0.00009423571,0.000051660092,0.0000063503667,0.000009693971,0.00003700761,0.000025955173],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99655,0.00017640418,0.0020278022,0.00032558115,0.00044527985,0.0004748942],"domain_scores_gemma":[0.99760276,0.00040442456,0.0010586218,0.00017562766,0.0005201917,0.00023837367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014845289,0.00035709963,0.0009996393,0.001229027,0.00006238889,0.00003887909,0.00043758517,0.00008274394,0.00023197506],"category_scores_gemma":[0.00022287336,0.00030334265,0.00029715945,0.00166206,0.0001379768,0.00020291217,0.00013723485,0.0005780049,0.000002390488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005788998,0.00079834205,0.00891036,0.00046350315,0.0016732075,0.000045272067,0.0019695628,0.00019335888,0.96720713,0.0001217687,0.000010997672,0.018027589],"study_design_scores_gemma":[0.005175284,0.00012209949,0.004346048,0.00015756857,0.0022046291,0.00001119672,0.0010039934,0.045795392,0.93901026,0.000026769883,0.0017859219,0.0003608226],"about_ca_topic_score_codex":0.0000095884625,"about_ca_topic_score_gemma":0.0000059348185,"teacher_disagreement_score":0.045602035,"about_ca_system_score_codex":0.00009695086,"about_ca_system_score_gemma":0.0001362914,"threshold_uncertainty_score":0.9999419},"labels":[],"label_agreement":null},{"id":"W1967097735","doi":"10.1016/j.talanta.2011.09.032","title":"Simultaneous determination of alcohol and carbohydrate content in commercial beverages by ultrasound frequency analysis","year":2011,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Chemistry; Ethanol; Chromatography; Sucrose; Ultrasound; Alcohol; Distilled water; Carbohydrate; Analytical Chemistry (journal); Ultrasonic sensor; Volume (thermodynamics); Food science; Biochemistry; Acoustics","score_opus":0.02981192306217358,"score_gpt":0.26690382913537986,"score_spread":0.23709190607320627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967097735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938808,0.00040607815,0.0000616942,0.000007752163,0.000009221503,0.000029918181,0.000080853024,0.000023097014,0.0055005965],"genre_scores_gemma":[0.99931216,0.00015960238,0.00008956603,0.000022686092,0.000011046809,0.000004701869,0.000058917976,0.00000985947,0.00033143538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992859,0.000013877621,0.00024831487,0.00018784987,0.000114901035,0.00014919505],"domain_scores_gemma":[0.999453,0.00017995406,0.0001229483,0.00016122207,0.00003722926,0.000045629236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005796347,0.00011537463,0.0002931447,0.00018022608,0.000028012151,0.000010188258,0.00011520851,0.00008565295,0.0004192629],"category_scores_gemma":[0.00010115003,0.00010978629,0.000064736996,0.00045018646,0.000075059776,0.000046888003,0.000020155221,0.000092882685,0.0000019638464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026716682,0.00013589776,0.42938602,0.000033310815,0.00021820821,0.00002605921,0.0007215875,0.0000016806796,0.5689318,0.00002129176,0.000021439115,0.00047597126],"study_design_scores_gemma":[0.0005649462,0.000039086786,0.03359404,0.000011719188,0.0010006923,0.000009287467,0.00050318224,0.00038276543,0.96348166,0.00018455903,0.000011735576,0.00021630655],"about_ca_topic_score_codex":0.0035603803,"about_ca_topic_score_gemma":0.0005961626,"teacher_disagreement_score":0.39579198,"about_ca_system_score_codex":0.00003667695,"about_ca_system_score_gemma":0.000008607473,"threshold_uncertainty_score":0.53822535},"labels":[],"label_agreement":null},{"id":"W1968133935","doi":"10.1016/s0039-9140(03)00042-0","title":"Measurement of lactate in whole human blood with near-infrared transmission spectroscopy","year":2003,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Merck Canada","keywords":"Chemistry; Correlation coefficient; Standard error; Analytical Chemistry (journal); Partial least squares regression; Calibration; Coefficient of determination; Infrared spectroscopy; Whole blood; Near-infrared spectroscopy; Chromatography; Statistics; Mathematics; Optics; Internal medicine","score_opus":0.01603768751135475,"score_gpt":0.2543816350955855,"score_spread":0.23834394758423072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968133935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93820786,0.00059768395,0.000077499615,0.000029144252,0.000006279826,0.000048354606,0.0000063000493,0.00003455375,0.06099232],"genre_scores_gemma":[0.9978795,0.00002702057,0.00042841237,0.000013041561,0.000012494782,0.0000069847933,0.000010365087,0.000020376256,0.0016018217],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988421,0.000017485512,0.00025618536,0.0002410811,0.0003755673,0.00026757742],"domain_scores_gemma":[0.9995019,0.000012121322,0.00010132989,0.0002666353,0.000043119973,0.00007490928],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013953402,0.00016316505,0.0002900311,0.00008842036,0.00006081427,0.00002100729,0.00014312347,0.00007204422,0.001177595],"category_scores_gemma":[0.00001747056,0.00013454292,0.00005908272,0.00041715198,0.00006514969,0.000055751047,0.000008526622,0.00016930862,0.000008300924],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005259386,0.00032646258,0.023306357,0.00009708322,0.00007969531,0.000023422994,0.00021130231,0.000008672935,0.9756447,0.00007045177,0.00014447447,0.00003479082],"study_design_scores_gemma":[0.00147808,0.00010036826,0.0016128733,0.00009931638,0.0001502523,0.000013202765,0.00021335995,0.000009935556,0.9945472,0.00016268018,0.0014447279,0.00016798697],"about_ca_topic_score_codex":0.000084673484,"about_ca_topic_score_gemma":0.000047979633,"teacher_disagreement_score":0.059671618,"about_ca_system_score_codex":0.000060927123,"about_ca_system_score_gemma":0.00007437494,"threshold_uncertainty_score":0.9997355},"labels":[],"label_agreement":null},{"id":"W1968749808","doi":"10.1007/s10916-009-9374-6","title":"Monitoring of Medication Intake Using a Camera System","year":2009,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Context (archaeology); Histogram; Event (particle physics); Histogram of oriented gradients; Pattern recognition (psychology); Image (mathematics)","score_opus":0.030953637906845816,"score_gpt":0.3295340018790064,"score_spread":0.2985803639721606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968749808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844388,0.0075359135,0.0054907547,0.0003143458,0.0006872016,0.00002873976,0.0000016219794,0.000020746596,0.0014818751],"genre_scores_gemma":[0.99794877,0.00018356362,0.00025604072,0.000008633206,0.0015462235,5.4090435e-7,4.377103e-7,0.000007585545,0.00004822989],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969067,0.000038812257,0.0010276233,0.00009844551,0.001754728,0.00017368572],"domain_scores_gemma":[0.9982045,0.000121960984,0.0010335535,0.00015938394,0.00019985223,0.00028069335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008451588,0.00010977154,0.00053317193,0.00018498332,0.00004158606,0.000022815399,0.0004081135,0.00022836639,0.00013882513],"category_scores_gemma":[0.00046317975,0.000082081846,0.00014590178,0.00033977136,0.000043718093,0.00008877989,0.000016056396,0.0003856507,0.0000020535886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003142011,0.0011824413,0.10960219,0.006701466,0.0018455365,0.0009731453,0.001874491,0.00066451106,0.8474272,0.002203827,0.0026634163,0.024547571],"study_design_scores_gemma":[0.009003704,0.0010499532,0.010011478,0.034629993,0.0035327587,0.01514854,0.060610563,0.038683176,0.8208189,0.00015167244,0.005032044,0.0013272262],"about_ca_topic_score_codex":0.00011184915,"about_ca_topic_score_gemma":1.5822044e-7,"teacher_disagreement_score":0.09959071,"about_ca_system_score_codex":0.0002692621,"about_ca_system_score_gemma":0.0001970029,"threshold_uncertainty_score":0.33472005},"labels":[],"label_agreement":null},{"id":"W1968764626","doi":"10.1017/s0021859614000392","title":"Maize yield forecasting by linear regression and artificial neural networks in Jilin, China","year":2014,"lang":"en","type":"article","venue":"The Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pacific Institute for Climate Solutions; University of Victoria; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Jilin University; University of East Anglia; Kwansei Gakuin University","keywords":"Yield (engineering); Fertilizer; Linear regression; Artificial neural network; Regression analysis; Statistics; Mathematics; Precipitation; Regression; Econometrics; Agronomy; Meteorology; Computer science; Geography; Artificial intelligence; Biology","score_opus":0.018725791157007267,"score_gpt":0.2530796815767757,"score_spread":0.23435389041976842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968764626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99785733,0.00051282544,0.00020216794,0.00070181047,0.00006885431,0.000016524684,4.6460863e-7,0.000005852618,0.00063418766],"genre_scores_gemma":[0.9993145,0.0000743179,0.000113346,0.00004325965,0.00032074214,2.3567861e-7,4.1059707e-7,0.0000024078786,0.00013081984],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990031,0.000022170814,0.00029031042,0.00011079155,0.0003219019,0.00025169988],"domain_scores_gemma":[0.9992501,0.00020102628,0.00029426816,0.000080337835,0.00007944905,0.000094797266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079940236,0.00010260098,0.00017007314,0.00004877546,0.00024699408,0.00006303661,0.0003923544,0.000047290505,0.000049253227],"category_scores_gemma":[0.0005116015,0.000043182703,0.00004078386,0.0007808237,0.00021431262,0.00026664726,0.000077427016,0.0003846311,4.7823943e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006419325,0.0000456543,0.0062416266,0.000010464742,0.000008777649,0.000003178212,0.00038546335,0.004770371,0.9814457,0.000028236665,0.0010409113,0.005955413],"study_design_scores_gemma":[0.00061013765,0.00033106067,0.114743724,0.0002920867,0.00017470992,0.0010172795,0.0031200284,0.13099544,0.74770766,0.0004087955,0.000114485316,0.00048458402],"about_ca_topic_score_codex":0.00004573815,"about_ca_topic_score_gemma":0.000013703102,"teacher_disagreement_score":0.23373803,"about_ca_system_score_codex":0.00004166426,"about_ca_system_score_gemma":0.000012184958,"threshold_uncertainty_score":0.18997039},"labels":[],"label_agreement":null},{"id":"W1968789570","doi":"10.1007/s11746-000-0023-7","title":"Determination of peroxide value by fourier transform near‐infrared spectroscopy","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Peroxide value; Fourier transform infrared spectroscopy; Near-infrared spectroscopy; Calibration; Gravimetric analysis; Reproducibility; Triphenylphosphine oxide; Materials science; Analytical Chemistry (journal); Dilution; Chemistry; Triphenylphosphine; Chromatography; Organic chemistry; Optics; Mathematics; Physics","score_opus":0.00505588204495799,"score_gpt":0.2504675432949801,"score_spread":0.24541166125002212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968789570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9739552,0.0006253473,0.00021129125,0.0007186423,0.00002702784,0.000015697917,0.000026195434,0.000016889762,0.024403697],"genre_scores_gemma":[0.95503503,0.0022638307,0.019778466,0.0006306027,0.00029609897,0.000003980723,0.0000060430275,0.000054280197,0.021931686],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99825996,0.000019759114,0.0006019199,0.0001816524,0.0006333449,0.0003033802],"domain_scores_gemma":[0.9982858,0.00011823062,0.0009612748,0.0003653381,0.00014590747,0.00012348307],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023423012,0.0002212571,0.0005530964,0.000019466592,0.00014736006,0.000040986884,0.0006387972,0.000099044526,0.0012879717],"category_scores_gemma":[0.00008161738,0.00016380964,0.0009399341,0.0006201171,0.0005662264,0.00013653917,0.000023681336,0.00051129,0.0000025117856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012090495,0.00020204303,0.000676498,0.00011144892,0.0002703295,0.0000021008514,0.0009839969,0.000021616235,0.96680576,0.0000015668052,0.011428188,0.019375555],"study_design_scores_gemma":[0.00060658983,0.0000625308,0.00013259758,0.000045946264,0.0003410238,0.00006440856,0.0008413453,0.00047847864,0.98861593,0.00014343036,0.008487963,0.00017976921],"about_ca_topic_score_codex":0.000055700442,"about_ca_topic_score_gemma":5.056723e-7,"teacher_disagreement_score":0.021810165,"about_ca_system_score_codex":0.00031444992,"about_ca_system_score_gemma":0.0001723203,"threshold_uncertainty_score":0.99962497},"labels":[],"label_agreement":null},{"id":"W1969418619","doi":"10.2116/analsci.25.627","title":"Automated and Simultaneous Determination of Free Fatty Acids and Peroxide Values in Edible Oils by FTIR Spectroscopy Using Spectral Reconstitution","year":2009,"lang":"en","type":"article","venue":"Analytical Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Northwest A and F University","keywords":"Chemistry; Fourier transform infrared spectroscopy; Peroxide value; Peroxide; Spectroscopy; Chromatography; Edible oil; Food science; Organic chemistry; Chemical engineering","score_opus":0.02130945263193915,"score_gpt":0.32366310646561147,"score_spread":0.3023536538336723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969418619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941708,0.00041227828,0.0006205425,0.00019511503,0.000012790595,0.000031059306,0.000008915737,0.000053420496,0.0044950745],"genre_scores_gemma":[0.9926298,0.0001345763,0.0071266755,0.00003413542,0.000028260602,4.6634315e-7,0.000002513056,0.0000024450094,0.000041129286],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988416,0.000012793275,0.0002728321,0.00033525797,0.0002553292,0.0002821822],"domain_scores_gemma":[0.99947894,0.00019594519,0.00008964315,0.00011391248,0.000031541713,0.00009004836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023664415,0.00012845137,0.00025675324,0.00017877689,0.00013247806,0.00007509249,0.00016509854,0.00009169027,0.00009536446],"category_scores_gemma":[0.00048194107,0.00010922396,0.000034060835,0.0007900912,0.00067666394,0.00023125374,0.000031588483,0.000106648,6.081401e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021142041,0.00010795772,0.020794617,0.00002895636,0.000010796583,0.000012475581,0.00008502498,0.00010223828,0.97658926,0.00061539555,0.000042038333,0.0015900983],"study_design_scores_gemma":[0.00026005047,0.00012730864,0.0018950285,0.000034810462,0.00007261357,0.000022103777,0.00022276418,0.28237197,0.7116859,0.0031581852,0.0000042019174,0.00014509527],"about_ca_topic_score_codex":0.00019708228,"about_ca_topic_score_gemma":0.000028644237,"teacher_disagreement_score":0.28226972,"about_ca_system_score_codex":0.00007876113,"about_ca_system_score_gemma":0.000051403553,"threshold_uncertainty_score":0.44540238},"labels":[],"label_agreement":null},{"id":"W1969881184","doi":"10.1002/cem.679","title":"The GIFI approach to non‐linear PLS modeling","year":2001,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tembec","funders":"Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse","keywords":"Principal component analysis; Linear regression; Coding (social sciences); Principal component regression; Computer science; Set (abstract data type); Regression; Regression analysis; Data set; Transformation (genetics); Linear model; Mathematics; Data mining; Statistics; Artificial intelligence; Machine learning; Chemistry","score_opus":0.037947435695457506,"score_gpt":0.2965185254049664,"score_spread":0.25857108970950893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969881184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68960303,0.0043283594,0.19287266,0.0010289007,0.0002658981,0.00007903221,0.0000046830196,0.000041725652,0.11177575],"genre_scores_gemma":[0.98546636,0.0011863485,0.00735698,0.00022969926,0.0009303804,0.0000031819332,0.0000018751837,0.00003591106,0.004789268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775374,0.000010630134,0.00075305643,0.00019628383,0.00084483007,0.00044144466],"domain_scores_gemma":[0.99810064,0.00027324763,0.0004143158,0.0003829136,0.00052862766,0.0003002554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075568346,0.00021055063,0.0004395393,0.0009039123,0.00022179307,0.00012572295,0.0008465873,0.00015232454,0.000120705445],"category_scores_gemma":[0.0015663974,0.00014179385,0.00030689643,0.0054667084,0.00004042924,0.00016140034,0.0000935688,0.00061025604,0.000026071915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036237151,0.008654639,0.051553264,0.0011722893,0.0071118847,0.0007196094,0.0056120274,0.24674572,0.42383763,0.002903398,0.12031878,0.12774704],"study_design_scores_gemma":[0.0053966455,0.0005794917,0.00038108716,0.0001563594,0.0017855474,0.0024617694,0.012401984,0.22040127,0.569927,0.0026260884,0.1817835,0.002099261],"about_ca_topic_score_codex":0.0000075485777,"about_ca_topic_score_gemma":3.7971796e-7,"teacher_disagreement_score":0.29586336,"about_ca_system_score_codex":0.00020808057,"about_ca_system_score_gemma":0.00010389666,"threshold_uncertainty_score":0.5782185},"labels":[],"label_agreement":null},{"id":"W1970078894","doi":"10.1016/j.meatsci.2006.06.031","title":"Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique","year":2006,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":141,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Hyperspectral imaging; Chemistry; Artificial intelligence; Computer science","score_opus":0.019005238592808005,"score_gpt":0.27648367439986105,"score_spread":0.25747843580705304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970078894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9825295,0.00038444117,0.01291685,0.00005495817,0.000025759276,0.000082607075,0.000020464327,0.000047640227,0.003937764],"genre_scores_gemma":[0.9855039,0.0000063465204,0.014207346,0.000009726585,0.00006366668,0.000010978318,0.0000020260547,0.00000550412,0.00019051063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991827,0.000002121157,0.00013891148,0.00024520236,0.00018766597,0.0002434226],"domain_scores_gemma":[0.99964225,0.00003109716,0.000074970136,0.00012867444,0.00008190331,0.000041109084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020436895,0.00007558745,0.0001188392,0.0001363407,0.00018356345,0.000034965964,0.00014516494,0.000030681727,0.000030887117],"category_scores_gemma":[0.00008740743,0.00006987908,0.00003446284,0.00065428705,0.00045918208,0.00017036595,0.000030946285,0.000050124094,1.9367033e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006417709,0.000024871262,0.0374258,0.000026370857,0.0000025301297,8.222004e-7,0.000021419319,0.000016072814,0.9617116,0.0006820224,0.000033707653,0.0000483902],"study_design_scores_gemma":[0.00014646849,0.00001298661,0.0013100974,0.000015443056,0.000043457763,0.000030312804,0.00011735792,0.0070482106,0.99034715,0.000753307,0.000110549634,0.000064646185],"about_ca_topic_score_codex":0.0003919836,"about_ca_topic_score_gemma":0.0000063910466,"teacher_disagreement_score":0.036115702,"about_ca_system_score_codex":0.00009989498,"about_ca_system_score_gemma":0.0000815232,"threshold_uncertainty_score":0.28495863},"labels":[],"label_agreement":null},{"id":"W1970153111","doi":"10.1094/cchem-87-2-0137","title":"Evaluating Sugar‐Snap Cookie Quality Using Digital Image Analysis","year":2010,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Commission; Library science; Phone; Political science; Computer science; Law; Philosophy","score_opus":0.058256660677430065,"score_gpt":0.4068988691038569,"score_spread":0.34864220842642685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970153111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86799425,0.00009002359,0.00028074547,0.00002215777,0.000026522113,0.00002358988,0.00010189499,0.00014943736,0.13131137],"genre_scores_gemma":[0.99427956,0.0000047278127,0.0015404746,0.000024194953,0.00045277816,0.000007196945,0.00031576815,0.000036533504,0.003338749],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977295,0.000009376343,0.0005567911,0.00065353845,0.00052877056,0.0005220684],"domain_scores_gemma":[0.99812114,0.00020791889,0.000327826,0.0008649709,0.00021001988,0.000268142],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027616182,0.00033972037,0.00056637265,0.00008527793,0.00020248277,0.00023355652,0.00047033982,0.0003357109,0.015259639],"category_scores_gemma":[0.00077162473,0.0003484445,0.00047389662,0.0011406317,0.00022952628,0.00023405241,0.00015579231,0.00071867416,0.000035002977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023681858,0.000111987734,0.022526266,0.00015260722,0.0006332019,0.000008929354,0.000035007866,0.00003184427,0.97566104,0.000011717443,0.000068551264,0.000735144],"study_design_scores_gemma":[0.0004482318,0.000004552566,0.0012936974,0.0000069777157,0.0014466683,0.000020575462,0.0003340172,0.0025104198,0.99315816,0.00016611283,0.00013889723,0.0004716992],"about_ca_topic_score_codex":0.00018249905,"about_ca_topic_score_gemma":0.0000113169235,"teacher_disagreement_score":0.12797262,"about_ca_system_score_codex":0.00014276784,"about_ca_system_score_gemma":0.00013879727,"threshold_uncertainty_score":0.99989676},"labels":[],"label_agreement":null},{"id":"W1970667965","doi":"10.1109/igarss.2013.6723248","title":"Hyperspectral imaging for the characterization of athabasca oil sands drill core","year":2013,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Oil sands; Hyperspectral imaging; Asphalt; Geology; Remote sensing; Characterization (materials science); Mineralogy; Materials science","score_opus":0.016095642439809283,"score_gpt":0.2575577368335673,"score_spread":0.24146209439375801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970667965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9630859,0.0002050339,0.0029997795,0.0009135516,0.000045164994,0.000045025667,0.000021255115,0.000059824106,0.03262449],"genre_scores_gemma":[0.9844412,0.000074298674,0.00044956553,0.00013782676,0.00014270344,0.000035946432,0.000044256496,0.000012746652,0.0146614155],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994505,0.0000015934671,0.00015465537,0.0001321519,0.000099603276,0.00016147262],"domain_scores_gemma":[0.9994905,0.00010315249,0.00008545544,0.00019645672,0.00009469927,0.000029718609],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000037724876,0.00008950139,0.0001315246,0.000039497652,0.000073180454,0.000029423838,0.0001595743,0.00003462427,0.005345844],"category_scores_gemma":[0.000045300214,0.00005769791,0.000101431324,0.00018171528,0.000049221348,0.000104211766,0.000021183481,0.0000544321,0.00001432119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007049567,0.000029021263,0.008875676,0.0000385613,0.000050692957,1.00553805e-7,0.0000871157,4.5807565e-7,0.9865756,0.0006617969,0.00063096546,0.0030429529],"study_design_scores_gemma":[0.00028864888,0.000008231088,0.0041975556,0.0000068766553,0.0001081897,0.0000031737118,0.00071172643,0.0035341966,0.9896181,0.00013933357,0.0012799029,0.00010401964],"about_ca_topic_score_codex":0.0001408798,"about_ca_topic_score_gemma":0.000002500238,"teacher_disagreement_score":0.021355366,"about_ca_system_score_codex":0.000024882065,"about_ca_system_score_gemma":0.000015177974,"threshold_uncertainty_score":0.9955634},"labels":[],"label_agreement":null},{"id":"W1971332480","doi":"10.1007/s11694-010-9104-2","title":"Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples","year":2010,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"University of Manitoba","keywords":"Hyperspectral imaging; Linear discriminant analysis; Principal component analysis; Quadratic classifier; Moisture; Remote sensing; Mathematics; Near-infrared spectroscopy; Environmental science; Pattern recognition (psychology); Geography; Artificial intelligence; Statistics; Computer science; Support vector machine; Physics; Meteorology; Optics","score_opus":0.057170545226089484,"score_gpt":0.3337119666151768,"score_spread":0.2765414213890873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971332480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99722004,0.0001373073,0.0016783456,0.0001323019,0.00006436158,0.0000759018,0.0004909253,0.000017399054,0.00018343655],"genre_scores_gemma":[0.9960602,0.000060122336,0.0036313175,0.000016701459,0.00003476566,7.3901145e-7,0.000086836626,0.000009312536,0.000099997866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989768,0.000023379567,0.0005034059,0.00022138057,0.00014685116,0.00012814038],"domain_scores_gemma":[0.99917996,0.00017252452,0.00033894592,0.00015161204,0.000108363995,0.000048570404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015785944,0.00012978818,0.00029445472,0.00005582398,0.0002584043,0.000042423264,0.000041266743,0.00010984995,0.00003336066],"category_scores_gemma":[0.00012522374,0.00011976015,0.00007903841,0.000101239086,0.0002448397,0.000114519135,0.000027343898,0.00009770176,8.002635e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001511915,0.000040908853,0.006141875,0.0003102902,0.000100867685,4.6302322e-8,0.0006716039,0.0000054418983,0.9876816,0.00073187455,0.0000068772547,0.0041573998],"study_design_scores_gemma":[0.0007364845,0.000053095995,0.028712982,0.000022927208,0.00015177188,0.00000750483,0.0026807487,0.0006580807,0.96523273,0.0015872135,0.000028496026,0.00012794047],"about_ca_topic_score_codex":0.000115304596,"about_ca_topic_score_gemma":0.00008875554,"teacher_disagreement_score":0.02257111,"about_ca_system_score_codex":0.000036809983,"about_ca_system_score_gemma":0.000019569972,"threshold_uncertainty_score":0.4883677},"labels":[],"label_agreement":null},{"id":"W1971360061","doi":"10.1002/cjce.5450820416","title":"Analysis of Solvent‐Diluted Bitumen from Oil Sands Froth Treatment Using NIR Spectroscopy","year":2004,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Devon Energy (Canada); Natural Resources Canada","funders":"","keywords":"Asphalt; Asphaltene; Oil sands; Solvent; Spectroscopy; Analytical Chemistry (journal); Materials science; Chromatography; Chemistry; Composite material; Organic chemistry","score_opus":0.013387500545664266,"score_gpt":0.23331500115600215,"score_spread":0.2199275006103379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971360061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997109,0.0013747334,0.00095924886,0.00013709573,0.000051286654,0.000011511501,0.00006244105,0.000011129165,0.00028354244],"genre_scores_gemma":[0.99762857,0.000022732374,0.0020390062,0.000021063912,0.00022013977,0.0000010490324,0.000017365717,0.000022706226,0.000027350396],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988044,0.0000048632705,0.00047420632,0.00014050283,0.00022946123,0.00034658576],"domain_scores_gemma":[0.998939,0.000083013365,0.00025206417,0.00026541526,0.00007938835,0.00038116882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083340936,0.00020739093,0.00059139536,0.00046841352,0.00005942453,0.000042725555,0.00034972504,0.000115523144,0.0006213681],"category_scores_gemma":[0.00008222883,0.0001575661,0.0004098667,0.0010967439,0.00007241982,0.00006847108,0.000012085416,0.00026897647,0.000001121611],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001744399,0.000029600877,0.0014668615,0.00001251548,0.0027432793,0.000033528846,0.0002782951,0.050701957,0.94460285,0.000034102763,0.000008045204,0.00007150262],"study_design_scores_gemma":[0.0006528805,0.000022135919,0.000099791,0.00005607157,0.0029021797,0.000017280374,0.000051651707,0.0045906464,0.99128914,0.000080285725,0.000082399056,0.00015555957],"about_ca_topic_score_codex":0.015294117,"about_ca_topic_score_gemma":0.0006992551,"teacher_disagreement_score":0.04668625,"about_ca_system_score_codex":0.0012194462,"about_ca_system_score_gemma":0.0005013638,"threshold_uncertainty_score":0.99126315},"labels":[],"label_agreement":null},{"id":"W1971564594","doi":"10.1007/s00216-011-4761-5","title":"Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry","year":2011,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Discrete wavelet transform; Noise (video); Wavelet transform; Calibration; Computer science; Second-generation wavelet transform; Raman spectroscopy; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Optics; Statistics; Physics; Image (mathematics)","score_opus":0.05244763605360843,"score_gpt":0.29472992054262215,"score_spread":0.24228228448901373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971564594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8575896,0.0047627077,0.02971595,0.0013075628,0.000025870893,0.0002922866,0.0014720716,0.0002467225,0.10458724],"genre_scores_gemma":[0.99089634,0.00039481622,0.0040036533,0.00012768261,0.00008530122,0.00002459609,0.00017384884,0.000034696546,0.0042590564],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972606,0.0000134856955,0.0006089019,0.0010179922,0.00035638633,0.0007426478],"domain_scores_gemma":[0.9981115,0.0005972719,0.00013330264,0.0003750568,0.00015842868,0.00062443514],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020135596,0.00052220374,0.0010538267,0.00020224955,0.000203981,0.0001192998,0.0002960021,0.0003987521,0.0044119447],"category_scores_gemma":[0.00019522909,0.00043543306,0.0004966834,0.0014091738,0.00092675857,0.00018144662,0.00009567253,0.0004068118,0.000007365737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012514904,0.012277992,0.0775505,0.00841284,0.12566917,0.00046864658,0.0025633285,0.0000073817714,0.6171665,0.10840381,0.02281539,0.01214949],"study_design_scores_gemma":[0.0030682143,0.000577384,0.0019771706,0.000052453288,0.030812345,0.000058063062,0.0059594032,0.04708635,0.8956443,0.010358961,0.0021731842,0.0022321662],"about_ca_topic_score_codex":0.00014006188,"about_ca_topic_score_gemma":0.000014466956,"teacher_disagreement_score":0.27847776,"about_ca_system_score_codex":0.00007708005,"about_ca_system_score_gemma":0.000043244356,"threshold_uncertainty_score":0.99980974},"labels":[],"label_agreement":null},{"id":"W1972399486","doi":"10.3390/s140407248","title":"Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":340,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Food safety; Quality (philosophy); Food quality; Remote sensing; Computer science; Spectral imaging; Environmental science; Artificial intelligence; Geography; Medicine; Physics","score_opus":0.09448051066697802,"score_gpt":0.4226196049282389,"score_spread":0.32813909426126087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972399486","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086074905,0.99127126,0.000042855594,0.000021590444,0.00015384515,0.00021262294,0.00011303433,0.00002319009,0.0073008337],"genre_scores_gemma":[0.0006066158,0.99591684,0.0029268882,0.000010027733,0.00020397932,0.000031558164,0.00011630607,0.000034336943,0.00015345583],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981083,0.000056462144,0.0008703737,0.00044463025,0.00021633263,0.0003038948],"domain_scores_gemma":[0.9985386,0.00056628714,0.00048326844,0.00027289943,0.00007194332,0.00006698526],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045700427,0.0003120677,0.0016155837,0.00020629946,0.0000418583,0.000017095439,0.00017551902,0.0001905937,0.000072739764],"category_scores_gemma":[0.0003120664,0.00027594483,0.00022806782,0.00035435386,0.00005578389,0.00002126496,0.00005793388,0.0002813485,0.000001006361],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021262596,0.00016879894,0.0011163347,0.03673183,0.00068087265,0.0000027892104,0.00007412748,0.000002554979,0.000073518764,0.0013175157,0.00015671439,0.9596537],"study_design_scores_gemma":[0.0006234694,0.000020117855,0.00013840281,0.0020266084,0.00066664687,0.000006741065,0.00023056853,0.000023326571,0.00071233837,0.00008836128,0.9949753,0.0004880823],"about_ca_topic_score_codex":0.000016740356,"about_ca_topic_score_gemma":0.0000121058465,"teacher_disagreement_score":0.9948186,"about_ca_system_score_codex":0.0003901904,"about_ca_system_score_gemma":0.00022157631,"threshold_uncertainty_score":0.99996924},"labels":[],"label_agreement":null},{"id":"W1972828727","doi":"10.1366/000370203322640198","title":"Revealing System Dynamics through Decomposition of the Perturbation Domain in Two-Dimensional Correlation Spectroscopy","year":2003,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Spectroscopy; Perturbation (astronomy); Frequency domain; Physics; Chemical physics; Materials science; Chemistry; Statistical physics; Mathematics; Mathematical analysis; Quantum mechanics","score_opus":0.0070612624986020715,"score_gpt":0.26769107454339913,"score_spread":0.26062981204479707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972828727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86013705,0.00023104978,0.021655405,0.00010785727,0.00018181496,0.00027698328,0.000022188586,0.00010225511,0.1172854],"genre_scores_gemma":[0.9791237,0.000012261501,0.020316623,0.000078244666,0.0000911901,0.000045146782,0.00007949861,0.00004374595,0.00020955483],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977224,0.00006662289,0.0007112024,0.0005196276,0.00051620055,0.0004639674],"domain_scores_gemma":[0.9986559,0.00018548591,0.0004690823,0.0005695329,0.00006254263,0.000057449266],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031590645,0.00032845334,0.00048831914,0.00015384838,0.00023419368,0.00003873544,0.0002932779,0.0001726968,0.00031848365],"category_scores_gemma":[0.000046677058,0.00028196484,0.00016738143,0.0011327073,0.00012561388,0.0001295181,0.000047523088,0.0004739476,0.000021792524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007163592,0.0001380679,0.00714167,0.000102769154,0.000049157632,0.0000021538783,0.0002004383,0.0030046632,0.75275207,0.23646195,0.00006815257,0.000007239481],"study_design_scores_gemma":[0.0012668405,0.0000240699,0.00065249676,0.000101290556,0.0001214375,0.000024055336,0.0010022877,0.0042001456,0.9736831,0.018603409,0.000028228476,0.00029263707],"about_ca_topic_score_codex":0.00011219127,"about_ca_topic_score_gemma":0.000084455736,"teacher_disagreement_score":0.22093101,"about_ca_system_score_codex":0.0012846685,"about_ca_system_score_gemma":0.00011449002,"threshold_uncertainty_score":0.9999632},"labels":[],"label_agreement":null},{"id":"W1973457817","doi":"10.1007/s11947-013-1079-7","title":"Potential of Machine Vision Techniques for Detecting Fecal and Microbial Contamination of Food Products: A Review","year":2013,"lang":"en","type":"review","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mycotoxin; Contamination; Agriculture; Food products; Human health; Machine vision; Food processing; Food safety; Food contaminant; Environmental science; Biotechnology; Biochemical engineering; Computer science; Food science; Artificial intelligence; Biology; Engineering; Environmental health; Ecology; Medicine","score_opus":0.015991692736074554,"score_gpt":0.309358212875392,"score_spread":0.29336652013931747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973457817","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016456608,0.9969332,0.00035701538,0.0000806384,0.000016805205,0.0006639888,0.0001685559,0.00010115953,0.00003296819],"genre_scores_gemma":[0.024050685,0.97425705,0.0012729116,0.0000055807213,0.000054249736,0.0002049287,0.000082835744,0.00003125763,0.00004048058],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984747,0.000010246523,0.0007186718,0.00048984983,0.00009977807,0.00020673058],"domain_scores_gemma":[0.99846506,0.000051196264,0.0009240074,0.00025387088,0.000278967,0.000026901094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015036398,0.00033114833,0.0016733946,0.0004963295,0.000060909137,0.000012632474,0.0002553259,0.00071361684,0.000016617869],"category_scores_gemma":[0.00033021197,0.00025323548,0.00015030977,0.0008033045,0.00025208105,0.000058242804,0.00016006404,0.00028831104,2.064889e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007277867,0.00006542888,0.0000036808792,0.20269455,0.00018203509,2.6997145e-7,0.0000029948696,7.075724e-10,0.023225745,0.00007365778,0.000022913066,0.77372146],"study_design_scores_gemma":[0.00035769,0.0016569152,3.2455043e-7,0.027113974,0.00315976,0.00022293284,0.000045568864,0.000004200024,0.878864,0.00041324506,0.08772037,0.0004410358],"about_ca_topic_score_codex":0.0000054611696,"about_ca_topic_score_gemma":0.0000033045835,"teacher_disagreement_score":0.8556382,"about_ca_system_score_codex":0.000025402116,"about_ca_system_score_gemma":0.00005913026,"threshold_uncertainty_score":0.999992},"labels":[],"label_agreement":null},{"id":"W1973820022","doi":"10.1364/ao.45.005270","title":"Correction of instrument line shape in Fourier transform spectrometry using matrix inversion","year":2006,"lang":"en","type":"article","venue":"Applied Optics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Inversion (geology); Optics; Fourier transform; Spectrometer; Physics; Algorithm; Computer science; Geology","score_opus":0.013739596721222509,"score_gpt":0.25758255400629476,"score_spread":0.24384295728507224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973820022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94598544,0.000099588826,0.0022217832,0.000030295905,0.0000768782,0.00006312119,0.0000063073153,0.000035209167,0.0514814],"genre_scores_gemma":[0.98911345,0.0000488725,0.010093479,0.000013941804,0.000117029595,0.0000036240804,0.00003212302,0.00001965349,0.0005578434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894077,0.0000026715386,0.00037017098,0.00020614611,0.00024205123,0.00023819892],"domain_scores_gemma":[0.9995785,0.000048417063,0.00012987162,0.00017886193,0.000026679347,0.000037693742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007812673,0.00015318292,0.0002673602,0.0002890029,0.00004315349,0.000013915966,0.00011689719,0.00015987437,0.0005346549],"category_scores_gemma":[0.000010912331,0.00015990913,0.000073081625,0.00092593784,0.00005033184,0.000052329768,0.000026753461,0.0002191168,0.0000055963646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016680067,0.00054457295,0.0048511894,0.00020848736,0.00007943643,0.000007854657,0.00012106626,0.005118694,0.979796,0.0027956585,0.00018894364,0.00612128],"study_design_scores_gemma":[0.00074972026,0.00002682272,0.0001584464,0.000017694249,0.00012293099,0.0000041897965,0.00048279035,0.0750089,0.92231166,0.00079811766,0.00014265327,0.00017605566],"about_ca_topic_score_codex":0.00015560108,"about_ca_topic_score_gemma":0.000023173376,"teacher_disagreement_score":0.06989021,"about_ca_system_score_codex":0.00023381735,"about_ca_system_score_gemma":0.000037386486,"threshold_uncertainty_score":0.6520905},"labels":[],"label_agreement":null},{"id":"W1974286700","doi":"10.1081/pre-120002904","title":"NEURAL NETWORK IDENTIFICATION OF STYRENE FREE RADICAL POLYMERIZATION","year":2002,"lang":"en","type":"article","venue":"Polymer Reaction Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polymerization; Monomer; Styrene; Bifunctional; Radical polymerization; Artificial neural network; Polymer; Materials science; Bulk polymerization; Polymer chemistry; Chemistry; Biological system; Copolymer; Computer science; Organic chemistry; Artificial intelligence; Catalysis","score_opus":0.011240619799419125,"score_gpt":0.21658609819173086,"score_spread":0.20534547839231174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974286700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95832574,0.01117468,0.023761256,0.00034673337,0.000569248,0.00006836416,0.000023605186,0.00051402533,0.0052163163],"genre_scores_gemma":[0.99831474,0.000103193735,0.00009469145,0.000012849547,0.00034167457,0.00000897991,0.000025594007,0.000024748731,0.0010735593],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990954,0.000004863793,0.0003298667,0.00017615026,0.00019302791,0.00020068215],"domain_scores_gemma":[0.99942493,0.0000429502,0.00014708377,0.0003007029,0.000020849959,0.00006350081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043756125,0.00012400761,0.0001656765,0.00012890443,0.000045924276,0.00001724192,0.00014418138,0.00009067913,0.00084792153],"category_scores_gemma":[0.000070884234,0.00013759873,0.000077835364,0.00055659,0.000023095205,0.00015795833,0.000022079501,0.00013717035,0.000011394333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004698874,0.000042930864,0.0008686696,0.000036183646,0.00005625017,7.744412e-7,0.000034951914,0.002387358,0.9941781,0.0002834041,0.00016777976,0.0019388843],"study_design_scores_gemma":[0.00020023658,0.0000064789992,0.0009136519,0.000009455374,0.000088864916,0.000010836476,0.00001937749,0.064995155,0.9333163,0.00000772641,0.0003011251,0.00013079747],"about_ca_topic_score_codex":0.000041248582,"about_ca_topic_score_gemma":5.523517e-7,"teacher_disagreement_score":0.062607795,"about_ca_system_score_codex":0.000043954446,"about_ca_system_score_gemma":0.0000034787718,"threshold_uncertainty_score":0.9284146},"labels":[],"label_agreement":null},{"id":"W1974747878","doi":"10.1016/j.jpba.2006.01.033","title":"Raman spectroscopy for tablet coating thickness quantification and coating characterization in the presence of strong fluorescent interference","year":2006,"lang":"en","type":"article","venue":"Journal of Pharmaceutical and Biomedical Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Dane O. Kildsig Center for Pharmaceutical Processing Research; Purdue University; National Science Foundation","keywords":"Coating; Raman spectroscopy; Calibration; Chemistry; Analytical Chemistry (journal); Second derivative; Partial least squares regression; Univariate; Chemometrics; Fluorescence; Biological system; Optics; Chromatography; Multivariate statistics; Mathematics; Statistics","score_opus":0.032820074925932606,"score_gpt":0.34091630470417306,"score_spread":0.30809622977824047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974747878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9007504,0.00046272203,0.09738137,0.0012458302,0.00002297392,0.000053698284,0.00003515133,0.0000040186274,0.000043813605],"genre_scores_gemma":[0.9984356,0.00022935415,0.0010598128,0.000043777636,0.00015261283,0.0000037707173,0.000053713502,0.000004090891,0.000017247678],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854374,0.000057284105,0.00069608894,0.00016630598,0.00034974044,0.00018685978],"domain_scores_gemma":[0.99875164,0.0004975823,0.0004589249,0.000081873266,0.0001313553,0.000078647856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080004754,0.00011245961,0.00038538358,0.0002814465,0.00007923027,0.00006185107,0.00018790587,0.000077248405,0.000104773644],"category_scores_gemma":[0.00026399092,0.0000704778,0.00011276519,0.0009945119,0.00023634238,0.000113203234,0.000035030727,0.0002619244,1.0982151e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001101077,0.00026278562,0.043602362,0.00018860582,0.00020183672,0.0000032737832,0.00014482696,0.0000125608585,0.9530018,0.0004378065,0.000023594775,0.002010456],"study_design_scores_gemma":[0.0016612145,0.00019548384,0.029152574,0.00017942945,0.003796256,0.00004092062,0.0018143152,0.1123166,0.8495331,0.00059987063,0.00046266362,0.00024757374],"about_ca_topic_score_codex":0.0000413778,"about_ca_topic_score_gemma":0.0000085003885,"teacher_disagreement_score":0.11230404,"about_ca_system_score_codex":0.00002604184,"about_ca_system_score_gemma":0.000026583573,"threshold_uncertainty_score":0.28740013},"labels":[],"label_agreement":null},{"id":"W1975152382","doi":"10.1016/j.chemolab.2014.09.007","title":"The undecimated wavelet transform–multivariate image analysis (UWT-MIA) for simultaneous extraction of spectral and spatial information","year":2014,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet transform; Pattern recognition (psychology); Artificial intelligence; Extraction (chemistry); Computer science; Multivariate statistics; Wavelet; Harmonic wavelet transform; Image (mathematics); Discrete wavelet transform; Computer vision; Machine learning; Chemistry; Chromatography","score_opus":0.009908549670736046,"score_gpt":0.26719433069633486,"score_spread":0.2572857810255988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975152382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4116641,0.0031799024,0.5814009,0.000097562996,0.0002884327,0.00047022826,0.00046877135,0.00009851024,0.0023316483],"genre_scores_gemma":[0.9983134,0.00095395383,0.00027342408,0.000016160777,0.000105226376,0.000027722934,0.00010718671,0.000016962316,0.00018591086],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983582,0.000028937617,0.0007361152,0.00025657733,0.0003197473,0.0003004529],"domain_scores_gemma":[0.9972262,0.0012069072,0.00046160814,0.00027915122,0.000696945,0.00012921043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006435878,0.0002378554,0.00050702406,0.0007132302,0.00024513772,0.0002603326,0.00016896187,0.00019587048,0.000038775936],"category_scores_gemma":[0.0012958809,0.00018227249,0.0001197829,0.003284748,0.0001258591,0.0002664312,0.000023967881,0.00016885622,0.0000034673421],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020972278,0.0011682431,0.01736277,0.009591252,0.013801852,0.00001407536,0.004282069,0.004833553,0.67739874,0.0113526145,0.0016420638,0.25645557],"study_design_scores_gemma":[0.0008793842,0.00022860806,0.00042654734,0.000030157968,0.0014878884,0.0000089423875,0.0021688251,0.38131383,0.5809676,0.0001297377,0.031898182,0.00046028118],"about_ca_topic_score_codex":0.0003801581,"about_ca_topic_score_gemma":0.00002466733,"teacher_disagreement_score":0.58664936,"about_ca_system_score_codex":0.00010517699,"about_ca_system_score_gemma":0.000042408283,"threshold_uncertainty_score":0.74328566},"labels":[],"label_agreement":null},{"id":"W1975372021","doi":"10.1208/pt040224","title":"Evaluation of the conformity index and the mahalanobis distance as a tool for process analysis: A technical note","year":2003,"lang":"en","type":"article","venue":"AAPS PharmSciTech","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Purdue Pharma (Canada)","funders":"Purdue Pharma; Purdue University","keywords":"Mahalanobis distance; Conformity; Index (typography); Process (computing); Conformity assessment; Computer science; Process engineering; Data mining; Statistics; Mathematics; Engineering; Psychology; Artificial intelligence; Operations management; World Wide Web; Social psychology","score_opus":0.018201092736317988,"score_gpt":0.3447071638900421,"score_spread":0.3265060711537241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975372021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97246397,0.0012165069,0.012876889,0.00039939917,0.000030670886,0.00064128137,0.000056448112,0.00004781431,0.012267009],"genre_scores_gemma":[0.99915886,0.000026638887,0.00009968317,0.00010792911,0.000019432076,0.0002325804,0.0000060098237,0.000008669174,0.00034017378],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986005,0.00004753117,0.0002884053,0.00024551863,0.00063879753,0.00017924706],"domain_scores_gemma":[0.99875444,0.0002418115,0.00022848255,0.00040128588,0.0003442825,0.000029706007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013590103,0.00013331021,0.00028566565,0.00007642199,0.00015148554,0.000035714544,0.00029778082,0.000101795595,0.0003698675],"category_scores_gemma":[0.0014194153,0.00007482532,0.00020082731,0.0014057698,0.00022211237,0.00007764186,0.00003304051,0.00017233347,9.1818606e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046362793,0.0026195096,0.44774356,0.0027026837,0.014655815,0.000005151427,0.005345179,0.0025185796,0.36776462,0.09549055,0.002227065,0.054291006],"study_design_scores_gemma":[0.0054018763,0.000023476085,0.0022015797,0.000029171266,0.010727094,0.000017918132,0.00047130234,0.0170505,0.9315789,0.030840779,0.0013552632,0.0003021405],"about_ca_topic_score_codex":0.000024476907,"about_ca_topic_score_gemma":0.000027793469,"teacher_disagreement_score":0.5638143,"about_ca_system_score_codex":0.00007295025,"about_ca_system_score_gemma":0.00015320379,"threshold_uncertainty_score":0.404979},"labels":[],"label_agreement":null},{"id":"W1975850935","doi":"10.1366/000370203322640206","title":"Identification and Interpretation of Generalized Two-Dimensional Correlation Spectroscopy Features through Decomposition of the Perturbation Domain","year":2003,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Superposition principle; Correlation; Perturbation (astronomy); Spectroscopy; Frequency domain; Correlation coefficient; Mathematics; Statistical physics; Mathematical analysis; Physics; Statistics; Geometry","score_opus":0.0060754380490582755,"score_gpt":0.2704943756277722,"score_spread":0.2644189375787139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975850935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.953194,0.0006004869,0.030898707,0.00011816081,0.0001130496,0.00022629248,0.000018314715,0.000041026888,0.014789912],"genre_scores_gemma":[0.98534024,0.000045368644,0.014113999,0.00007188101,0.000049648235,0.00003120102,0.00006794501,0.0000245453,0.0002551661],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838203,0.000058597445,0.000565549,0.00038165992,0.00039707395,0.00021508012],"domain_scores_gemma":[0.99870384,0.00014121034,0.00061871775,0.0003965773,0.00010016653,0.000039479404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020588019,0.00022961671,0.00035364996,0.00011198417,0.00017433598,0.000034196193,0.00016999824,0.0001388981,0.00031582796],"category_scores_gemma":[0.00006698209,0.00019024804,0.00012403405,0.0005367108,0.00020227676,0.00015069646,0.000033888962,0.00021713336,0.000004088473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012767134,0.000098920245,0.0011491835,0.00005409158,0.00007463025,1.4088671e-7,0.00047609876,0.0006568758,0.9187649,0.07840647,0.00015343464,0.000037585396],"study_design_scores_gemma":[0.00091449486,0.000030331865,0.0016480953,0.000026984302,0.00018993553,0.000009710668,0.00023993559,0.0009104173,0.94020927,0.055640418,0.000020717702,0.00015970618],"about_ca_topic_score_codex":0.000036887257,"about_ca_topic_score_gemma":0.000009446969,"teacher_disagreement_score":0.032146197,"about_ca_system_score_codex":0.0001418356,"about_ca_system_score_gemma":0.00005677416,"threshold_uncertainty_score":0.77580905},"labels":[],"label_agreement":null},{"id":"W1976380316","doi":"10.1007/s11306-013-0612-z","title":"Regularized projection pursuit for data with a small sample-to-variable ratio","year":2013,"lang":"en","type":"article","venue":"Metabolomics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Projection pursuit; Univariate; Multivariate statistics; Overfitting; Principal component analysis; Computer science; Projection (relational algebra); Data mining; Algorithm; Artificial intelligence; Statistics; Mathematics; Pattern recognition (psychology); Machine learning","score_opus":0.04884875004525439,"score_gpt":0.27919155292625153,"score_spread":0.23034280288099715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976380316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13330825,0.00026294056,0.86118394,0.00057775003,0.00010671879,0.0007349688,0.00033617648,0.00019503641,0.0032942253],"genre_scores_gemma":[0.025689071,0.000044993805,0.9567511,0.00059272896,0.00046990757,0.0006797103,0.0013865783,0.000082249586,0.014303644],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987128,0.000011505292,0.00024909858,0.0005155286,0.0001626601,0.0003484277],"domain_scores_gemma":[0.99843454,0.00019472674,0.00012492364,0.00097022456,0.00017111041,0.0001044517],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023368666,0.00019296339,0.00034982018,0.00011000662,0.00013452665,0.0001908879,0.0006364627,0.000120519886,0.0011649905],"category_scores_gemma":[0.0006414392,0.00015711386,0.000045537705,0.0005477797,0.00003435559,0.00023730866,0.00014788847,0.00013144019,0.000034231543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050406845,0.00033750155,0.0032156492,0.00023221485,0.00087690173,8.535332e-7,0.00016676799,0.0001189064,0.96317333,0.007404036,0.016449848,0.007519933],"study_design_scores_gemma":[0.0032313927,0.00015553455,0.00030241697,0.000024593788,0.0015116949,0.000017946551,0.00058167294,0.033397548,0.65326923,0.0035026812,0.3031718,0.0008334543],"about_ca_topic_score_codex":0.0008091451,"about_ca_topic_score_gemma":0.000053462194,"teacher_disagreement_score":0.3099041,"about_ca_system_score_codex":0.000055034932,"about_ca_system_score_gemma":0.00012211183,"threshold_uncertainty_score":0.99974805},"labels":[],"label_agreement":null},{"id":"W1976656507","doi":"10.1016/j.watres.2004.05.003","title":"Application of multi-wavelength fluorometry for on-line monitoring of an anaerobic digestion process","year":2004,"lang":"en","type":"article","venue":"Water Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Biotechnology Research Institute","funders":"","keywords":"Partial least squares regression; Anaerobic digestion; Fluorescence spectroscopy; Nonlinear regression; Chemometrics; Fluorometer; Linear regression; Biological system; Chemistry; Process engineering; Regression analysis; Analytical Chemistry (journal); Chromatography; Fluorescence; Computer science; Engineering; Machine learning; Optics","score_opus":0.10578467013937534,"score_gpt":0.4267122820152645,"score_spread":0.32092761187588914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976656507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960375,0.000055212564,0.0035380612,0.000046582525,0.000010337503,0.00015364985,0.000015490343,0.000023842216,0.00011934243],"genre_scores_gemma":[0.9965119,0.000018024493,0.0029805596,0.0000013562238,0.0001092123,0.00009983112,0.00006599034,0.00001959767,0.00019354264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998792,0.000013643218,0.00023608841,0.0002571087,0.00040638956,0.00029476345],"domain_scores_gemma":[0.99915624,0.000038829538,0.00005328467,0.00031242156,0.00037313605,0.000066110035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041466864,0.0000894816,0.00017975325,0.00033486125,0.00008193177,0.0000129669415,0.00025273842,0.00010148479,0.000030131936],"category_scores_gemma":[0.00012164223,0.00006864553,0.000048218455,0.0005374103,0.00008161107,0.000087686574,0.000031992742,0.00019187092,0.0000062029976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119911034,0.0005622406,0.002900248,0.00044529367,0.000034123546,5.4710785e-7,0.0003260659,0.0009366155,0.99119353,0.00007220504,9.38794e-7,0.0034082897],"study_design_scores_gemma":[0.00069384975,0.00022138197,0.00048520367,0.00003741214,0.000016427774,6.886855e-7,0.00043943917,0.0010703732,0.9963973,0.000550193,0.000016148257,0.00007154964],"about_ca_topic_score_codex":0.00014700738,"about_ca_topic_score_gemma":0.0000027548347,"teacher_disagreement_score":0.0052038115,"about_ca_system_score_codex":0.00010517647,"about_ca_system_score_gemma":0.000035544064,"threshold_uncertainty_score":0.27992836},"labels":[],"label_agreement":null},{"id":"W1976729575","doi":"10.3390/ijms12031921","title":"Detecting Molecular Features of Spectra Mainly Associated with Structural and Non-Structural Carbohydrates in Co-Products from BioEthanol Production Using DRIFT with Uni- and Multivariate Molecular Spectral Analyses","year":2011,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Beef Cattle Research Council","keywords":"Biofuel; Multivariate statistics; Chemistry; Biology; Biotechnology; Mathematics","score_opus":0.029568313988594493,"score_gpt":0.31407921851619797,"score_spread":0.28451090452760347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976729575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978384,0.0010528353,0.0007174149,0.00007333078,0.000073281095,0.000061620434,0.000012801902,0.000009163796,0.00016116696],"genre_scores_gemma":[0.9827712,0.000022043074,0.017112238,0.000019031218,0.000050261908,8.2195515e-7,0.0000051730603,0.000016072978,0.0000031313962],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805963,0.000060499075,0.00042759374,0.0004152175,0.00077601447,0.0002610351],"domain_scores_gemma":[0.9986066,0.00005653955,0.00072350714,0.00013062338,0.0004005962,0.00008210082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025652585,0.00025227718,0.00038919802,0.00058075105,0.00008572322,0.00011108085,0.00040066123,0.000083373205,0.000024326424],"category_scores_gemma":[0.0002806972,0.00018308079,0.00006537516,0.00076887803,0.0005716079,0.00035580675,0.000054405977,0.00030701896,4.281422e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001665795,0.000027675464,0.048126776,0.0000108077265,0.0005944056,0.00036443505,0.0007036547,0.0015624344,0.948268,0.00005755497,1.4717003e-7,0.00011756017],"study_design_scores_gemma":[0.0007011701,0.00018297131,0.046848986,0.00017022021,0.00024422634,0.00031453656,0.0010470279,0.0012848171,0.9483067,0.0006832212,8.955868e-8,0.00021603484],"about_ca_topic_score_codex":0.0013870011,"about_ca_topic_score_gemma":0.00010127522,"teacher_disagreement_score":0.016394822,"about_ca_system_score_codex":0.000096701864,"about_ca_system_score_gemma":0.00015626395,"threshold_uncertainty_score":0.7465818},"labels":[],"label_agreement":null},{"id":"W1977086895","doi":"10.1016/j.jhydrol.2009.02.030","title":"Distribution and assessment of surface water contamination by application of chemometric and deterministic models","year":2009,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Sixth Framework Programme; Ministerio de Educación, Cultura y Deporte; Generalitat de Catalunya; European Commission","keywords":"Contamination; Environmental science; Water contamination; Surface water; Distribution (mathematics); Surface (topology); Hydrology (agriculture); Statistics; Soil science; Mathematics; Environmental engineering; Geology; Ecology; Geotechnical engineering; Biology","score_opus":0.009090486922841137,"score_gpt":0.2868571700362118,"score_spread":0.27776668311337066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977086895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97145885,0.0007091453,0.027249463,0.000207409,0.0000077825225,0.000022765884,0.000012950527,0.0000023952282,0.0003292594],"genre_scores_gemma":[0.9993091,0.0002775889,0.00032891685,0.00001661511,0.000014541943,4.438733e-7,0.000021716589,0.0000029842126,0.000028053726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992821,0.000014604951,0.00038636543,0.00008879209,0.00013263868,0.000095462034],"domain_scores_gemma":[0.9992762,0.00008287072,0.00040657143,0.00007350034,0.00012120411,0.000039619896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020536715,0.000072184506,0.00030033814,0.00010422586,0.00002068061,0.0000050630856,0.00006676794,0.00009344341,0.000020146364],"category_scores_gemma":[0.0000346734,0.000055480723,0.000035919482,0.0001531654,0.000064590204,0.00008583905,0.000013490123,0.0001163805,8.026368e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005749736,0.00018210575,0.01039629,0.00006504528,0.00006722386,0.0000019463919,0.00004912369,0.0002768626,0.9847219,0.00028720713,0.000052997908,0.003841805],"study_design_scores_gemma":[0.0010492487,0.0005654477,0.009747329,0.000009877746,0.00032376975,0.00012687013,0.00005041276,0.038209353,0.94653285,0.0031542678,0.00013498646,0.00009556575],"about_ca_topic_score_codex":0.00000705011,"about_ca_topic_score_gemma":3.0548432e-7,"teacher_disagreement_score":0.038189024,"about_ca_system_score_codex":0.000039612023,"about_ca_system_score_gemma":0.000015048538,"threshold_uncertainty_score":0.22624382},"labels":[],"label_agreement":null},{"id":"W1977934683","doi":"10.1016/j.jmr.2007.05.008","title":"Robust baseline correction algorithm for signal dense NMR spectra","year":2007,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Chenomx (Canada)","funders":"","keywords":"Baseline (sea); Spectral line; NMR spectra database; SIGNAL (programming language); Algorithm; Spectrometer; Computer science; Suite; Line (geometry); Mathematics; Physics; Optics; Geology; Geometry","score_opus":0.017853272680422992,"score_gpt":0.26312970785111023,"score_spread":0.24527643517068723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977934683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08201928,0.052612826,0.8537977,0.00040916877,0.0008342411,0.0001412214,0.00003268136,0.00005364213,0.010099236],"genre_scores_gemma":[0.50913894,0.004456503,0.40322825,0.00080983294,0.010758528,0.000016500479,0.000021827822,0.00016683982,0.0714028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843466,0.000013440411,0.0006404362,0.00018283621,0.00038597826,0.00034264097],"domain_scores_gemma":[0.9984643,0.0004908637,0.00041028994,0.00015730805,0.00033178946,0.00014543904],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008323817,0.00016579272,0.0003291828,0.00019104787,0.000087391,0.00003481931,0.0002271783,0.00012085314,0.0023019998],"category_scores_gemma":[0.00030255015,0.00014665947,0.00022992484,0.0004097826,0.000056763267,0.000096164935,0.000015157439,0.00033538646,0.000007781836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005707183,0.00037980304,0.0013229402,0.000056079083,0.00006276326,0.00019601813,0.00008553464,0.00025259992,0.047819644,0.00003222604,0.031059785,0.91816187],"study_design_scores_gemma":[0.0045524817,0.0015662266,0.006367582,0.00021372414,0.0007003714,0.0018372567,0.0008985934,0.048399232,0.7045609,0.00054501917,0.22967407,0.0006845883],"about_ca_topic_score_codex":0.000011297856,"about_ca_topic_score_gemma":0.000009608261,"teacher_disagreement_score":0.9174773,"about_ca_system_score_codex":0.00013792496,"about_ca_system_score_gemma":0.0000817934,"threshold_uncertainty_score":0.99861},"labels":[],"label_agreement":null},{"id":"W1978448217","doi":"10.1021/ie060832r","title":"Soft Sensor for Snack Food Textural Properties Using On-Line Vibrational Measurements","year":2006,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Soft sensor; Computer science; Process (computing); Product (mathematics); Fourier transform; Line (geometry); Partial least squares regression; Process engineering; Biological system; Mathematics; Engineering; Machine learning","score_opus":0.36672700802298097,"score_gpt":0.3757617145076268,"score_spread":0.009034706484645827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978448217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955176,0.00027192844,0.000680788,0.00025113605,0.00011159763,0.00026399014,0.00012745918,0.00019247812,0.0025829922],"genre_scores_gemma":[0.99390966,0.0000017562191,0.00031739494,0.0000070114625,0.0033115007,0.00008540747,0.000118589174,0.00006454344,0.0021841652],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731296,0.000013684429,0.00041653516,0.00048331698,0.0009968503,0.00077666796],"domain_scores_gemma":[0.9988064,0.00026536142,0.00007496941,0.0003379044,0.00037088,0.00014444806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045036248,0.0002959498,0.00031747035,0.00014154935,0.0002555841,0.00016447136,0.0003636012,0.00040138204,0.00048565964],"category_scores_gemma":[0.0011256161,0.00028164466,0.00015190257,0.0006221253,0.0000943938,0.00010512845,0.00007688606,0.00083857024,0.000009444211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015044995,0.00012802979,0.00028530916,0.0001727502,0.00013160799,0.0000027199087,0.000007906421,0.025146758,0.97322214,0.000018883557,0.0006643341,0.00006908943],"study_design_scores_gemma":[0.001250457,0.000063304265,0.000004639319,0.00009599583,0.000045583736,0.000009850464,0.000059568145,0.015646374,0.9810913,0.000027879814,0.0014106749,0.0002943535],"about_ca_topic_score_codex":0.00005820416,"about_ca_topic_score_gemma":0.000001037113,"teacher_disagreement_score":0.009500385,"about_ca_system_score_codex":0.0005071344,"about_ca_system_score_gemma":0.00025870715,"threshold_uncertainty_score":0.9999636},"labels":[],"label_agreement":null},{"id":"W1980198114","doi":"10.1016/j.jpba.2008.02.016","title":"Detection of Lipitor® counterfeits: A comparison of NIR and Raman spectroscopy in combination with chemometrics","year":2008,"lang":"en","type":"article","venue":"Journal of Pharmaceutical and Biomedical Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Pfizer","keywords":"Chemometrics; Chemistry; Raman spectroscopy; Near-infrared spectroscopy; Principal component analysis; Partial least squares regression; Analytical Chemistry (journal); Linear discriminant analysis; Spectroscopy; Biological system; Chromatography; Pattern recognition (psychology); Artificial intelligence; Optics; Mathematics; Statistics","score_opus":0.025304753181626467,"score_gpt":0.33320260858604206,"score_spread":0.3078978554044156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980198114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915673,0.0023560075,0.0057137543,0.00021035234,0.000021095637,0.000023408658,0.0000068651484,0.0000044182943,0.000096762014],"genre_scores_gemma":[0.9978811,0.001432951,0.0005660276,0.000019611653,0.00006598527,7.4449827e-7,0.000004085382,0.000005543347,0.000023931434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99828833,0.000029821456,0.00079116743,0.0001597173,0.00056003185,0.00017095271],"domain_scores_gemma":[0.99873996,0.00023414407,0.0005379934,0.00007868685,0.00020165951,0.0002075819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031195607,0.00013846582,0.00081665436,0.0012910463,0.00004359081,0.000010332737,0.00011915998,0.00012683298,0.00018653499],"category_scores_gemma":[0.0001529063,0.000096239986,0.00014222997,0.0036848134,0.0005057591,0.00009769659,0.000034276705,0.00036823185,1.9364364e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075010554,0.0018061253,0.41544357,0.0003061819,0.0017339328,0.000031406278,0.0003102661,0.0000093969265,0.5749413,0.000018882693,0.000042708507,0.004606131],"study_design_scores_gemma":[0.0029875191,0.0007520943,0.024774896,0.00005679238,0.0031340935,0.000094645526,0.00057421654,0.0077812714,0.9592145,0.00006342477,0.00040963021,0.00015689112],"about_ca_topic_score_codex":0.000043252923,"about_ca_topic_score_gemma":0.00000968103,"teacher_disagreement_score":0.3906687,"about_ca_system_score_codex":0.00005792115,"about_ca_system_score_gemma":0.000042061227,"threshold_uncertainty_score":0.39245528},"labels":[],"label_agreement":null},{"id":"W1981880459","doi":"10.1002/pen.10030","title":"In‐line color monitoring of polymers during extrusion using a charge coupled device spectrometer: Color changeovers and residence time distributions","year":2003,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Residence time distribution; Materials science; Extrusion; Spectrometer; Residence time (fluid dynamics); Dimensionless quantity; Analytical Chemistry (journal); Spectral line; Optics; Composite material; Mechanics; Chemistry; Chromatography; Mineralogy; Physics; Engineering","score_opus":0.014578938913203785,"score_gpt":0.25981448213092956,"score_spread":0.24523554321772578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981880459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960397,0.0035240976,0.0001682736,0.00002772275,0.000053003983,0.000036688776,0.000010931427,0.000028676974,0.000110938025],"genre_scores_gemma":[0.9988692,0.00018514364,0.0006587611,0.000003540114,0.000031429776,0.000005230746,7.703342e-7,0.000009767725,0.00023618648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883807,0.000005268148,0.00020986176,0.00032168746,0.00022986812,0.00039524244],"domain_scores_gemma":[0.9995269,0.000080045495,0.00007614131,0.00015218051,0.000030252764,0.00013448496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019647997,0.00015277835,0.00023461311,0.00032142838,0.00017097798,0.00003972509,0.00013215492,0.00005401333,0.00012503823],"category_scores_gemma":[0.00019415015,0.00015670473,0.000026511387,0.0013016218,0.00018179919,0.00025691887,0.00006344341,0.000124038,9.733532e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009224163,0.000018841301,0.0044647474,0.00006090521,0.000008617962,0.000005602118,0.00013173363,0.00006667327,0.9950885,0.00012833823,1.6818832e-7,0.000016631473],"study_design_scores_gemma":[0.00027377252,0.000019274816,0.0020685056,0.00008498927,0.000036486235,0.000035579313,0.00013910637,0.021416532,0.9757564,0.000003142661,0.0000040145956,0.00016220604],"about_ca_topic_score_codex":0.0001377895,"about_ca_topic_score_gemma":0.0000010344619,"teacher_disagreement_score":0.021349858,"about_ca_system_score_codex":0.00011798742,"about_ca_system_score_gemma":0.000052944626,"threshold_uncertainty_score":0.63902336},"labels":[],"label_agreement":null},{"id":"W1982401390","doi":"10.1002/cjs.5550330206","title":"Third order point process intensity estimation for reaction time experiment data","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parametric statistics; Intensity (physics); Nonparametric statistics; Context (archaeology); Point process; Process (computing); Point (geometry); Computer science; Nonlinear system; Simple (philosophy); Econometrics; Experimental data; Mathematics; Statistics; Applied mathematics; Physics; Optics","score_opus":0.046610960332954324,"score_gpt":0.31108739892367204,"score_spread":0.26447643859071773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982401390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14298,0.001783811,0.836623,0.005078448,0.0005404271,0.00029257342,0.0043136664,0.00005111065,0.008336943],"genre_scores_gemma":[0.8447536,0.000029319974,0.15272765,0.00027437,0.00051113823,0.000003234452,0.00047584908,0.000027354383,0.0011974837],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920505,0.0000041958606,0.0003316204,0.00012359387,0.00014224586,0.00019329319],"domain_scores_gemma":[0.9985897,0.000074355405,0.00028290917,0.00021539543,0.000557821,0.00027981936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016037509,0.000098664204,0.00019944948,0.00014247018,0.00010891461,0.00005203677,0.00025502994,0.00006113756,0.0004817267],"category_scores_gemma":[0.0008183915,0.00009520828,0.000025064983,0.00015049282,0.000045850506,0.00023817344,0.000009604121,0.00014882426,0.000015994443],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047108874,0.0003654409,0.0015308317,0.00062035065,0.0012041979,0.00019553382,0.0036055946,0.0020352602,0.088476874,0.0030740637,0.84273034,0.055690397],"study_design_scores_gemma":[0.0047452054,0.0006530201,0.0012678609,0.00040073757,0.0028371825,0.0013715937,0.006436446,0.35909644,0.4640244,0.013221563,0.14417228,0.0017732875],"about_ca_topic_score_codex":0.00048424417,"about_ca_topic_score_gemma":0.0012469543,"teacher_disagreement_score":0.7017736,"about_ca_system_score_codex":0.0003163653,"about_ca_system_score_gemma":0.0007279628,"threshold_uncertainty_score":0.52745694},"labels":[],"label_agreement":null},{"id":"W1982875966","doi":"10.1016/j.vibspec.2004.01.020","title":"In vivo lactate measurement in human tissue by near-infrared diffuse reflectance spectroscopy","year":2004,"lang":"en","type":"article","venue":"Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal Children's Hospital; McGill University","funders":"","keywords":"Diffuse reflectance infrared fourier transform; Diffuse reflection; In vivo; Near infrared reflectance spectroscopy; Infrared; Near-infrared spectroscopy; Reflectivity; Infrared spectroscopy; Spectroscopy; Chemistry; Materials science; Analytical Chemistry (journal); Biomedical engineering; Optics; Medicine; Chromatography; Biochemistry; Biology; Physics; Organic chemistry","score_opus":0.01695001485320184,"score_gpt":0.2943607636782236,"score_spread":0.27741074882502176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982875966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9205886,0.0016207669,0.008915502,0.0029118843,0.0001692265,0.00039757305,0.00013090999,0.00026223992,0.06500327],"genre_scores_gemma":[0.9828464,0.00016383182,0.014588563,0.0006129903,0.00030600323,0.00014069091,0.0000935799,0.00008595197,0.0011620088],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963077,0.00003840411,0.00082033005,0.0008817397,0.001052913,0.0008989587],"domain_scores_gemma":[0.99887824,0.000053087297,0.0002438745,0.0005294865,0.00009803126,0.00019726882],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026780827,0.0005077713,0.00061201287,0.0003373414,0.00025139083,0.00021448273,0.00051142985,0.00024221693,0.0057004318],"category_scores_gemma":[0.0001270235,0.0005498345,0.00011749119,0.0014662237,0.00017018257,0.0005478115,0.000068276204,0.00063202024,0.000094034134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012452212,0.00085692585,0.0023370397,0.000043071657,0.00004301135,0.000048738322,0.00020193626,0.00020408927,0.9890003,0.0033334296,0.0038034534,0.0000034477705],"study_design_scores_gemma":[0.0030400306,0.00014540234,0.00060502463,0.00006434174,0.00003179226,0.000008524483,0.00007678595,0.00006985597,0.9768257,0.01707462,0.0015206691,0.0005372159],"about_ca_topic_score_codex":0.0005962658,"about_ca_topic_score_gemma":0.001037266,"teacher_disagreement_score":0.06384125,"about_ca_system_score_codex":0.0021910423,"about_ca_system_score_gemma":0.00034253031,"threshold_uncertainty_score":0.9996953},"labels":[],"label_agreement":null},{"id":"W1984735140","doi":"10.1021/ac048894j","title":"Comparison of Spectra Using a Bayesian Approach. An Argument Using Oil Spills as an Example","year":2004,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of Environment and Conservation","funders":"","keywords":"Chemistry; Oil spill; Bayesian probability; Argument (complex analysis); Environmental chemistry; Statistics; Petroleum engineering","score_opus":0.08563568260132544,"score_gpt":0.36473012271807675,"score_spread":0.27909444011675133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984735140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9596916,0.00018601955,0.006175297,0.000016915132,0.0000116891415,0.00002350262,0.000012790416,0.000085648855,0.03379653],"genre_scores_gemma":[0.9858825,0.000009098128,0.013252163,0.00003848364,0.000288824,0.0000026811788,0.00009481476,0.00004954333,0.00038184176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99734116,0.000014103078,0.0006970538,0.00076956867,0.00057435886,0.0006037768],"domain_scores_gemma":[0.9982185,0.00004282412,0.00027511615,0.0008704396,0.00010039482,0.0004927466],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015690937,0.00039037198,0.0007813334,0.000086611144,0.00015184423,0.00008214353,0.0004902932,0.00032876924,0.002425655],"category_scores_gemma":[0.00007314572,0.00039856776,0.00024560472,0.0007003454,0.00026665573,0.00021417331,0.000087024906,0.00044455283,0.000006170062],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006521896,0.0022837534,0.005965198,0.00046734867,0.00028858948,0.000025600913,0.00032438926,0.0043962337,0.98545986,0.00051911833,0.000008230924,0.00019645637],"study_design_scores_gemma":[0.0006816094,0.000050440674,0.000033971817,0.00004937762,0.0006577941,0.00006238663,0.0022202583,0.055435877,0.93959135,0.0007074271,0.00004938533,0.00046013328],"about_ca_topic_score_codex":0.001527604,"about_ca_topic_score_gemma":0.000007468142,"teacher_disagreement_score":0.051039644,"about_ca_system_score_codex":0.00044080004,"about_ca_system_score_gemma":0.00022982218,"threshold_uncertainty_score":0.99984664},"labels":[],"label_agreement":null},{"id":"W1985444020","doi":"10.1007/s11119-011-9237-2","title":"Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet","year":2011,"lang":"en","type":"article","venue":"Precision Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Reseau canadien de recherche respiratoire; Deutsche Forschungsgemeinschaft","keywords":"Heterodera schachtii; Rhizoctonia solani; Rhizoctonia; Hyperspectral imaging; Biology; Sugar beet; Agronomy; Horticulture; Remote sensing","score_opus":0.023693814629906714,"score_gpt":0.25234650075340137,"score_spread":0.22865268612349465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985444020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99523085,0.0004927375,0.00093611516,0.00022202007,0.000062603685,0.0001345572,0.00017052899,0.0000998548,0.0026507264],"genre_scores_gemma":[0.99641114,0.000112713795,0.0015256298,0.0002515992,0.000056894783,0.000015505413,0.00005104673,0.000027430535,0.0015480514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983176,0.000027312113,0.00034651265,0.0005718571,0.00036624406,0.00037049112],"domain_scores_gemma":[0.99897826,0.00012356944,0.0001687946,0.00039388175,0.00010626584,0.00022924412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082697734,0.0003338678,0.0004111459,0.00013012247,0.00012127332,0.000089739166,0.00027971657,0.00014920636,0.0006635485],"category_scores_gemma":[0.00012981758,0.00022172401,0.00011184873,0.0004077346,0.00005373583,0.0002112677,0.00011897983,0.00029064761,0.000033607426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014467769,0.0002533371,0.014585452,0.00004229971,0.00009757969,0.0000081199805,0.0007309616,0.000004434668,0.9649516,0.000077168166,0.018352315,0.00075207616],"study_design_scores_gemma":[0.00042137323,0.00011873952,0.012602923,0.000093494265,0.0001726291,0.00000979141,0.00023580056,0.000043588916,0.9840846,0.000114502436,0.0017679107,0.0003346303],"about_ca_topic_score_codex":0.00014931992,"about_ca_topic_score_gemma":0.000016325883,"teacher_disagreement_score":0.019133039,"about_ca_system_score_codex":0.00006729417,"about_ca_system_score_gemma":0.000009672017,"threshold_uncertainty_score":0.90416425},"labels":[],"label_agreement":null},{"id":"W1985774050","doi":"10.1007/s11746-001-0276-1","title":"Multivariate model for the prediction of soluble condensed tannins in crude extracts of polyphenols from canola and rapeseed hulls","year":2001,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; St. Francis Xavier University","funders":"","keywords":"Canola; Polyphenol; Rapeseed; Partial least squares regression; Proanthocyanidin; Chemistry; Vanillin; Multivariate statistics; Chromatography; Tannin; Mathematics; Food science; Statistics; Organic chemistry","score_opus":0.026036614253249858,"score_gpt":0.2839173620071357,"score_spread":0.2578807477538858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985774050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99694157,0.0009762478,0.0011535096,0.00046385187,0.000031397085,0.000033903718,0.00008079303,0.000005943071,0.00031278515],"genre_scores_gemma":[0.9958082,0.0008891431,0.0020730032,0.000121063655,0.00010252913,0.0000048913266,0.0000029332277,0.00001783472,0.0009804463],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987181,0.000015644891,0.0005788325,0.00015095952,0.0003235588,0.00021292918],"domain_scores_gemma":[0.9974513,0.00052756333,0.0014802278,0.00027520058,0.00020094661,0.00006474377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025480738,0.00015397975,0.00050366856,0.000028135588,0.00007987248,0.000015810916,0.00039619074,0.000089375964,0.000037856476],"category_scores_gemma":[0.00026064148,0.00009993115,0.00039422605,0.0003959193,0.00038609156,0.00007976229,0.000060997234,0.00034095885,5.4803124e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023314066,0.00014578617,0.008388866,0.000051607694,0.00044602368,6.6233974e-7,0.0018313254,0.0009723923,0.98681545,0.0000018774717,0.000696885,0.00041595407],"study_design_scores_gemma":[0.0019175134,0.000039938128,0.0052145417,0.00009829829,0.00047305712,0.000018321745,0.004666158,0.034026295,0.9531659,0.00015777162,0.00010538568,0.00011683266],"about_ca_topic_score_codex":0.0006332335,"about_ca_topic_score_gemma":0.000015707374,"teacher_disagreement_score":0.03364959,"about_ca_system_score_codex":0.00014152932,"about_ca_system_score_gemma":0.00017533089,"threshold_uncertainty_score":0.40750742},"labels":[],"label_agreement":null},{"id":"W1986524464","doi":"10.1002/cjce.20164","title":"Covariance‐oriented qualitative and quantitative calibration analysis for multistage batch processes","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong University of Science and Technology; National Natural Science Foundation of China","keywords":"Covariance; Calibration; Computer science; Process (computing); Latent variable; Multivariate statistics; Quality (philosophy); Analysis of covariance; Data mining; Statistics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.02240422313607481,"score_gpt":0.2974635880471675,"score_spread":0.2750593649110927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986524464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8352703,0.0035382851,0.15970923,0.0011568597,0.000042721786,0.00007101518,0.00008373438,0.000018846067,0.00010900058],"genre_scores_gemma":[0.99464875,0.000008869524,0.0051688263,0.000040901832,0.0000778836,0.0000027369888,0.000012993188,0.000008112668,0.000030923402],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993082,0.0000075716753,0.00027270702,0.00010058002,0.00011473381,0.00019616517],"domain_scores_gemma":[0.9989103,0.00041549673,0.00015218795,0.00008034356,0.00022453559,0.00021715512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019706134,0.00011308775,0.0002663064,0.00019430279,0.000075963275,0.000055947356,0.00013698998,0.0000585396,0.000042704924],"category_scores_gemma":[0.0012860387,0.00008776052,0.000092405164,0.0008121872,0.00005320196,0.00012461246,0.0000032937508,0.00020438575,1.5415058e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003778817,0.00011393195,0.0018619082,0.0008672707,0.0045380197,0.00004491259,0.03675675,0.0349386,0.88993394,0.028789159,0.00063007727,0.0011475441],"study_design_scores_gemma":[0.0010405878,0.00010710424,0.00023372695,0.00010495887,0.001904958,0.00003115347,0.0021059762,0.04548145,0.94595623,0.0017709411,0.0008343894,0.00042849532],"about_ca_topic_score_codex":0.0003464842,"about_ca_topic_score_gemma":0.00019037345,"teacher_disagreement_score":0.15937844,"about_ca_system_score_codex":0.000115935974,"about_ca_system_score_gemma":0.00022899591,"threshold_uncertainty_score":0.35787702},"labels":[],"label_agreement":null},{"id":"W1986786163","doi":"10.1016/j.biosystemseng.2010.09.006","title":"Pork meat quality classification using Visible/Near-Infrared spectroscopic data","year":2010,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Linear discriminant analysis; Loin; Near-infrared spectroscopy; Mathematics; Quality (philosophy); Processed meat; Pattern recognition (psychology); Food science; Artificial intelligence; Chemistry; Computer science; Statistics; Biology; Physics","score_opus":0.07116780092309166,"score_gpt":0.32988299775728935,"score_spread":0.2587151968341977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986786163","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98918056,0.0004891343,0.004003295,0.000032567546,0.00058081886,0.000086929285,0.00007316418,0.00040967375,0.005143853],"genre_scores_gemma":[0.9923977,0.000011024455,0.006032911,0.000008158657,0.00065125996,0.000005858302,0.00013949402,0.000045985875,0.00070756284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982847,0.000010958545,0.0004841593,0.0005258371,0.0003107172,0.00038364594],"domain_scores_gemma":[0.9979958,0.00008911264,0.00017662636,0.0015412633,0.00005644516,0.0001407858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002939606,0.00025167834,0.00036505426,0.0001327887,0.00012896986,0.000176944,0.0006724558,0.00023901061,0.00053211005],"category_scores_gemma":[0.00031308152,0.00025664284,0.00007255232,0.0005813652,0.000031542622,0.0002539561,0.00014094567,0.00040389146,0.000044725923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003769002,0.000027448332,0.009058486,0.00027733503,0.0000747639,0.0000027265014,0.000028684237,0.000038703754,0.98964614,0.000665488,0.00013090218,0.000045576013],"study_design_scores_gemma":[0.00041735417,0.000008425565,0.004958784,0.00008593073,0.00015711182,0.000027918986,0.0001981058,0.0662795,0.91734487,0.000015292997,0.009939374,0.0005673522],"about_ca_topic_score_codex":0.000187034,"about_ca_topic_score_gemma":0.000014847278,"teacher_disagreement_score":0.07230127,"about_ca_system_score_codex":0.000102655715,"about_ca_system_score_gemma":0.000074155854,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W1987116698","doi":"10.1071/aseg2001ab121","title":"The AVO modelling volume","year":2001,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Shell (Canada)","funders":"","keywords":"Volume (thermodynamics); Biot number; Porosity; Line (geometry); Saturation (graph theory); Geology; Mathematics; Mechanics; Mineralogy; Geometry; Geotechnical engineering; Physics; Thermodynamics","score_opus":0.020236230065264656,"score_gpt":0.26221245236283275,"score_spread":0.2419762222975681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987116698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7428424,0.0023742376,0.001747442,0.00096667244,0.0001393777,0.000056736168,0.000010644626,0.00027600233,0.25158653],"genre_scores_gemma":[0.9717979,0.0005735058,0.00030184514,0.00010932847,0.00027099167,0.000010632929,0.000014978485,0.000024913134,0.026895948],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986338,0.0000071528725,0.00030900165,0.000286813,0.00028783103,0.00047540065],"domain_scores_gemma":[0.99900854,0.00017617593,0.00013676683,0.00047926293,0.00006362057,0.00013564106],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015366646,0.00018095091,0.000168131,0.000050871236,0.00037949745,0.00012278072,0.0003531509,0.00011851672,0.0015943238],"category_scores_gemma":[0.000096736614,0.00013454778,0.00012625092,0.00030455572,0.0000830972,0.00012961475,0.000039446964,0.0003277134,0.0004372566],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009080922,0.002992097,0.0059361774,0.00031782984,0.0024753248,0.0020407245,0.00086290773,0.15251277,0.06272544,0.015784705,0.09111451,0.66232944],"study_design_scores_gemma":[0.0015370307,0.00007116312,0.019377222,0.00005942028,0.0005837367,0.0003781294,0.0019315684,0.04447699,0.38868278,0.020977592,0.52044404,0.0014803228],"about_ca_topic_score_codex":0.00011420994,"about_ca_topic_score_gemma":0.000016149099,"teacher_disagreement_score":0.6608491,"about_ca_system_score_codex":0.00006338958,"about_ca_system_score_gemma":0.000043979144,"threshold_uncertainty_score":0.99931836},"labels":[],"label_agreement":null},{"id":"W1987794588","doi":"10.5539/jfr.v1n4p55","title":"Near-infrared Spectroscopy for Rapid Estimation of Dry Matter Content in Whole Unpeeled Potato Tubers","year":2012,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dry matter; Near-infrared spectroscopy; Chemistry; Penetration depth; Water content; Food science; Animal science; Analytical Chemistry (journal); Chromatography; Physics; Biology; Optics; Geology","score_opus":0.09318442292819977,"score_gpt":0.38269925241670555,"score_spread":0.28951482948850576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987794588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914575,0.0021342484,0.0006488264,0.0007148125,0.00006566637,0.00011552713,0.000017160311,0.0000049016944,0.004841307],"genre_scores_gemma":[0.992428,0.000043343367,0.005796548,0.00004186257,0.00019322112,0.000011094553,0.0000062813215,0.000021576036,0.0014580915],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979023,0.00006693545,0.00064424216,0.00012227648,0.0007172703,0.00054699567],"domain_scores_gemma":[0.99843246,0.00045302176,0.00029220895,0.00021164228,0.00043212983,0.00017854296],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014471274,0.00012184193,0.00041121064,0.00048992556,0.00008008251,0.000052568266,0.00030674628,0.00012016401,0.0011226566],"category_scores_gemma":[0.000583196,0.00010147422,0.00019781054,0.0006202246,0.00013244317,0.00023572524,0.00004737929,0.0005603171,0.000021714799],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012159505,0.00091719086,0.069080934,0.0005220637,0.00047114253,0.000008299322,0.0011023686,0.00016732032,0.9075829,0.00014306692,0.017756645,0.0010321499],"study_design_scores_gemma":[0.0017444647,0.00047627374,0.0052006156,0.00009736843,0.000062400686,0.0000149720845,0.0011456914,0.00048799207,0.9888348,0.0006894063,0.0011378019,0.000108233035],"about_ca_topic_score_codex":0.000034014723,"about_ca_topic_score_gemma":0.000003417454,"teacher_disagreement_score":0.08125191,"about_ca_system_score_codex":0.00025856533,"about_ca_system_score_gemma":0.00017107825,"threshold_uncertainty_score":0.99979043},"labels":[],"label_agreement":null},{"id":"W1987931506","doi":"10.1366/000370208786049079","title":"Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order Savitzky—Golay Filter","year":2008,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Binary Golay code; Smoothing; Filter (signal processing); Algorithm; Computer science; Noise (video); Subtraction; Mathematics; Artificial intelligence; Computer vision; Arithmetic","score_opus":0.01169312569555545,"score_gpt":0.2528082975957487,"score_spread":0.24111517190019324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987931506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8974743,0.00004240823,0.07509525,0.00026426537,0.00008737343,0.00047772878,0.00005220126,0.0008636661,0.025642786],"genre_scores_gemma":[0.97650385,0.000025459996,0.019330734,0.0019331947,0.00029442803,0.0004107703,0.00030091018,0.00010749633,0.0010931748],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99625456,0.000029872608,0.000691269,0.0011856952,0.00083812885,0.0010004722],"domain_scores_gemma":[0.9979357,0.00013290257,0.00027225088,0.0011060212,0.00016529435,0.00038780962],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018398253,0.00075669447,0.0007607767,0.00039021324,0.0005718466,0.000134312,0.00059536815,0.00025578612,0.008647227],"category_scores_gemma":[0.000022444758,0.000701586,0.00012786021,0.0013308061,0.00016907166,0.00024811536,0.00008373468,0.00055972935,0.0008809902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073232315,0.00077506993,0.0008934162,0.000052893796,0.00013286193,0.00002656557,0.0003432741,0.0033348526,0.988924,0.00043623,0.0041634906,0.0001850122],"study_design_scores_gemma":[0.0016378802,0.00070480513,0.0011090192,0.000039444607,0.000111681526,0.0000063588736,0.000056860772,0.026477234,0.9686443,0.00015850448,0.00025790883,0.00079597917],"about_ca_topic_score_codex":0.000058497917,"about_ca_topic_score_gemma":0.00001388127,"teacher_disagreement_score":0.079029515,"about_ca_system_score_codex":0.0005128009,"about_ca_system_score_gemma":0.00022292434,"threshold_uncertainty_score":0.99989694},"labels":[],"label_agreement":null},{"id":"W1988334036","doi":"10.1002/cjce.20343","title":"Bitumen content estimation of Athabasca oil sand from broad band infrared reflectance spectra","year":2010,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Suncor Energy (Canada); Syncrude (Canada); University of Winnipeg; Laurentian University; Alberta Environment and Protected Areas; University of Alberta","funders":"Syncrude; Suncor Energy Incorporated","keywords":"Oil sands; Asphalt; Hyperspectral imaging; Mineralogy; Dispersion (optics); Geology; Soil science; Calibration; Wavelet; Reflectivity; Environmental science; Remote sensing; Materials science; Mathematics; Optics; Composite material; Statistics; Computer science; Artificial intelligence","score_opus":0.01215634679729443,"score_gpt":0.22065864898225301,"score_spread":0.20850230218495858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988334036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99613005,0.0007328436,0.0002936482,0.00022138373,0.00010617115,0.000009993244,0.00002126399,0.00000932119,0.0024753483],"genre_scores_gemma":[0.9978365,0.000007699315,0.0017450033,0.00002698249,0.00023990803,0.0000010358061,0.000005432235,0.000015842137,0.00012162777],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915266,0.000003055174,0.00035283127,0.000089626046,0.0001822193,0.00021961828],"domain_scores_gemma":[0.9991145,0.0001277943,0.00018974658,0.00019547243,0.0000914892,0.00028097513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107939675,0.00012665687,0.0002659483,0.00011043138,0.000037620943,0.000038578695,0.00033049996,0.000111089816,0.00061679],"category_scores_gemma":[0.0004456551,0.00010050306,0.000110723784,0.00022847291,0.000073654504,0.00008934892,0.000007845804,0.00057523773,0.0000019304466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012694291,0.0000066169946,0.00026393315,0.000025350444,0.00009490569,0.000010237942,0.00013804848,0.0005443483,0.9983737,0.00006571465,0.00015100419,0.0003134697],"study_design_scores_gemma":[0.00032584844,0.000008362884,0.00013727321,0.00006579929,0.00009611438,0.000034457255,0.000016447593,0.0024291421,0.9963067,0.00031936067,0.00015354222,0.000106911786],"about_ca_topic_score_codex":0.0017867952,"about_ca_topic_score_gemma":0.00027324507,"teacher_disagreement_score":0.0023537206,"about_ca_system_score_codex":0.00012994441,"about_ca_system_score_gemma":0.000222716,"threshold_uncertainty_score":0.67534184},"labels":[],"label_agreement":null},{"id":"W1988586734","doi":"10.1080/03610918.2011.595984","title":"Model-Based Classification via Mixtures of Multivariate<i>t</i>-Factor Analyzers","year":2011,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Multivariate statistics; Artificial intelligence; Convergence (economics); Mixture model; Wine; Selection (genetic algorithm); Pattern recognition (psychology); Computer science; Statistical classification; Mathematics; Statistics; Chemistry","score_opus":0.2087419360183242,"score_gpt":0.43038155111568227,"score_spread":0.22163961509735808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988586734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05214433,0.00018646481,0.94534,0.000027737453,0.000013454879,0.00008630065,0.00008095375,0.00004301306,0.0020777967],"genre_scores_gemma":[0.8450287,0.000036260422,0.15451655,0.000028097582,0.000004567776,0.000013201744,0.00033253306,0.000012632812,0.000027439899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897486,0.00005824563,0.00050478126,0.00019592488,0.00014963394,0.000116578536],"domain_scores_gemma":[0.99839497,0.0005670788,0.00032185917,0.00048078355,0.00018599868,0.000049285794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010760975,0.00013253743,0.00020373704,0.0002878802,0.000115771734,0.000019627385,0.0002441323,0.00010213912,0.00010160341],"category_scores_gemma":[0.0001474804,0.00014593646,0.000033001226,0.00045517684,0.00017011185,0.00010622237,0.000051055566,0.00015350795,0.0000020842642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021238763,0.0015520875,0.04967517,0.00029585813,0.0001691346,0.000001194055,0.005252779,0.8052916,0.038574584,0.032310322,0.00005044393,0.06661442],"study_design_scores_gemma":[0.00049956114,0.000016128583,0.01581951,0.000015916938,0.000069556765,2.2054618e-7,0.00012511875,0.97110254,0.0031054437,0.009093724,0.000016552061,0.00013573251],"about_ca_topic_score_codex":0.0001113856,"about_ca_topic_score_gemma":0.00004309379,"teacher_disagreement_score":0.7928844,"about_ca_system_score_codex":0.00006682008,"about_ca_system_score_gemma":0.000052090883,"threshold_uncertainty_score":0.5951116},"labels":[],"label_agreement":null},{"id":"W1989081764","doi":"10.1364/fts.2003.fmc3","title":"Detailed lineshape measurements using a high resolution, high divergence fourier transform spectrometer","year":2003,"lang":"en","type":"article","venue":"Fourier Transform Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Fourier transform; Citation; Divergence (linguistics); Fourier transform spectroscopy; Spectrometer; Photonics; Resolution (logic); Fourier transform infrared spectroscopy; High resolution; Computer science; Optics; Physics; Library science; Remote sensing; Geology; Artificial intelligence","score_opus":0.034780505579548975,"score_gpt":0.2753671570304361,"score_spread":0.24058665145088715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989081764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82251936,0.0020739988,0.13041526,0.00086282834,0.00084945565,0.0007435047,0.00025974214,0.0006193447,0.041656516],"genre_scores_gemma":[0.91460097,0.00046697355,0.08106345,0.00025019958,0.0005629352,0.00010259636,0.00010883855,0.00018989248,0.0026541515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99364215,0.000083783234,0.0012664002,0.001370815,0.0016109407,0.002025939],"domain_scores_gemma":[0.9975765,0.00010691507,0.00030907686,0.0011202527,0.00029897317,0.00058831915],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062032836,0.0010802938,0.0012125699,0.00059092423,0.0008262304,0.00020326083,0.0008401578,0.00056274934,0.019990837],"category_scores_gemma":[0.00012920066,0.0010350689,0.00069407857,0.0020356888,0.00032290077,0.0007260214,0.000034901695,0.00095897255,0.00016883973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011173433,0.0014979065,0.017667918,0.000529697,0.002449929,0.00013535454,0.00072196947,0.0002748084,0.96650505,0.006662817,0.0014086284,0.0010286008],"study_design_scores_gemma":[0.0034454188,0.00028461663,0.0004301898,0.00007905184,0.0014614809,0.00008029919,0.00023317494,0.0009481636,0.97638285,0.0106756855,0.0046206536,0.0013584117],"about_ca_topic_score_codex":0.00034067853,"about_ca_topic_score_gemma":0.00026276798,"teacher_disagreement_score":0.092081614,"about_ca_system_score_codex":0.0010094092,"about_ca_system_score_gemma":0.000390045,"threshold_uncertainty_score":0.99920994},"labels":[],"label_agreement":null},{"id":"W1989184543","doi":"10.1016/s0168-1605(02)00109-5","title":"Prediction of food thermal process evaluation parameters using neural networks","year":2002,"lang":"en","type":"article","venue":"International Journal of Food Microbiology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mathematics; Approximation error; Slab; Logarithm; Statistics; Artificial neural network; Absolute deviation; Traverse; Mean absolute error; Mean absolute percentage error; Thermal; Mean squared error; Thermodynamics; Physics; Mathematical analysis; Computer science; Artificial intelligence; Geodesy","score_opus":0.062150427028143836,"score_gpt":0.30393543726732886,"score_spread":0.24178501023918503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989184543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962045,0.0015973754,0.0012214988,0.00008344185,0.0004914845,0.00003389185,0.000052004783,0.000007732429,0.0003080518],"genre_scores_gemma":[0.9993025,0.00003744036,0.00023458237,0.000055506905,0.00032339356,0.0000015716797,0.000022166761,0.0000099752215,0.000012875864],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894774,0.000040993917,0.00054242363,0.00012699401,0.00019363019,0.00014823434],"domain_scores_gemma":[0.99837846,0.00007944471,0.00072955765,0.000083105704,0.00069285,0.00003659355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018539406,0.00011549263,0.00023021786,0.00022792583,0.000028360704,0.000016648877,0.00034260013,0.00013584211,0.00086385035],"category_scores_gemma":[0.00012477441,0.00010096636,0.00016210495,0.00015212355,0.00008339774,0.00014609743,0.000027144346,0.00023468315,8.652942e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001573663,0.0002459185,0.006147579,0.000017092181,0.0014203177,0.0000049595856,0.00021911706,0.06028583,0.9282496,0.000018220833,0.00011399861,0.0031199965],"study_design_scores_gemma":[0.0020858531,0.00077622227,0.00037517463,0.000065440836,0.000576875,0.0011596817,0.00027207582,0.114533775,0.8796894,0.00024527003,0.000053280644,0.0001669711],"about_ca_topic_score_codex":0.0000038027865,"about_ca_topic_score_gemma":0.0000019569263,"teacher_disagreement_score":0.054247946,"about_ca_system_score_codex":0.00013508902,"about_ca_system_score_gemma":0.000031651318,"threshold_uncertainty_score":0.94585556},"labels":[],"label_agreement":null},{"id":"W1989290329","doi":"10.1021/ie0602299","title":"Non-Negative Matrix Factorization for Detection and Diagnosis of Plantwide Oscillations","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-negative matrix factorization; Matrix decomposition; Principal component analysis; Singular value decomposition; Computer science; Measure (data warehouse); Pattern recognition (psychology); Matrix (chemical analysis); Basis (linear algebra); Representation (politics); Plot (graphics); Filter (signal processing); Factorization; Algorithm; Biological system; Data mining; Artificial intelligence; Mathematics; Statistics; Physics; Chemistry","score_opus":0.07277474749003028,"score_gpt":0.37059805807331103,"score_spread":0.29782331058328076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989290329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99546444,0.0000961863,0.0030731224,0.000030502102,0.000055106397,0.0001530723,0.00008745653,0.00005274327,0.0009873545],"genre_scores_gemma":[0.9987203,0.00003685155,0.00010545936,6.48844e-7,0.00056391227,0.000054006963,0.00003130943,0.000023012959,0.0004645118],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873,0.0000041208623,0.00030380837,0.0002548295,0.00033649025,0.00037073105],"domain_scores_gemma":[0.9980954,0.0013041234,0.00008081093,0.00017058347,0.00022379136,0.00012526961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005305223,0.00013681834,0.00021424587,0.00017950553,0.000118622826,0.000035688146,0.00013595774,0.0003358847,0.00014826156],"category_scores_gemma":[0.0019754858,0.00014762435,0.000060046666,0.00078692497,0.0000686255,0.00007486888,0.00004834082,0.0004478857,9.3759166e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075246586,0.00003120401,0.006008174,0.0001841581,0.00006846533,0.0000012012848,0.00006361043,0.00033129647,0.992606,0.000010177488,0.0000774861,0.0005429789],"study_design_scores_gemma":[0.000662948,0.000028065662,0.0002953569,0.000046833782,0.000039518734,0.000003171661,0.00023155764,0.0012777965,0.9964002,0.000038165475,0.0008464273,0.000129954],"about_ca_topic_score_codex":0.00009564298,"about_ca_topic_score_gemma":0.0000029324203,"teacher_disagreement_score":0.005712817,"about_ca_system_score_codex":0.00018639419,"about_ca_system_score_gemma":0.000059180933,"threshold_uncertainty_score":0.60199463},"labels":[],"label_agreement":null},{"id":"W1989528514","doi":"10.1007/s11746-000-0192-4","title":"Determination of iodine value with a fourier transform‐near infrared based global calibration using disposable vials: An international collaborative study","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; ABB (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reproducibility; Repeatability; Calibration; Spectrum analyzer; Certified reference materials; Iodine value; Analytical Chemistry (journal); Mathematics; Computer science; Statistics; Chemistry; Chromatography; Detection limit; Telecommunications","score_opus":0.01009113822222718,"score_gpt":0.2978619476316899,"score_spread":0.2877708094094627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989528514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996932,0.00003278575,0.0011232651,0.00017147731,0.000021322527,0.000039574585,0.000060673,0.000010738683,0.0016081448],"genre_scores_gemma":[0.97949004,0.000033673146,0.019941209,0.00012260849,0.00012466614,0.0000034162529,0.00000784746,0.00001534386,0.00026120525],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99849284,0.000048387923,0.00046862022,0.00016997302,0.0006609909,0.00015918304],"domain_scores_gemma":[0.99831736,0.00006118864,0.0009299341,0.00023684678,0.0003709106,0.000083734776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024181118,0.00017503698,0.00039189603,0.000020694666,0.0001495285,0.00007241037,0.0004173728,0.0000545259,0.00031253468],"category_scores_gemma":[0.00006547428,0.00012223536,0.000234754,0.0009836686,0.00030666805,0.00037449654,0.000015749576,0.00020078247,1.6023024e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0077911066,0.008466146,0.22153035,0.00046595326,0.0046016374,0.000048788315,0.019296478,0.030011041,0.65243345,0.000018528955,0.0006925667,0.05464394],"study_design_scores_gemma":[0.005303216,0.0007939737,0.003211661,0.0001686426,0.0014739925,0.00009431898,0.014241896,0.16889507,0.8046878,0.00011369142,0.0004979854,0.0005177411],"about_ca_topic_score_codex":0.000106432686,"about_ca_topic_score_gemma":0.000007554991,"teacher_disagreement_score":0.21831869,"about_ca_system_score_codex":0.00042839028,"about_ca_system_score_gemma":0.00042540874,"threshold_uncertainty_score":0.49846134},"labels":[],"label_agreement":null},{"id":"W1990011345","doi":"10.1002/cem.924","title":"Mathematical improvements to maximum likelihood parallel factor analysis: experimental studies","year":2005,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Covariance; Algorithm; Computer science; Set (abstract data type); Covariance matrix; Experimental data; Data set; Variety (cybernetics); Statistics; Data mining; Mathematics; Artificial intelligence","score_opus":0.03897354445166925,"score_gpt":0.3532158939684374,"score_spread":0.31424234951676816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990011345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773233,0.009726849,0.008449433,0.00076498865,0.00010514014,0.00007197301,0.00001768961,0.00004101902,0.0034996371],"genre_scores_gemma":[0.9760682,0.00026705503,0.021722613,0.00040528295,0.0005285374,0.0000074888258,0.00000260993,0.000030475292,0.0009677632],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970464,0.000013109871,0.0011112782,0.00027970815,0.00105291,0.00049656926],"domain_scores_gemma":[0.9978832,0.00022074087,0.0006699045,0.0003537824,0.00044748478,0.0004248886],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032558196,0.00032059988,0.0010548675,0.0023642331,0.00008665906,0.00009097196,0.0005755358,0.00014876416,0.002689921],"category_scores_gemma":[0.0009104764,0.00025670015,0.0006479036,0.005675254,0.000051129424,0.0002765477,0.00016056818,0.000376769,0.000082680555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084571243,0.010389653,0.057441503,0.0007862075,0.050501753,0.00025198277,0.0068935566,0.00086136285,0.8024078,0.00031697715,0.023039242,0.04626423],"study_design_scores_gemma":[0.0016230228,0.00031916695,0.00042239926,0.0000347297,0.0026443722,0.00004783804,0.0051313937,0.00015196357,0.98429585,0.00089790893,0.0039390405,0.0004923468],"about_ca_topic_score_codex":0.0000015309831,"about_ca_topic_score_gemma":6.9905013e-7,"teacher_disagreement_score":0.181888,"about_ca_system_score_codex":0.00058425276,"about_ca_system_score_gemma":0.0000649504,"threshold_uncertainty_score":0.9999885},"labels":[],"label_agreement":null},{"id":"W1990065601","doi":"10.1366/10-06107","title":"Noise Reduction Methods Applied to Two-Dimensional Correlation Spectroscopy (2D-COS) Reveal Complementary Benefits of Pre- and Post-Treatment","year":2011,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Noise reduction; Noise (video); Spectroscopy; Spectral line; Wavelet; Chemistry; Two-dimensional nuclear magnetic resonance spectroscopy; Reduction (mathematics); Correlation coefficient; Algorithm; Mathematics; Biological system; Analytical Chemistry (journal); Nuclear magnetic resonance; Computer science; Physics; Artificial intelligence; Statistics","score_opus":0.02496002037023115,"score_gpt":0.313011113262371,"score_spread":0.28805109289213987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990065601","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9607137,0.000428677,0.004517792,0.00009766324,0.00014175646,0.00065815996,0.00013754671,0.00018633384,0.033118382],"genre_scores_gemma":[0.7874766,0.00005210095,0.21121435,0.00014129849,0.00024390935,0.000162379,0.00027400575,0.000070121416,0.00036520697],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969498,0.000036346188,0.00081898505,0.0010355694,0.00045860262,0.00070068054],"domain_scores_gemma":[0.99823076,0.0001459586,0.00043357868,0.0007432259,0.00011439017,0.00033210852],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029403175,0.00060777005,0.00087113446,0.00040399225,0.0002679525,0.000037602724,0.00030451687,0.0001946654,0.0021492704],"category_scores_gemma":[0.000021154881,0.00059359334,0.00015524968,0.00072708895,0.00018374017,0.00011536386,0.00016743869,0.00031136913,0.00005790234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012860303,0.00054537266,0.0013234488,0.00005770763,0.00032538464,0.0000015311053,0.0013369212,0.00026075443,0.9858438,0.007278363,0.0002718473,0.0014688771],"study_design_scores_gemma":[0.0017771716,0.00047451508,0.008549364,0.000022410704,0.00062464795,0.000025065465,0.0006431022,0.000069072936,0.9850294,0.0021512713,0.0001142947,0.0005196877],"about_ca_topic_score_codex":0.00087480893,"about_ca_topic_score_gemma":0.00003553188,"teacher_disagreement_score":0.20669656,"about_ca_system_score_codex":0.00046599496,"about_ca_system_score_gemma":0.00009638588,"threshold_uncertainty_score":0.99965155},"labels":[],"label_agreement":null},{"id":"W1990543748","doi":"10.1366/0003702053085043","title":"New Developments in Planar Array Infrared Spectroscopy","year":2005,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Spectrograph; Optics; Infrared; Planar; Infrared spectroscopy; Spectroscopy; Near-infrared spectroscopy; Spectral resolution; Materials science; Curvature; Computer science; Physics; Spectral line; Mathematics; Computer graphics (images)","score_opus":0.01019163872623411,"score_gpt":0.25540856581985766,"score_spread":0.24521692709362355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990543748","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16874735,0.00073243096,0.0093189385,0.0005124409,0.00010276872,0.00024571086,0.000019125457,0.00048506577,0.8198362],"genre_scores_gemma":[0.7865139,0.00026716926,0.19565429,0.00093261525,0.0010763484,0.00009260855,0.00012157675,0.00013133066,0.015210145],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964343,0.000011965028,0.0007750535,0.00095244346,0.00059144036,0.0012348515],"domain_scores_gemma":[0.99850667,0.000098898345,0.00023495684,0.0007441706,0.000027390315,0.00038789163],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016268544,0.0006137536,0.0007559176,0.00051219895,0.00016647046,0.00013047358,0.00077727286,0.0003266892,0.007978122],"category_scores_gemma":[0.00003915148,0.0006463655,0.00014240005,0.0013772851,0.00008267515,0.00021844837,0.0000669948,0.00075678853,0.0011877032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018902085,0.0002030171,0.0069985143,0.000031836433,0.000116893854,0.000013672194,0.00038244415,0.000017441698,0.971993,0.0049696397,0.014443098,0.00064137817],"study_design_scores_gemma":[0.0018073558,0.000029598907,0.0015713526,0.000021694841,0.000069532536,0.000010384172,0.0002648305,0.000008195216,0.95125693,0.0034432963,0.040844925,0.00067191775],"about_ca_topic_score_codex":0.00013023644,"about_ca_topic_score_gemma":0.00018556546,"teacher_disagreement_score":0.80462605,"about_ca_system_score_codex":0.0008932084,"about_ca_system_score_gemma":0.00035908172,"threshold_uncertainty_score":0.99959874},"labels":[],"label_agreement":null},{"id":"W1990743281","doi":"10.1007/s11694-007-9022-0","title":"Near-infrared spectroscopy and imaging in food quality and safety","year":2007,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":167,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Food safety; Quality (philosophy); Chemometrics; Computer science; Identification (biology); Software; Agriculture; Food quality; Traceability; Systems engineering; Agricultural engineering; Biochemical engineering; Biotechnology; Risk analysis (engineering); Business; Artificial intelligence; Engineering; Machine learning; Geography; Food science; Software engineering","score_opus":0.039466054438937755,"score_gpt":0.34378653279914423,"score_spread":0.3043204783602065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990743281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99072605,0.00096948404,0.0041154814,0.00076474523,0.000049713428,0.00015473369,0.00014707376,0.00005343784,0.0030192502],"genre_scores_gemma":[0.99402046,0.00045338596,0.004902099,0.00038275277,0.000057914156,0.000001448407,0.00009165528,0.000017370436,0.00007290445],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981309,0.00006434495,0.00074566517,0.00049870013,0.00018652958,0.00037383861],"domain_scores_gemma":[0.9989623,0.00042117568,0.00023713248,0.00016499373,0.00005554798,0.0001588707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011685375,0.00024276944,0.0004242894,0.00011986161,0.0005002545,0.00015884459,0.0000409675,0.00013139877,0.000019494506],"category_scores_gemma":[0.00018395779,0.00025242753,0.000053633234,0.00023545924,0.00029047654,0.0002653988,0.000057538742,0.00020223417,2.6886732e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064877383,0.0003478006,0.37286097,0.0024372337,0.00061550195,0.000008423232,0.012428099,0.00000745221,0.1753741,0.021068165,0.0001100242,0.4082545],"study_design_scores_gemma":[0.014043001,0.0006638317,0.51870644,0.000247528,0.0002902098,0.00011649031,0.033109203,0.0017147368,0.39794534,0.028215121,0.0034070234,0.0015410675],"about_ca_topic_score_codex":0.0002676201,"about_ca_topic_score_gemma":0.0004511477,"teacher_disagreement_score":0.40671343,"about_ca_system_score_codex":0.000092884795,"about_ca_system_score_gemma":0.000033927623,"threshold_uncertainty_score":0.9999928},"labels":[],"label_agreement":null},{"id":"W1990960805","doi":"10.1021/ie800997x","title":"Potential of Hyperspectral Imaging for Quality Control of Polymer Blend Films","year":2009,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Hyperspectral imaging; Low-density polyethylene; Materials science; Polystyrene; Extrusion; Polymer; Chemical imaging; Spectral imaging; Composite material; Polyethylene; Polymer blend; Optics; Remote sensing","score_opus":0.060196742912284815,"score_gpt":0.368030534841555,"score_spread":0.3078337919292702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990960805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99433696,0.0013593222,0.00048875506,0.00036156276,0.00007166852,0.00015429355,0.00025371322,0.00007694923,0.0028967531],"genre_scores_gemma":[0.99836904,0.000013090104,0.00011422082,0.0000050467634,0.0005922535,0.000016078144,0.000032012715,0.000024224028,0.00083400926],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977522,0.00001728832,0.00058738585,0.00035852686,0.00064313697,0.0006414844],"domain_scores_gemma":[0.998483,0.00046999834,0.00015384477,0.00042740768,0.00030658595,0.0001591377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000718432,0.00021005535,0.0005292475,0.00014627463,0.00006551631,0.000029700508,0.0004704518,0.00028061308,0.00084414147],"category_scores_gemma":[0.0012192742,0.00022242445,0.00026520452,0.0006080383,0.00013316744,0.00007287611,0.00003945916,0.00069834135,0.0000010259743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027655874,0.00014800325,0.0003897797,0.00018653176,0.0001292333,0.000004403422,0.000022735388,0.0004795581,0.99750966,0.00007999737,0.00038355985,0.0003899956],"study_design_scores_gemma":[0.0022808756,0.00003984657,0.000060456892,0.000055679684,0.00008436616,0.0000070979877,0.0002733982,0.0021121807,0.99474615,0.00003751795,0.00011106984,0.00019138432],"about_ca_topic_score_codex":0.000081885664,"about_ca_topic_score_gemma":8.0006856e-8,"teacher_disagreement_score":0.0040320824,"about_ca_system_score_codex":0.00010957017,"about_ca_system_score_gemma":0.00018753386,"threshold_uncertainty_score":0.9242757},"labels":[],"label_agreement":null},{"id":"W1990961313","doi":"10.3390/s131115673","title":"Building Kinetic Models for Determining Vitamin C Content in Fresh Jujube and Predicting Its Shelf Life Based on Near-Infrared Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Shelf life; Ziziphus; Chemistry; Vitamin C; Calibration; Food science; Analytical Chemistry (journal); Botany; Chromatography; Mathematics; Statistics; Biology","score_opus":0.03246688765576662,"score_gpt":0.2672232564851352,"score_spread":0.23475636882936857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990961313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934732,0.00018224848,0.0011552711,0.00018202285,0.00004662579,0.00025374012,0.00002546958,0.000104592706,0.0045768493],"genre_scores_gemma":[0.9862853,0.000016547368,0.012384615,0.00030145116,0.00011909861,0.00008616135,0.000009872692,0.00004999246,0.0007469428],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829674,0.000020247602,0.00040980478,0.0005007777,0.00023380734,0.0005386216],"domain_scores_gemma":[0.99879384,0.0005158931,0.00014780043,0.0002738493,0.00007480446,0.00019383554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012441105,0.00027871312,0.00040522512,0.00019422888,0.00013846964,0.0001456035,0.00016374722,0.00016394568,0.00032336926],"category_scores_gemma":[0.0006401382,0.0002740963,0.000098979894,0.00027305153,0.000053050488,0.000115600305,0.00004164983,0.00025780915,0.000008511029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002518799,0.00029910996,0.037214287,0.0006903448,0.00018048967,0.00003958087,0.00090525,0.048513368,0.9096026,0.0002779709,0.0010675979,0.00095752307],"study_design_scores_gemma":[0.0011876132,0.00008054425,0.0010305354,0.000116646945,0.000059502963,0.0000034463071,0.0005126333,0.64954114,0.3469507,0.0002402147,0.00003528583,0.00024172654],"about_ca_topic_score_codex":0.00017198408,"about_ca_topic_score_gemma":0.000009823005,"teacher_disagreement_score":0.6010278,"about_ca_system_score_codex":0.00014365646,"about_ca_system_score_gemma":0.00004610159,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W1991024677","doi":"10.13031/2013.20043","title":"Thermal Imaging to Identify Western Canadian Wheat Classes","year":2005,"lang":"en","type":"article","venue":"2005 Tampa, FL July 17-20, 2005","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kernel (algebra); Materials science; Thermal; Mathematics; Meteorology; Geography","score_opus":0.014249496001412966,"score_gpt":0.2962014182447372,"score_spread":0.28195192224332427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991024677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6461793,0.0046419706,0.00026514204,0.011085594,0.00037055745,0.00028809876,0.00055973703,0.00055058877,0.336059],"genre_scores_gemma":[0.81106985,0.0000997371,0.0013057326,0.005845725,0.0030852633,0.000052009196,0.0002171796,0.0001519664,0.17817254],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996106,0.000031498024,0.0006250792,0.00082470855,0.0005745949,0.0018380957],"domain_scores_gemma":[0.99748033,0.00009983549,0.00015539852,0.0009012558,0.00010937397,0.0012538094],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024414046,0.0005771077,0.00057130604,0.00064304937,0.00036670457,0.0003515531,0.00085900817,0.0002647576,0.013552722],"category_scores_gemma":[0.00007001446,0.0005969319,0.00027018535,0.0006673874,0.0001066619,0.00054286467,0.00012231353,0.0005919385,0.004374697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004696765,0.00023691829,0.03418055,0.00006686797,0.00022169061,0.00013034082,0.0005815679,0.0014538391,0.2425257,0.000017563965,0.71575755,0.004780416],"study_design_scores_gemma":[0.00059985096,0.00001160202,0.0026985542,0.000047730653,0.00021455923,0.00004603771,0.00033289587,0.00029119864,0.13520424,0.000015432659,0.85977066,0.00076721836],"about_ca_topic_score_codex":0.0094895195,"about_ca_topic_score_gemma":0.18295881,"teacher_disagreement_score":0.1734693,"about_ca_system_score_codex":0.0010607839,"about_ca_system_score_gemma":0.00044882155,"threshold_uncertainty_score":0.9996482},"labels":[],"label_agreement":null},{"id":"W1991169069","doi":"10.1016/j.aca.2011.08.006","title":"Fast and simple methods for the optimization of kurtosis used as a projection pursuit index","year":2011,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Projection pursuit; Kurtosis; Simple (philosophy); Index (typography); Chromatography; Biological system; Statistics","score_opus":0.04350831249063949,"score_gpt":0.34330201469824595,"score_spread":0.2997937022076065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991169069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5061991,0.0003246372,0.39231464,0.0022091805,0.00014724373,0.0013691082,0.00011425497,0.00033689506,0.09698491],"genre_scores_gemma":[0.981343,0.00012910728,0.0176377,0.000068162335,0.00005252177,0.00004000151,0.000018054921,0.000021673455,0.00068980176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991171,0.000021431173,0.00026651967,0.00026045024,0.00013620555,0.00019830554],"domain_scores_gemma":[0.99891317,0.00041354605,0.00019439403,0.00032547582,0.00009666836,0.000056738183],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022800287,0.00014610156,0.0002656641,0.00013873518,0.00012417114,0.00003594463,0.0002448183,0.00011853222,0.0013933876],"category_scores_gemma":[0.00040462264,0.00010652038,0.00012714721,0.000519854,0.000113240625,0.000102021804,0.0000624325,0.00011627295,0.0000011160702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011814298,0.0013599795,0.029681267,0.00072862476,0.00525545,0.00000130199,0.005109241,0.0001731841,0.9321407,0.0031463562,0.0046989946,0.016523484],"study_design_scores_gemma":[0.0007339618,0.00013062304,0.0011778973,0.000014409393,0.0017568151,0.000009102267,0.0014986029,0.16842557,0.82339513,0.0016905692,0.00089795334,0.00026935618],"about_ca_topic_score_codex":0.00012710041,"about_ca_topic_score_gemma":0.000010527248,"teacher_disagreement_score":0.47514385,"about_ca_system_score_codex":0.0000317649,"about_ca_system_score_gemma":0.000043279844,"threshold_uncertainty_score":0.99951947},"labels":[],"label_agreement":null},{"id":"W1991598493","doi":"10.13031/2013.36284","title":"Near-Infrared (NIR) Hyperspectral Imaging - An Emerging Analytical Tool for Classification of Western Canadian Wheat Classes from Different Locations and Crop Years","year":2010,"lang":"en","type":"article","venue":"ASABE/CSBE North Central Intersectional Meeting","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Manitoba","keywords":"Hyperspectral imaging; Principal component analysis; Near-infrared spectroscopy; Remote sensing; Crop; Linear discriminant analysis; Environmental science; Geography; Artificial intelligence; Computer science; Optics; Physics","score_opus":0.019457626335951047,"score_gpt":0.2834142386609672,"score_spread":0.26395661232501616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991598493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99735725,0.00006826164,0.0007665189,0.00035127674,0.00026462565,0.00009841006,0.00020525805,0.00005425193,0.0008341382],"genre_scores_gemma":[0.9977611,0.000010994505,0.0011351677,0.00009828168,0.00040665412,0.000022931381,0.00037081566,0.000032238502,0.00016183105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983378,0.000018544264,0.00044271562,0.00047098793,0.00022775176,0.0005022008],"domain_scores_gemma":[0.9989238,0.0002019533,0.00016764626,0.00025450194,0.00014866545,0.00030347056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076188764,0.00022343462,0.00028214118,0.0001857274,0.0002784645,0.00015693747,0.00020091252,0.000114287686,0.00046391174],"category_scores_gemma":[0.00019590605,0.00023778535,0.00013164202,0.00020194192,0.00018432044,0.00027397578,0.00003263383,0.00036609088,0.0000026019504],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004756747,0.000096558535,0.91280967,0.00003453322,0.0001488708,0.000002647258,0.00046916533,0.00008343854,0.0847987,0.0002701612,0.00017268569,0.0010660036],"study_design_scores_gemma":[0.0006095827,0.000039683804,0.9307538,0.000053614724,0.00033431555,0.000016511503,0.002351481,0.043358464,0.020890823,0.0002299917,0.00096002273,0.00040168737],"about_ca_topic_score_codex":0.021157462,"about_ca_topic_score_gemma":0.062812194,"teacher_disagreement_score":0.06390788,"about_ca_system_score_codex":0.00023239094,"about_ca_system_score_gemma":0.0001661367,"threshold_uncertainty_score":0.98536074},"labels":[],"label_agreement":null},{"id":"W1991823828","doi":"10.1002/lite.201400074","title":"Portable FTIR analyzers for the rapid determination of total <i>trans</i> fat","year":2015,"lang":"en","type":"article","venue":"Lipid Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Fourier transform infrared spectroscopy; Attenuated total reflection; Chemistry; Infrared; Infrared spectroscopy; Analytical Chemistry (journal); Chromatography; Organic chemistry; Optics; Physics","score_opus":0.01904722976397432,"score_gpt":0.2713424947822299,"score_spread":0.25229526501825555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991823828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8809577,0.005323982,0.07462493,0.0063310554,0.00055640313,0.00044678565,0.00013818541,0.00079990097,0.030821048],"genre_scores_gemma":[0.99659026,0.000056823745,0.0018965873,0.000034173598,0.000110924986,0.000055012944,0.000016543432,0.000014066816,0.0012256213],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923193,0.0000036298861,0.00022728369,0.00019108217,0.00012178782,0.00022429482],"domain_scores_gemma":[0.99922997,0.00010887129,0.00013901133,0.0003730531,0.00011401643,0.000035052613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009823415,0.00011252401,0.00023039604,0.00020194071,0.00005722916,0.00000842385,0.00028057824,0.00022925453,0.00030218656],"category_scores_gemma":[0.00019638966,0.00008588482,0.00009958453,0.0007226055,0.00017470296,0.000052726376,0.00003622923,0.0001371833,0.0000031848756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011673367,0.00021489206,0.0059956396,0.00014281314,0.00029065638,0.0000035525527,0.00018525276,0.00003554959,0.9197995,0.002843682,0.0060981144,0.06427359],"study_design_scores_gemma":[0.0005200429,0.000081924845,0.000025205583,0.000004809913,0.00021285235,0.0000139917065,0.00091911183,0.00082555565,0.9815178,0.0009817759,0.0147886975,0.000108218264],"about_ca_topic_score_codex":0.000020632702,"about_ca_topic_score_gemma":0.000006092494,"teacher_disagreement_score":0.11563254,"about_ca_system_score_codex":0.000040350456,"about_ca_system_score_gemma":0.000033320233,"threshold_uncertainty_score":0.35022813},"labels":[],"label_agreement":null},{"id":"W1992288428","doi":"10.1080/17480272.2014.965743","title":"Using near-infrared hyperspectral images on subalpine fir board. Part 1: Moisture content estimation","year":2014,"lang":"en","type":"article","venue":"Wood Material Science and Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CRB Innovations (Canada); FPInnovations; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; FPInnovations","keywords":"Hyperspectral imaging; Absorbance; Partial least squares regression; Water content; Remote sensing; Moisture; Mathematics; Environmental science; Near-infrared spectroscopy; Ranging; Sample (material); Analytical Chemistry (journal); Materials science; Statistics; Geography; Chemistry; Physics; Optics; Environmental chemistry; Meteorology; Geology; Chromatography","score_opus":0.020358890940712394,"score_gpt":0.24490556316993878,"score_spread":0.2245466722292264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992288428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970439,0.00004434581,0.0009415759,0.00006516273,0.00020912118,0.000028221508,0.000008258219,0.00009663299,0.0015627944],"genre_scores_gemma":[0.9942869,0.000009953719,0.005349058,0.000036798847,0.00021495773,0.0000040577384,0.000004892213,0.000010485668,0.0000828907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990877,0.0000025974819,0.00012981117,0.00024615286,0.0002388952,0.0002948259],"domain_scores_gemma":[0.99965155,0.000017018989,0.00003886509,0.00014953648,0.00004225136,0.00010075517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015576927,0.0001350917,0.00016071973,0.00007207295,0.00018156174,0.0002622609,0.00013817221,0.00005126932,0.00013005853],"category_scores_gemma":[0.00017657272,0.0001190793,0.00002482692,0.00028979967,0.000105522355,0.00020339026,0.00003938715,0.00007371517,0.000004327124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000106354655,0.000011720704,0.0000743426,0.00003686658,0.000007042327,0.0000013252344,0.000039632014,0.0016792009,0.9976286,0.00018416537,0.00005235109,0.00027408876],"study_design_scores_gemma":[0.00020148704,0.00003374325,0.0010118249,0.000034427856,0.00003170013,0.0000079151605,0.000064705986,0.044692528,0.9533005,0.00003392206,0.00041368444,0.00017357226],"about_ca_topic_score_codex":0.000069137954,"about_ca_topic_score_gemma":4.3069284e-7,"teacher_disagreement_score":0.04432814,"about_ca_system_score_codex":0.000094934156,"about_ca_system_score_gemma":0.000025973392,"threshold_uncertainty_score":0.48559126},"labels":[],"label_agreement":null},{"id":"W1994315740","doi":"10.1016/j.aca.2011.03.021","title":"Estimation of the age of a weathered mixture of volatile organic compounds","year":2011,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Ingenuity; University of Alberta","keywords":"Partial least squares regression; Chemistry; Multivariate statistics; Statistics; Mean squared error; Principal component analysis; Nonlinear regression; Linear discriminant analysis; Principal component regression; Regression analysis; Mathematics","score_opus":0.018480670647928424,"score_gpt":0.2425923839809574,"score_spread":0.22411171333302898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994315740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96013886,0.00001986903,0.000042071446,0.000057119672,0.000012453511,0.000037071917,0.000025729709,0.000014058363,0.03965275],"genre_scores_gemma":[0.9985544,0.000014510718,0.00057279563,0.000017486102,0.000014130278,0.0000011717104,0.000015640184,0.000013981355,0.0007958871],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910206,0.000013694976,0.00038889656,0.00014948545,0.00021545935,0.00013040578],"domain_scores_gemma":[0.9988921,0.00008818858,0.00036419934,0.00055143825,0.00007356831,0.000030461531],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000055048928,0.00012400535,0.0003680521,0.00008634946,0.000029743227,0.0000039619645,0.00042865728,0.00010788489,0.0027625714],"category_scores_gemma":[0.00015649974,0.00008814438,0.00019498437,0.0006647817,0.00018609017,0.000046412642,0.00007330785,0.00013370637,0.0000023216521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033368364,0.00023332785,0.0072124884,0.00012512263,0.00027909584,3.9256233e-7,0.0004869015,4.7285252e-7,0.99086887,0.00023478075,0.00049249287,0.000032673815],"study_design_scores_gemma":[0.00022856667,0.000029158755,0.005549601,0.00004219766,0.0005344132,0.0000025709066,0.00015296113,0.0014818748,0.99088246,0.0009649741,0.00004542611,0.00008582315],"about_ca_topic_score_codex":0.000050303624,"about_ca_topic_score_gemma":0.000012674073,"teacher_disagreement_score":0.038856864,"about_ca_system_score_codex":0.0000243282,"about_ca_system_score_gemma":0.000054799333,"threshold_uncertainty_score":0.99814904},"labels":[],"label_agreement":null},{"id":"W1994389634","doi":"10.1007/s12161-008-9031-6","title":"Perspectives on Quantitative Mid-FTIR Spectroscopy in Relation to Edible Oil and Lubricant Analysis: Evolution and Integration of Analytical Methodologies","year":2008,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Iodine value; Partial least squares regression; Lubricant; Process engineering; Saponification value; Biological system; Materials science; Biochemical engineering; Chemistry; Computer science; Mathematics; Chemical engineering; Engineering; Food science; Statistics; Organic chemistry; Biology","score_opus":0.11644741957879756,"score_gpt":0.4321710739153724,"score_spread":0.31572365433657484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994389634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7585838,0.001195731,0.23268025,0.0005544703,0.000020718397,0.00004431983,0.000017652477,0.00004777371,0.0068553197],"genre_scores_gemma":[0.6640136,0.00029876013,0.33533847,0.000018644558,0.000033003744,0.000011333167,0.0000065105633,0.000011732804,0.00026794075],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975614,0.00041085816,0.00061123096,0.00072618865,0.00034168115,0.0003486738],"domain_scores_gemma":[0.99681145,0.002315255,0.00019357847,0.0003246347,0.00017058625,0.00018447182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011088251,0.00028987665,0.00095622893,0.0014108804,0.00010868622,0.00002203793,0.00014586569,0.00018649582,0.00011910877],"category_scores_gemma":[0.005480788,0.0002478626,0.00018549134,0.003701874,0.00041779518,0.00015046586,0.000083226754,0.0004121307,0.0000021474928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034244068,0.0015701121,0.0736474,0.00032010395,0.0056245346,0.000032857915,0.009442193,0.0022028645,0.5700834,0.3268364,0.00013225994,0.0066834977],"study_design_scores_gemma":[0.0023416555,0.0057966523,0.30080053,0.00021804788,0.0066993707,0.000038225848,0.04325415,0.15660247,0.46440208,0.018280098,0.00009262302,0.0014741089],"about_ca_topic_score_codex":0.00019683536,"about_ca_topic_score_gemma":0.00004543249,"teacher_disagreement_score":0.3085563,"about_ca_system_score_codex":0.00027132747,"about_ca_system_score_gemma":0.000058133788,"threshold_uncertainty_score":0.9999974},"labels":[],"label_agreement":null},{"id":"W1995240098","doi":"10.1039/c2an35091b","title":"Fluorescence excitation–emission matrix (EEM) spectroscopy and cavity ring-down (CRD) absorption spectroscopy of oil-contaminated jet fuel using fiber-optic probes","year":2012,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gastops (Canada); Queen's University","funders":"","keywords":"Spectroscopy; Fluorescence spectroscopy; Cavity ring-down spectroscopy; Fluorescence; Jet (fluid); Absorption (acoustics); Absorption spectroscopy; Excitation; Materials science; Analytical Chemistry (journal); Matrix (chemical analysis); Fiber; Optical fiber; Chemistry; Emission spectrum; Optics; Spectral line; Chromatography; Physics","score_opus":0.01971407332663892,"score_gpt":0.2967052730429595,"score_spread":0.2769911997163206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995240098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923613,0.0033470416,0.00036744704,0.0000947363,0.000048285572,0.00007954851,0.000023330074,0.00007956795,0.0035987499],"genre_scores_gemma":[0.9920056,0.00040399004,0.0055650235,0.000015721014,0.00025364477,0.000010252625,0.0000401685,0.000036887508,0.0016687122],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790686,0.00007530575,0.00055857556,0.00040383788,0.00045335208,0.0006020688],"domain_scores_gemma":[0.99837726,0.00019636616,0.00048781047,0.0006094817,0.00014074236,0.0001883207],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047842215,0.0003648584,0.0005694856,0.0002644949,0.0002807104,0.00008022195,0.00036246734,0.00017168994,0.0011314786],"category_scores_gemma":[0.00014787965,0.00027167445,0.00018112536,0.0010442011,0.00026321303,0.00036412664,0.00012445633,0.00035606953,0.000035177738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009095656,0.00016525373,0.025282398,0.0003358348,0.00019388551,0.0000027418869,0.0005572594,0.000024442113,0.9729523,0.00020856733,0.00007387134,0.00011247544],"study_design_scores_gemma":[0.0004934404,0.000048567075,0.00544783,0.00012309723,0.0010380149,0.000036198315,0.0008670187,0.0029781978,0.9881833,0.00034047943,0.00010567494,0.0003381711],"about_ca_topic_score_codex":0.00061518943,"about_ca_topic_score_gemma":0.000022211208,"teacher_disagreement_score":0.019834567,"about_ca_system_score_codex":0.00022913353,"about_ca_system_score_gemma":0.0000593533,"threshold_uncertainty_score":0.99997354},"labels":[],"label_agreement":null},{"id":"W1995538127","doi":"10.1109/crv.2013.10","title":"Application of Support Vector Machine to Detect Microbial Spoilage of Mushrooms","year":2013,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Classifier (UML); Robot; Mushroom; Histogram; Scale-invariant feature transform; Feature extraction; Image (mathematics)","score_opus":0.006170788903614,"score_gpt":0.24385283388585907,"score_spread":0.23768204498224507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995538127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614977,0.000047088597,0.0089817075,0.00010792326,0.00001466674,0.00010219148,0.000029491051,0.000043480963,0.029175738],"genre_scores_gemma":[0.9946156,0.0000046350556,0.0017397802,0.00006777426,0.000042764485,0.000019866382,0.00002261975,0.000011682993,0.003475245],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929756,0.0000031760374,0.00027201048,0.00016976731,0.00011785364,0.00013965454],"domain_scores_gemma":[0.9994225,0.000035855377,0.00010801405,0.00029364863,0.00007525146,0.0000647182],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000043084932,0.000097909986,0.00022387861,0.0000970588,0.00001506481,0.0000059586073,0.00019025737,0.0000700311,0.016515218],"category_scores_gemma":[0.000036789297,0.00008477407,0.00008286887,0.00037722793,0.00003278916,0.000040058887,0.000044907694,0.000058325615,0.00015319345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001300692,0.000049216505,0.002196072,0.00007015438,0.00003196076,1.7968134e-7,0.00003064236,0.000003854195,0.99441296,0.00004029093,0.0015667676,0.0015849057],"study_design_scores_gemma":[0.00017814546,0.00003626721,0.0012409488,0.0000028877773,0.00003971602,0.000001978519,0.000047616548,0.000081964754,0.99717045,0.000048717815,0.0010635228,0.00008780775],"about_ca_topic_score_codex":0.0008349724,"about_ca_topic_score_gemma":0.000028235132,"teacher_disagreement_score":0.03311792,"about_ca_system_score_codex":0.000027036665,"about_ca_system_score_gemma":0.000017171202,"threshold_uncertainty_score":0.9843838},"labels":[],"label_agreement":null},{"id":"W1995857351","doi":"10.1016/j.aca.2011.10.065","title":"Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets","year":2011,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Reseau canadien de recherche respiratoire","keywords":"Chemometrics; Hyperspectral imaging; Chemical imaging; Principal component analysis; Chemistry; Raman spectroscopy; Resolution (logic); Analytical Chemistry (journal); Homogeneity (statistics); Process analytical technology; Biological system; Pattern recognition (psychology); Artificial intelligence; Chromatography; Statistics; Computer science; Mathematics; Optics","score_opus":0.04111728166313753,"score_gpt":0.26841091175289145,"score_spread":0.22729363008975392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995857351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98301953,0.000089835485,0.000010353664,0.00029871127,0.00003222956,0.000063665924,0.0000776647,0.00004859194,0.016359432],"genre_scores_gemma":[0.99857706,0.000147943,0.00081835134,0.000048454236,0.00012733771,0.000004297411,0.000034859804,0.000025938669,0.00021574186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830633,0.000029339197,0.0004910981,0.00037519704,0.00035764335,0.00044040094],"domain_scores_gemma":[0.9985454,0.00033677067,0.00032385383,0.0006109806,0.000074193435,0.000108791304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011566585,0.00027035672,0.00048751576,0.00012753754,0.00012050322,0.00002181161,0.00063759467,0.00014295697,0.0027224012],"category_scores_gemma":[0.00022670855,0.00019211603,0.00019898528,0.0005270305,0.0003243184,0.00012461194,0.00012316206,0.00045199666,0.000009093549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015359897,0.00028832373,0.026459238,0.00008049936,0.00036609493,0.0000028846093,0.00024873356,0.0000012266412,0.9718344,0.00012063134,0.00033122225,0.00011311176],"study_design_scores_gemma":[0.00043886204,0.000068271016,0.051476847,0.000041239826,0.0008737997,0.000006924713,0.00028859449,0.003173791,0.9428582,0.00052396895,0.00005099255,0.00019848596],"about_ca_topic_score_codex":0.00013612946,"about_ca_topic_score_gemma":0.000005911053,"teacher_disagreement_score":0.028976211,"about_ca_system_score_codex":0.00006951871,"about_ca_system_score_gemma":0.000049523464,"threshold_uncertainty_score":0.9981893},"labels":[],"label_agreement":null},{"id":"W1996212606","doi":"10.1016/s0169-7439(02)00138-7","title":"Comparison of principal components regression and partial least squares regression through generic simulations of complex mixtures","year":2003,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":210,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Partial least squares regression; Principal component regression; Statistics; Latent variable; Principal component analysis; Multivariate statistics; Mathematics; Calibration; Regression; Regression analysis; Chemometrics; Linear regression; Chemistry; Chromatography","score_opus":0.10002483650554103,"score_gpt":0.3595029654042023,"score_spread":0.2594781288986613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996212606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96837914,0.028178664,0.0018804865,0.000009558643,0.00015893302,0.00013671276,0.00014923704,0.0000300593,0.0010772236],"genre_scores_gemma":[0.99852777,0.000790515,0.0003768668,0.00001081278,0.0000634282,0.0000072687435,0.00006641257,0.000026103087,0.00013083192],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978009,0.00008133959,0.00092753855,0.000410777,0.00050470885,0.00027473166],"domain_scores_gemma":[0.9979863,0.0003045469,0.0007625165,0.00038388505,0.00041038965,0.00015232954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002214102,0.00030676925,0.000859005,0.00044997173,0.00016432701,0.000051779833,0.00018241383,0.00023856679,0.00018364553],"category_scores_gemma":[0.00047073662,0.00024412808,0.00008109902,0.0021340738,0.00024467296,0.00012318254,0.000083128645,0.00020414889,0.0000014903138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089280475,0.0008261839,0.28045008,0.0019623698,0.00028692314,0.0000034643003,0.0009694161,0.0008253208,0.7105494,0.00252914,0.00090725,0.00060116453],"study_design_scores_gemma":[0.00046146775,0.00011255878,0.0011223874,0.00021553709,0.0001521351,0.000004506473,0.0017219383,0.0071843797,0.9748549,0.000029035084,0.01385478,0.00028637834],"about_ca_topic_score_codex":0.000062115665,"about_ca_topic_score_gemma":0.0000011649336,"teacher_disagreement_score":0.2793277,"about_ca_system_score_codex":0.00006215294,"about_ca_system_score_gemma":0.000049958486,"threshold_uncertainty_score":0.9955254},"labels":[],"label_agreement":null},{"id":"W1996757933","doi":"10.1007/s11947-010-0333-5","title":"Applications of Thermal Imaging in Agriculture and Food Industry—A Review","year":2010,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":351,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Agriculture; Bruise; Environmental science; Agricultural engineering; Aerospace; Imaging technology; Remote sensing; Computer science; Process engineering; Engineering; Ecology; Geography; Aerospace engineering; Medicine; Biology","score_opus":0.00526099946124909,"score_gpt":0.2493293778387484,"score_spread":0.2440683783774993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996757933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9575298,0.03894243,0.000021060014,0.0014920572,0.000004892355,0.00007003079,0.000016345375,0.000064180036,0.0018592355],"genre_scores_gemma":[0.99824786,0.0014521575,0.000113664646,0.00005137938,0.00002300206,0.000054946766,0.0000051529873,0.000004956298,0.000046867586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994777,0.0000011917039,0.00015191532,0.00019396488,0.000048177957,0.00012706165],"domain_scores_gemma":[0.9996916,0.000010323577,0.000086620166,0.00014217284,0.000043419106,0.000025861196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034862256,0.00009455961,0.00019238822,0.00013804184,0.000034682256,0.0000065842473,0.0001473503,0.00031012492,0.000055713834],"category_scores_gemma":[0.00003466626,0.000071284565,0.000015865468,0.0007779487,0.00017141782,0.000036019574,0.000065394925,0.00048202366,4.5494912e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002691605,0.00011735175,0.13808349,0.0015420167,0.000037854858,9.2883073e-7,0.000029464105,3.001993e-8,0.80774325,0.0032079278,0.000055836143,0.049179163],"study_design_scores_gemma":[0.00030555515,0.00006226668,0.0019842435,0.00018238765,0.00009714256,0.000073708106,0.0005716589,0.0000035784374,0.98893607,0.0021803,0.005410343,0.00019274672],"about_ca_topic_score_codex":0.0000058412766,"about_ca_topic_score_gemma":0.000028680874,"teacher_disagreement_score":0.18119283,"about_ca_system_score_codex":0.0000037668,"about_ca_system_score_gemma":0.0000123875225,"threshold_uncertainty_score":0.29069},"labels":[],"label_agreement":null},{"id":"W1996909697","doi":"10.1366/000370205774430846","title":"A New Fourier Transform Infrared Method for the Determination of Moisture in Edible Oils","year":2005,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Sultan Qaboos University","keywords":"Acetonitrile; Chemistry; Moisture; Analytical Chemistry (journal); Extraction (chemistry); Gravimetric analysis; Water content; Fourier transform infrared spectroscopy; Infrared spectroscopy; Calibration curve; Chromatography; Sample preparation; Detection limit; Chemical engineering; Organic chemistry","score_opus":0.01129570720396853,"score_gpt":0.30239231493251456,"score_spread":0.291096607728546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996909697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005123818,0.0007996821,0.90035677,0.0012795657,0.000038906077,0.00039121392,0.000024842531,0.00006902786,0.09191615],"genre_scores_gemma":[0.22424111,0.00017799839,0.7689895,0.00032579937,0.0007056057,0.00026767023,0.000032259864,0.000054899356,0.0052051526],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863565,0.0000061098294,0.000409695,0.0003135466,0.00025631255,0.00037869957],"domain_scores_gemma":[0.9989653,0.00038456963,0.00015695473,0.00038403124,0.00003573847,0.000073399875],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023913074,0.00021906981,0.0003736905,0.00016016835,0.00008549858,0.00003140577,0.0003592131,0.00018102142,0.0010205013],"category_scores_gemma":[0.000047528218,0.000171994,0.00015445147,0.00062193297,0.000041823438,0.000089162495,0.0000215582,0.00025923623,0.0000073350025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002841056,0.00012249051,0.00013131615,0.00010651695,0.00008016735,6.4338883e-7,0.0007318139,0.000053379412,0.9410273,0.00586946,0.0036842308,0.047908574],"study_design_scores_gemma":[0.0013681612,0.000033580924,0.00006146911,0.000009976991,0.0001789163,0.0000023539224,0.00031205252,0.0020520927,0.97497755,0.009812522,0.011011059,0.00018027396],"about_ca_topic_score_codex":0.000115846095,"about_ca_topic_score_gemma":0.00021273221,"teacher_disagreement_score":0.21911728,"about_ca_system_score_codex":0.0001594247,"about_ca_system_score_gemma":0.00011154135,"threshold_uncertainty_score":0.9998927},"labels":[],"label_agreement":null},{"id":"W1997014250","doi":"10.1007/s11947-008-0110-x","title":"Wheat Class Identification Using Thermal Imaging","year":2008,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Drop (telecommunication); Moisture; Thermal; Materials science; Winter wheat; Water content; Horticulture; Analytical Chemistry (journal); Environmental science; Animal science; Meteorology; Agronomy; Composite material; Chemistry; Geography; Environmental chemistry; Biology; Geology","score_opus":0.01813225177467248,"score_gpt":0.260678884008534,"score_spread":0.24254663223386153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997014250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941222,0.0032765418,0.00042690348,0.00039433528,0.000019928168,0.000019605232,0.0000054790967,0.00023937288,0.0014956],"genre_scores_gemma":[0.99935544,0.00012030753,0.0001796909,0.000029558438,0.00005225101,0.000006777618,0.000005700995,0.00001174685,0.00023854432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929094,0.0000016577944,0.00015590967,0.00026366228,0.000082125174,0.0002056955],"domain_scores_gemma":[0.99965155,0.000007827313,0.00007966872,0.00017845417,0.000053600143,0.000028909093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025101497,0.00011361855,0.0001493048,0.00023818607,0.00020839849,0.000018480183,0.00016572111,0.00015469937,0.00007711489],"category_scores_gemma":[0.000024848448,0.000102820115,0.000027277105,0.00059550186,0.00024982193,0.00009349682,0.000058651545,0.00014909015,0.0000055598834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063536054,0.00003734016,0.04238288,0.000049580434,0.000031602078,0.0000073038445,0.00004227699,0.0000011539199,0.9543658,0.00031403409,0.000018210492,0.0027434856],"study_design_scores_gemma":[0.00018045226,0.000017011665,0.00058762537,0.000008571541,0.000050269024,0.00021121527,0.00047007916,0.0005354652,0.9968901,0.00060666644,0.000302507,0.00013998439],"about_ca_topic_score_codex":0.000010428249,"about_ca_topic_score_gemma":0.0000017938744,"teacher_disagreement_score":0.042524375,"about_ca_system_score_codex":0.000026438067,"about_ca_system_score_gemma":0.000021507796,"threshold_uncertainty_score":0.41928825},"labels":[],"label_agreement":null},{"id":"W1997187492","doi":"10.1016/j.ijpharm.2009.07.031","title":"Quality by design case study: An integrated multivariate approach to drug product and process development","year":2009,"lang":"en","type":"article","venue":"International Journal of Pharmaceutics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":211,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's Health Research Institute","funders":"U.S. Department of Energy","keywords":"Critical quality attributes; Quality by Design; Design for manufacturability; Multivariate statistics; Design of experiments; Multivariate analysis; Process (computing); Quality (philosophy); New product development; Process design; Product design; Process engineering; Computer science; Product (mathematics); Manufacturing engineering; Reliability engineering; Engineering; Mathematics; Statistics; Process integration; Mechanical engineering; Business; Machine learning","score_opus":0.10551673434089173,"score_gpt":0.4498971458583384,"score_spread":0.3443804115174467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997187492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9784989,0.00026166602,0.020460421,0.00025405764,0.00009545128,0.00011607488,0.000011402383,0.000023161314,0.00027886618],"genre_scores_gemma":[0.9848767,0.00001836113,0.01443676,0.00025952436,0.00017382532,0.000004410966,0.000008322483,0.000011694594,0.00021039028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983386,0.000073587995,0.0006037191,0.0002385355,0.000576,0.00016957546],"domain_scores_gemma":[0.9985261,0.00006587416,0.0003493399,0.00009977354,0.00072846754,0.000230478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007632899,0.00018267655,0.00025536766,0.00020553323,0.000076308126,0.00011640415,0.0004174644,0.000034609624,0.00006168814],"category_scores_gemma":[0.00017987321,0.00015218278,0.000038033177,0.00025330874,0.00002429078,0.00025430627,0.000033286182,0.00034487652,0.0000013710547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027552391,0.020514222,0.02712127,0.00013857412,0.004067529,0.0028274292,0.051008224,0.0048388606,0.6868577,0.000052055908,0.002035144,0.19778375],"study_design_scores_gemma":[0.0024281442,0.00011148474,0.00052696356,0.00002529511,0.00021077936,0.002343356,0.012730028,0.0018381705,0.97697717,0.00007188041,0.0023740125,0.00036270128],"about_ca_topic_score_codex":0.00004080157,"about_ca_topic_score_gemma":0.000002226811,"teacher_disagreement_score":0.2901195,"about_ca_system_score_codex":0.00018638217,"about_ca_system_score_gemma":0.00015760677,"threshold_uncertainty_score":0.62058336},"labels":[],"label_agreement":null},{"id":"W1997420766","doi":"10.1080/10408398.2012.733895","title":"Recent Research and Progress in Food, Feed and Nutrition with Advanced Synchrotron-based SR-IMS and DRIFT Molecular Spectroscopy","year":2014,"lang":"en","type":"review","venue":"Critical Reviews in Food Science and Nutrition","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Synchrotron; Synchrotron radiation; Molecular spectroscopy; Chemistry; Spectroscopy; Infrared spectroscopy; Food science; Nanotechnology; Materials science; Physics; Optics","score_opus":0.0711178568133539,"score_gpt":0.42472454436861956,"score_spread":0.35360668755526564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997420766","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01051391,0.9873314,0.000054097796,0.00044401956,0.000019995052,0.0013981217,0.000023565804,0.00002128447,0.00019364395],"genre_scores_gemma":[0.02902233,0.9676557,0.0019946054,0.00005023655,0.00006402039,0.001146079,0.00003490285,0.000029800389,0.0000023365308],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960639,0.00023236345,0.00078954845,0.0013430914,0.00075021124,0.000820911],"domain_scores_gemma":[0.9981793,0.00061272597,0.00015514175,0.000415876,0.00027745828,0.00035948158],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0020297475,0.00047736877,0.0017772723,0.0009123254,0.00028462175,0.00032359752,0.0002930253,0.0003225335,0.000019440311],"category_scores_gemma":[0.0014662365,0.00038355164,0.0000726461,0.0027923002,0.0028152547,0.00031512507,0.00013516066,0.00090468803,0.0000014547458],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022186998,0.0013185012,0.00014286472,0.1450245,0.000027186263,0.000042199317,0.000040435945,2.3142613e-8,0.0019956254,0.0040951576,0.000057907135,0.84703374],"study_design_scores_gemma":[0.0056667863,0.0108205965,0.00004543277,0.20490085,0.0007261541,0.00026913924,0.0004067321,0.00016565883,0.025179388,0.00867425,0.74092126,0.002223744],"about_ca_topic_score_codex":0.0000023729406,"about_ca_topic_score_gemma":0.000018395427,"teacher_disagreement_score":0.84481,"about_ca_system_score_codex":0.0002838511,"about_ca_system_score_gemma":0.0002105714,"threshold_uncertainty_score":0.9998985},"labels":[],"label_agreement":null},{"id":"W1997899467","doi":"10.1002/cjce.22222","title":"Input variable selection for an inferential predictor using the retrospective Taguchi method","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Taguchi methods; Statistics; Selection (genetic algorithm); Variable (mathematics); Feature selection; Computer science; Econometrics; Mathematics; Artificial intelligence","score_opus":0.028238112211210222,"score_gpt":0.2792065701028091,"score_spread":0.2509684578915989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997899467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8182792,0.00033104405,0.18023515,0.0002217262,0.00030186865,0.00008234066,0.0000272483,0.000028994513,0.00049243873],"genre_scores_gemma":[0.9857621,3.3071234e-7,0.01324895,0.000038782633,0.00087467255,0.0000035458474,0.000003483268,0.000024367164,0.000043746713],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913925,0.000013273331,0.00025673685,0.00010409841,0.00019260503,0.00029401583],"domain_scores_gemma":[0.998905,0.00013532612,0.00014362854,0.00014330931,0.00027571307,0.00039699444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046190934,0.00013074881,0.00021763206,0.00010712822,0.00011404637,0.0000954213,0.00034419147,0.0001149434,0.00007108126],"category_scores_gemma":[0.0009500285,0.000087110035,0.00009491294,0.00039131736,0.000041481322,0.00014230619,0.000012539625,0.00046932526,2.9100832e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009255199,0.0000307204,0.004898195,0.00006040433,0.0005246963,0.0000033410083,0.0009572222,0.062414043,0.9243157,0.0053322455,0.0012512614,0.00011962843],"study_design_scores_gemma":[0.00071786763,0.00007860918,0.000059329614,0.00003811488,0.0005211518,0.0002776689,0.00014084416,0.16497919,0.82815313,0.003616699,0.0011920637,0.00022530956],"about_ca_topic_score_codex":0.0020282909,"about_ca_topic_score_gemma":0.00017785978,"teacher_disagreement_score":0.16748293,"about_ca_system_score_codex":0.0006773852,"about_ca_system_score_gemma":0.0007200654,"threshold_uncertainty_score":0.35522443},"labels":[],"label_agreement":null},{"id":"W1998026468","doi":"10.1366/000370203322102843","title":"Raman Spectroscopy as a Tool for Measuring Mutual-Diffusion Coefficients in Hydrogels","year":2003,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Raman spectroscopy; Self-healing hydrogels; Diffusion; Chemistry; Penetration (warfare); Analytical Chemistry (journal); PEG ratio; Polymer; Thermal diffusivity; Polyethylene; Thermodynamics; Polymer chemistry; Optics; Chromatography; Organic chemistry","score_opus":0.015279951264206178,"score_gpt":0.2699779698088812,"score_spread":0.25469801854467505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998026468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8738173,0.00035610274,0.01240572,0.00005793831,0.00011672106,0.0005923267,0.000023267423,0.00027934436,0.11235128],"genre_scores_gemma":[0.98472077,0.000096568605,0.012336969,0.0002799843,0.00019777949,0.00039454454,0.000058556107,0.00013158108,0.0017832761],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958802,0.0000305074,0.0008046125,0.0012014804,0.00066779915,0.0014154295],"domain_scores_gemma":[0.99827933,0.00027296902,0.00027702676,0.0008668535,0.00006685666,0.00023695527],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00049271947,0.0006578738,0.0008716328,0.00047470254,0.00033891565,0.00016086272,0.00056904566,0.00035965137,0.0024597016],"category_scores_gemma":[0.00031438,0.00068562623,0.00028429137,0.0013012311,0.00014123844,0.00012801193,0.00008333276,0.0005810895,0.00029235575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003177405,0.00064822077,0.0029408021,0.00011421341,0.00008872742,0.000013311837,0.00025157726,0.00005629961,0.97612566,0.018890992,0.00040542005,0.00014702562],"study_design_scores_gemma":[0.003670371,0.00011250171,0.00008319443,0.00003903303,0.00014575325,0.000016955653,0.0005610903,0.00016660873,0.98160946,0.007666064,0.0051915697,0.00073737797],"about_ca_topic_score_codex":0.000054801047,"about_ca_topic_score_gemma":0.000032744836,"teacher_disagreement_score":0.11090344,"about_ca_system_score_codex":0.00069524464,"about_ca_system_score_gemma":0.00018556822,"threshold_uncertainty_score":0.99955946},"labels":[],"label_agreement":null},{"id":"W1998583416","doi":"10.1520/jai103344","title":"An Overview of Progress and New Developments in FTIR Lubricant Condition Monitoring Methodology","year":2011,"lang":"en","type":"article","venue":"Journal of ASTM International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thermal-Lube (Canada); McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Lubricant; Process engineering; Base oil; Materials science; Environmental science; Moisture; Computer science; Pulp and paper industry; Biochemical engineering; Chemical engineering; Engineering; Composite material","score_opus":0.28952573594579184,"score_gpt":0.445698969027354,"score_spread":0.15617323308156217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998583416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99702394,0.0010104767,0.0004642949,0.000050527688,0.00016102755,0.000011206349,0.0000031661732,0.0000026952252,0.0012726684],"genre_scores_gemma":[0.9434308,0.0003020255,0.0559998,0.000012286015,0.00015229954,7.6427847e-7,0.0000024708204,0.0000050313274,0.00009455969],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991193,0.00002685362,0.00045588656,0.00008415551,0.00023303223,0.000080774254],"domain_scores_gemma":[0.9991739,0.00006335851,0.00049082754,0.00005380231,0.00015375871,0.000064397515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000301465,0.0000683808,0.00021538034,0.00024747412,0.00001016325,0.000010457282,0.00019958819,0.000048688926,0.0007608548],"category_scores_gemma":[0.0001219567,0.000059148784,0.00004496232,0.00012438593,0.000035958823,0.00019449893,0.000027317485,0.00013444047,7.679584e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018727695,0.00026945613,0.902841,0.00004169902,0.0003400056,0.000038287246,0.0008918928,0.000002695088,0.07738175,0.0010350641,0.000078311496,0.01689258],"study_design_scores_gemma":[0.0007409877,0.00008563079,0.46219704,0.00012004562,0.000051851148,0.00013584972,0.00047761376,0.00002127848,0.53418356,0.0015600119,0.00034365372,0.000082508675],"about_ca_topic_score_codex":0.00004141167,"about_ca_topic_score_gemma":0.0000025039162,"teacher_disagreement_score":0.4568018,"about_ca_system_score_codex":0.00007043867,"about_ca_system_score_gemma":0.00007540326,"threshold_uncertainty_score":0.8330827},"labels":[],"label_agreement":null},{"id":"W1999004572","doi":"10.1007/s11947-012-1046-8","title":"Quantification of Mildew Damage in Soft Red Winter Wheat Based on Spectral Characteristics of Bulk Samples: A Comparison of Visible-Near-Infrared Imaging and Near-Infrared Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mildew; Partial least squares regression; Winter wheat; Infrared; Near-infrared spectroscopy; Wavelength; Analytical Chemistry (journal); Materials science; Environmental science; Mathematics; Remote sensing; Chemistry; Horticulture; Agronomy; Optics; Statistics; Biology; Physics; Environmental chemistry; Geology","score_opus":0.015471942628209336,"score_gpt":0.27456875438292183,"score_spread":0.2590968117547125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999004572","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99682325,0.00096707715,0.0010756595,0.00035350342,0.000018120076,0.00014272022,0.000077325436,0.00006811569,0.00047421403],"genre_scores_gemma":[0.99618,0.00008449313,0.0035517265,0.000017058956,0.000013922162,0.000014476517,0.000058674836,0.000020669188,0.000059003058],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846876,0.000009749139,0.0006436255,0.00041294112,0.00017202587,0.00029289647],"domain_scores_gemma":[0.9988381,0.00008159889,0.0004693693,0.00041086067,0.00014973948,0.00005033025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008196172,0.00023412883,0.00073558296,0.00040508033,0.00006543653,0.00003972159,0.0002743419,0.00025629322,0.00026168808],"category_scores_gemma":[0.00015035241,0.00022488773,0.000058465295,0.0007811821,0.00067890726,0.000091880385,0.00006865093,0.00027563327,0.0000015638866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014207566,0.0003136718,0.3107673,0.0007079128,0.000044570366,0.0000010421642,0.00022295162,0.0000033242611,0.6862509,0.000058271504,0.00008414971,0.0014038313],"study_design_scores_gemma":[0.00091224274,0.00042356778,0.040806178,0.00024712278,0.00007531648,0.0000038088142,0.0011609121,0.010405391,0.94411606,0.001600742,0.000040665815,0.000207974],"about_ca_topic_score_codex":0.000085683605,"about_ca_topic_score_gemma":0.000015505771,"teacher_disagreement_score":0.26996112,"about_ca_system_score_codex":0.000028937704,"about_ca_system_score_gemma":0.00005712786,"threshold_uncertainty_score":0.91706556},"labels":[],"label_agreement":null},{"id":"W1999286072","doi":"10.1007/s11947-012-0933-3","title":"Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging","year":2012,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":134,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Biology; Embryo; Eggshell; Hatchery; Poultry farming; Artificial intelligence; Computer science; Ecology; Fishery","score_opus":0.01716803917940725,"score_gpt":0.2737297116321293,"score_spread":0.25656167245272204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999286072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99285376,0.0061775716,0.00022741497,0.0000344041,0.00001778496,0.00003845989,0.0000030185138,0.00013051376,0.0005170483],"genre_scores_gemma":[0.9926235,0.000011554269,0.007288451,0.00000616518,0.000022874545,0.000006420216,0.0000010832175,0.0000132710575,0.000026673295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898076,0.0000034109285,0.0002805409,0.00025648624,0.0001016404,0.0003771381],"domain_scores_gemma":[0.9995558,0.000019848447,0.00014870102,0.0001512308,0.000055621753,0.0000687931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010964216,0.00016925234,0.0003369236,0.00020081173,0.00022031585,0.00002980555,0.00013421796,0.00018486382,0.000027937795],"category_scores_gemma":[0.00008758465,0.00015059375,0.000032698295,0.0005295794,0.0002620241,0.00013622845,0.00013702395,0.00021110002,5.660721e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003877294,0.000063920226,0.40118465,0.00023194189,0.0001034365,9.425822e-7,0.00074737705,9.651535e-8,0.58641565,0.00004557326,3.7133324e-7,0.011167268],"study_design_scores_gemma":[0.00032570862,0.00002707896,0.022913394,0.000027916065,0.00008617757,0.00004186335,0.0020082144,0.00009865361,0.9738815,0.00037688704,0.000026274158,0.00018636714],"about_ca_topic_score_codex":0.000016557186,"about_ca_topic_score_gemma":0.000002880711,"teacher_disagreement_score":0.38746583,"about_ca_system_score_codex":0.000037499492,"about_ca_system_score_gemma":0.000045680852,"threshold_uncertainty_score":0.6141035},"labels":[],"label_agreement":null},{"id":"W2000095052","doi":"10.1007/s10044-009-0170-1","title":"Representation and classification of high-dimensional biomedical spectral data","year":2009,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; National Research Council Institute for Biodiagnostics; University of Alberta","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Dimensionality reduction; Principal component analysis; Computer science; Linear subspace; Perceptron; Classifier (UML); Feature vector; k-nearest neighbors algorithm; Random subspace method; Support vector machine; Subspace topology; Artificial neural network; Mathematics","score_opus":0.03573797126209471,"score_gpt":0.332521243984583,"score_spread":0.2967832727224883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000095052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78627115,0.00048786605,0.21048598,0.0015771359,0.000004431596,0.00006792161,0.0002359564,0.000035819477,0.00083372585],"genre_scores_gemma":[0.99752116,0.0001360772,0.0007012528,0.00006019762,0.000070675494,0.000010612291,0.0013814507,0.0000033834442,0.00011520128],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991014,0.000008519316,0.00025363427,0.00037383812,0.00016853635,0.00009407365],"domain_scores_gemma":[0.9991704,0.000057880443,0.00013947174,0.00051967066,0.000038215432,0.00007436846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079005746,0.000082186656,0.00022671578,0.00020092052,0.00007617033,0.000024679637,0.00015664626,0.000053781416,0.00037300278],"category_scores_gemma":[0.000016193417,0.00007435754,0.000046728623,0.0010053401,0.00008913078,0.00006869418,0.000046799494,0.000067279645,0.000003901794],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020544732,0.00084489584,0.2149686,0.00006733861,0.00178439,0.0000018521904,0.00009526749,0.000028009717,0.4710004,0.0043524215,0.00096333574,0.30587295],"study_design_scores_gemma":[0.00050611707,0.000029901776,0.91239053,0.000007749125,0.004597095,0.000008940266,0.00022180997,0.027812855,0.05043207,0.0029662596,0.0007561157,0.00027056434],"about_ca_topic_score_codex":0.00018777732,"about_ca_topic_score_gemma":0.000029622579,"teacher_disagreement_score":0.6974219,"about_ca_system_score_codex":0.000010318008,"about_ca_system_score_gemma":0.000009396861,"threshold_uncertainty_score":0.4084119},"labels":[],"label_agreement":null},{"id":"W2000215959","doi":"10.3390/rs6020925","title":"Separating Crop Species in Northeastern Ontario Using Hyperspectral Data","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Algoma University; Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada; Northern Ontario Heritage Fund Corporation","keywords":"Canola; Crop; Hordeum vulgare; Hyperspectral imaging; Sowing; Agronomy; Growing season; Biology; Environmental science; Mathematics; Geography; Remote sensing; Poaceae","score_opus":0.07075312131496561,"score_gpt":0.30532313589400956,"score_spread":0.23457001457904395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000215959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94969064,0.00011118165,0.0053696064,0.000054393295,0.00006152223,0.0000246628,0.0000030976082,0.000058742487,0.04462617],"genre_scores_gemma":[0.9710444,0.000005166848,0.026094865,0.00006277605,0.00036630087,1.4634949e-9,0.00003013547,0.000028787834,0.0023675167],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985114,0.00002421257,0.00031828537,0.00048747248,0.00023012723,0.00042852756],"domain_scores_gemma":[0.9988929,0.0000886683,0.00012523033,0.0007812262,0.00003669263,0.00007527981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020002128,0.00020511352,0.00033697576,0.00014467571,0.00013167194,0.00010384511,0.00027773395,0.0001041998,0.00023522324],"category_scores_gemma":[0.00014914191,0.00021206384,0.000060690774,0.0003624737,0.000059886297,0.00016582233,0.0001760309,0.00036279412,0.000019223327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050540388,0.000054016775,0.018449027,0.000103748105,0.00013163836,0.00011436399,0.0020752246,0.0032839898,0.95803326,0.00004608813,0.000056671077,0.017601443],"study_design_scores_gemma":[0.0005638396,0.000013248794,0.00066211866,0.00015926526,0.0001535889,0.00017177143,0.0009911678,0.87477785,0.11932821,0.00020291582,0.0024701424,0.0005058632],"about_ca_topic_score_codex":0.02629325,"about_ca_topic_score_gemma":0.036443595,"teacher_disagreement_score":0.8714939,"about_ca_system_score_codex":0.00038559813,"about_ca_system_score_gemma":0.00009588238,"threshold_uncertainty_score":0.9811388},"labels":[],"label_agreement":null},{"id":"W2000316516","doi":"10.3168/jds.s0022-0302(02)74242-2","title":"Nondestructive Measurement of Light-induced Oxidation in Dairy Products by Fluorescence Spectroscopy and Imaging","year":2002,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Partenariat Canadien Contre Le Cancer; TINE","keywords":"Fluorescence; Fluorescence spectroscopy; Photodegradation; Spectroscopy; Chemistry; Light intensity; Analytical Chemistry (journal); Materials science; Optics; Chromatography; Photocatalysis","score_opus":0.022021700013257484,"score_gpt":0.26269887302586137,"score_spread":0.24067717301260388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000316516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996235,0.0014173572,0.00011018181,0.0006050603,0.000082485516,0.00003579209,0.0000014000587,0.0000059612153,0.0015067639],"genre_scores_gemma":[0.99717474,0.00011330423,0.002592362,0.000014847836,0.00006674046,7.1853736e-7,1.778082e-7,0.00000572372,0.000031401803],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980212,0.000015530703,0.0004655996,0.00026678265,0.00096958753,0.0002613088],"domain_scores_gemma":[0.99874085,0.000028183815,0.0005178738,0.00019699434,0.00041140066,0.00010472279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007609683,0.00012565116,0.00025709282,0.00038505555,0.000088936176,0.000054043892,0.000393372,0.000031778945,0.00008981673],"category_scores_gemma":[0.00064882345,0.00010735848,0.000036527694,0.0013957146,0.00023397883,0.000796943,0.00005016338,0.00025368846,0.0000013146423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017871496,0.00010451,0.0148378825,0.000031438754,0.000009669046,0.0000070773895,0.0003511796,0.0000015448998,0.9834905,0.000013063062,0.0003546959,0.0007805248],"study_design_scores_gemma":[0.0004055119,0.00006343176,0.0048300596,0.000088459434,0.000031336887,0.000063850166,0.0005321833,0.00017026003,0.9935767,0.00010400331,0.000028611925,0.000105568986],"about_ca_topic_score_codex":0.00001718874,"about_ca_topic_score_gemma":0.0000015809728,"teacher_disagreement_score":0.010086181,"about_ca_system_score_codex":0.00027747688,"about_ca_system_score_gemma":0.00014116682,"threshold_uncertainty_score":0.4377952},"labels":[],"label_agreement":null},{"id":"W2000973585","doi":"10.1366/000370204322842922","title":"Quantitative Determination of Moisture in Lubricants by Fourier Transform Infrared Spectroscopy","year":2004,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Analytical Chemistry (journal); Moisture; Fourier transform infrared spectroscopy; Infrared spectroscopy; Chemistry; Calibration curve; Acetone; Reagent; Standard addition; Calibration; Spectroscopy; Standard solution; Infrared; Chromatography; Detection limit; Organic chemistry; Chemical engineering; Mathematics; Optics","score_opus":0.01004470005551622,"score_gpt":0.2758486896813789,"score_spread":0.26580398962586266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000973585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7320028,0.0008596786,0.04281834,0.0003867985,0.000060922863,0.00038915666,0.00014273704,0.00017114828,0.22316843],"genre_scores_gemma":[0.9517547,0.00025067054,0.046820257,0.0001199577,0.00005642032,0.00009599879,0.000113905255,0.000066112865,0.0007219797],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973912,0.000012275411,0.0007166187,0.00064342143,0.0005439403,0.0006925085],"domain_scores_gemma":[0.9988626,0.00011669067,0.000325468,0.0004900615,0.00006198452,0.00014317188],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016280792,0.00044374933,0.00075426116,0.00037183706,0.000102088154,0.000042596304,0.00045571793,0.00028814396,0.00078526686],"category_scores_gemma":[0.000047950565,0.00045278957,0.00016618284,0.0013707948,0.0002351246,0.000187491,0.000038996266,0.0005295485,0.000035438425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036258565,0.00046679503,0.0008489458,0.00009809343,0.00006888477,0.000010772966,0.0007104882,0.000019089193,0.98903406,0.0074561913,0.000640497,0.0002835877],"study_design_scores_gemma":[0.0023474765,0.00017230584,0.0002596015,0.00003775287,0.000107118445,0.0000062209992,0.00098402,0.00006859899,0.97083443,0.02434422,0.00041153302,0.00042671306],"about_ca_topic_score_codex":0.00029238532,"about_ca_topic_score_gemma":0.00014980824,"teacher_disagreement_score":0.22244644,"about_ca_system_score_codex":0.000590103,"about_ca_system_score_gemma":0.0001551826,"threshold_uncertainty_score":0.9997924},"labels":[],"label_agreement":null},{"id":"W2001049580","doi":"10.1117/12.809112","title":"High-performance dispersive Raman and absorption spectroscopy as tools for drug identification","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"P&P Optica (Canada)","funders":"","keywords":"Raman spectroscopy; Spectrometer; Materials science; Absorption (acoustics); Optics; Spectroscopy; Spectral resolution; Analytical Chemistry (journal); Optoelectronics; Chemistry; Spectral line; Physics","score_opus":0.0089989739328104,"score_gpt":0.24034816172799164,"score_spread":0.23134918779518124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001049580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956646,0.0001405467,0.00006799995,0.002099465,0.000087544504,0.0003202926,0.000047558162,0.00007537181,0.0014966293],"genre_scores_gemma":[0.9690219,0.0002996911,0.029406583,0.00009374285,0.00040978138,0.0001034257,0.000031415995,0.00003406332,0.00059939333],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982086,5.966335e-9,0.0005748707,0.00041208125,0.00046109367,0.00034336772],"domain_scores_gemma":[0.998362,0.00014532184,0.00041748138,0.00006252701,0.000914415,0.000098241435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032529817,0.00028190858,0.00037936697,0.00010511305,0.00012654497,0.00021523391,0.0005764849,0.00016250683,0.000031932996],"category_scores_gemma":[0.0005465373,0.00024903417,0.00036429198,0.00030175204,0.00015310016,0.0008185296,0.000056143224,0.00022927429,0.0000016696538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012911507,0.00009896734,0.00019299096,0.00038363555,0.00023364343,2.0813934e-8,0.00013970931,0.000029262896,0.80525506,0.19202249,0.0011358304,0.00037929142],"study_design_scores_gemma":[0.000938012,0.00019940369,0.0013382733,0.00012211922,0.0003012238,0.0000075122025,0.001312572,0.0060795494,0.98599005,0.0029882004,0.00044796366,0.00027514552],"about_ca_topic_score_codex":0.0000075597222,"about_ca_topic_score_gemma":6.662903e-8,"teacher_disagreement_score":0.18903428,"about_ca_system_score_codex":0.00018637376,"about_ca_system_score_gemma":0.00002128269,"threshold_uncertainty_score":0.9999962},"labels":[],"label_agreement":null},{"id":"W2001203449","doi":"10.2333/bhmk.37.1","title":"Nonlinear Generalized Structured Component Analysis","year":2010,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Component (thermodynamics); Component analysis; Structural equation modeling; Transformation (genetics); Computer science; Mathematics; Multidimensional scaling; Scaling; Data transformation; Nonlinear system; Algorithm; Data mining; Statistics","score_opus":0.016115595234944997,"score_gpt":0.298695333522126,"score_spread":0.282579738287181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001203449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99576527,0.000173065,0.0002159347,0.00004118635,0.00015105218,0.000044918193,0.000110153465,0.00017423872,0.003324189],"genre_scores_gemma":[0.98718876,0.000022965722,0.009595578,0.000057654834,0.0002447701,0.000016036924,0.00033144702,0.000029003033,0.002513761],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99833417,0.000009893892,0.00038356593,0.00044247246,0.00042843856,0.00040146848],"domain_scores_gemma":[0.99873585,0.000059805265,0.00016390506,0.00072825764,0.000094781375,0.00021742952],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010888525,0.00026288687,0.00051631697,0.00087392103,0.00015046667,0.00008479362,0.00043738534,0.00026322747,0.02286788],"category_scores_gemma":[0.000084203806,0.00023765082,0.00046212395,0.003466768,0.000093128976,0.00008079331,0.000078269404,0.0005266062,0.0000610698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019663075,0.00023865735,0.17640027,0.000009762839,0.00050223316,0.000014576461,0.000029691833,0.000008447895,0.82100457,0.00010205073,0.00023491544,0.00143515],"study_design_scores_gemma":[0.000821839,0.000017845017,0.040861547,0.0000011448618,0.0051184194,0.000013124105,0.00006810636,0.00038914738,0.94398445,0.000037349157,0.008254663,0.000432386],"about_ca_topic_score_codex":0.00035282396,"about_ca_topic_score_gemma":0.00021285156,"teacher_disagreement_score":0.13553871,"about_ca_system_score_codex":0.000056323475,"about_ca_system_score_gemma":0.000040826482,"threshold_uncertainty_score":0.9780254},"labels":[],"label_agreement":null},{"id":"W2001550460","doi":"10.1088/1757-899x/42/1/012037","title":"Use of an ultrasonic reflectance technique to examine bubble size changes in dough","year":2012,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Bubble; Attenuation; Ultrasonic sensor; Ostwald ripening; Materials science; Transducer; Reflection (computer programming); Volume (thermodynamics); Optics; Ultrasound; Acoustics; Mechanics; Nanotechnology; Physics","score_opus":0.03386919903887468,"score_gpt":0.27064566806205564,"score_spread":0.23677646902318097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001550460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99923563,0.00006514199,0.0001484598,0.0000553116,0.000078247795,0.0000559882,0.000015116905,0.000057020054,0.00028905555],"genre_scores_gemma":[0.9943176,0.00011776056,0.0052745272,0.000022066204,0.00006160081,0.000050255454,0.0000019303984,0.00001111459,0.00014313625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890953,0.000005785142,0.00019550955,0.0002402255,0.00020318349,0.0004457781],"domain_scores_gemma":[0.99941814,0.00005414935,0.000056372333,0.00023835099,0.00009182502,0.00014114691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003848648,0.00015298645,0.00027114566,0.00017507837,0.00005003093,0.00012128384,0.0002407606,0.0000661909,0.0003030914],"category_scores_gemma":[0.00051634,0.00014452176,0.000008562103,0.000798757,0.00013507807,0.0012236888,0.00007733332,0.00006508972,0.0000015426318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017168712,0.000020302672,0.0002552648,0.00009428412,0.0000028278087,0.0000010276358,0.00031228276,0.000027494072,0.9986052,0.000514251,0.0000012828286,0.000148642],"study_design_scores_gemma":[0.00005222343,0.000052269883,0.0025740808,0.000072039424,0.0000084507465,0.000015459163,0.00020480249,0.000021962729,0.9965427,0.000015427806,0.000266742,0.0001738464],"about_ca_topic_score_codex":0.00011960355,"about_ca_topic_score_gemma":0.000035873392,"teacher_disagreement_score":0.0051260674,"about_ca_system_score_codex":0.000060986502,"about_ca_system_score_gemma":0.00005128597,"threshold_uncertainty_score":0.58934265},"labels":[],"label_agreement":null},{"id":"W2002330894","doi":"10.1016/j.biosystemseng.2009.12.009","title":"Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging","year":2010,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hyperspectral imaging; Mahalanobis distance; Pattern recognition (psychology); Artificial intelligence; Standard deviation; Linear discriminant analysis; Mathematics; Remote sensing; Computer science; Statistics; Geology","score_opus":0.011717468566090038,"score_gpt":0.22033285236562467,"score_spread":0.20861538379953462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002330894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971467,0.00042018353,0.0011339076,0.0000063039824,0.00020001963,0.000059008806,0.000023042883,0.0001451037,0.00086572475],"genre_scores_gemma":[0.99878055,0.000005283515,0.00084605115,0.0000017254459,0.00020203766,0.0000041074586,0.000005050025,0.000037633396,0.00011754291],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990107,0.0000036216015,0.0002886709,0.00025380324,0.00016240694,0.00028081055],"domain_scores_gemma":[0.99948376,0.000053585874,0.00007059152,0.00023009103,0.00005409749,0.00010788213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008171031,0.00020256989,0.0002997092,0.00014521333,0.00007929962,0.00014766764,0.00009262685,0.00012449584,0.000037621605],"category_scores_gemma":[0.00011213201,0.00021271846,0.00008877818,0.00030346788,0.00005661927,0.00022327487,0.000043474614,0.00027017656,0.000001321211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073421297,0.00001548928,0.0064067324,0.00018775438,0.00006492513,0.000009975518,0.00008991222,0.000083997096,0.9928984,0.000017455037,0.0000030369797,0.0002149526],"study_design_scores_gemma":[0.0002122689,0.000009982266,0.0011580479,0.000050768664,0.00007782277,0.00016262458,0.00036686144,0.052617073,0.94495034,0.000003654457,0.00016197692,0.00022860525],"about_ca_topic_score_codex":0.00006488044,"about_ca_topic_score_gemma":0.000003677467,"teacher_disagreement_score":0.052533075,"about_ca_system_score_codex":0.000077955985,"about_ca_system_score_gemma":0.000023526136,"threshold_uncertainty_score":0.8674407},"labels":[],"label_agreement":null},{"id":"W2002344648","doi":"10.1117/12.850371","title":"Near-infrared hyperspectral imaging for quality analysis of agricultural and food products","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Canada Research Chairs","keywords":"Hyperspectral imaging; Computer science; Pixel; Quality (philosophy); Food quality; Remote sensing; Image processing; Artificial intelligence; Food products; Chemical imaging; Spectral imaging; Computer vision; Image (mathematics)","score_opus":0.011913270515875101,"score_gpt":0.25161069805721,"score_spread":0.23969742754133488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002344648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99631804,0.00015801507,0.000015348218,0.0012547247,0.00007569608,0.00022664583,0.00015924047,0.000056547313,0.0017357441],"genre_scores_gemma":[0.925311,0.000028584072,0.07402526,0.000026128691,0.00025633606,0.000074195275,0.00002473027,0.000029485751,0.00022425468],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803686,8.501771e-9,0.0006807341,0.0004383047,0.0004921056,0.00035200786],"domain_scores_gemma":[0.99717116,0.00019919979,0.0005100434,0.000075038595,0.0019388144,0.000105710715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004415402,0.00028379215,0.00062188646,0.00013698512,0.00009810612,0.00011429095,0.00062928733,0.00016248967,0.000029021156],"category_scores_gemma":[0.0013274289,0.00022367568,0.0007897452,0.0008558096,0.00032767843,0.00036150985,0.00011118117,0.0003171483,1.3175921e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051188003,0.00011273686,0.005177151,0.000638397,0.002236241,8.547106e-9,0.00022691432,0.000024772768,0.84665734,0.1443599,0.00046182962,0.000053529697],"study_design_scores_gemma":[0.0009320123,0.00011947876,0.017598445,0.000051136925,0.0021954502,0.000006760506,0.0030555697,0.006474732,0.96808606,0.0007740867,0.0003456551,0.00036062702],"about_ca_topic_score_codex":0.000014825139,"about_ca_topic_score_gemma":4.856756e-7,"teacher_disagreement_score":0.14358582,"about_ca_system_score_codex":0.0000753345,"about_ca_system_score_gemma":0.000032037264,"threshold_uncertainty_score":0.91212296},"labels":[],"label_agreement":null},{"id":"W2002507631","doi":"10.1016/j.talanta.2010.10.025","title":"Cluster resolution: A metric for automated, objective and optimized feature selection in chemometric modeling","year":2010,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Chemistry; Chemometrics; Feature selection; Metric (unit); Cluster (spacecraft); Selection (genetic algorithm); Feature (linguistics); Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Biological system; Statistics; Chromatography; Computer science","score_opus":0.01134677093725919,"score_gpt":0.27877155771226114,"score_spread":0.267424786775002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002507631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932411,0.0007922179,0.0021789921,0.00014153539,0.000062286395,0.00016700964,0.00002922464,0.00030206022,0.0030855986],"genre_scores_gemma":[0.99297583,0.00008438858,0.005346452,0.000042244785,0.00015668826,0.00005778844,0.000038685714,0.00002466779,0.0012732742],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989607,0.000009147179,0.00019448163,0.0003770591,0.00014968096,0.00030897494],"domain_scores_gemma":[0.9994476,0.00019205188,0.000082203675,0.0001390356,0.00006524426,0.00007388775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019523758,0.00017753763,0.0002939598,0.00090668054,0.00011219879,0.00005592194,0.00011975001,0.0002934334,0.00012378392],"category_scores_gemma":[0.00040465948,0.00016704704,0.00007613006,0.0022433212,0.00002928049,0.00013111341,0.00004576857,0.0004304737,0.000003890116],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020994183,0.00080658233,0.026313245,0.0007400994,0.00067553844,0.000018695317,0.0010478285,0.010661223,0.9449657,0.00036364584,0.009164033,0.0031439723],"study_design_scores_gemma":[0.002515109,0.000034321427,0.00056613324,0.000014406266,0.00016411358,0.0000929171,0.00023936165,0.92486376,0.07079073,0.00020669105,0.00022090686,0.00029153918],"about_ca_topic_score_codex":0.00008293677,"about_ca_topic_score_gemma":0.00005771346,"teacher_disagreement_score":0.9142026,"about_ca_system_score_codex":0.00010497471,"about_ca_system_score_gemma":0.000040088446,"threshold_uncertainty_score":0.68119806},"labels":[],"label_agreement":null},{"id":"W2002763261","doi":"10.1117/12.628232","title":"Potential of near-infrared hyperspectral reflectance imaging for screening of farm feed contamination","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Broiler; Contamination; Meat and bone meal; Environmental science; Hyperspectral imaging; Food science; Remote sensing; Biology; Geography","score_opus":0.009786640830549772,"score_gpt":0.24884594436128527,"score_spread":0.23905930353073548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002763261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939927,0.00030258528,0.0006557551,0.00094070233,0.00006055738,0.00024849764,0.000081037506,0.000055463406,0.00366269],"genre_scores_gemma":[0.82489175,0.000051467105,0.17424423,0.00003294587,0.0003137476,0.00005714494,0.000014611559,0.000045085097,0.00034900822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783486,1.14487815e-8,0.00082926045,0.00034874072,0.00061639264,0.00037071903],"domain_scores_gemma":[0.99702734,0.00014535271,0.00072285044,0.000063967935,0.0019602247,0.00008026768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003732584,0.00027824208,0.0005364133,0.00012584764,0.00008439157,0.000060584916,0.0007315699,0.00016494453,0.000038022496],"category_scores_gemma":[0.0006486077,0.0002605208,0.0007757151,0.000411554,0.00027139275,0.00039035606,0.00009085663,0.00023835046,2.3413541e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022817105,0.00014741551,0.0006389115,0.00068880117,0.00053964916,3.7415532e-8,0.00020838574,0.00022780646,0.91467625,0.08159915,0.0003852691,0.0006601472],"study_design_scores_gemma":[0.0015316437,0.00012438177,0.0004713972,0.00019049541,0.00038856856,0.00000831421,0.0019951707,0.06550073,0.92851394,0.0005753787,0.0004572096,0.00024274779],"about_ca_topic_score_codex":0.000011328845,"about_ca_topic_score_gemma":1.8532278e-7,"teacher_disagreement_score":0.17358847,"about_ca_system_score_codex":0.00017714682,"about_ca_system_score_gemma":0.000040351337,"threshold_uncertainty_score":0.9999847},"labels":[],"label_agreement":null},{"id":"W2003421703","doi":"10.1007/s11746-003-0751-8","title":"Novel method for rapid monitoring of lipid oxidation by FTIR spectroscopy using disposable IR cards","year":2003,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Absorbance; Fourier transform infrared spectroscopy; Chemistry; Analytical Chemistry (journal); Infrared spectroscopy; Spectral line; Double bond; Chromatography; Polymer chemistry; Organic chemistry; Chemical engineering","score_opus":0.019903685349052888,"score_gpt":0.32361593876055644,"score_spread":0.30371225341150354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003421703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9727933,0.0009634235,0.024592089,0.00013836066,0.00015076646,0.000028627652,0.00003704193,0.000013868829,0.00128252],"genre_scores_gemma":[0.6313675,0.00069197203,0.36604688,0.00010180656,0.0006914151,0.0000055630885,0.0000036515669,0.000057319365,0.001033884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983362,0.00002771716,0.00059383165,0.00022046083,0.00047678995,0.00034502774],"domain_scores_gemma":[0.9971247,0.00028338886,0.0018260727,0.00034568543,0.00030959462,0.000110567285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005906098,0.00023384925,0.0006569334,0.000031618612,0.00016714404,0.000036010915,0.00045829185,0.000095469055,0.00007046668],"category_scores_gemma":[0.00043575052,0.00018228067,0.00091798254,0.00071375986,0.00020430362,0.00012139426,0.000042935622,0.00038191292,1.6925217e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004950539,0.00014744475,0.0025290519,0.00015707931,0.00044725637,1.4539161e-7,0.00019798758,0.000089276946,0.9940736,0.0000042907523,0.0018634923,0.00044081899],"study_design_scores_gemma":[0.0006805042,0.000042781005,0.00006087673,0.00006238868,0.00045732292,0.00004874207,0.0023306713,0.00023197543,0.99399716,0.00002753225,0.0018767138,0.00018333277],"about_ca_topic_score_codex":0.00005990341,"about_ca_topic_score_gemma":1.3341128e-7,"teacher_disagreement_score":0.34145477,"about_ca_system_score_codex":0.0005219475,"about_ca_system_score_gemma":0.00021925685,"threshold_uncertainty_score":0.74331903},"labels":[],"label_agreement":null},{"id":"W2003691366","doi":"10.1081/pre-120002903","title":"IN-LINE MONITORING OF EMULSION HOMO- AND COPOLYMERIZATIONS USING ATR-FTIR SPECTROMETRY","year":2002,"lang":"en","type":"article","venue":"Polymer Reaction Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"University of Waterloo","keywords":"Emulsion; Fourier transform infrared spectroscopy; Mass spectrometry; Chemistry; Line (geometry); Chromatography; Materials science; Analytical Chemistry (journal); Chemical engineering; Organic chemistry; Engineering","score_opus":0.022381814646590222,"score_gpt":0.2608873077937606,"score_spread":0.2385054931471704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003691366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99174887,0.0055744913,0.0016283968,0.00003722728,0.00013264077,0.000021862097,0.00000437024,0.000066181536,0.0007859309],"genre_scores_gemma":[0.99804044,0.00049492036,0.00077994465,0.0000035432129,0.0001973597,0.0000034175969,0.000002800941,0.000026989457,0.00045061545],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999209,0.0000032803882,0.0002589438,0.00018731081,0.00013793494,0.00020354323],"domain_scores_gemma":[0.9996244,0.000051701354,0.000086850145,0.00015693951,0.000016068681,0.00006404401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026250329,0.00014346655,0.00021539236,0.00044542417,0.000042455697,0.000014046475,0.00006219119,0.00008532744,0.00034809017],"category_scores_gemma":[0.00004201746,0.00016322773,0.000047524234,0.0008544377,0.0000209389,0.00020873465,0.000025498768,0.00017624717,0.0000026087346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029187618,0.00004921782,0.008766508,0.00006082077,0.00003324445,0.000002993941,0.00007604249,0.00039700526,0.99027133,0.000033188342,0.000002007434,0.0003047075],"study_design_scores_gemma":[0.00023031664,0.000009243638,0.0011843388,0.000045580913,0.00006109841,0.000026265543,0.00013756762,0.019451309,0.978668,0.0000024980968,0.000037574362,0.00014624296],"about_ca_topic_score_codex":0.00014244414,"about_ca_topic_score_gemma":6.1544023e-7,"teacher_disagreement_score":0.019054305,"about_ca_system_score_codex":0.00008723052,"about_ca_system_score_gemma":0.000003456798,"threshold_uncertainty_score":0.6656233},"labels":[],"label_agreement":null},{"id":"W2003833411","doi":"10.1109/whispers.2011.6080877","title":"MR-i &amp;#x2014; overview and first results of the ABB high speed hyperspectral imaging spectroradiometer","year":2011,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Hyperspectral imaging; Spectroradiometer; Modular design; Product line; Full spectral imaging; Spectral imaging; Image resolution; Computer science; Chemical imaging; Remote sensing; Imaging spectroscopy; Spectral resolution; Spectral signature; Moderate-resolution imaging spectroradiometer; Artificial intelligence; Computer vision; Optics; Physics; Engineering; Geology; Spectral line; Reflectivity; Satellite","score_opus":0.044348212489286734,"score_gpt":0.263424615975272,"score_spread":0.21907640348598528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003833411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8336315,0.0026296338,0.00006094069,0.00077907334,0.00007987833,0.000064916865,0.000041364314,0.00008080936,0.16263187],"genre_scores_gemma":[0.9891241,0.00044395195,0.0031897237,0.00013965125,0.0001102925,0.0000018304946,0.0000065265535,0.000022893322,0.0069609843],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986544,0.000014157333,0.0003682621,0.0003723986,0.00024985833,0.0003409073],"domain_scores_gemma":[0.99891216,0.000099911136,0.00018324931,0.00065977854,0.000054062635,0.00009084946],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013874934,0.00022150466,0.00034754534,0.0001271976,0.00010934331,0.000029244206,0.00038252375,0.00006087758,0.004484504],"category_scores_gemma":[0.00012266375,0.00014767851,0.0001715847,0.00060249533,0.00021109536,0.00011327066,0.00013079781,0.00019811519,0.000028612181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065342017,0.0009223688,0.15240593,0.0007321143,0.0011186007,0.000022030415,0.0036576164,0.0000033050576,0.8039194,0.017338714,0.018220227,0.0010063027],"study_design_scores_gemma":[0.0013024944,0.000023462657,0.019818708,0.00005367733,0.00037752648,0.000057589878,0.00048822208,0.000028766097,0.969863,0.0022177638,0.0054017273,0.00036706612],"about_ca_topic_score_codex":0.0018900201,"about_ca_topic_score_gemma":0.0001714159,"teacher_disagreement_score":0.16594364,"about_ca_system_score_codex":0.00006815837,"about_ca_system_score_gemma":0.000025273997,"threshold_uncertainty_score":0.9964255},"labels":[],"label_agreement":null},{"id":"W2003942577","doi":"10.1016/s0031-9422(03)00505-3","title":"Evidence for cross-linking in tomato cutin using HR-MAS NMR spectroscopy","year":2003,"lang":"en","type":"article","venue":"Phytochemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":113,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; The Scarborough Hospital","funders":"National Science Foundation","keywords":"Cutin; Nuclear magnetic resonance spectroscopy; Spectroscopy; Chemistry; Botany; Stereochemistry; Biology; Physics","score_opus":0.09988101193156608,"score_gpt":0.3968588662281827,"score_spread":0.29697785429661666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003942577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97615147,0.004888693,0.002409928,0.000028341028,0.00006152815,0.0001194471,0.00001905268,0.00011578994,0.016205784],"genre_scores_gemma":[0.98884094,0.000073662864,0.009403791,0.000074438394,0.00029822421,0.000070764756,0.000017550936,0.00005343743,0.0011672191],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99771506,0.000012587663,0.00052805303,0.00070154056,0.0002993883,0.0007433977],"domain_scores_gemma":[0.9985769,0.00035318744,0.0002309895,0.0005942868,0.0001016901,0.00014298079],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002832369,0.0003543527,0.00045368305,0.00009096878,0.00017734646,0.00013592649,0.00040383317,0.00026799453,0.001210556],"category_scores_gemma":[0.0010371293,0.00039551748,0.00022635648,0.00066898443,0.00012151975,0.00020891515,0.000058903617,0.0003739664,0.00001349326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006429485,0.00010388284,0.053115733,0.0005438136,0.000050968927,0.00001161674,0.000057537312,0.00015103626,0.94566745,0.00010028948,0.00009869928,0.000034703244],"study_design_scores_gemma":[0.00071951636,0.000011504196,0.00016705072,0.00033129455,0.000099616525,0.000033475895,0.00017219264,0.00087170524,0.99547863,0.0013512447,0.0003340553,0.00042971986],"about_ca_topic_score_codex":0.000027229804,"about_ca_topic_score_gemma":0.00000389558,"teacher_disagreement_score":0.052948684,"about_ca_system_score_codex":0.0004305837,"about_ca_system_score_gemma":0.00020519772,"threshold_uncertainty_score":0.9998497},"labels":[],"label_agreement":null},{"id":"W2004365658","doi":"10.1016/s1386-1425(02)00223-8","title":"Fourier transform Raman spectroscopy of Syncrude Sweet Blend distillation fractions: the 200–1800 cm−1 region","year":2003,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada); Natural Resources Canada","funders":"","keywords":"Naphtha; Distillation; Raman spectroscopy; Analytical Chemistry (journal); Chemistry; Carbon fibers; Spectroscopy; Vacuum distillation; Cyclopentane; Organic chemistry; Materials science; Catalysis","score_opus":0.010252116323863303,"score_gpt":0.25633291684182113,"score_spread":0.24608080051795783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004365658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86225075,0.0068450016,0.06965021,0.0037243357,0.000320681,0.0009384948,0.0000972861,0.00033020598,0.05584305],"genre_scores_gemma":[0.9916069,0.0018530067,0.005172424,0.00030832563,0.00025664293,0.00007898215,0.00010390712,0.00012971273,0.0004901015],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99556226,0.00016950999,0.0009896407,0.0011916107,0.00088427274,0.0012027305],"domain_scores_gemma":[0.997421,0.0001527397,0.0005600636,0.0013631966,0.00015115517,0.0003517901],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034710456,0.00086555764,0.0009652405,0.0004148733,0.0005694352,0.00022452403,0.00061796966,0.00039600115,0.000946066],"category_scores_gemma":[0.00024000334,0.000711038,0.00062997284,0.001459597,0.00062109693,0.000252178,0.00008247724,0.0008193737,0.000019009514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022318754,0.0003151908,0.0006375633,0.00011614176,0.00057275547,0.000073993,0.00012809607,0.0000051613715,0.98896396,0.0077619595,0.0011267489,0.000075241616],"study_design_scores_gemma":[0.0012545516,0.00030731718,0.000112494534,0.000069771886,0.0009707381,0.00033790624,0.00038286418,0.00017285957,0.97567636,0.0064435527,0.0134937735,0.00077781064],"about_ca_topic_score_codex":0.00008207242,"about_ca_topic_score_gemma":0.000034776225,"teacher_disagreement_score":0.12935616,"about_ca_system_score_codex":0.00025823424,"about_ca_system_score_gemma":0.00021474535,"threshold_uncertainty_score":0.9999672},"labels":[],"label_agreement":null},{"id":"W2004460890","doi":"10.1255/jnirs.857","title":"Quantification of Absorber through a Scattering Medium of Different Thickness Using Evanescent Light Piping","year":2010,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Scattering; Opacity; Optics; Materials science; Analyte; Wavelength; Light scattering; Absorption (acoustics); Scattering coefficient; Chemistry; Optoelectronics; Physics","score_opus":0.025046077337027926,"score_gpt":0.30822261795932065,"score_spread":0.28317654062229275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004460890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925088,0.00040501758,0.004295676,0.00026400248,0.00046604534,0.000054856435,0.000008837774,0.000017154007,0.0019795948],"genre_scores_gemma":[0.9852746,0.00013801936,0.014011888,0.000030079167,0.00039778554,0.0000013538298,0.000003700683,0.000037001897,0.00010554017],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975651,0.000026885993,0.0011886472,0.00022126264,0.0006805305,0.00031758563],"domain_scores_gemma":[0.99727607,0.000120729404,0.0016451739,0.0004758692,0.00037251072,0.0001096347],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027979305,0.00025459263,0.00075929175,0.00023199712,0.00010752149,0.000060192746,0.000493488,0.00021089976,0.0011372361],"category_scores_gemma":[0.00022131891,0.0002094991,0.0003227147,0.0004849949,0.00019422671,0.00030071608,0.00007225655,0.0007647301,0.0000035882065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013931931,0.00027774714,0.009752482,0.0002885593,0.00019775901,0.000007668995,0.00069971144,0.00005503248,0.98823893,0.00014150856,0.00017438296,0.00002689346],"study_design_scores_gemma":[0.0006066883,0.00008469378,0.002214405,0.00024133922,0.00032651747,0.000116353964,0.00044988684,0.00037229253,0.9942164,0.00075055665,0.00043512147,0.00018577585],"about_ca_topic_score_codex":0.00003221962,"about_ca_topic_score_gemma":0.000005595019,"teacher_disagreement_score":0.009716212,"about_ca_system_score_codex":0.0001271386,"about_ca_system_score_gemma":0.00017063845,"threshold_uncertainty_score":0.9997759},"labels":[],"label_agreement":null},{"id":"W2004527040","doi":"10.1007/s00216-007-1180-8","title":"Vibrational overtone combination spectroscopy (VOCSY)—a new way of using IR and NIR data","year":2007,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre","funders":"","keywords":"Overtone; Principal component analysis; Partial least squares regression; Spectroscopy; Near-infrared spectroscopy; Chemistry; Matrix (chemical analysis); Chemometrics; Component analysis; Biological system; Representation (politics); Infrared spectroscopy; Analytical Chemistry (journal); Spectral line; Artificial intelligence; Computer science; Optics; Physics; Chromatography; Machine learning","score_opus":0.0413570246977108,"score_gpt":0.3192636032327359,"score_spread":0.2779065785350251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004527040","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9579989,0.0014415594,0.014585997,0.00095589366,0.000022286955,0.00005438382,0.00010409085,0.000064210726,0.02477271],"genre_scores_gemma":[0.99423075,0.00016849632,0.0036575957,0.00010111766,0.00028115924,3.3887113e-7,0.00016698305,0.000019212612,0.0013743254],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99780774,0.0000065497557,0.0006041747,0.00066517544,0.00047885394,0.00043751826],"domain_scores_gemma":[0.99842113,0.0003311035,0.00015631993,0.00053286104,0.000093779396,0.00046479393],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029893976,0.00028659674,0.00051353546,0.00009303621,0.00010463842,0.00006831164,0.00033188958,0.0002852054,0.0018041789],"category_scores_gemma":[0.00038001733,0.00025274084,0.000095012176,0.00057294476,0.0005238781,0.00019699876,0.00032523845,0.00032965743,0.0000034583452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021073541,0.0004013231,0.03882921,0.00045484593,0.0005451359,0.0000310809,0.000021823887,0.0000028405534,0.9522928,0.0052993963,0.0008339252,0.0010768521],"study_design_scores_gemma":[0.0009828403,0.000035044366,0.0027477504,0.00004767554,0.0009872273,0.000069163245,0.00019459169,0.04130034,0.9499887,0.0028404654,0.00038031707,0.0004258579],"about_ca_topic_score_codex":0.00011015658,"about_ca_topic_score_gemma":0.000008235153,"teacher_disagreement_score":0.041297503,"about_ca_system_score_codex":0.000070911155,"about_ca_system_score_gemma":0.00010519869,"threshold_uncertainty_score":0.9999925},"labels":[],"label_agreement":null},{"id":"W2004609402","doi":"10.1016/j.foodchem.2007.06.015","title":"Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries","year":2007,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Government; Alberta Water Research Institute","keywords":"Chemometrics; Partial least squares regression; Principal component analysis; Linear discriminant analysis; Wine; Mathematics; Near-infrared spectroscopy; Calibration; Analytical Chemistry (journal); Chemistry; Statistics; Food science; Chromatography; Optics; Physics","score_opus":0.01648469175218077,"score_gpt":0.27689928095807526,"score_spread":0.2604145892058945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004609402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992424,0.00067879795,0.0011185596,0.00019197381,0.000026172713,0.0001962275,0.00009287845,0.000083646526,0.0051877145],"genre_scores_gemma":[0.998748,0.00003184148,0.00033523887,0.00009267068,0.00024126016,0.000046178924,0.000045982328,0.000035739788,0.00042308215],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805677,0.000008598399,0.00050033745,0.0005325386,0.0005393107,0.0003624325],"domain_scores_gemma":[0.99795145,0.00074905035,0.0002732223,0.00076059357,0.00011192028,0.0001537389],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020207447,0.00033623478,0.00046256577,0.00010704221,0.0002045598,0.000086748514,0.00043981505,0.00020116332,0.00030831364],"category_scores_gemma":[0.00033484076,0.00026195205,0.00008982468,0.0008762872,0.00015590055,0.000049553848,0.000181208,0.0003551599,0.000007605606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048995763,0.00068061496,0.07297209,0.0002409395,0.0003418823,0.0000033244116,0.0008681339,0.000007647204,0.92366415,0.00004335269,0.00046544246,0.00022248915],"study_design_scores_gemma":[0.00050849473,0.00031121154,0.016641859,0.00004588692,0.00020030061,0.0000018901322,0.0037185058,0.000063462794,0.9773611,0.00024916834,0.0006374129,0.0002607112],"about_ca_topic_score_codex":0.000028802046,"about_ca_topic_score_gemma":0.0000037897735,"teacher_disagreement_score":0.05633023,"about_ca_system_score_codex":0.00013692095,"about_ca_system_score_gemma":0.000038769747,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W2005251888","doi":"10.1366/13-07242","title":"Near-Infrared Spectral Image Analysis of Pork Marbling Based on Gabor Filter and Wide Line Detector Techniques","year":2014,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Calibration; Marbled meat; Detector; Hyperspectral imaging; Near-infrared spectroscopy; Filter (signal processing); Artificial intelligence; Line (geometry); Pattern recognition (psychology); Computer science; Linear regression; Wavelength; Mathematics; Computer vision; Optics; Statistics; Physics","score_opus":0.007782626778182132,"score_gpt":0.25286939251878177,"score_spread":0.24508676574059962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005251888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85592043,0.00011829913,0.046893474,0.00021254117,0.00002778342,0.00021287326,0.00009422601,0.00044779474,0.09607256],"genre_scores_gemma":[0.9392906,0.0000331826,0.059559893,0.00044492792,0.00014804315,0.00004632116,0.000094735726,0.00006082096,0.00032151447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997671,0.000023395967,0.00056964875,0.0007299481,0.00043545853,0.0005705572],"domain_scores_gemma":[0.9980844,0.0004694478,0.00033443945,0.0008579893,0.000068936126,0.00018476682],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002736867,0.0004422151,0.0009546649,0.00062148715,0.0001619862,0.0001231911,0.0003565612,0.00022957241,0.0031007729],"category_scores_gemma":[0.00016228942,0.0004182628,0.00029370285,0.0017655337,0.00025463692,0.00007101137,0.00006637557,0.00041513174,0.000018118624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035070238,0.00018343347,0.0068634683,0.00009778806,0.0005683148,0.0000046450573,0.000048766746,0.00009851928,0.9905399,0.00058945303,0.00038663333,0.00026839634],"study_design_scores_gemma":[0.00060087634,0.00016419773,0.002197487,0.00002294809,0.0015270868,8.228692e-7,0.000050552415,0.011302392,0.98246294,0.0007230137,0.00053874776,0.0004089049],"about_ca_topic_score_codex":0.000057334575,"about_ca_topic_score_gemma":0.000014112977,"teacher_disagreement_score":0.09575105,"about_ca_system_score_codex":0.000121963385,"about_ca_system_score_gemma":0.00005346575,"threshold_uncertainty_score":0.9998269},"labels":[],"label_agreement":null},{"id":"W2005666926","doi":"10.1021/jf0725068","title":"Correcting for Underlying Absorption Interferences in Fourier Transform Infrared <i>trans</i> Analysis of Edible Oils Using Two-Dimensional Correlation Techniques","year":2008,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fourier transform; Spectral line; Chemistry; Analytical Chemistry (journal); Fourier transform infrared spectroscopy; Absorption (acoustics); Infrared; Spectroscopy; Wavenumber; Biological system; Mathematics; Optics; Chromatography; Physics","score_opus":0.03698844479521787,"score_gpt":0.28120267830149065,"score_spread":0.24421423350627278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005666926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952815,0.0005839539,0.0034371228,0.000037985556,0.000029382081,0.000036353493,0.000017570514,0.000011212719,0.0005648869],"genre_scores_gemma":[0.99562925,0.000069719325,0.004055822,0.000007400758,0.0000996721,0.0000027685942,0.000027662458,0.0000053781932,0.00010229895],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99882483,0.000006747948,0.0006302736,0.00014601967,0.00023019697,0.00016193403],"domain_scores_gemma":[0.99897355,0.00013769523,0.0005190495,0.00005527622,0.00024876534,0.00006566314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014157247,0.00015715566,0.0004671796,0.00016093571,0.00009663277,0.000018615592,0.00010085102,0.00015452706,0.000073175324],"category_scores_gemma":[0.000049553462,0.00011068555,0.0002688831,0.00072227203,0.00006019562,0.00024123937,0.000011162873,0.0002752369,2.304077e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087529996,0.0000837579,0.0048829135,0.00013967537,0.00056343956,0.0000014834642,0.00041477405,0.0018540163,0.99107057,0.0000012798032,0.000028666844,0.0008718956],"study_design_scores_gemma":[0.0006067096,0.00009174878,0.0021256781,0.00015064856,0.0010187976,0.00024668055,0.0012217596,0.0015675846,0.9927035,0.000102356375,0.0000054285665,0.0001590855],"about_ca_topic_score_codex":0.000021903143,"about_ca_topic_score_gemma":0.000010056834,"teacher_disagreement_score":0.0027572354,"about_ca_system_score_codex":0.000091897884,"about_ca_system_score_gemma":0.00004325045,"threshold_uncertainty_score":0.45136255},"labels":[],"label_agreement":null},{"id":"W2005668729","doi":"10.1002/bip.20010","title":"Study of protein aggregation using two‐dimensional correlation infrared spectroscopy and spectral simulations","year":2004,"lang":"en","type":"article","venue":"Biopolymers","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Chemistry; Spectral line; Spectroscopy; Intramolecular force; Infrared spectroscopy; Intermolecular force; Fourier transform infrared spectroscopy; Protein aggregation; Fourier transform; Two-dimensional nuclear magnetic resonance spectroscopy; Crystallography; Stereochemistry; Physics; Optics; Molecule; Biochemistry","score_opus":0.02158844768821854,"score_gpt":0.2957239253540189,"score_spread":0.2741354776658004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005668729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979416,0.00032707633,0.00038813497,0.000021450238,0.00003403878,0.00016074191,0.0000140931625,0.000043239914,0.0010695859],"genre_scores_gemma":[0.9978845,0.0000017498653,0.0017770532,0.000008916395,0.000067051864,0.000004288513,0.000023705901,0.000015635342,0.00021705877],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902123,0.000011988674,0.0002873523,0.00025100517,0.0002501633,0.00017823197],"domain_scores_gemma":[0.99948347,0.00003115216,0.00019543496,0.00017984111,0.000045518937,0.00006460477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041233245,0.00014934555,0.00019910959,0.00019615355,0.00014343666,0.00002175255,0.00006843784,0.000071127695,0.0003232848],"category_scores_gemma":[0.00004366643,0.00015217326,0.000043869884,0.00050895626,0.000091266564,0.00014445995,0.000029034663,0.00011448307,0.0000023238188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006814549,0.0004147991,0.019035771,0.000020160465,0.000108579756,0.0000046763466,0.000539388,0.015965424,0.96345395,0.00032018407,0.0000023278906,0.00006657117],"study_design_scores_gemma":[0.0021239463,0.00013085551,0.0013956595,0.00003307422,0.00018709328,0.000007947496,0.0011450701,0.0034240722,0.9905168,0.00085100374,0.0000021053518,0.00018237124],"about_ca_topic_score_codex":0.0005321613,"about_ca_topic_score_gemma":0.00003391912,"teacher_disagreement_score":0.02706283,"about_ca_system_score_codex":0.0001366537,"about_ca_system_score_gemma":0.00007436209,"threshold_uncertainty_score":0.62054455},"labels":[],"label_agreement":null},{"id":"W2006179021","doi":"10.1080/10402000600781432","title":"FTIR Condition Monitoring of In-Service Lubricants: Ongoing Developments and Future Perspectives","year":2006,"lang":"en","type":"article","venue":"Tribology Transactions","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thermal-Lube (Canada); McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Lubricant; Process engineering; Absorbance; Materials science; Context (archaeology); Crankcase; Analytical Chemistry (journal); Chromatography; Mechanical engineering; Chemistry; Chemical engineering; Engineering; Composite material","score_opus":0.010742204071868605,"score_gpt":0.2696861324934641,"score_spread":0.2589439284215955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006179021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935107,0.0009591995,0.0010849151,0.00029375215,0.00005319264,0.000030606712,0.000021490774,0.000039377035,0.0040067234],"genre_scores_gemma":[0.9981302,0.000350929,0.00112684,0.000009544781,0.00009341819,0.000015434685,0.000014218621,0.000008817747,0.00025057094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993359,0.000011488771,0.00021098103,0.00019436522,0.000076836826,0.00017043274],"domain_scores_gemma":[0.9996827,0.00007418621,0.000069694004,0.00008825467,0.000058015503,0.000027104466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039034287,0.00010500577,0.00020507569,0.00020457066,0.000090159425,0.00000650145,0.00006594157,0.00012662979,0.00036772716],"category_scores_gemma":[0.0000035678188,0.00011196117,0.000035251676,0.0005733111,0.00006442491,0.00009592427,0.0000032824166,0.00018063081,0.0000025218628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015118593,0.0009868848,0.31269222,0.0003320476,0.00041739328,0.000021518972,0.004124077,0.00018139958,0.6738333,0.0014121665,0.000031308882,0.0058164746],"study_design_scores_gemma":[0.0014063482,0.0000214658,0.2771528,0.0000355845,0.0001879214,0.00004816272,0.018447086,0.000020964055,0.70156425,0.00036216996,0.0005144376,0.00023881026],"about_ca_topic_score_codex":0.00034176902,"about_ca_topic_score_gemma":0.00012671702,"teacher_disagreement_score":0.035539404,"about_ca_system_score_codex":0.00008445212,"about_ca_system_score_gemma":0.0000345101,"threshold_uncertainty_score":0.4565644},"labels":[],"label_agreement":null},{"id":"W2006423707","doi":"10.1016/j.aca.2013.10.019","title":"Assessment of the oxidative stability of lubricant oil using fiber-coupled fluorescence excitation–emission matrix spectroscopy","year":2013,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; Queen's University; Gastops (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Chemistry; Lubricant; Fluorescence; Fluorescence spectroscopy; Antioxidant; Analytical Chemistry (journal); Spectroscopy; Matrix (chemical analysis); Fluorescence spectrometry; Photochemistry; Chromatography; Organic chemistry","score_opus":0.019974911500147532,"score_gpt":0.30977911111789375,"score_spread":0.2898041996177462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006423707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99066365,0.000017779263,0.00005746382,0.00035006937,0.000020194651,0.00008515557,0.000028068287,0.000024956233,0.008752685],"genre_scores_gemma":[0.99249965,0.00005213304,0.0066794036,0.00001993781,0.00004066889,0.000009017333,0.000012811689,0.000020444397,0.0006659488],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99792963,0.000053963253,0.00068131264,0.00041265055,0.00058092194,0.00034150475],"domain_scores_gemma":[0.99770015,0.00036961783,0.0006423533,0.00085839874,0.00031080615,0.00011867546],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018823228,0.00024998366,0.0005521741,0.00012548092,0.00012664728,0.00003414869,0.0005983404,0.00011727692,0.0072730095],"category_scores_gemma":[0.00041877094,0.00017709198,0.00028068517,0.001092305,0.00033388493,0.00017685814,0.00018149184,0.00029853947,0.0000059443],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013896099,0.0002469331,0.016811157,0.00016021542,0.00016251388,2.2236583e-7,0.00008447975,0.000003028774,0.9822305,0.000093095514,0.00016887573,0.000025093419],"study_design_scores_gemma":[0.00028701924,0.00002967397,0.018582726,0.00008065458,0.00038896597,0.0000019752663,0.0005298888,0.027072504,0.9524329,0.00039153657,0.000016041584,0.00018613486],"about_ca_topic_score_codex":0.0002814997,"about_ca_topic_score_gemma":0.000003661919,"teacher_disagreement_score":0.02979761,"about_ca_system_score_codex":0.00025828532,"about_ca_system_score_gemma":0.0002714542,"threshold_uncertainty_score":0.99363446},"labels":[],"label_agreement":null},{"id":"W2006762949","doi":"10.1366/000370205774783151","title":"Multicomponent Peak Modeling of Protein Secondary Structures: Comparison of Gaussian with Lorentzian Analytical Methods for Plant Feed and Seed Molecular Biology and Chemistry Research","year":2005,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Gaussian; Plant biology; Chemistry; Statistical physics; Analytical Chemistry (journal); Biological system; Physics; Computational chemistry; Botany; Biology; Chromatography","score_opus":0.038758535750331,"score_gpt":0.4135714329441596,"score_spread":0.3748128971938286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006762949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9358786,0.0012727886,0.05950027,0.00011679662,0.000004358662,0.00033420147,0.000055523367,0.000029055665,0.002808431],"genre_scores_gemma":[0.8445008,0.000020821946,0.15522812,0.000007661492,0.00005899498,0.000055843564,0.00007307631,0.000030140503,0.000024551866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980802,0.00003560464,0.0005737165,0.0005814089,0.0002340426,0.00049503666],"domain_scores_gemma":[0.99893975,0.00020554494,0.0002181468,0.00037364455,0.00009323294,0.00016965142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039604344,0.0002822509,0.00079505926,0.00018197589,0.00011636606,0.00002638895,0.0002320582,0.00023244019,0.00011265751],"category_scores_gemma":[0.000057421174,0.00023533641,0.00006758658,0.00030113113,0.00047166913,0.000032001077,0.00011020231,0.0005032726,2.6386007e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072507793,0.00015800574,0.00059631525,0.0005419055,0.00025794917,7.388655e-7,0.00024757814,0.0001793276,0.9909932,0.005715338,0.000004328298,0.00058021024],"study_design_scores_gemma":[0.0014753436,0.00013213293,0.00008067673,0.00003504858,0.00016422593,0.0000059290096,0.0010403086,0.06965048,0.9247121,0.0024302055,0.00005540502,0.00021810277],"about_ca_topic_score_codex":0.00006751534,"about_ca_topic_score_gemma":0.000006373109,"teacher_disagreement_score":0.095727846,"about_ca_system_score_codex":0.000082995815,"about_ca_system_score_gemma":0.00009303914,"threshold_uncertainty_score":0.95967406},"labels":[],"label_agreement":null},{"id":"W2007498636","doi":"10.1002/mats.200300015","title":"A Protocol for the Estimation of Parameters in Process Models: Case Studies with Polymerization Scenarios","year":2004,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimation theory; Process (computing); Protocol (science); Computer science; Plot (graphics); Estimation; Nonlinear system; Sensitivity (control systems); Confidence interval; Mathematical optimization; Mathematics; Algorithm; Statistics; Engineering","score_opus":0.034396166866908204,"score_gpt":0.35759476333021056,"score_spread":0.32319859646330235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007498636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69105864,0.000097025535,0.29826507,0.000061733066,0.000002603237,0.010391377,0.000016952303,0.00002026218,0.00008634359],"genre_scores_gemma":[0.9830817,0.0000011924121,0.0013922375,0.00002551025,0.000004160129,0.015460127,0.000008300988,0.000010421759,0.000016363258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994825,0.000019826923,0.0001748114,0.00014319617,0.00007942719,0.00010021227],"domain_scores_gemma":[0.99936455,0.00032259923,0.0000942602,0.00013469624,0.00006554995,0.000018343093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012301886,0.000097583405,0.0001345967,0.0000744866,0.00015909378,0.000017856406,0.00005158073,0.00003811326,0.000009366899],"category_scores_gemma":[0.00016146098,0.000064867694,0.000028732891,0.00032554558,0.00012252884,0.00009583874,0.00001106051,0.000054422897,7.1538125e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022066305,0.00009350396,0.00009260025,0.00021548248,0.00011038985,0.000017734303,0.0016509155,0.9861248,0.0017214466,0.009361608,1.2291771e-7,0.00039070362],"study_design_scores_gemma":[0.00369086,0.000143419,0.0000090452695,0.00016148406,0.00038947279,0.00015627415,0.005190764,0.4767605,0.342568,0.17068484,0.000002610175,0.00024271714],"about_ca_topic_score_codex":0.000014769211,"about_ca_topic_score_gemma":0.000018140416,"teacher_disagreement_score":0.5093643,"about_ca_system_score_codex":0.000023286198,"about_ca_system_score_gemma":0.00003458687,"threshold_uncertainty_score":0.2645228},"labels":[],"label_agreement":null},{"id":"W2007668179","doi":"10.1364/ol.32.003302","title":"Improved locally adaptive least-squares detection of differences in images","year":2007,"lang":"en","type":"article","venue":"Optics Letters","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Clutter; Least-squares function approximation; Optics; Computer science; Recursive least squares filter; Intensity (physics); Change detection; Window (computing); Artificial intelligence; Computer vision; Algorithm; Mathematics; Adaptive filter; Physics; Radar; Statistics","score_opus":0.012316231699047058,"score_gpt":0.23971284598699982,"score_spread":0.22739661428795277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007668179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96020687,0.00014987182,0.036936514,0.00012546357,0.00003651091,0.00003211697,0.000005422149,0.000027165346,0.0024800561],"genre_scores_gemma":[0.99793434,0.000026912156,0.0017041358,0.000104582585,0.00006490464,0.0000034203988,0.000002346681,0.000011501676,0.00014784148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991277,0.0000067262863,0.0002637187,0.00019354031,0.00015674568,0.00025157747],"domain_scores_gemma":[0.99945766,0.00017463161,0.00012443133,0.00015763658,0.000041028954,0.00004459554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010966553,0.00013034919,0.0002128909,0.00018620322,0.00003053201,0.00001580533,0.00014786454,0.00008388464,0.000081055405],"category_scores_gemma":[0.000086890584,0.00012312498,0.00006831796,0.0003567336,0.00011746433,0.00006746936,0.000028308654,0.00018586437,0.0000028612965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008256467,0.00005839824,0.009454907,0.000040258634,0.000045018005,0.000009072043,0.000093467315,0.000018210054,0.988434,0.000025670794,0.000011483989,0.0017269588],"study_design_scores_gemma":[0.00034091636,0.00005520612,0.0074641933,0.000021140459,0.000046971567,0.0000023036844,0.00083491986,0.00077609665,0.9902814,0.000030106148,0.000006154669,0.00014061303],"about_ca_topic_score_codex":0.00017296354,"about_ca_topic_score_gemma":0.00012629892,"teacher_disagreement_score":0.037727475,"about_ca_system_score_codex":0.000074129224,"about_ca_system_score_gemma":0.000015321468,"threshold_uncertainty_score":0.5020891},"labels":[],"label_agreement":null},{"id":"W2007782751","doi":"10.1007/s11746-005-5165-5","title":"Determination of the fatty acid composition of canola, flax, and solin by near‐infrared spectroscopy","year":2005,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Canola; Iodine value; Oleic acid; Linoleic acid; Chemistry; Linolenic acid; Composition (language); Cultivar; Food science; Fatty acid; Botany; Agronomy; Biology; Biochemistry","score_opus":0.005842838275842538,"score_gpt":0.25380392441319594,"score_spread":0.2479610861373534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007782751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963974,0.0008967922,0.0001939637,0.0007940078,0.000025297002,0.000015744024,0.000026378537,0.000006241564,0.0016442046],"genre_scores_gemma":[0.9921468,0.00048106306,0.006430665,0.00021911468,0.00012556059,0.0000011611833,0.0000022741144,0.000014675393,0.0005787075],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987403,0.000025609927,0.00049068645,0.00012997631,0.00043560014,0.00017782461],"domain_scores_gemma":[0.99763197,0.00008964072,0.0017346406,0.0002960676,0.00018382045,0.000063854175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017682966,0.00015441679,0.0004192724,0.000015786185,0.0001314647,0.0000239417,0.0004564251,0.00007490057,0.00007229345],"category_scores_gemma":[0.00007955449,0.00010114762,0.00045391917,0.00041779247,0.00067340344,0.00009076198,0.00008829638,0.0003741171,2.4582585e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004326851,0.00010221993,0.005199395,0.00008227325,0.000120672215,1.8855333e-7,0.0005546696,0.000012380755,0.98909014,0.0000010079781,0.003283939,0.0015098403],"study_design_scores_gemma":[0.0004432498,0.000032519085,0.001603816,0.00005964743,0.00023557857,0.00003898545,0.000597884,0.0004381792,0.9960338,0.000031115058,0.00038632628,0.00009889343],"about_ca_topic_score_codex":0.000048810085,"about_ca_topic_score_gemma":0.0000020425364,"teacher_disagreement_score":0.006943661,"about_ca_system_score_codex":0.00024873985,"about_ca_system_score_gemma":0.00012337136,"threshold_uncertainty_score":0.41246802},"labels":[],"label_agreement":null},{"id":"W2008080854","doi":"10.1016/j.talanta.2007.06.004","title":"Determination of peroxide value of edible oils by FTIR spectroscopy with the use of the spectral reconstitution technique","year":2007,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Diluent; Chemistry; Dilution; Peroxide; Fourier transform infrared spectroscopy; Peroxide value; Gravimetric analysis; Analytical Chemistry (journal); Triphenylphosphine oxide; Reagent; Nuclear chemistry; Chromatography; Triphenylphosphine; Organic chemistry; Chemical engineering","score_opus":0.016625108663705705,"score_gpt":0.26244831368695937,"score_spread":0.24582320502325367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008080854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9829322,0.00009203906,0.010021774,0.00008147017,0.000021082644,0.0001164856,0.00004284828,0.000019547037,0.0066725393],"genre_scores_gemma":[0.9955415,0.000048456142,0.0038845835,0.000017721393,0.00003657098,0.000005880445,0.000009816522,0.000010080159,0.00044538517],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909544,0.000016838165,0.00029428597,0.00014781568,0.00026633387,0.00017929214],"domain_scores_gemma":[0.9989947,0.0001614865,0.00036665404,0.0003776838,0.000073170726,0.000026276191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022096938,0.00012080427,0.0002191653,0.000077585384,0.000055249497,0.000008371677,0.00025549743,0.000098402255,0.00012226148],"category_scores_gemma":[0.00007370486,0.00007008563,0.00009323284,0.00050581363,0.0003625396,0.00009369136,0.000034241406,0.00017955905,6.758987e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075396354,0.000084813495,0.0062780404,0.00007450033,0.000033069216,0.0000010627159,0.00007665657,0.000004585508,0.99232876,0.0004616858,0.00046484979,0.0001165972],"study_design_scores_gemma":[0.00018852505,0.000060268427,0.001009576,0.000064161155,0.00013546587,0.000029392088,0.00015804985,0.0000292672,0.9979775,0.00008769589,0.0001804051,0.000079690246],"about_ca_topic_score_codex":0.0002468076,"about_ca_topic_score_gemma":0.00006359015,"teacher_disagreement_score":0.012609292,"about_ca_system_score_codex":0.000066997236,"about_ca_system_score_gemma":0.00006217629,"threshold_uncertainty_score":0.2858009},"labels":[],"label_agreement":null},{"id":"W2008359378","doi":"10.1016/s0959-1524(00)00025-1","title":"Jackknife and bootstrap methods in the identification of dynamic models","year":2001,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Jackknife resampling; Statistics; Mathematics; Statistic; Regression; Confidence interval; Computer science","score_opus":0.029748335272851225,"score_gpt":0.3928778989986307,"score_spread":0.3631295637257795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008359378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8848742,0.006000199,0.10678152,0.0006258647,0.000021702292,0.000049010072,0.0000038338935,0.000004598283,0.001639039],"genre_scores_gemma":[0.9989088,0.00038487234,0.00050032296,0.000050662115,0.0000378922,0.0000033223994,4.2130725e-7,0.0000054545417,0.00010822485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896574,0.00003902594,0.00059283647,0.00008167313,0.00021470845,0.00010599665],"domain_scores_gemma":[0.99886894,0.00020047613,0.0006260319,0.0001077955,0.00016681683,0.000029951945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007725528,0.00007672038,0.0002620172,0.00015287874,0.000026798285,0.000028698143,0.00023767067,0.000058976293,0.000060161816],"category_scores_gemma":[0.00020089858,0.0000509647,0.0000709299,0.00032357365,0.00004652221,0.0002003842,0.0000048922334,0.0002069754,2.457125e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049610494,0.0005050309,0.01368885,0.0004282217,0.00038829917,0.00004090695,0.0018436381,0.0033523554,0.95702964,0.0005265859,0.000052875796,0.021647496],"study_design_scores_gemma":[0.012396883,0.00036135025,0.020486588,0.00028551702,0.0024623796,0.0014651747,0.02109709,0.27973086,0.55793715,0.10270287,0.0003631419,0.0007109877],"about_ca_topic_score_codex":0.000005753652,"about_ca_topic_score_gemma":0.000003160551,"teacher_disagreement_score":0.39909247,"about_ca_system_score_codex":0.000027145146,"about_ca_system_score_gemma":0.000050054095,"threshold_uncertainty_score":0.207828},"labels":[],"label_agreement":null},{"id":"W2008592976","doi":"10.1021/jf034874r","title":"Rapid Prediction of Solid Wood Lignin Content Using Transmittance Near-Infrared Spectroscopy","year":2004,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lignin; Transmittance; Spectroscopy; Infrared spectroscopy; Infrared; Near-infrared spectroscopy; Materials science; Chemistry; Analytical Chemistry (journal); Environmental chemistry; Organic chemistry; Optics; Optoelectronics","score_opus":0.024250122282843522,"score_gpt":0.23462183204028833,"score_spread":0.21037170975744482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008592976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99273336,0.005171046,0.00012098779,0.0001297347,0.000051431758,0.000029701117,0.00007527851,0.000018509421,0.0016699263],"genre_scores_gemma":[0.9970147,0.0006702916,0.0016406652,0.000015639927,0.00037861647,0.0000011659931,0.000009828996,0.000010138415,0.00025897718],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856436,0.0000054259017,0.0006641044,0.00018437767,0.00033418325,0.0002475423],"domain_scores_gemma":[0.9988209,0.000025959685,0.0006203234,0.00011463025,0.00025080197,0.0001673762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007318168,0.00022951972,0.00050086726,0.00003014344,0.0001045472,0.0000395407,0.00019022952,0.00018033493,0.00013709824],"category_scores_gemma":[0.00003606111,0.00015293522,0.0002676587,0.00029248113,0.00012258314,0.00021389045,0.000022148211,0.00037118924,3.8537326e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005313402,0.00016226606,0.00045807203,0.00021828302,0.00034221722,0.0000047251406,0.00021788345,0.00010007651,0.99823725,0.0000042726597,0.00015008338,0.000051749106],"study_design_scores_gemma":[0.0015558118,0.00023407565,0.0025945778,0.00020245674,0.00037726108,0.00054766913,0.0016904694,0.000009406077,0.9924163,0.00013264289,0.00007558773,0.00016370868],"about_ca_topic_score_codex":0.0000059898507,"about_ca_topic_score_gemma":4.791088e-7,"teacher_disagreement_score":0.005820905,"about_ca_system_score_codex":0.00013891935,"about_ca_system_score_gemma":0.00008050656,"threshold_uncertainty_score":0.62365174},"labels":[],"label_agreement":null},{"id":"W2008720504","doi":"10.1080/10520290500166282","title":"Preparation of soybean seed samples for FT-IR microspectroscopy","year":2005,"lang":"en","type":"article","venue":"Biotechnic & Histochemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Brookhaven National Laboratory; Canadian Light Source","keywords":"Imbibition; Microtome; Sample preparation; Infrared; Chemistry; Materials science; Analytical Chemistry (journal); Chromatography; Germination; Mineralogy; Optics; Botany; Biology","score_opus":0.015191843997326909,"score_gpt":0.3083851469972603,"score_spread":0.29319330299993335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008720504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9363171,0.006463073,0.02236819,0.0006595539,0.000088523644,0.00044928692,0.0004593786,0.00075204653,0.03244285],"genre_scores_gemma":[0.9725108,0.0000592072,0.019667938,0.000059823436,0.00036321755,0.000094178016,0.00019665007,0.000044091787,0.0070040636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826,0.000004021098,0.00055084366,0.0005327815,0.00024840856,0.00040390834],"domain_scores_gemma":[0.9986057,0.00009015638,0.00037607172,0.0007018652,0.00012098066,0.000105221836],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012964083,0.0002995617,0.00043986252,0.00009356381,0.00013582455,0.000023065031,0.0004516019,0.00045493353,0.00091516465],"category_scores_gemma":[0.00017094592,0.0003231087,0.00029437017,0.00033011383,0.0001914596,0.00008579714,0.000058197435,0.00023152852,0.0000127929425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012092556,0.0001669042,0.00019707305,0.00026946867,0.00007102874,3.593351e-7,0.000048290236,0.000005396803,0.9931568,0.00016957756,0.0055874167,0.0002067436],"study_design_scores_gemma":[0.00057859207,0.000046975292,0.00003888242,0.000032414024,0.00018073902,0.000010419258,0.00007887789,0.00011754098,0.98055935,0.0001872053,0.017849743,0.00031926818],"about_ca_topic_score_codex":0.000033272256,"about_ca_topic_score_gemma":0.0000056419876,"teacher_disagreement_score":0.036193732,"about_ca_system_score_codex":0.00049794675,"about_ca_system_score_gemma":0.00014491136,"threshold_uncertainty_score":0.99999815},"labels":[],"label_agreement":null},{"id":"W2008729476","doi":"10.1021/ie061348r","title":"Use of Wavelet Packet Transform in Characterization of Surface Quality","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"McMaster University","keywords":"Wavelet packet decomposition; Discrete wavelet transform; Second-generation wavelet transform; Wavelet; Wavelet transform; Stationary wavelet transform; Pattern recognition (psychology); Lifting scheme; Computer science; Harmonic wavelet transform; Artificial intelligence; Feature extraction; Mathematics","score_opus":0.20649842342983085,"score_gpt":0.3939064944857694,"score_spread":0.18740807105593857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008729476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99810666,0.000030662697,0.000073871444,0.0000510725,0.000024101695,0.000077193006,0.00009646855,0.000030033341,0.0015099249],"genre_scores_gemma":[0.9989025,0.00003766281,0.000046329365,0.0000011128923,0.00015355999,0.0000035828173,0.00010041282,0.000023621356,0.00073123793],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99781454,0.00001777442,0.00073314284,0.00026491832,0.0006588524,0.0005108009],"domain_scores_gemma":[0.99856055,0.0006341071,0.00012453807,0.00035292166,0.00020740191,0.00012051246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015502943,0.0001629655,0.0004156449,0.00016593571,0.000024396957,0.000020411815,0.00027840547,0.00044408566,0.0005352333],"category_scores_gemma":[0.001156337,0.00017768296,0.00009357523,0.0015391402,0.00010323604,0.0001298545,0.0000497272,0.00087650045,0.0000012242768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016124034,0.00013174504,0.0052850945,0.00035943813,0.00004700117,0.0000062988174,0.00008470302,0.00016698349,0.9930884,0.000023383993,0.000017022247,0.0006286394],"study_design_scores_gemma":[0.00060040347,0.000014246733,0.0014703025,0.000092655566,0.000012534219,0.0000018873664,0.00011415094,0.0002452715,0.9966648,0.000005626248,0.0006319336,0.00014616383],"about_ca_topic_score_codex":0.00031407856,"about_ca_topic_score_gemma":0.0000039179367,"teacher_disagreement_score":0.003814792,"about_ca_system_score_codex":0.00021223564,"about_ca_system_score_gemma":0.00012504584,"threshold_uncertainty_score":0.7245701},"labels":[],"label_agreement":null},{"id":"W2009270465","doi":"10.1366/000370202760076857","title":"Application of Maximum Likelihood Principal Components Regression to Fluorescence Emission Spectra","year":2002,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Partial least squares regression; Principal component regression; Calibration; Principal component analysis; Heteroscedasticity; Covariance; Statistics; Analytical Chemistry (journal); Chemometrics; Chemistry; Observational error; Covariance matrix; Mathematics; Chromatography","score_opus":0.016169673453431368,"score_gpt":0.2648209557928366,"score_spread":0.24865128233940523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009270465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8598613,0.000515532,0.023487214,0.00049774867,0.000088295514,0.00051352713,0.00003747134,0.0004178307,0.114581086],"genre_scores_gemma":[0.98198986,0.00012514975,0.01683128,0.00010702931,0.00022658346,0.00008662005,0.000046468245,0.000056422334,0.0005306015],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99729145,0.000011690447,0.0005923641,0.0007766936,0.0006541265,0.00067365594],"domain_scores_gemma":[0.99826753,0.00006725942,0.0003177175,0.00093376817,0.00006665485,0.00034707942],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012141187,0.00040175006,0.00056727364,0.00025882135,0.00017772315,0.00003787996,0.0006534979,0.00022202256,0.0025195586],"category_scores_gemma":[0.000038896957,0.00036679537,0.00014455049,0.0010303591,0.00009720347,0.00007639516,0.00016463576,0.0003780622,0.00038698787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001207847,0.00043212023,0.0029762874,0.00009521943,0.000045380293,0.0000034059715,0.00019305514,0.0000060759626,0.9906246,0.0008416016,0.0016512478,0.0030102034],"study_design_scores_gemma":[0.00063432124,0.00006677952,0.00085671735,0.000048888953,0.00009337823,0.0000060727302,0.00012123326,0.000819617,0.9930765,0.0017086883,0.0021958444,0.0003719393],"about_ca_topic_score_codex":0.000054480843,"about_ca_topic_score_gemma":0.0000028311013,"teacher_disagreement_score":0.12212855,"about_ca_system_score_codex":0.00023457433,"about_ca_system_score_gemma":0.000020806436,"threshold_uncertainty_score":0.9998784},"labels":[],"label_agreement":null},{"id":"W2009346339","doi":"10.1016/s1537-5110(03)00083-7","title":"Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification","year":2003,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Pattern recognition (psychology); Backpropagation; Artificial intelligence; Classifier (UML); Artificial neural network; Parametric statistics; Computer science; Mathematics; Statistics","score_opus":0.02408964711865329,"score_gpt":0.28661857903370264,"score_spread":0.26252893191504934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009346339","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9765753,0.0029986291,0.01957372,0.000007691207,0.00021801589,0.00013017062,0.000011021293,0.000058586727,0.00042685168],"genre_scores_gemma":[0.9984658,0.0000093512845,0.0010571156,0.0000021016278,0.00009973948,0.000037177506,0.00000862829,0.000019534948,0.00030055904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991367,0.000005484883,0.00035547154,0.00018928538,0.000105309024,0.00020778622],"domain_scores_gemma":[0.99944854,0.00013252841,0.00014556554,0.00017566982,0.000042093146,0.000055585795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016162003,0.00012544087,0.00030539438,0.0001445322,0.000039713508,0.00003083593,0.00008194341,0.00009849412,0.000012923055],"category_scores_gemma":[0.00020000068,0.0001264528,0.000071770024,0.0006005718,0.000012625004,0.000044459714,0.0000103105485,0.00007793899,0.000001062049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019202827,0.00009931992,0.07760427,0.0020182868,0.00025096736,9.3348297e-7,0.00018831446,0.009653878,0.906689,0.0020399268,0.0011455044,0.00029037948],"study_design_scores_gemma":[0.0006944325,0.00004114727,0.0038490775,0.00009260819,0.00020346945,0.000011049364,0.00041382766,0.20615754,0.78519994,0.000028747496,0.0029635648,0.0003445901],"about_ca_topic_score_codex":0.0000115080975,"about_ca_topic_score_gemma":0.0000010846313,"teacher_disagreement_score":0.19650367,"about_ca_system_score_codex":0.000038615184,"about_ca_system_score_gemma":0.000009422642,"threshold_uncertainty_score":0.5156595},"labels":[],"label_agreement":null},{"id":"W2009727312","doi":"10.1016/j.neunet.2007.04.018","title":"Nonlinear principal component analysis of noisy data","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Freie Universität Berlin","keywords":"Overfitting; Principal component analysis; Nonlinear system; Regularization (linguistics); Artificial neural network; Artificial intelligence; Computer science; Component (thermodynamics); Pattern recognition (psychology); Mathematics; Algorithm","score_opus":0.03856100644950894,"score_gpt":0.32030128146221,"score_spread":0.28174027501270105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009727312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9865104,0.0008004758,0.0076092966,0.00004850072,0.00007937379,0.000029443689,0.00006837056,0.00006627124,0.004787893],"genre_scores_gemma":[0.9976592,0.00005140415,0.00080398866,0.00010334932,0.00033198323,7.13482e-7,0.0007291987,0.00001387452,0.00030627815],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858886,0.000008665922,0.00041953113,0.00036297226,0.00027069665,0.00034930083],"domain_scores_gemma":[0.99847573,0.00020778518,0.0001918371,0.00095794286,0.0000534973,0.00011320304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022993262,0.00015441191,0.00042473793,0.00021565166,0.000055787346,0.000017035443,0.0006656226,0.00013144418,0.0010911769],"category_scores_gemma":[0.000046251094,0.00013859021,0.00016845914,0.001770803,0.00007673002,0.000074830634,0.00026849835,0.0002759591,0.0000033693677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007348496,0.0017536147,0.7399837,0.00018950549,0.012033896,0.00021703701,0.00019361408,0.14399876,0.072770126,0.00027721288,0.0038828482,0.02396488],"study_design_scores_gemma":[0.0002994411,0.000021683645,0.013341773,0.000005305359,0.0029311415,0.0000039743813,0.00007155183,0.9585782,0.022414004,0.0000032269934,0.0021107409,0.0002188991],"about_ca_topic_score_codex":0.00011414926,"about_ca_topic_score_gemma":0.00013391917,"teacher_disagreement_score":0.8145795,"about_ca_system_score_codex":0.000031543932,"about_ca_system_score_gemma":0.000009016859,"threshold_uncertainty_score":0.99982196},"labels":[],"label_agreement":null},{"id":"W2009736783","doi":"10.2333/bhmk.30.145","title":"Relationships Between two Methods for Dealing with Missing Data in Principal Component Analysis","year":2003,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Missing data; Principal component analysis; Equating; Homogeneity (statistics); Correspondence analysis; Variety (cybernetics); Computer science; Data mining; Statistics; Econometrics; Mathematics","score_opus":0.21027230704143302,"score_gpt":0.4587870013282969,"score_spread":0.2485146942868639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009736783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73470277,0.0009853713,0.26134908,0.000034186607,0.000016269696,0.00014034573,0.0000981499,0.00006815266,0.0026057048],"genre_scores_gemma":[0.73252165,0.000010503195,0.26681095,0.000005167381,0.00002909528,0.000019637579,0.00041412457,0.00002325412,0.0001656235],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981864,0.000098486824,0.00050574326,0.000577661,0.00024285977,0.00038884103],"domain_scores_gemma":[0.9975965,0.0010212788,0.00022023029,0.0009424453,0.00006555496,0.00015401088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015408267,0.0002185363,0.00054283533,0.0008097052,0.00022037583,0.00007246377,0.00042954198,0.00014135869,0.00039619466],"category_scores_gemma":[0.00078739505,0.00020205877,0.0001287835,0.0029261347,0.000051889798,0.00016864539,0.00007106437,0.00039210444,0.000002432359],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019010748,0.00012363325,0.98731446,0.000026491169,0.00037715657,0.0000040039895,0.00007280497,0.00011050243,0.0060115512,0.00034975642,0.0000041858134,0.0055864174],"study_design_scores_gemma":[0.0059125894,0.00011467784,0.4118019,0.000093500654,0.031143393,0.00002510447,0.0017559684,0.005685257,0.5283748,0.00066630164,0.012545783,0.0018807069],"about_ca_topic_score_codex":0.00031480993,"about_ca_topic_score_gemma":0.00014824417,"teacher_disagreement_score":0.5755126,"about_ca_system_score_codex":0.00017422874,"about_ca_system_score_gemma":0.000074988355,"threshold_uncertainty_score":0.82397175},"labels":[],"label_agreement":null},{"id":"W2010474483","doi":"10.1016/j.scienta.2012.06.037","title":"Determination of nutrients in hydroponic solutions using mid-infrared spectroscopy","year":2012,"lang":"en","type":"article","venue":"Scientia Horticulturae","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Ministry of Education of the People's Republic of China","keywords":"Partial least squares regression; Chemistry; Greenhouse; Nutrient; Calibration; Coefficient of determination; Analytical Chemistry (journal); Environmental chemistry; Mathematics; Horticulture; Statistics","score_opus":0.028745833742668456,"score_gpt":0.30272340664786224,"score_spread":0.27397757290519376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010474483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99165773,0.0009069236,0.00081725896,0.000016640792,0.00017486779,0.000060418857,0.00001083772,0.000033135755,0.006322184],"genre_scores_gemma":[0.9960085,0.00002313514,0.0031551525,0.000008411427,0.00007863941,0.0000067807887,0.000021790047,0.000009381642,0.00068817777],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985055,0.000016975873,0.00036095982,0.00023048057,0.00033095197,0.00055515027],"domain_scores_gemma":[0.9993363,0.000028940687,0.0001858315,0.00024924288,0.00008246337,0.00011722446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001970105,0.00014128261,0.00021937427,0.00024612402,0.00014137103,0.00003473791,0.00020519689,0.00009417141,0.00080989517],"category_scores_gemma":[0.00012937987,0.00012331764,0.00010654544,0.0013840151,0.00014246031,0.0003936461,0.00007475948,0.00012855195,0.000022109123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008377936,0.00028377172,0.033771705,0.000036084268,0.000017777154,9.623969e-7,0.00046043925,0.000019828218,0.9651613,0.00008505693,0.000099886,0.00005479523],"study_design_scores_gemma":[0.00034936288,0.000010750355,0.008093475,0.000030339155,0.000134998,0.000008815521,0.00068185764,0.0015769324,0.98858136,0.00016608599,0.00019933928,0.00016668926],"about_ca_topic_score_codex":0.00006568509,"about_ca_topic_score_gemma":0.000016753005,"teacher_disagreement_score":0.025678229,"about_ca_system_score_codex":0.0002575698,"about_ca_system_score_gemma":0.000043376313,"threshold_uncertainty_score":0.88677835},"labels":[],"label_agreement":null},{"id":"W2010647744","doi":"10.1016/j.compag.2006.03.001","title":"Classification of vitreousness in durum wheat using soft X-rays and transmitted light images","year":2006,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kernel (algebra); Moisture; Water content; Mathematics; Materials science; Composite material; Engineering","score_opus":0.006135020871072464,"score_gpt":0.22071717043574882,"score_spread":0.21458214956467636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010647744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98837286,0.009234598,0.0014259352,0.00028422888,0.000015020525,0.00004821767,0.0000039754077,0.000019320925,0.0005958236],"genre_scores_gemma":[0.998739,0.0005571404,0.00048537494,0.00001610365,0.000050910254,0.0000031337274,0.00002402071,0.0000072722632,0.00011706407],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991422,0.000012664694,0.00024033675,0.0002600212,0.00009501429,0.00024974303],"domain_scores_gemma":[0.999729,0.000041518066,0.00007942452,0.00009121077,0.000030280042,0.000028570244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000544402,0.00015045254,0.00026242225,0.00011420957,0.000044070886,0.000032786855,0.00009218778,0.00014783334,0.000007694742],"category_scores_gemma":[0.0000041900585,0.00011704339,0.000035397323,0.0005122272,0.00003997838,0.00007578061,0.00002005422,0.0002313965,1.14031614e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024116267,0.00017294746,0.030077921,0.0001575798,0.000025635169,0.0000075633693,0.00022176358,0.00028695032,0.9667213,0.0007210208,0.00044219493,0.0011410178],"study_design_scores_gemma":[0.0026867178,0.00010504696,0.12889563,0.00027336736,0.00021207696,0.00011575271,0.00079820544,0.02150022,0.8390687,0.0033113505,0.0021904358,0.0008424637],"about_ca_topic_score_codex":0.000107718784,"about_ca_topic_score_gemma":0.00018802437,"teacher_disagreement_score":0.12765256,"about_ca_system_score_codex":0.00008467644,"about_ca_system_score_gemma":0.000019526467,"threshold_uncertainty_score":0.4772891},"labels":[],"label_agreement":null},{"id":"W2010769867","doi":"10.1002/jrs.2353","title":"Surface enhanced Raman scattering of trans‐p‐coumaric and syringic acids","year":2009,"lang":"en","type":"article","venue":"Journal of Raman Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Raman spectroscopy; Raman scattering; Syringic acid; Chemistry; Spectral line; Infrared; Analytical Chemistry (journal); Physical chemistry; Organic chemistry; Optics; Physics","score_opus":0.00987065529330173,"score_gpt":0.2728558627519553,"score_spread":0.26298520745865356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010769867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871368,0.0029205075,0.0045413054,0.0004199644,0.00007083974,0.000039476403,0.000004178589,0.000029497905,0.004837443],"genre_scores_gemma":[0.99233735,0.00095369644,0.0060320045,0.00008115036,0.00020756692,4.2027432e-7,0.0000015693685,0.000023561823,0.00036266886],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981081,0.00001997534,0.0007780785,0.00025035132,0.00043924866,0.00040428195],"domain_scores_gemma":[0.9986456,0.00008932818,0.00065594644,0.00028783752,0.00013142475,0.00018987434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026206006,0.0002830596,0.00077113014,0.0002672351,0.00008899336,0.00007232665,0.00037623776,0.00014567045,0.00081690185],"category_scores_gemma":[0.00005154374,0.00025371954,0.0002644448,0.0005320435,0.00013037099,0.00025086993,0.000024584493,0.00049815467,0.0000031695006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019162749,0.0001646566,0.003393365,0.00009925879,0.00015574474,0.000022661347,0.0002827883,0.000083000254,0.99485886,0.00008811649,0.00012969314,0.0005302522],"study_design_scores_gemma":[0.0011735167,0.00045836915,0.0032202834,0.00012066793,0.00032012584,0.00013373877,0.00034754208,0.000059453014,0.9929191,0.00085688254,0.0001573385,0.0002329705],"about_ca_topic_score_codex":0.000009583925,"about_ca_topic_score_gemma":0.0000016797783,"teacher_disagreement_score":0.005200574,"about_ca_system_score_codex":0.00012939375,"about_ca_system_score_gemma":0.00007263085,"threshold_uncertainty_score":0.9999915},"labels":[],"label_agreement":null},{"id":"W2012585937","doi":"10.1002/app.23962","title":"Inline monitoring of styrene/butyl acrylate miniemulsion polymerization with attenuated total reflectance/Fourier transform infrared spectroscopy","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Attenuated total reflection; Miniemulsion; Fourier transform infrared spectroscopy; Styrene; Butyl acrylate; Spectroscopy; Materials science; Infrared spectroscopy; Acrylate; Copolymer; Polymer chemistry; Analytical Chemistry (journal); Polymer; Chemistry; Polymerization; Organic chemistry; Chemical engineering","score_opus":0.006655934779760161,"score_gpt":0.2514911526847974,"score_spread":0.24483521790503723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012585937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9802239,0.0011691455,0.0013216372,0.00010964576,0.000103123,0.00004789485,0.000008960383,0.000025748177,0.016989958],"genre_scores_gemma":[0.9942396,0.00006486235,0.004609201,0.000016087357,0.00035893908,0.0000028517256,0.0000041360986,0.000023511779,0.000680786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741924,0.00000550722,0.0007564882,0.000297704,0.0010639639,0.0004571144],"domain_scores_gemma":[0.9984519,0.00004731245,0.00083123415,0.0002832085,0.00023196255,0.00015440868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023176981,0.0002493132,0.00046435554,0.00050841324,0.00019596597,0.000074633914,0.0004949602,0.000102930826,0.00013063125],"category_scores_gemma":[0.000010798968,0.00019355702,0.00010343339,0.0024836466,0.00042943168,0.0004510006,0.0000350961,0.00031418595,0.0000018216991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044942033,0.00014074,0.0027716302,0.000025807974,0.00004589308,0.000008298406,0.00021109331,0.000096670316,0.9950507,0.00013399831,0.000021047781,0.001044692],"study_design_scores_gemma":[0.0009939853,0.00016844542,0.0013441577,0.000068418776,0.00014124573,0.000051098745,0.00042644414,0.00024028342,0.99616104,0.00016514491,0.0000264097,0.00021330794],"about_ca_topic_score_codex":0.000029509134,"about_ca_topic_score_gemma":0.0000018083873,"teacher_disagreement_score":0.016309172,"about_ca_system_score_codex":0.00015531102,"about_ca_system_score_gemma":0.00032038314,"threshold_uncertainty_score":0.7893026},"labels":[],"label_agreement":null},{"id":"W2012618052","doi":"10.1016/j.biosystemseng.2011.08.004","title":"Influence of growing location on features extracted from colour images of wheat and detection of foreign material represented by barley","year":2011,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Geography; Agricultural engineering; Engineering","score_opus":0.006941906135764931,"score_gpt":0.19976294395740404,"score_spread":0.19282103782163912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012618052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99873227,0.00054901454,0.0003488234,9.67531e-7,0.000033027212,0.000049210776,0.0000660482,0.000035475077,0.0001851557],"genre_scores_gemma":[0.99973756,0.000023092678,0.00015408083,6.900168e-7,0.000032539167,0.0000060672105,0.0000144944415,0.000012764552,0.000018703235],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933636,0.0000076199613,0.00029373114,0.00014857856,0.000121323406,0.00009237593],"domain_scores_gemma":[0.9994873,0.00005698836,0.00017553096,0.00017106601,0.00008026637,0.000028869328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056473174,0.000106368505,0.0002433781,0.000111013345,0.000013220504,0.000005906799,0.00008085333,0.000111513225,0.000018968087],"category_scores_gemma":[0.000118479424,0.00010350818,0.000031560063,0.00020557918,0.000020566134,0.00010930701,0.00001804371,0.000061382445,1.8677224e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065368506,0.00002486096,0.0029764355,0.00044134946,0.0000743206,5.6938933e-7,0.00008351766,0.00026476322,0.99600947,0.000018491188,0.000008890357,0.00003194371],"study_design_scores_gemma":[0.0001905259,0.000037675083,0.014532908,0.00022041998,0.00005585639,0.0000025290567,0.00017874522,0.00022959051,0.98446494,0.000004072659,0.000003775383,0.0000789447],"about_ca_topic_score_codex":0.0014946568,"about_ca_topic_score_gemma":0.0000022592922,"teacher_disagreement_score":0.011556473,"about_ca_system_score_codex":0.000025207048,"about_ca_system_score_gemma":0.000006168472,"threshold_uncertainty_score":0.4220941},"labels":[],"label_agreement":null},{"id":"W2013821852","doi":"10.1021/jf0110093","title":"Quantification of Exopolysaccharide, Lactic Acid, and Lactose Concentrations in Culture Broth by Near-Infrared Spectroscopy","year":2002,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Lactic acid; Lactose; Chemistry; Correlation coefficient; Ultrafiltration (renal); Chromatography; Partial least squares regression; Lactobacillus rhamnosus; Lactobacillus; Food science; Fermentation; Bacteria; Biology; Mathematics","score_opus":0.013195493243413287,"score_gpt":0.2315841743128835,"score_spread":0.2183886810694702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013821852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.979319,0.017261032,0.000017554617,0.00027162983,0.000020595273,0.00002739041,0.00003583748,0.000009385299,0.003037569],"genre_scores_gemma":[0.99618673,0.0019978916,0.00027156487,0.000017112612,0.00011316585,0.0000019448796,0.000015539206,0.000005864963,0.0013901581],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989725,0.000008332629,0.0004734912,0.00016675744,0.00020222501,0.00017671677],"domain_scores_gemma":[0.99913365,0.000040338517,0.00048411003,0.000096301694,0.0001215292,0.0001240519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004926975,0.0001710798,0.00033806934,0.00002404254,0.00007299923,0.0000752213,0.00012943508,0.00015776178,0.00041368062],"category_scores_gemma":[0.00007762208,0.00011526119,0.00008347424,0.00029862212,0.000110697445,0.00024063993,0.000020526231,0.00034397564,7.0625873e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019553625,0.00014849193,0.004613441,0.00013393608,0.00008889235,0.0000017061618,0.00034877463,0.0000012110919,0.9908622,0.000011215715,0.0036880688,0.0000825232],"study_design_scores_gemma":[0.00070932804,0.000079062214,0.0035731327,0.00005665162,0.00012809329,0.00014724593,0.0015770557,0.000020460668,0.99317473,0.000045536057,0.00033882816,0.00014987691],"about_ca_topic_score_codex":0.0000071099516,"about_ca_topic_score_gemma":0.00000223391,"teacher_disagreement_score":0.016867753,"about_ca_system_score_codex":0.000053332846,"about_ca_system_score_gemma":0.00001552107,"threshold_uncertainty_score":0.4700215},"labels":[],"label_agreement":null},{"id":"W2014228296","doi":"10.1080/10942911003687223","title":"Fungal Damage Detection in Wheat Using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging","year":2010,"lang":"en","type":"article","venue":"International Journal of Food Properties","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hyperspectral imaging; Principal component analysis; Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Mahalanobis distance; Mathematics; Remote sensing; Computer science; Geology","score_opus":0.027341657025809207,"score_gpt":0.2611208559030126,"score_spread":0.2337791988772034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014228296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970804,0.00053872156,0.00008396606,0.00018289371,0.00027801175,0.000022091821,0.000009664713,0.000013158435,0.0017911012],"genre_scores_gemma":[0.999007,0.000021751455,0.0004546345,0.000029960896,0.00034280727,0.0000010749018,0.0000011925121,0.000014390428,0.00012717306],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990119,0.000007957507,0.00035069068,0.00013155515,0.00033754949,0.00016039115],"domain_scores_gemma":[0.9994289,0.000032404027,0.00013725503,0.00007522647,0.0002577713,0.00006841897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010440912,0.00012758377,0.00018477191,0.00023392383,0.00006283648,0.0003761567,0.00019543416,0.00006358675,0.0001238639],"category_scores_gemma":[0.00019014107,0.0001057391,0.000086036955,0.00011564591,0.000119471086,0.00068933325,0.000059616505,0.000470902,9.400327e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024844092,0.00008395569,0.03019364,0.000012476129,0.0001842291,0.00010233312,0.00039340917,0.00004391157,0.96345377,0.000020219019,0.000010775941,0.00525284],"study_design_scores_gemma":[0.00069096853,0.000095520045,0.0036184555,0.000065149616,0.00006541425,0.0017079063,0.0014908318,0.007031708,0.9845067,0.0003456963,0.00019665583,0.00018499694],"about_ca_topic_score_codex":0.000026428159,"about_ca_topic_score_gemma":0.00003575592,"teacher_disagreement_score":0.026575185,"about_ca_system_score_codex":0.00012747702,"about_ca_system_score_gemma":0.00009230754,"threshold_uncertainty_score":0.43119153},"labels":[],"label_agreement":null},{"id":"W2014308315","doi":"10.1016/j.aca.2014.08.007","title":"Characterization of heteroscedastic measurement noise in the absence of replicates","year":2014,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noise (video); Heteroscedasticity; Standard deviation; Amplitude; Chemistry; Smoothing; Statistics; Analytical Chemistry (journal); Mathematics; Optics; Physics; Artificial intelligence; Computer science; Chromatography","score_opus":0.022972769214210095,"score_gpt":0.2562322383054055,"score_spread":0.23325946909119538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014308315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939321,0.0000060293214,0.00010779786,0.0003667384,0.0000076527585,0.000042610507,0.000007480636,0.000011547347,0.005518004],"genre_scores_gemma":[0.9997254,0.000036994064,0.000042372158,0.00008321188,0.000031550073,0.000006869102,0.000015450505,0.000008630436,0.00004952221],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988477,0.000026322123,0.00039233948,0.00021160774,0.00036261522,0.00015938583],"domain_scores_gemma":[0.99895555,0.00013024903,0.00025378086,0.0005352764,0.0000948256,0.000030344057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003208963,0.000114251096,0.00027874482,0.000115509174,0.000024724726,0.000012151674,0.00038444178,0.000057544585,0.0001965183],"category_scores_gemma":[0.00045601142,0.00008296358,0.000091664566,0.000520832,0.00008977924,0.000051666662,0.000033322092,0.00011213025,0.0000037392344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015558126,0.00014802878,0.008651625,0.000081961516,0.000038097398,1.3716931e-7,0.00009547513,5.7706393e-7,0.99066377,0.00022984407,0.000025222345,0.0000497],"study_design_scores_gemma":[0.00019507739,0.000033150005,0.03291511,0.000048677073,0.00011288242,0.0000016564804,0.00006764012,0.0014298336,0.9647516,0.00021042756,0.00013638994,0.00009757506],"about_ca_topic_score_codex":0.000018736482,"about_ca_topic_score_gemma":0.000006449652,"teacher_disagreement_score":0.025912195,"about_ca_system_score_codex":0.000032394586,"about_ca_system_score_gemma":0.000021697973,"threshold_uncertainty_score":0.33831567},"labels":[],"label_agreement":null},{"id":"W2014523986","doi":"10.13031/2013.16742","title":"Mass Determination of Wheat Kernels from X-ray Images","year":2004,"lang":"en","type":"article","venue":"2004, Ottawa, Canada August 1 - 4, 2004","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canada Research Chairs","keywords":"Computer science; Artificial intelligence","score_opus":0.007631644707811827,"score_gpt":0.2303643464779783,"score_spread":0.22273270177016646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014523986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79240614,0.014374189,0.029672638,0.0019525535,0.0010281806,0.00047488115,0.0028566194,0.00048502535,0.1567498],"genre_scores_gemma":[0.9839633,0.00006979438,0.004054268,0.00026345107,0.0002783214,0.00002011123,0.00020439715,0.000067726636,0.0110786185],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741495,0.000018606215,0.00063947594,0.0005620485,0.0007671023,0.00059780286],"domain_scores_gemma":[0.9984429,0.00011281781,0.00033610637,0.0006647415,0.00017449341,0.00026898138],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009470187,0.0003990479,0.00056291703,0.00017879908,0.00013635127,0.000054601354,0.00053806935,0.0002491505,0.0076440275],"category_scores_gemma":[0.00015135675,0.00041268778,0.00016822293,0.00064045325,0.00013164188,0.00023781674,0.00004528957,0.00032275164,0.000033114527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009773215,0.000539649,0.01682664,0.0004625302,0.00063750934,0.00056231895,0.00020224447,0.0070436974,0.8211258,0.00032759568,0.14790203,0.004272224],"study_design_scores_gemma":[0.0015682833,0.00003776285,0.0014571452,0.00009465223,0.00035356713,0.000011536183,0.00042479034,0.00006271031,0.98475504,0.001520527,0.009097268,0.0006167155],"about_ca_topic_score_codex":0.6456123,"about_ca_topic_score_gemma":0.25879315,"teacher_disagreement_score":0.38681915,"about_ca_system_score_codex":0.001324326,"about_ca_system_score_gemma":0.0012816498,"threshold_uncertainty_score":0.9998325},"labels":[],"label_agreement":null},{"id":"W2015415108","doi":"10.1016/j.jspr.2015.01.006","title":"Hyperspectral imaging to classify and monitor quality of agricultural materials","year":2015,"lang":"en","type":"article","venue":"Journal of Stored Products Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":198,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Hyperspectral imaging; Multispectral image; Grading (engineering); Precision agriculture; Remote sensing; Computer science; Imaging spectrometer; Environmental science; Agriculture; Artificial intelligence; Geography; Engineering; Spectrometer; Optics","score_opus":0.16724858313164076,"score_gpt":0.43525574496070446,"score_spread":0.2680071618290637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015415108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99252385,0.0024654157,0.0000034347534,0.0030589227,0.00011855261,0.000055398945,0.000012381053,0.0000069524563,0.0017551079],"genre_scores_gemma":[0.99581254,0.0000681036,0.0019849774,0.0000075600374,0.00095011643,0.0000019031446,0.0000016657326,0.0000111201,0.0011620343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977338,0.00012868809,0.00053546677,0.00020988392,0.0010545978,0.00033759637],"domain_scores_gemma":[0.9970167,0.0001258252,0.00026601597,0.0002204879,0.002075726,0.00029519928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023820568,0.00011446511,0.00042012468,0.00032174197,0.00006923595,0.00008333769,0.00032019903,0.000057116118,0.00012786563],"category_scores_gemma":[0.002957866,0.00007982076,0.000049685455,0.00070977135,0.00015635762,0.00021346762,0.00011656365,0.00039819494,0.0000055358905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026404744,0.00009420766,0.003535874,0.00012719375,0.00007550258,0.000013769797,0.00067613553,0.0000021697037,0.98931485,0.000072100505,0.0055174367,0.0003067364],"study_design_scores_gemma":[0.00045948345,0.00009738749,0.015002849,0.000038019858,0.000030003457,0.00011325231,0.0066166464,3.7747023e-7,0.97701555,0.00014105836,0.00038896583,0.000096399046],"about_ca_topic_score_codex":0.00014188948,"about_ca_topic_score_gemma":0.0000011170764,"teacher_disagreement_score":0.0122992685,"about_ca_system_score_codex":0.00026379814,"about_ca_system_score_gemma":0.00020433325,"threshold_uncertainty_score":0.35410556},"labels":[],"label_agreement":null},{"id":"W2015699006","doi":"10.5740/jaoacint.13-246","title":"Evaluation of the Thermo Scientific™ SureTect™ Listeria monocytogenes Assay","year":2014,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Listeria monocytogenes; Food science; Salmonella; Listeria; Processed meat; Significant difference; Cooked meat; Chemistry; Biology; Mathematics; Bacteria","score_opus":0.049287989833965824,"score_gpt":0.3297338154620023,"score_spread":0.2804458256280365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015699006","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9726003,0.00022167481,0.0006978701,0.00047370413,0.0009655563,0.000019936695,0.000009780564,0.0000039979736,0.025007175],"genre_scores_gemma":[0.9977992,0.0000053543517,0.0001833952,0.000026542828,0.00038936158,0.0000011393153,0.0000024266099,0.0000069729203,0.0015855646],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979574,0.00008535045,0.00039116206,0.00009609696,0.001386184,0.00008380041],"domain_scores_gemma":[0.9975827,0.000113769354,0.0006693268,0.00018232336,0.001420794,0.000031134678],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024804536,0.000077459954,0.00014756488,0.00014435682,0.00007217589,0.000068050744,0.0005383457,0.000054678625,0.0029587129],"category_scores_gemma":[0.0011597936,0.0000503673,0.00019829586,0.0002176291,0.00009056356,0.00011851292,0.000052699303,0.0001342794,0.0000047351054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006542509,0.00014784507,0.018539593,0.000016788475,0.00059978076,6.678504e-7,0.00017873917,0.00077110244,0.9666122,0.000735137,0.0019631097,0.010369612],"study_design_scores_gemma":[0.0006314821,0.00002198124,0.007796075,0.000032510678,0.00038668877,0.000039193394,0.00009974454,0.005199327,0.97859436,0.0023968385,0.0047305077,0.00007130225],"about_ca_topic_score_codex":0.000005737255,"about_ca_topic_score_gemma":0.0000047714475,"teacher_disagreement_score":0.025198938,"about_ca_system_score_codex":0.00015384323,"about_ca_system_score_gemma":0.0001449103,"threshold_uncertainty_score":0.9979527},"labels":[],"label_agreement":null},{"id":"W2015886346","doi":"10.1366/11-06550","title":"A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra","year":2012,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; CMC Microsystems","keywords":"Baseline (sea); Stripping (fiber); Spectral line; Raman spectroscopy; Statistics; Algorithm; Analytical Chemistry (journal); Mathematics; Chemistry; Optics; Physics; Materials science; Chromatography","score_opus":0.021024111907642713,"score_gpt":0.30533275726531933,"score_spread":0.28430864535767664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015886346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053911507,0.00032768678,0.9274838,0.0003345126,0.00010685422,0.0004970695,0.00007117867,0.0017383847,0.0155290365],"genre_scores_gemma":[0.56774557,0.0000074531827,0.43026513,0.00041928637,0.0005577884,0.0002640826,0.00031216547,0.00009474127,0.00033379637],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996878,0.000040423245,0.0006682454,0.0007190007,0.00040771568,0.0012865626],"domain_scores_gemma":[0.99767554,0.0007590416,0.00037601485,0.00074648316,0.00008907786,0.00035381818],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00088344567,0.0005896609,0.00073523395,0.00035524438,0.0003702516,0.00014950418,0.00044556023,0.00033255605,0.0023394898],"category_scores_gemma":[0.0002614638,0.0005981412,0.00030448672,0.0008920757,0.0000743662,0.0001678337,0.000058404607,0.00043910553,0.00014049806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032742356,0.00046940218,0.0006399625,0.00027382403,0.0002223877,0.0000029714022,0.0001413243,0.0024694374,0.9873182,0.0054210937,0.0015785209,0.0011354081],"study_design_scores_gemma":[0.001731808,0.00005293245,0.00016674896,0.000019345152,0.00041731328,0.000008656821,0.00011353272,0.1645692,0.8306355,0.0009172512,0.00080962747,0.00055806665],"about_ca_topic_score_codex":0.00006120515,"about_ca_topic_score_gemma":0.000009719004,"teacher_disagreement_score":0.51383406,"about_ca_system_score_codex":0.0004649044,"about_ca_system_score_gemma":0.00015191431,"threshold_uncertainty_score":0.999647},"labels":[],"label_agreement":null},{"id":"W2015919431","doi":"10.1007/s11746-000-0167-5","title":"<i>Trans</i> determination of edible oils by fourier transform near‐infrared spectroscopy","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Food and Drug Administration","keywords":"Calibration; Analytical Chemistry (journal); Fourier transform infrared spectroscopy; Chemistry; Partial least squares regression; Chemometrics; Attenuated total reflection; Spectroscopy; Reproducibility; Near-infrared spectroscopy; Infrared spectroscopy; Chromatography; Mathematics; Statistics; Optics; Organic chemistry; Physics","score_opus":0.005276268336758625,"score_gpt":0.24816295370102234,"score_spread":0.24288668536426372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015919431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9585664,0.0009776304,0.0008963656,0.001283559,0.00005008149,0.0000236983,0.00005614146,0.00002595978,0.038120158],"genre_scores_gemma":[0.9478557,0.0048674527,0.026559055,0.0009620734,0.0004818185,0.000006743086,0.000010642819,0.000074131785,0.019182377],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980762,0.000018398026,0.00066055683,0.00019876659,0.0006933735,0.00035273426],"domain_scores_gemma":[0.99826634,0.00011839131,0.00091601827,0.0003809356,0.00017421176,0.00014409995],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002592502,0.00024460338,0.0006103028,0.000022179789,0.00015900459,0.000047853715,0.00068693457,0.00012536367,0.0016373858],"category_scores_gemma":[0.000054853313,0.0001838516,0.0009886841,0.0007863404,0.0006032825,0.00016651094,0.000018032615,0.00061617,0.0000016224403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011656303,0.00019155517,0.0003774277,0.00013507022,0.00020475627,0.000001839518,0.00074264465,0.000008562638,0.961035,3.6909674e-7,0.017640634,0.01954555],"study_design_scores_gemma":[0.000748013,0.00006839235,0.0000352456,0.00005284396,0.00036481023,0.00005597995,0.00049402524,0.00018235284,0.9804401,0.00011017699,0.01725623,0.00019185955],"about_ca_topic_score_codex":0.00004792684,"about_ca_topic_score_gemma":8.535992e-7,"teacher_disagreement_score":0.025662689,"about_ca_system_score_codex":0.00024316175,"about_ca_system_score_gemma":0.00018985175,"threshold_uncertainty_score":0.99927527},"labels":[],"label_agreement":null},{"id":"W2016386623","doi":"10.1016/j.foodchem.2012.05.011","title":"Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools","year":2012,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Extreme learning machine; Calibration; Mean squared error; Artificial neural network; Nonlinear regression; Correlation coefficient; Feature selection; Linear regression; Near-infrared spectroscopy; Nonlinear system; Coefficient of determination; Regression; Biological system; Mathematics; Spectroscopy; Content (measure theory); Regression analysis; Algorithm; Partial least squares regression; Artificial intelligence; Statistics; Computer science; Physics; Optics","score_opus":0.04573867536578833,"score_gpt":0.26318464792868784,"score_spread":0.21744597256289952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016386623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920608,0.0019176434,0.0016802432,0.00001734655,0.000060924758,0.00010363866,0.00007068524,0.000056909557,0.0040318375],"genre_scores_gemma":[0.9837864,0.000016598866,0.01579324,0.00002039754,0.00021849795,0.00001549959,0.000052624146,0.00003294987,0.00006378913],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980441,0.000022412823,0.00046948984,0.00038527275,0.0006081457,0.00047060457],"domain_scores_gemma":[0.99902725,0.00006647093,0.00027220606,0.00031996952,0.00014395731,0.00017013864],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030901848,0.00032318727,0.0004506554,0.00009189054,0.00010930426,0.000050158218,0.00012701002,0.0002547424,0.0005469692],"category_scores_gemma":[0.0002588392,0.0002994347,0.00011240131,0.0005041767,0.00008837663,0.000094106486,0.000059937753,0.00034695974,0.0000016833314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016388592,0.00065989495,0.0013599973,0.00028558244,0.00008054402,0.0000016306545,0.000068017194,0.0005079751,0.9963109,9.813749e-7,0.00004121105,0.0005194195],"study_design_scores_gemma":[0.0012857836,0.00011307058,0.00020159656,0.00023791102,0.00009798588,0.000013147501,0.00022448818,0.05237152,0.94506127,0.0000025822646,0.00011719527,0.00027343052],"about_ca_topic_score_codex":0.000029351177,"about_ca_topic_score_gemma":3.9112442e-7,"teacher_disagreement_score":0.051863544,"about_ca_system_score_codex":0.0004767254,"about_ca_system_score_gemma":0.00016962297,"threshold_uncertainty_score":0.99994576},"labels":[],"label_agreement":null},{"id":"W2016728078","doi":"10.1109/isspit.2007.4458196","title":"Prostate Tissue Classification Methods and Critiques","year":2007,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Region of interest; Support vector machine; Computer science; Feature extraction; Classifier (UML); Pixel; Feature vector; Computer vision; Mathematics","score_opus":0.03477596375335172,"score_gpt":0.4333955498574142,"score_spread":0.39861958610406245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016728078","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20550005,0.0017738617,0.16615446,0.0006435885,0.000027810227,0.000051212857,0.0000031236611,0.00027415733,0.6255717],"genre_scores_gemma":[0.8830174,0.00016827254,0.09711023,0.00013532708,0.00006507911,0.0000042672123,0.000007828257,0.000009776094,0.019481823],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994798,0.000007110982,0.00013735681,0.0001640633,0.000066091074,0.00014558116],"domain_scores_gemma":[0.99961215,0.0001254277,0.000033574746,0.00012735113,0.00004059761,0.00006092326],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002783728,0.00007121,0.00009713307,0.00006424709,0.000047927435,0.000028747625,0.000052875104,0.00006593322,0.0013412684],"category_scores_gemma":[0.00009707099,0.00006107424,0.000015539015,0.00018611117,0.000046515623,0.00006681562,0.000017786526,0.000075971046,0.000009628379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009220243,0.000025381989,0.0031410188,0.000036058187,0.000017631737,0.0000020544062,0.00009513969,2.3365459e-8,0.9368995,0.00412367,0.0005466653,0.05510367],"study_design_scores_gemma":[0.00007815963,0.000009800435,0.0022365353,0.0000023891832,0.000032017208,0.000006204177,0.0006900912,0.000041143772,0.9809825,0.0013326638,0.014504932,0.000083582105],"about_ca_topic_score_codex":0.00004334427,"about_ca_topic_score_gemma":0.000008171318,"teacher_disagreement_score":0.67751735,"about_ca_system_score_codex":0.000023851857,"about_ca_system_score_gemma":0.0000073347037,"threshold_uncertainty_score":0.9995716},"labels":[],"label_agreement":null},{"id":"W2016941099","doi":"10.1366/0003702001950571","title":"Derivative Preprocessing and Optimal Corrections for Baseline Drift in Multivariate Calibration","year":2000,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":148,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Covariance; Calibration; Noise (video); Statistics; Covariance matrix; Derivative (finance); Mathematics; Preprocessor; Projection (relational algebra); Algorithm; Multivariate statistics; Filter (signal processing); Computer science; Artificial intelligence","score_opus":0.011890003575146173,"score_gpt":0.2810251296443119,"score_spread":0.26913512606916573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016941099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87392,0.00022657604,0.10349855,0.00017317926,0.000026936925,0.0002741543,0.000036352492,0.00017690181,0.02166735],"genre_scores_gemma":[0.96970266,0.00006062297,0.028471911,0.00011576941,0.00015254317,0.00019137247,0.00007871136,0.00003218027,0.001194219],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987686,0.000008823611,0.0002934507,0.0004861906,0.000111690024,0.00033119757],"domain_scores_gemma":[0.99944067,0.00018850555,0.00008336287,0.00018946432,0.00002415167,0.00007383709],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012609237,0.00020437664,0.0002768189,0.00011940353,0.00018864391,0.00007549822,0.00010122251,0.0001255485,0.0018480275],"category_scores_gemma":[0.000055920955,0.00020961062,0.000043747506,0.0004649332,0.00007384865,0.00013904713,0.000018358867,0.00019960859,0.000007707094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043579692,0.00022853549,0.0022278083,0.000081618906,0.000060464336,0.0000016797707,0.00076209015,0.00095802365,0.9909082,0.001084311,0.00023118268,0.0030202586],"study_design_scores_gemma":[0.0012485847,0.000033746568,0.00042811973,0.00001801379,0.000075720935,0.0000027548376,0.0005394018,0.025399951,0.970275,0.0010716956,0.00064241834,0.00026461753],"about_ca_topic_score_codex":0.000099624995,"about_ca_topic_score_gemma":0.000036039575,"teacher_disagreement_score":0.09578267,"about_ca_system_score_codex":0.00008882988,"about_ca_system_score_gemma":0.000048777492,"threshold_uncertainty_score":0.99906445},"labels":[],"label_agreement":null},{"id":"W2016983161","doi":"10.1208/pt060229","title":"Monitoring the fluidized bed granulation process based onS-statistic analysis of a pressure time series","year":2005,"lang":"en","type":"article","venue":"AAPS PharmSciTech","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Technische Universiteit Delft","keywords":"Granulation; Granule (geology); Statistic; Fluidized bed; Fluidization; Mathematics; Chromatography; Chemistry; Biological system; Materials science; Statistics; Composite material; Organic chemistry","score_opus":0.01272848937830527,"score_gpt":0.3015199022131861,"score_spread":0.2887914128348808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016983161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951968,0.0011643212,0.0014218236,0.0004376625,0.000024919294,0.00011369819,0.00015430893,0.00016165474,0.0013247833],"genre_scores_gemma":[0.9976331,0.00003027535,0.0010803195,0.000032369244,0.00014705333,0.000040234845,0.000075875265,0.00001897047,0.0009418207],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868846,0.000025931764,0.00035313074,0.0002831183,0.00040428716,0.00024508007],"domain_scores_gemma":[0.9989331,0.00023530198,0.0002180489,0.00039667133,0.00016095057,0.000055962817],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018888467,0.00018509307,0.00041720024,0.00030210664,0.00015157605,0.000048190297,0.00034972234,0.00009054432,0.0026290524],"category_scores_gemma":[0.00016705255,0.00014228735,0.00017919959,0.0023838116,0.00009283613,0.00018260007,0.000027336033,0.00016675652,0.000013886203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019332005,0.00020482318,0.0380269,0.00021264174,0.0024477344,0.0000025636841,0.0004916926,0.0036063744,0.95321614,0.000038761824,0.00024241455,0.0013166516],"study_design_scores_gemma":[0.00047945132,0.000011830651,0.0010476212,0.000018474197,0.00491687,0.0000010137836,0.00010930549,0.015184857,0.97763956,0.00007930425,0.0003529604,0.00015878033],"about_ca_topic_score_codex":0.000029483012,"about_ca_topic_score_gemma":0.0000041411963,"teacher_disagreement_score":0.036979277,"about_ca_system_score_codex":0.000040965966,"about_ca_system_score_gemma":0.000047984653,"threshold_uncertainty_score":0.9982827},"labels":[],"label_agreement":null},{"id":"W2017274545","doi":"10.1016/j.biortech.2007.12.063","title":"Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques","year":2008,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. Department of Energy","keywords":"Chemical composition; Chemistry; Partial least squares regression; Lignin; Calibration; Biomass (ecology); Raw material; Chemometrics; Xylan; Analytical Chemistry (journal); Mathematics; Chromatography; Cellulose; Agronomy","score_opus":0.027623636222872297,"score_gpt":0.2829019218904999,"score_spread":0.2552782856676276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017274545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98396856,0.00028558238,0.010829697,0.00023284162,0.0000054387947,0.00004893995,0.00007433639,0.00024420518,0.0043104114],"genre_scores_gemma":[0.9899937,0.000076846976,0.009559751,0.000024253688,0.000018540537,0.000013027455,0.00005828358,0.0000129090995,0.00024270771],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991261,0.0000061846818,0.00027997297,0.000256584,0.00015829608,0.00017286699],"domain_scores_gemma":[0.99934983,0.000048123693,0.00017175337,0.000298943,0.000088663626,0.000042670563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003739466,0.00013670212,0.00033906198,0.00086154713,0.00023415915,0.000009985769,0.00015079649,0.00033873672,0.0004010748],"category_scores_gemma":[0.00002882522,0.00013860695,0.00012388341,0.002106601,0.00069255166,0.000057822035,0.00003191884,0.0002029219,0.0000015524485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004955587,0.00023392981,0.22896762,0.000028978733,0.0011980125,0.000007912973,0.00020246282,0.000040208033,0.7649174,0.0009449923,0.000158344,0.0032505963],"study_design_scores_gemma":[0.00048590914,0.00007772396,0.04626266,0.000020879119,0.0031215758,0.00015668281,0.0013198069,0.029552387,0.91129774,0.00052755966,0.006746921,0.0004301658],"about_ca_topic_score_codex":0.00005608889,"about_ca_topic_score_gemma":0.0000059352187,"teacher_disagreement_score":0.18270497,"about_ca_system_score_codex":0.00008067731,"about_ca_system_score_gemma":0.0000369956,"threshold_uncertainty_score":0.56522274},"labels":[],"label_agreement":null},{"id":"W2017457079","doi":"10.1366/0003702011951957","title":"Assessment of the Structure and Predictive Ability of Models Developed for Monitoring Key Analytes in a Submerged Fungal Bioprocess Using Near-Infrared Spectroscopy","year":2001,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Biological system; Analyte; Partial least squares regression; Bioprocess; Chemometrics; Univariate; Matrix (chemical analysis); Parametric statistics; Bivariate analysis; Chemistry; Mathematics; Statistics; Multivariate statistics; Chromatography; Engineering","score_opus":0.023684409650747567,"score_gpt":0.30880956693671807,"score_spread":0.2851251572859705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017457079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98523396,0.00034785745,0.010772375,0.000021433965,0.00005395775,0.00045545347,0.0001540258,0.000035035424,0.002925877],"genre_scores_gemma":[0.95852757,0.00009566772,0.041141756,0.0000089777695,0.000095418,0.000045990713,0.000015644442,0.00004130961,0.000027662596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99755,0.000023114151,0.00075588725,0.0006459576,0.00046007687,0.00056497747],"domain_scores_gemma":[0.9985107,0.00014676867,0.0005130908,0.0005651658,0.00016704304,0.00009721457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002399758,0.00038892767,0.0008196409,0.00018345588,0.00019655828,0.00004969949,0.00045106377,0.00023409342,0.00010714236],"category_scores_gemma":[0.00007373145,0.00032309425,0.00014881062,0.0014491417,0.00032451015,0.00016287464,0.0001405811,0.00040738966,8.708969e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026811732,0.00011923815,0.253857,0.00024361747,0.00014781207,7.520401e-7,0.00030100445,0.0016165108,0.74302655,0.00039217214,0.0000038623707,0.000023358223],"study_design_scores_gemma":[0.0011543048,0.000054021,0.0698061,0.000060575836,0.00025378593,0.000004332718,0.00068330416,0.018077208,0.90070266,0.008938427,0.00000353918,0.00026175453],"about_ca_topic_score_codex":0.00013862421,"about_ca_topic_score_gemma":0.000030672058,"teacher_disagreement_score":0.18405087,"about_ca_system_score_codex":0.00038547238,"about_ca_system_score_gemma":0.00033952115,"threshold_uncertainty_score":0.9999221},"labels":[],"label_agreement":null},{"id":"W2017816005","doi":"10.1002/jae.885","title":"gnuplot 4.0: a portable interactive plotting utility","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":176,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Plotter; Unix; Computer science; Bitmap; Software; Computer graphics (images); Operating system; Terminal (telecommunication); IBM; Telecommunications","score_opus":0.018379706595669246,"score_gpt":0.24112397783564252,"score_spread":0.22274427123997326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017816005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7311108,0.0008729691,0.0014434836,0.00004370834,0.00009564718,0.00002900464,0.000012084768,0.000032540975,0.26635978],"genre_scores_gemma":[0.99664897,0.00007384586,0.0019912154,0.000060233113,0.00047270578,0.000002457601,0.000006233443,0.000024686035,0.00071963185],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835986,0.0000039275897,0.0008774882,0.00021508314,0.00023338453,0.00031023647],"domain_scores_gemma":[0.9981094,0.00031585575,0.0010965674,0.00021151677,0.0001512421,0.00011542994],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040343183,0.00018220546,0.0005187731,0.0009669683,0.000084549356,0.00009078496,0.0003220279,0.00011851793,0.0027831157],"category_scores_gemma":[0.0001740006,0.00017157251,0.00021933993,0.0016407976,0.00004634163,0.00024256443,0.00005562788,0.0004590432,0.00003595888],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035237654,0.011010845,0.6243196,0.0020665233,0.006755054,0.00052569516,0.0015081692,0.007662511,0.15784892,0.028109627,0.06723825,0.08943102],"study_design_scores_gemma":[0.0038129392,0.00019219414,0.016410094,0.000049709182,0.0010861177,0.00022008228,0.0036593156,0.0012483655,0.8631262,0.02029323,0.088742085,0.001159627],"about_ca_topic_score_codex":0.000028823755,"about_ca_topic_score_gemma":0.0000025800944,"teacher_disagreement_score":0.7052773,"about_ca_system_score_codex":0.00027203214,"about_ca_system_score_gemma":0.00008182958,"threshold_uncertainty_score":0.9981285},"labels":[],"label_agreement":null},{"id":"W2017839394","doi":"10.1139/v99-231","title":"Infrared titration of aqueous NaOH by aqueous HCl","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chemistry; Titration; Aqueous solution; Infrared spectroscopy; Analytical Chemistry (journal); Infrared; Molecule; Titration curve; Inorganic chemistry; Salt (chemistry); Spectral line; Complexometric titration; Physical chemistry; Chromatography; Organic chemistry","score_opus":0.004948785698222732,"score_gpt":0.20515092381452693,"score_spread":0.2002021381163042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017839394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79622835,0.0046737297,0.000018169163,0.0001419127,0.000029614643,0.000013847139,0.000079111705,0.000009033664,0.19880623],"genre_scores_gemma":[0.97052366,0.00013951433,0.000111588284,0.000090561036,0.00020460285,9.877158e-7,0.000030410954,0.000020236572,0.028878445],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988373,0.0000065088125,0.0005110185,0.0001353312,0.00021815007,0.00029168636],"domain_scores_gemma":[0.9987407,0.000039929317,0.000306603,0.00023336834,0.00015982494,0.0005195905],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000082337894,0.000160365,0.00032290674,0.00007604517,0.000071026705,0.00003970774,0.00036193564,0.00021011771,0.03333597],"category_scores_gemma":[0.00012436623,0.00016628695,0.00016587361,0.00029143292,0.00012367783,0.00010680535,0.0000042341735,0.00034173278,0.0000140570355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006213629,0.00007345654,0.001845915,0.00022911253,0.00028917013,0.00023564431,0.0003022405,0.000052454325,0.9100891,0.000005505574,0.076313205,0.010502094],"study_design_scores_gemma":[0.0003912492,0.000020631627,0.00002176774,0.000048958012,0.000095242525,0.00031953247,0.0001973097,0.000017453247,0.95449555,0.00027985364,0.043944996,0.00016744736],"about_ca_topic_score_codex":0.0007772737,"about_ca_topic_score_gemma":0.00016248491,"teacher_disagreement_score":0.1742953,"about_ca_system_score_codex":0.00022401423,"about_ca_system_score_gemma":0.00089750043,"threshold_uncertainty_score":0.9675477},"labels":[],"label_agreement":null},{"id":"W2017905682","doi":"10.1016/j.ijfoodmicro.2015.03.021","title":"Rapid detection of Listeria monocytogenes in milk using confocal micro-Raman spectroscopy and chemometric analysis","year":2015,"lang":"en","type":"article","venue":"International Journal of Food Microbiology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Program for Changjiang Scholars and Innovative Research Team in University; Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology of the People's Republic of China","keywords":"Listeria monocytogenes; Chemometrics; Food science; Listeria; Principal component analysis; Raman spectroscopy; Microbiology; Food microbiology; Chemistry; Biology; Bacteria; Chromatography; Artificial intelligence; Computer science","score_opus":0.04667829119550647,"score_gpt":0.3121464388777983,"score_spread":0.2654681476822918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017905682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99319446,0.0039990633,0.002298764,0.000058887665,0.00022485132,0.000021802101,0.00004142399,0.0000050304257,0.0001556975],"genre_scores_gemma":[0.99751574,0.00023720127,0.0020060374,0.000036352834,0.00015799075,6.134311e-7,0.000014511562,0.000009617802,0.000021923348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987799,0.00003880172,0.0006936417,0.00018504508,0.00012708822,0.00017552213],"domain_scores_gemma":[0.9986153,0.00009112488,0.000672837,0.000107761094,0.0004321112,0.00008086765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026942423,0.00015254284,0.0005326856,0.001811874,0.000016243628,0.000027777975,0.00032286916,0.0001624334,0.00013978862],"category_scores_gemma":[0.00017268331,0.00014233578,0.00019735178,0.00093076576,0.00012798456,0.000100797886,0.00007927893,0.00020195823,9.542126e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003245965,0.000105444844,0.021153882,0.0000117207965,0.0015373247,0.000015624615,0.00013214172,0.00006486617,0.97616553,0.000008423594,0.000009949896,0.0004704992],"study_design_scores_gemma":[0.0013330211,0.00024511255,0.0014158433,0.00001531624,0.00047208418,0.000563838,0.0003644833,0.000119560355,0.9950306,0.00010600357,0.00021494935,0.00011920946],"about_ca_topic_score_codex":0.0000941829,"about_ca_topic_score_gemma":0.00005342708,"teacher_disagreement_score":0.019738039,"about_ca_system_score_codex":0.00026902294,"about_ca_system_score_gemma":0.000092724214,"threshold_uncertainty_score":0.5804285},"labels":[],"label_agreement":null},{"id":"W2019676906","doi":"10.1590/s1516-89132012000200003","title":"Grape quality mapping for vineyard differential harvesting","year":2012,"lang":"en","type":"article","venue":"Brazilian Archives of Biology and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Constellation Brands (Canada)","funders":"","keywords":"Vineyard; Wine grape; Titratable acid; Wine; Quality (philosophy); Mathematics; Environmental science; Agricultural engineering; Horticulture; Food science; Biology; Engineering; Physics","score_opus":0.02333360638358303,"score_gpt":0.31420813415823784,"score_spread":0.29087452777465483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019676906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9815842,0.0017072653,0.010987374,0.00043088218,0.00005727688,0.00006296444,0.00005541281,0.00012749869,0.0049871104],"genre_scores_gemma":[0.9954079,0.00009459601,0.0037794965,0.000020247715,0.00009280591,0.00002345476,0.000037363738,0.000009801464,0.0005343339],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990401,0.000019129899,0.0002919009,0.00023729185,0.000027733733,0.00038381416],"domain_scores_gemma":[0.9991148,0.00041642098,0.0001737298,0.00021808808,0.000016682998,0.000060274473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008185191,0.00014359267,0.00036444215,0.0003406029,0.00011918711,0.0000040396894,0.00020580999,0.0002417571,0.00010326527],"category_scores_gemma":[0.00036716904,0.00012873841,0.00008608698,0.000221026,0.00074717216,0.00004009442,0.00012979808,0.00018053857,0.0000014244138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003726808,0.000075021824,0.30907688,0.00013059458,0.000121527715,1.4232099e-7,0.000075543525,2.29862e-8,0.61903226,0.02809038,0.000014318673,0.04334601],"study_design_scores_gemma":[0.0012410876,0.00013170394,0.06599502,0.00003889112,0.00014985805,0.000028152912,0.0007463302,0.000090736874,0.88301444,0.042522985,0.0056546396,0.00038615524],"about_ca_topic_score_codex":0.000013481936,"about_ca_topic_score_gemma":0.0000047010794,"teacher_disagreement_score":0.26398215,"about_ca_system_score_codex":0.0000047858184,"about_ca_system_score_gemma":0.000012246657,"threshold_uncertainty_score":0.52497995},"labels":[],"label_agreement":null},{"id":"W2020957519","doi":"10.13031/2013.12943","title":"HYPERSPECTRAL IMAGE CLASSIFICATION TO DETECT WEED INFESTATIONS AND NITROGEN STATUS IN CORN","year":2003,"lang":"en","type":"article","venue":"Transactions of the ASAE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Weed; Agronomy; Weed control; Mahalanobis distance; Sowing; Aerial image; Mathematics; Artificial intelligence; Computer science; Biology; Image (mathematics)","score_opus":0.0185671718522244,"score_gpt":0.27571039428685984,"score_spread":0.25714322243463544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020957519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9825006,0.00012634369,0.007975546,0.00026209358,0.000023791248,0.00009957136,0.00003880089,0.00002707473,0.008946171],"genre_scores_gemma":[0.99502534,0.000039999606,0.004469204,0.000021690365,0.0000052256637,0.000032021104,0.0000020160765,0.000009912889,0.00039460172],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993855,0.000020672725,0.00017992893,0.00014407975,0.000100357116,0.00016948658],"domain_scores_gemma":[0.99952847,0.000077314675,0.000052113093,0.00024255796,0.000041193973,0.000058332575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005924455,0.00008564481,0.00012448139,0.00013661025,0.00008912213,0.000015039375,0.00010154549,0.000048862606,0.0004098632],"category_scores_gemma":[0.00008130522,0.00007414647,0.000063088606,0.00067206024,0.000067205525,0.00008988264,0.0000028547565,0.00014039426,0.000007174751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022588385,0.00009162194,0.007329038,0.000025299656,0.000054795153,3.9749875e-7,0.00038010348,0.00011458682,0.9905939,0.0005100141,0.000025791709,0.0008518605],"study_design_scores_gemma":[0.000489002,0.0000258492,0.025037294,0.000010695148,0.0001576521,0.000008173857,0.0016242845,0.00008947121,0.9704627,0.0015447679,0.00041796587,0.00013213462],"about_ca_topic_score_codex":0.0004784566,"about_ca_topic_score_gemma":0.0002675658,"teacher_disagreement_score":0.020131193,"about_ca_system_score_codex":0.00008356283,"about_ca_system_score_gemma":0.000052973974,"threshold_uncertainty_score":0.44877145},"labels":[],"label_agreement":null},{"id":"W2021303886","doi":"10.1021/ac000875w","title":"Three-Way Analysis of Fluorescence Spectra of Polycyclic Aromatic Hydrocarbons with Quenching by Nitromethane","year":2001,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Chemistry; Fluoranthene; Anthracene; Pyrene; Analytical Chemistry (journal); Quenching (fluorescence); Fluorescence; Emission spectrum; Photochemistry; Spectral line; Chromatography; Organic chemistry","score_opus":0.01074847431710201,"score_gpt":0.25317454878958195,"score_spread":0.24242607447247994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021303886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622717,0.00057452335,0.0035945154,0.000092805356,0.00000397361,0.000028733963,0.000027333568,0.000055811022,0.03335064],"genre_scores_gemma":[0.9976529,0.000076980585,0.0011236011,0.000019107973,0.000043533866,0.000008118273,0.00009108698,0.000026825872,0.0009578292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99761903,0.000009286324,0.0007068409,0.0005242706,0.0006515909,0.0004890005],"domain_scores_gemma":[0.9982272,0.00031384034,0.00039194754,0.0007941522,0.000022858516,0.00025004358],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014044867,0.00033923864,0.0010841286,0.00024981794,0.00005314166,0.000022110611,0.0005210562,0.0002173556,0.0018036796],"category_scores_gemma":[0.00022926791,0.00029102265,0.00043042187,0.0039472017,0.00040551947,0.00008143796,0.000053247444,0.00038198536,0.000004194259],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000922566,0.0004287364,0.10118996,0.00012740861,0.004024246,0.000032263506,0.000036732665,0.000017442957,0.89371645,0.000024169702,0.00014049331,0.00016985013],"study_design_scores_gemma":[0.0004492616,0.00004049122,0.0019207492,0.00006069204,0.007066817,0.000023250901,0.00021900609,0.008601748,0.9807977,0.0004275613,0.00003166618,0.00036107315],"about_ca_topic_score_codex":0.0005256472,"about_ca_topic_score_gemma":0.00003318316,"teacher_disagreement_score":0.09926921,"about_ca_system_score_codex":0.00010441672,"about_ca_system_score_gemma":0.00006816089,"threshold_uncertainty_score":0.99995416},"labels":[],"label_agreement":null},{"id":"W2021683927","doi":"10.1088/1741-2560/6/5/058001","title":"On the risk of extracting relevant information from random data","year":2009,"lang":"en","type":"letter","venue":"Journal of Neural Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Computer science; Random forest; Set (abstract data type); Feature selection; Data mining; Data set; Artificial intelligence; Selection (genetic algorithm); Process (computing); Feature (linguistics); Pattern recognition (psychology); Machine learning; Information retrieval","score_opus":0.021084712236211372,"score_gpt":0.24292152453018553,"score_spread":0.22183681229397417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021683927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6875284,0.008083668,0.019143565,0.27693945,0.002452051,0.00031989688,0.0015394008,0.00019473859,0.0037988587],"genre_scores_gemma":[0.9226991,0.0023791925,0.0029952824,0.057687055,0.013270686,0.0000031473187,0.00057181844,0.00012663064,0.00026707756],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847925,0.000018447332,0.00074886525,0.00009843114,0.00048306756,0.00017194662],"domain_scores_gemma":[0.9963305,0.0016814751,0.001377389,0.00049042626,0.00008215377,0.000038030656],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0002646536,0.00020942354,0.00047202228,0.0002079684,0.00004038286,0.00006106591,0.0007783131,0.0002447126,0.00027055026],"category_scores_gemma":[0.0024843512,0.00013350406,0.00021196675,0.00017935615,0.000011495585,0.0004191172,0.000052827592,0.002809003,0.0000031300574],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037287737,0.000045199507,0.00015206418,0.0004981712,0.0022980222,0.00037273768,0.00029860713,0.05614037,0.040631626,0.000009859824,0.89183927,0.0073412005],"study_design_scores_gemma":[0.0057576727,0.00035011803,0.0005155316,0.0024653093,0.006668243,0.0006628062,0.0004897817,0.25854012,0.17027153,0.0005474752,0.5520795,0.0016519219],"about_ca_topic_score_codex":0.000025013622,"about_ca_topic_score_gemma":1.682242e-7,"teacher_disagreement_score":0.33975977,"about_ca_system_score_codex":0.000058846497,"about_ca_system_score_gemma":0.000031864543,"threshold_uncertainty_score":0.9994916},"labels":[],"label_agreement":null},{"id":"W2022085241","doi":"10.1366/14-07578","title":"Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats","year":2015,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"","keywords":"Pattern recognition (psychology); Autocorrelation; Wavelet; Computer science; Histogram; Sample (material); Artificial intelligence; Mathematics; Statistics; Chemistry; Image (mathematics)","score_opus":0.039156824744267425,"score_gpt":0.27525096720501635,"score_spread":0.23609414246074892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022085241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6210469,0.00018764919,0.0015222172,0.0001770203,0.000056537312,0.00013373044,0.00011963091,0.00041416456,0.37634215],"genre_scores_gemma":[0.9906501,0.000050471215,0.0069530066,0.0003128295,0.00024096356,0.000037627637,0.00028343403,0.00007910051,0.0013924395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980037,0.000028388531,0.00047927015,0.00049090496,0.0004904653,0.00050723215],"domain_scores_gemma":[0.9986598,0.00016992979,0.0002806293,0.00049753755,0.00009733269,0.0002947978],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014693842,0.00030350848,0.0005227966,0.00025862915,0.000099050696,0.00008159147,0.00043046838,0.00019542439,0.003604756],"category_scores_gemma":[0.00009469088,0.0003060417,0.0001394132,0.0008378392,0.00016680453,0.00022550575,0.00016992397,0.00047453816,0.00027635036],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009804906,0.0010930675,0.013925024,0.00039654982,0.00081237365,0.000059119764,0.0023596005,0.000024594834,0.9387999,0.00070949097,0.031912852,0.008926941],"study_design_scores_gemma":[0.0011903036,0.0001061394,0.00079786,0.000035791978,0.00013662499,0.000008779634,0.0015967381,0.00008036802,0.9893106,0.0056239874,0.00077478145,0.0003379895],"about_ca_topic_score_codex":0.00006138494,"about_ca_topic_score_gemma":0.0000036649437,"teacher_disagreement_score":0.37494972,"about_ca_system_score_codex":0.00012358764,"about_ca_system_score_gemma":0.0002001545,"threshold_uncertainty_score":0.99993914},"labels":[],"label_agreement":null},{"id":"W2022234741","doi":"10.1002/xrs.894","title":"Chemometrics and its applications to x‐ray spectrometry","year":2006,"lang":"en","type":"article","venue":"X-Ray Spectrometry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Chemometrics; Principal component analysis; Linear discriminant analysis; Computer science; Artificial intelligence; Pattern recognition (psychology); Multivariate statistics; Partial least squares regression; Principal component regression; k-nearest neighbors algorithm; Algorithm; Machine learning; Data mining","score_opus":0.011671572625489278,"score_gpt":0.26386302060390876,"score_spread":0.25219144797841947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022234741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7829338,0.013891493,0.019921513,0.0013925837,0.00014191051,0.0005701617,0.00026280654,0.00094015343,0.17994562],"genre_scores_gemma":[0.97081566,0.0003818634,0.01013917,0.00029992513,0.001243768,0.00013805272,0.000085140295,0.00010750137,0.016788932],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962354,0.00001529595,0.0006957502,0.0011733719,0.0007886009,0.0010916198],"domain_scores_gemma":[0.9975653,0.00064711436,0.00024652402,0.0009037003,0.00016877161,0.00046856247],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027873486,0.0005954224,0.00077121845,0.002481754,0.00034157277,0.00022875663,0.0007326076,0.00033949135,0.004912044],"category_scores_gemma":[0.00058102235,0.00061974814,0.00023991826,0.010803568,0.000101060265,0.00022591342,0.00025300024,0.0006410791,0.00069987145],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031140058,0.00093974266,0.018090824,0.00026209664,0.00022206662,0.00003216627,0.000042031257,0.000035405432,0.96636164,0.0069882725,0.0059255827,0.001069014],"study_design_scores_gemma":[0.0012147966,0.00022551918,0.021485655,0.000028983332,0.00051094015,0.00007537465,0.00036687308,0.0001430445,0.9192839,0.0027742926,0.05239267,0.0014979551],"about_ca_topic_score_codex":0.00008781542,"about_ca_topic_score_gemma":0.00001134311,"teacher_disagreement_score":0.18788189,"about_ca_system_score_codex":0.00046292847,"about_ca_system_score_gemma":0.00007212817,"threshold_uncertainty_score":0.9996254},"labels":[],"label_agreement":null},{"id":"W2022468990","doi":"10.1117/12.477617","title":"&lt;title&gt;New initial basic probability assignments for multiple classifiers&lt;/title&gt;","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada); Université Laval","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Bayes classifier; Confusion matrix; Pattern recognition (psychology); Random subspace method; Machine learning; Naive Bayes classifier; Probabilistic classification; Artificial neural network; Support vector machine","score_opus":0.02705736426912208,"score_gpt":0.2521689967791152,"score_spread":0.2251116325099931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022468990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8954576,0.00022171464,0.00013558217,0.00087428687,0.00020858773,0.00027828006,0.00008320618,0.00010013501,0.102640584],"genre_scores_gemma":[0.87721914,0.00019166656,0.10201805,0.0001737382,0.0018449035,0.00024229944,0.00003553519,0.00013197112,0.018142728],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986412,7.88511e-9,0.0003741457,0.00029258456,0.00040816862,0.00028391875],"domain_scores_gemma":[0.99907297,0.000119167584,0.00019108554,0.00005649252,0.00045629457,0.00010401437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016653212,0.00020800743,0.0002743565,0.000060607013,0.00005791314,0.0000583426,0.00049595674,0.00019192017,0.00096137106],"category_scores_gemma":[0.0006377013,0.00018067854,0.0004547496,0.00021879784,0.00011954994,0.00017074292,0.00007296511,0.00019353503,0.000020896478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006641764,0.00027279495,0.00039131945,0.000710906,0.0007018183,1.0357719e-7,0.000100273486,0.000013852695,0.6355735,0.22117633,0.1395017,0.001490992],"study_design_scores_gemma":[0.0027450325,0.00028956603,0.00021346247,0.0002598241,0.00067854545,0.000013926168,0.0004231726,0.046894457,0.7312988,0.00708707,0.20922823,0.0008678896],"about_ca_topic_score_codex":0.000001899834,"about_ca_topic_score_gemma":7.219424e-8,"teacher_disagreement_score":0.21408926,"about_ca_system_score_codex":0.00017841905,"about_ca_system_score_gemma":0.000023975981,"threshold_uncertainty_score":0.9999519},"labels":[],"label_agreement":null},{"id":"W2023162320","doi":"10.1016/j.talanta.2006.10.042","title":"An automated FTIR method for the routine quantitative determination of moisture in lubricants: An alternative to Karl Fischer titration","year":2006,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Security Establishment; Thermal-Lube (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Navy","keywords":"Acetonitrile; Fourier transform infrared spectroscopy; Chemistry; Extraction (chemistry); Moisture; Analytical Chemistry (journal); Lubricant; Gravimetric analysis; Chromatography; Chemical engineering; Organic chemistry","score_opus":0.023465829818202093,"score_gpt":0.3748547531582596,"score_spread":0.3513889233400575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023162320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95832616,0.00006796795,0.03909234,0.00017998069,0.000023368439,0.00017454955,0.000116065814,0.00008231178,0.0019372447],"genre_scores_gemma":[0.98518735,0.0000041637663,0.014090256,0.000048898677,0.000053786465,0.000044468703,0.00016646949,0.000014219406,0.00039035824],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992226,0.000038620678,0.00023649148,0.00021472857,0.00014370949,0.00014385847],"domain_scores_gemma":[0.999271,0.00024720086,0.00015115846,0.00019944426,0.00010173387,0.000029461029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002112304,0.000113967675,0.00018498716,0.00011202426,0.000049157214,0.00002478955,0.0001941818,0.000069441216,0.00007451879],"category_scores_gemma":[0.000087229215,0.00008582393,0.00004145541,0.00042175865,0.00002676805,0.00015134091,0.000013590386,0.000078471014,0.0000021297553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020119065,0.00037104057,0.005712543,0.00004751942,0.000053667838,0.0000033914641,0.001649325,0.0008619035,0.98729753,0.001565035,0.0004885107,0.0017483442],"study_design_scores_gemma":[0.0005104063,0.00014471114,0.016452372,0.00001674926,0.00009087435,0.0000036066378,0.0013877641,0.2121924,0.7684834,0.00048166807,0.00009277795,0.00014325717],"about_ca_topic_score_codex":0.0023033135,"about_ca_topic_score_gemma":0.0012802561,"teacher_disagreement_score":0.21881412,"about_ca_system_score_codex":0.00006517195,"about_ca_system_score_gemma":0.000022734197,"threshold_uncertainty_score":0.34997982},"labels":[],"label_agreement":null},{"id":"W2023689729","doi":"10.1366/000370210790918300","title":"Alleviating the Effects of Light Scattering in Multivariate Calibration of Near-Infrared Spectra by Path Length Distribution Correction","year":2010,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Scattering; Path length; Calibration; Light scattering; Spectral line; Optics; Spectroscopy; Optical path; Chemistry; Computational physics; Statistics; Physics; Mathematics","score_opus":0.0036988787918722883,"score_gpt":0.22806927460021326,"score_spread":0.22437039580834098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023689729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98513323,0.00012762837,0.009273423,0.000092849,0.00023623974,0.00024111349,0.000030448327,0.0000728641,0.004792229],"genre_scores_gemma":[0.99846655,0.000041548385,0.0010185447,0.000021674783,0.00011448431,0.00005414514,0.00013240209,0.000028657027,0.00012200841],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984621,0.000022951139,0.00053007563,0.00035489642,0.00028769372,0.00034228223],"domain_scores_gemma":[0.99871016,0.00033218143,0.0004391499,0.0004274196,0.000036748876,0.000054320484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020226516,0.00024260633,0.00040595958,0.000066995824,0.00013710817,0.000046716796,0.0002707232,0.00019213844,0.00023076375],"category_scores_gemma":[0.00020418836,0.00020367012,0.00009971419,0.0006474595,0.00012539398,0.00011089956,0.000055332417,0.00056659634,0.0000032962027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007487852,0.0001805765,0.0037555392,0.0001508143,0.000052795145,9.1318975e-7,0.00038031887,0.000014799546,0.9937101,0.00078699546,0.0006497327,0.0002425544],"study_design_scores_gemma":[0.0007166283,0.000049642287,0.0021027066,0.000042065443,0.00008265129,0.0000020745,0.00020266378,0.0025942859,0.9935129,0.0003873639,0.00012347159,0.00018352718],"about_ca_topic_score_codex":0.0003125977,"about_ca_topic_score_gemma":0.000045957007,"teacher_disagreement_score":0.01333333,"about_ca_system_score_codex":0.00010621417,"about_ca_system_score_gemma":0.000052590356,"threshold_uncertainty_score":0.8305426},"labels":[],"label_agreement":null},{"id":"W2025679637","doi":"10.1007/s12551-011-0045-8","title":"Deriving biomedical diagnostics from NMR spectroscopic data","year":2011,"lang":"en","type":"review","venue":"Biophysical Reviews","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Principal component analysis; Computer science; Pattern recognition (psychology); Data mining; Artificial intelligence","score_opus":0.18271310793565862,"score_gpt":0.3964295354087718,"score_spread":0.21371642747311317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025679637","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000044880717,0.9945175,0.000307714,0.000012330981,0.00021850695,0.00034336426,0.0009000434,0.00013820267,0.0035578283],"genre_scores_gemma":[0.0000020062043,0.98895323,0.0018536416,0.00006761481,0.0030659784,0.00013688636,0.004746035,0.00012708202,0.0010475216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953941,0.00013185335,0.0016258685,0.0016667681,0.00042582914,0.00075559097],"domain_scores_gemma":[0.99374396,0.0010418206,0.0009888099,0.003726659,0.000031406482,0.00046735266],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00023834819,0.0010710082,0.0051467377,0.00017135534,0.00012176279,0.00010984282,0.003312461,0.0007291306,0.011089454],"category_scores_gemma":[0.0020918231,0.00076554157,0.0011142018,0.0011745113,0.00029358335,0.00014955536,0.0013267125,0.0012045036,0.005379762],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018329063,0.00048116487,0.000005384713,0.017231798,0.0004928904,0.0000370111,0.000012112622,6.17268e-10,0.00012984553,0.0002100672,0.03468294,0.94671494],"study_design_scores_gemma":[0.000094915464,0.000025080286,0.000001161623,0.0104763275,0.006436411,0.000005036528,0.000003328731,0.0000045366533,0.00011271764,0.00010955555,0.9819897,0.0007412595],"about_ca_topic_score_codex":0.0001369469,"about_ca_topic_score_gemma":0.000004766365,"teacher_disagreement_score":0.94730675,"about_ca_system_score_codex":0.00020312442,"about_ca_system_score_gemma":0.0002807664,"threshold_uncertainty_score":0.99947953},"labels":[],"label_agreement":null},{"id":"W2025838029","doi":"10.1897/07-236.1","title":"Comment on “Toxicity of weathered<i>Exxon Valdez</i>crude oil to pink salmon embryos”","year":2008,"lang":"en","type":"letter","venue":"Environmental Toxicology and Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bay; Marine fisheries; Fishery; Service (business); Oil spill; Oceanography; Library science; Fish <Actinopterygii>; Environmental science; Biology; Environmental protection; Business; Geology; Computer science","score_opus":0.011241840285305693,"score_gpt":0.22828658576387345,"score_spread":0.21704474547856775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025838029","genre_codex":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82145995,0.0018983696,0.000009010457,0.12374252,0.00013789123,0.00011527213,0.0007655223,0.00010406753,0.051767375],"genre_scores_gemma":[0.40274107,0.004047046,0.00028157973,0.53044116,0.0024104463,0.00018188391,0.0014179877,0.00022031872,0.058258492],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99716145,0.000037989616,0.0006117832,0.0010114793,0.00049132144,0.00068599754],"domain_scores_gemma":[0.9983416,0.00023810279,0.00038937168,0.0008059983,0.000007302122,0.0002175928],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007947597,0.0007496695,0.0010095111,0.00009405058,0.00019932102,0.000016009102,0.00057128584,0.0019449,0.008408509],"category_scores_gemma":[0.000036446472,0.0007759185,0.00030026227,0.00013849126,0.0005526861,0.000036515146,0.00031795207,0.0019927048,0.00009292194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009389052,0.00051003107,0.00064554974,0.00041465025,0.00032085055,0.00019860032,0.00008651151,0.0000049665573,0.5649706,0.0000010372321,0.43234724,0.00040605786],"study_design_scores_gemma":[0.00047865845,0.000114665505,0.00006530042,0.000060891787,0.00019678414,0.000087133274,0.00009008468,0.0000022079137,0.6620973,0.00001786501,0.33630678,0.00048233342],"about_ca_topic_score_codex":0.000011305757,"about_ca_topic_score_gemma":6.048717e-7,"teacher_disagreement_score":0.4187189,"about_ca_system_score_codex":0.00050303293,"about_ca_system_score_gemma":0.000040292616,"threshold_uncertainty_score":0.99946916},"labels":[],"label_agreement":null},{"id":"W2026029795","doi":"10.1094/cchem.2003.80.5.511","title":"Measurement of Hard Vitreous Kernels in Durum Wheat by Machine Vision","year":2003,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kernel (algebra); Machine vision; Sample (material); Reproducibility; Artificial intelligence; Statistics; Chemistry; Mathematics; Computer science; Chromatography","score_opus":0.012050155956871892,"score_gpt":0.2487865666955805,"score_spread":0.2367364107387086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026029795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83649534,0.004571993,0.0000607134,0.00007473241,0.000021593001,0.00004023008,0.000043605854,0.000053403906,0.15863837],"genre_scores_gemma":[0.9951756,0.00015437233,0.0000968619,0.000025158588,0.000035289308,0.0000110388555,0.00004098234,0.000027745333,0.0044329297],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830514,0.000012865093,0.00042448734,0.00040230592,0.00049351866,0.00036166506],"domain_scores_gemma":[0.9991658,0.00003124477,0.00014946715,0.000433222,0.00008339902,0.00013690427],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019349715,0.00025090348,0.00039927647,0.00004296418,0.000040488692,0.00001850334,0.00024310235,0.00021627606,0.004049344],"category_scores_gemma":[0.00018474388,0.0002474537,0.00012961899,0.0003547572,0.00007282382,0.000051662297,0.00003883882,0.00029294068,0.000013444104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004008627,0.0003147051,0.014255998,0.00036730146,0.00005471864,0.000009808447,0.000024052057,0.0000064980227,0.98202646,0.000006197255,0.002639117,0.0002550457],"study_design_scores_gemma":[0.00085806154,0.000013955227,0.0002877708,0.000065920765,0.00006284001,0.000016095493,0.00010856903,0.000030731007,0.9954523,0.00007961533,0.002793549,0.00023060039],"about_ca_topic_score_codex":0.00013737119,"about_ca_topic_score_gemma":0.000008731187,"teacher_disagreement_score":0.15868028,"about_ca_system_score_codex":0.00028721723,"about_ca_system_score_gemma":0.000070434224,"threshold_uncertainty_score":0.9999978},"labels":[],"label_agreement":null},{"id":"W2026491537","doi":"10.1016/s1537-5110(03)00034-5","title":"Cereal Grain and Dockage Identification using Machine Vision","year":2003,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":142,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Chaff; Canola; Agronomy; Rapeseed; Mathematics; Husk; Biology; Botany","score_opus":0.009425266751114963,"score_gpt":0.2451204880893673,"score_spread":0.23569522133825233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026491537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899765,0.0025319639,0.005857197,0.000008082496,0.00014322806,0.000042096694,0.000011414837,0.00012925178,0.0013002673],"genre_scores_gemma":[0.99881005,0.000025714868,0.00065584655,0.0000026225334,0.00006767845,0.0000035224452,0.000008543764,0.00002090234,0.0004051346],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930376,0.000007166182,0.00020901611,0.00019851855,0.00011422872,0.00016733474],"domain_scores_gemma":[0.9996505,0.000026587657,0.000061149556,0.00017839787,0.00001809039,0.00006530495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000133021,0.00012610546,0.00015492638,0.00012380663,0.000060748152,0.00006054416,0.00006159409,0.00007895638,0.00007745927],"category_scores_gemma":[0.000073901305,0.00012465526,0.000037614835,0.0002606284,0.000008670037,0.00008311223,0.000014912215,0.00008409145,0.00000499331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011955817,0.000011284445,0.0019958995,0.00020251959,0.00002868793,0.0000028626582,0.000032585194,0.00011622242,0.9969497,0.0005964081,0.000015523234,0.000047162906],"study_design_scores_gemma":[0.00025411494,0.000006456922,0.0004017463,0.00005719182,0.00006694445,0.00006553376,0.00010270368,0.018294776,0.97792774,0.000011614535,0.0025885433,0.00022261134],"about_ca_topic_score_codex":0.00007833882,"about_ca_topic_score_gemma":0.0000023836735,"teacher_disagreement_score":0.019021885,"about_ca_system_score_codex":0.00007227983,"about_ca_system_score_gemma":0.0000073395954,"threshold_uncertainty_score":0.5083294},"labels":[],"label_agreement":null},{"id":"W2026601897","doi":"10.1094/cchem-86-2-0164","title":"Measurement of Spaghetti Speck Count, Size, and Color Using an Automated Imaging System","year":2009,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian International Grains Institute","funders":"","keywords":"Chemistry; Statistics; Mathematics","score_opus":0.021092443450119357,"score_gpt":0.28284029604147914,"score_spread":0.2617478525913598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026601897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96611565,0.0011277853,0.000042008036,0.000026174674,0.000013471983,0.000039834602,0.000020771591,0.0003610078,0.03225332],"genre_scores_gemma":[0.9992596,0.000014379282,0.0003553143,0.00002701103,0.00015622645,0.0000022631905,0.000011310824,0.000020832063,0.0001530673],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99847895,0.0000083670975,0.00037741312,0.00038549537,0.00042727296,0.00032252425],"domain_scores_gemma":[0.99904156,0.000028270619,0.00024141461,0.0003196768,0.00019369744,0.00017535544],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016985634,0.00024790107,0.00040428698,0.000031478336,0.00010058172,0.000051392966,0.00020532534,0.00013427342,0.00028088884],"category_scores_gemma":[0.000082237006,0.00025195876,0.00007519645,0.000260307,0.000091301954,0.000105459425,0.00003460894,0.00016299661,0.0000016471923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035454075,0.00012739182,0.003411337,0.0006552173,0.000060995073,0.000019078783,0.000035468198,0.000024810426,0.995365,0.000012689716,0.0001498003,0.00010275213],"study_design_scores_gemma":[0.0006005643,0.000013617761,0.002046976,0.00015859665,0.0002518474,0.000107865584,0.0007515672,0.009637039,0.9860819,0.000014870824,0.00007131105,0.00026385052],"about_ca_topic_score_codex":0.00010218635,"about_ca_topic_score_gemma":0.0000014826189,"teacher_disagreement_score":0.03314397,"about_ca_system_score_codex":0.00041669118,"about_ca_system_score_gemma":0.00010122062,"threshold_uncertainty_score":0.99999326},"labels":[],"label_agreement":null},{"id":"W2026886896","doi":"10.1021/ie801382d","title":"Annatto Powder Production in a Spouted Bed: An Experimental and CFD Study","year":2008,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais","keywords":"Draft tube; Extraction (chemistry); Factorial experiment; Computational fluid dynamics; Inlet; Conical surface; Materials science; Tube (container); Chromatography; Pulp and paper industry; Environmental science; Process engineering; Chemistry; Mechanical engineering; Composite material; Mathematics; Mechanics; Engineering; Physics","score_opus":0.1447522660008526,"score_gpt":0.38449518800646626,"score_spread":0.23974292200561367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026886896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99829006,0.0002840319,2.739902e-7,0.000060800725,0.000049795006,0.0001652142,0.0000059573335,0.000111710695,0.001032161],"genre_scores_gemma":[0.9978099,0.000015363223,0.000011329361,0.0000020599152,0.0008055225,0.00008588738,0.000020698684,0.000038378545,0.0012108489],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978494,0.000029762652,0.0003120913,0.00061823684,0.00061761204,0.00057289505],"domain_scores_gemma":[0.9991357,0.00008511209,0.0000348442,0.00042656928,0.00009454916,0.00022324815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005028811,0.00023102731,0.00031155473,0.00023320393,0.00015564031,0.000059053174,0.00028327026,0.00029551028,0.00063982455],"category_scores_gemma":[0.0006169125,0.00025079542,0.000039426985,0.0011620312,0.00012125788,0.00019101387,0.00013631987,0.0012222602,0.0000060529765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099687946,0.0007012762,0.026499271,0.00003199577,0.00004552404,0.00011242776,0.0006336844,0.000121208664,0.97145545,8.334827e-7,0.00021351207,0.00008511434],"study_design_scores_gemma":[0.0012921967,0.0000783873,0.0007318723,0.000025433268,0.000012671287,0.000074526804,0.0030182393,0.00021106374,0.99406916,0.0000017587442,0.00024149843,0.000243218],"about_ca_topic_score_codex":0.00023274941,"about_ca_topic_score_gemma":0.0000023664306,"teacher_disagreement_score":0.025767399,"about_ca_system_score_codex":0.00029242365,"about_ca_system_score_gemma":0.00011343403,"threshold_uncertainty_score":0.99999446},"labels":[],"label_agreement":null},{"id":"W2027152940","doi":"10.2202/1556-3758.1089","title":"Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network","year":2007,"lang":"en","type":"article","venue":"International Journal of Food Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Hyperspectral imaging; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Artificial neural network; Computer science; Feature selection; Reflectivity; Mathematics; Optics; Physics","score_opus":0.027386943244014486,"score_gpt":0.30383807350381653,"score_spread":0.27645113025980206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027152940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94964325,0.0016199069,0.047618408,0.00007557881,0.00041338135,0.000010927069,0.0000026972145,0.00003287944,0.00058296334],"genre_scores_gemma":[0.9936487,0.000012981005,0.0051678065,0.000015828806,0.0011323628,2.3361946e-7,0.0000014378381,0.000012972047,0.000007668875],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897003,0.000004838357,0.0004424214,0.000097527445,0.00030815543,0.00017699861],"domain_scores_gemma":[0.9993072,0.00009690305,0.0002724982,0.000066827044,0.0001750908,0.00008144219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604223,0.00010262644,0.00016786906,0.00017916823,0.000032340446,0.000062202846,0.00018520404,0.000040382667,0.000018209774],"category_scores_gemma":[0.00008332263,0.00010158269,0.00008919666,0.00015424537,0.000015762784,0.00017661873,0.00002498271,0.00021145998,2.817425e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016503065,0.00006634872,0.050272647,0.00012935756,0.0009456513,0.00016472027,0.00029174387,0.045454293,0.89021784,0.008940546,0.000042093936,0.0033097004],"study_design_scores_gemma":[0.003451425,0.00014301299,0.07982138,0.00085494365,0.0007285687,0.008270167,0.007060326,0.49758753,0.39975715,0.00037641037,0.00072360545,0.0012255041],"about_ca_topic_score_codex":0.000010532221,"about_ca_topic_score_gemma":9.777757e-7,"teacher_disagreement_score":0.49046072,"about_ca_system_score_codex":0.00029870268,"about_ca_system_score_gemma":0.000018803734,"threshold_uncertainty_score":0.41424218},"labels":[],"label_agreement":null},{"id":"W2027665225","doi":"10.13031/2013.13576","title":"SOFT XRAY INSPECTION OF WHEAT KERNELS INFESTED BY SITOPHILUS ORYZAE","year":2003,"lang":"en","type":"article","venue":"Transactions of the ASAE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sitophilus; Rice weevil; Histogram; Biology; Mathematics; Artificial intelligence; Pattern recognition (psychology); Agronomy; Computer science; Image (mathematics)","score_opus":0.010030992981771451,"score_gpt":0.24066328810667859,"score_spread":0.23063229512490713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027665225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9519818,0.0007003634,0.019539895,0.0002134616,0.0001813381,0.000086540815,0.00007296721,0.00011851739,0.027105153],"genre_scores_gemma":[0.99493676,0.00007585616,0.00015208492,0.000019032808,0.000016546535,0.0000068719323,0.000003262769,0.000015471274,0.0047741076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999181,0.000022844048,0.00028988335,0.00015578863,0.00020109079,0.00014941827],"domain_scores_gemma":[0.99923706,0.00007966877,0.00014805255,0.00041897336,0.00007514378,0.00004109015],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006946528,0.0001249075,0.00022969632,0.00008754876,0.00012545093,0.00000764483,0.0002191613,0.00011616716,0.0016512123],"category_scores_gemma":[0.00005061582,0.00010079382,0.00020550727,0.0006797882,0.00013041294,0.000089013745,0.0000041203143,0.00019770357,0.000008176945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026907628,0.00038039108,0.0025337012,0.000097795346,0.00023937864,2.5235065e-7,0.00021885533,0.00062349846,0.99446386,0.00014121868,0.00084872,0.00042542638],"study_design_scores_gemma":[0.00037142393,0.000025133973,0.0004876135,0.000017227325,0.00025415933,0.000008743005,0.0003080057,0.000035098215,0.9969855,0.00029774886,0.0011089108,0.0001004343],"about_ca_topic_score_codex":0.0002185557,"about_ca_topic_score_gemma":0.000026144318,"teacher_disagreement_score":0.042955,"about_ca_system_score_codex":0.00006100911,"about_ca_system_score_gemma":0.000044304306,"threshold_uncertainty_score":0.99926144},"labels":[],"label_agreement":null},{"id":"W2027914747","doi":"10.1016/j.jfoodeng.2006.11.020","title":"Detection of sprouted wheat kernels using soft X-ray image analysis","year":2007,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial neural network; Artificial intelligence; Histogram; Pattern recognition (psychology); Kernel (algebra); Statistical analysis; Mathematics; Germination; Support vector machine; Computer science; Image (mathematics); Biology; Horticulture; Statistics","score_opus":0.011167235723771039,"score_gpt":0.25037838451587763,"score_spread":0.2392111487921066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027914747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6448364,0.0004874845,0.35448346,0.0000030920423,0.00004584103,0.0000072961675,0.0000017022112,0.000013774844,0.00012090089],"genre_scores_gemma":[0.9920857,0.000016920021,0.00765729,0.0000025587372,0.00020090757,1.4471715e-7,6.0642907e-7,0.00001626228,0.000019599542],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988895,0.0000031495895,0.0005458925,0.000090797424,0.00026630997,0.00020433337],"domain_scores_gemma":[0.999206,0.0000715061,0.0003375178,0.00013219505,0.000160351,0.00009241499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028485563,0.00012621257,0.0003928452,0.0007645471,0.000025804029,0.000019579982,0.00013637012,0.000090876005,0.000104455496],"category_scores_gemma":[0.00014055225,0.000119516946,0.0003393797,0.0013771483,0.000017319067,0.00016685428,0.000016155316,0.0002493998,4.9104636e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025522253,0.000033950317,0.0010103264,0.00006669827,0.0012993142,0.00001233705,0.000054793087,0.035717286,0.96142644,0.0000028007084,0.0000011883819,0.00034932935],"study_design_scores_gemma":[0.00024374167,0.000058863967,0.0016285725,0.00002919333,0.0014737733,0.00003967121,0.00014456196,0.014119594,0.982116,0.000007974431,0.000027993485,0.00011007747],"about_ca_topic_score_codex":0.000012992906,"about_ca_topic_score_gemma":0.0000033464405,"teacher_disagreement_score":0.34724927,"about_ca_system_score_codex":0.00015020065,"about_ca_system_score_gemma":0.000018520373,"threshold_uncertainty_score":0.48737597},"labels":[],"label_agreement":null},{"id":"W2028521312","doi":"","title":"ACCURATE ANALYTIC POTENTIALS FOR THE $A\\,^3\\Pi_1$ and $X\\,^1\\Sigma^+$ STATES OF ${\\rm IBr}$ FROM A COMBINED-ISOTOPOLOGUE DIRECT-POTENTIAL-FIT DATA ANALYSIS","year":2011,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Isotopologue; Sigma; Pi; Physics; Mathematics; Quantum mechanics; Spectral line; Geometry","score_opus":0.0893513352528785,"score_gpt":0.3131876051397043,"score_spread":0.22383626988682578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028521312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6987405,0.011584146,0.26315746,0.0010056251,0.00025687236,0.00083851034,0.008349253,0.0004133534,0.01565429],"genre_scores_gemma":[0.9929256,0.0010803079,0.0026601248,0.000103381964,0.00007163358,0.000029332365,0.0009898806,0.000029430757,0.0021103339],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977811,0.000035705383,0.00068552734,0.00078922743,0.0002898931,0.00041851934],"domain_scores_gemma":[0.9966351,0.00085953897,0.00044266944,0.0017727661,0.00015883078,0.00013114253],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037528214,0.00032847511,0.0008878643,0.00029384377,0.00022530516,0.00008986685,0.0011835699,0.0001667302,0.007080807],"category_scores_gemma":[0.00026872437,0.00022257124,0.00032820576,0.0012160103,0.0003377424,0.00024599713,0.000451464,0.0001543122,0.0000072811035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064067487,0.007350701,0.3356655,0.0019117373,0.26706877,0.00015598866,0.007121789,0.0046064453,0.2927129,0.0050944043,0.05967248,0.012232549],"study_design_scores_gemma":[0.0026980222,0.00019814011,0.012358933,0.00003155959,0.049534846,0.0000049465752,0.00683964,0.18651389,0.7322667,0.0074603395,0.0010231037,0.0010698625],"about_ca_topic_score_codex":0.0064840917,"about_ca_topic_score_gemma":0.00055075233,"teacher_disagreement_score":0.43955383,"about_ca_system_score_codex":0.000024698104,"about_ca_system_score_gemma":0.000046040786,"threshold_uncertainty_score":0.99382687},"labels":[],"label_agreement":null},{"id":"W2028562374","doi":"10.4141/s03-090","title":"Near-infrared spectroscopy for soil water determination in small soil volumes","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Absorbance; Soil water; Water content; Soil test; Soil science; Near-infrared spectroscopy; Chemistry; Partial least squares regression; Soil texture; Spectroscopy; Analytical Chemistry (journal); Environmental science; Environmental chemistry; Geology; Mathematics","score_opus":0.015218668361449778,"score_gpt":0.25261095434300157,"score_spread":0.2373922859815518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028562374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934908,0.0002701683,0.00088710856,0.000646868,0.00017427269,0.000038566803,0.000009299401,0.000007602311,0.004475301],"genre_scores_gemma":[0.99621594,0.000017759383,0.002542382,0.00016194557,0.00016129698,0.000004270634,0.000002458193,0.000013903709,0.00088006526],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99834555,0.0000061310657,0.00040883519,0.0002483494,0.00025334425,0.0007377809],"domain_scores_gemma":[0.99872035,0.000036226702,0.00017502924,0.00019703021,0.00028432842,0.00058705965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055156206,0.00015278574,0.0002564598,0.000589294,0.00037238924,0.0002844555,0.0006813206,0.00009388506,0.00026216148],"category_scores_gemma":[0.0003796596,0.00012767897,0.000116473406,0.0007901507,0.00052223395,0.00044066156,0.000018547795,0.00025068954,0.000011579742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011154512,0.00017826648,0.09239515,0.0002229053,0.0000715514,0.00053877186,0.006232248,0.011535638,0.87826675,0.0005672303,0.00096067874,0.00891926],"study_design_scores_gemma":[0.00089117663,0.00009512039,0.0026435931,0.000065151726,0.000040851603,0.00010022728,0.00051391346,0.001251549,0.9883897,0.005090504,0.00069361675,0.00022463799],"about_ca_topic_score_codex":0.01039758,"about_ca_topic_score_gemma":0.09426571,"teacher_disagreement_score":0.110122904,"about_ca_system_score_codex":0.0011035578,"about_ca_system_score_gemma":0.0031912073,"threshold_uncertainty_score":0.9961923},"labels":[],"label_agreement":null},{"id":"W2029570222","doi":"10.1002/pen.20022","title":"Biaxial deformation of polyamide‐6: Assessment of orientation by means of infrared trichroism","year":2004,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Materials science; Polyamide; Infrared spectroscopy; Composite material; Orientation (vector space); Planar; Deformation (meteorology); Infrared; Crystallography; Optics; Geometry; Chemistry; Organic chemistry; Mathematics; Physics","score_opus":0.006062704098783059,"score_gpt":0.2551456018153072,"score_spread":0.24908289771652414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029570222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9859088,0.00049396616,0.01258097,0.000010583576,0.000028465322,0.000014934847,0.00001127694,0.000012312118,0.0009386568],"genre_scores_gemma":[0.9987525,0.000031641,0.0011544678,0.000002328193,0.000007634143,0.0000015662365,0.0000041227927,0.0000030508108,0.00004272934],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999321,9.781664e-7,0.00021872284,0.000097524186,0.0002558649,0.00010595177],"domain_scores_gemma":[0.9996788,0.000018664303,0.00011558845,0.00009938174,0.00004817979,0.000039374827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010703396,0.000060448794,0.00013167706,0.00018890621,0.000026206502,0.000007025455,0.000105591236,0.000027519198,0.000020777408],"category_scores_gemma":[0.000039506842,0.00005614776,0.000024971281,0.0007769637,0.000114941824,0.00016164235,0.000019548026,0.00004247135,8.931809e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023084258,0.000030654723,0.0025451735,0.00007811285,0.00000978301,6.453984e-8,0.0002912516,0.0013473444,0.9943282,0.0011136386,0.0000017452516,0.000251735],"study_design_scores_gemma":[0.00026103744,0.000027654287,0.002974638,0.000019715539,0.00002482807,0.0000014206902,0.00017409738,0.005272048,0.99115646,0.000031879037,0.0000030078845,0.000053225325],"about_ca_topic_score_codex":0.00014278713,"about_ca_topic_score_gemma":4.0450144e-7,"teacher_disagreement_score":0.012843626,"about_ca_system_score_codex":0.0000401393,"about_ca_system_score_gemma":0.00007049496,"threshold_uncertainty_score":0.22896393},"labels":[],"label_agreement":null},{"id":"W2029728010","doi":"10.1111/1541-0420.00076","title":"Parametric Modeling of Reaction Time Experiment Data","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Australian National University","keywords":"Parametric statistics; Normality; Nonparametric statistics; Nonlinear system; Computation; Computer science; Parametric model; Applied mathematics; Algorithm; Intensity (physics); Mathematics; Statistics; Optics; Physics","score_opus":0.09530555923954115,"score_gpt":0.33124065971598504,"score_spread":0.2359351004764439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029728010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8594833,0.0143115595,0.015874095,0.000028076049,0.00019227318,0.0001017198,0.00013335259,0.00018222179,0.109693386],"genre_scores_gemma":[0.9935764,0.0003160294,0.0046300096,0.000010707656,0.000042524836,0.0000033098383,0.00010378527,0.000018679892,0.0012985569],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865806,0.000013362747,0.00034311766,0.00035983312,0.00039664583,0.00022895077],"domain_scores_gemma":[0.9985835,0.00016209493,0.00016240324,0.00091467716,0.00009376583,0.000083542844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002896072,0.0001395859,0.0002631038,0.0017849802,0.00005022851,0.00002306107,0.00042026406,0.00013465974,0.00061098195],"category_scores_gemma":[0.0014702718,0.00013553853,0.00006525073,0.009837436,0.00003094168,0.00013996716,0.00009207379,0.000107486245,0.000067239525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021551212,0.0007168612,0.0015817605,0.00010940474,0.0002413176,0.0000042700967,0.000036590558,0.00011191651,0.9922071,0.0004055686,0.001880939,0.0026827215],"study_design_scores_gemma":[0.00059425284,0.000051966803,0.000038337126,0.000012418531,0.00028237735,0.000012205921,0.00034604588,0.0601153,0.9264017,0.00017029158,0.011594393,0.00038071082],"about_ca_topic_score_codex":0.000057994057,"about_ca_topic_score_gemma":1.4152714e-7,"teacher_disagreement_score":0.13409308,"about_ca_system_score_codex":0.00011617029,"about_ca_system_score_gemma":0.000050791004,"threshold_uncertainty_score":0.6689824},"labels":[],"label_agreement":null},{"id":"W2029902465","doi":"10.1007/s10651-011-0176-x","title":"Iterating Masuyama’s method to reduce sampling variation","year":2011,"lang":"en","type":"article","venue":"Environmental and Ecological Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University; University of Virginia","keywords":"Sampling (signal processing); Variance (accounting); Mathematics; Variation (astronomy); Boundary (topology); Statistics; Importance sampling; Systematic sampling; Sampling design; Computer science; Monte Carlo method; Mathematical analysis; Population","score_opus":0.05180758817391254,"score_gpt":0.2953489950867703,"score_spread":0.24354140691285778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029902465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5978262,0.00006212511,0.3945132,0.000034606674,0.000033313492,0.00005838457,0.00017549761,0.000040084873,0.0072566066],"genre_scores_gemma":[0.654006,0.000020202948,0.34516075,0.00015673274,0.00003958233,0.000010281879,0.000027233924,0.000006233658,0.0005729705],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992436,0.000015945661,0.000184307,0.0002576327,0.000096409065,0.0002020868],"domain_scores_gemma":[0.9995847,0.00015105336,0.00005724249,0.00008120055,0.0000026842429,0.00012312352],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000092859795,0.00011769442,0.00015091388,0.000025856187,0.00014411213,0.00002494577,0.000073617375,0.000080349426,0.01357196],"category_scores_gemma":[0.000062679974,0.00010087213,0.000020589161,0.000050419923,0.000036646856,0.00003888588,0.00009353827,0.00011511933,0.00008379622],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012723038,0.0009388078,0.06445801,0.00006901837,0.00017624404,0.000055251145,0.0025424848,0.0000784722,0.86267626,0.007984658,0.0006055499,0.060288],"study_design_scores_gemma":[0.0006531011,0.00042084182,0.84336317,0.000009717527,0.0002970188,0.000034473167,0.0013569958,0.0018686475,0.13288529,0.015564604,0.0028145427,0.00073158316],"about_ca_topic_score_codex":0.000027699809,"about_ca_topic_score_gemma":0.000002105086,"teacher_disagreement_score":0.77890515,"about_ca_system_score_codex":0.00007355508,"about_ca_system_score_gemma":0.000002899653,"threshold_uncertainty_score":0.9873298},"labels":[],"label_agreement":null},{"id":"W2030043102","doi":"10.5539/jas.v4n4p129","title":"Qualitative Sorting of Potatoes by Color Analysis in Machine Vision System","year":2012,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Artificial intelligence; Computer vision; Computer science; Computer graphics (images); Machine vision; HSL and HSV; Image processing; Mathematics; Algorithm; Medicine; Image (mathematics)","score_opus":0.014043228540934564,"score_gpt":0.34115047155317346,"score_spread":0.3271072430122389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030043102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99719226,0.001187904,0.000083186496,0.00008538714,0.000035028857,0.0000150065625,0.000006225616,0.0000050598105,0.0013899391],"genre_scores_gemma":[0.99919844,0.00001891542,0.00058787956,0.0000061016553,0.000046252342,4.9613163e-7,0.0000017250946,0.0000016616812,0.000138521],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983757,0.000034748213,0.00060285797,0.00010841892,0.0006130388,0.0002652033],"domain_scores_gemma":[0.9984045,0.00016649999,0.000951183,0.00007010089,0.0002695202,0.00013817567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011990326,0.00009708784,0.00038593923,0.00028233393,0.000083348656,0.00003216318,0.00033140238,0.000040027706,0.000079833226],"category_scores_gemma":[0.00031112204,0.000050897608,0.00016596685,0.0037688988,0.0001404391,0.00063237874,0.000043513723,0.0001615939,0.0000014839444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013552898,0.000111869645,0.046209756,0.00003494806,0.00011327859,0.0000012138842,0.0034110586,0.00013280033,0.94968355,0.00009868267,0.000112208545,0.000077104894],"study_design_scores_gemma":[0.00028650713,0.00007744923,0.15965238,0.00007425718,0.0005038121,0.00004199219,0.06735821,0.0002146965,0.7716163,0.000007895288,0.000015419935,0.00015111605],"about_ca_topic_score_codex":0.000112360445,"about_ca_topic_score_gemma":0.000010715072,"teacher_disagreement_score":0.17806727,"about_ca_system_score_codex":0.0002526368,"about_ca_system_score_gemma":0.00003119467,"threshold_uncertainty_score":0.20755443},"labels":[],"label_agreement":null},{"id":"W2031138951","doi":"10.1016/j.compag.2005.12.001","title":"Application of support vector machine technology for weed and nitrogen stress detection in corn","year":2006,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":182,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Weed; Weed control; Hyperspectral imaging; Support vector machine; Artificial intelligence; Agronomy; Growing season; Machine learning; Mathematics; Computer science; Biology","score_opus":0.002770211771245033,"score_gpt":0.2104692686554075,"score_spread":0.20769905688416246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031138951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870273,0.0038326334,0.008665225,0.000116282885,0.000012481829,0.0001560013,0.000019910563,0.00003072908,0.00013946259],"genre_scores_gemma":[0.9992196,0.00011010744,0.000443801,0.000008681294,0.00003819063,0.000049405615,0.0000801807,0.000005527193,0.0000444976],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937916,0.00000355175,0.00017512293,0.00020866601,0.000048815433,0.00018466519],"domain_scores_gemma":[0.9997614,0.00003217664,0.00008345928,0.0000763935,0.00003001163,0.000016559035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039053182,0.00010450233,0.00019286187,0.00012802874,0.000031088963,0.000010475821,0.00007678803,0.00016364676,0.0000026653004],"category_scores_gemma":[0.000005992964,0.00008520548,0.000024543426,0.0004370612,0.000031552772,0.00002783028,0.000025183797,0.00016291814,9.800458e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007540209,0.00025124784,0.102199614,0.00025254337,0.000051465046,0.0000018681374,0.000049565293,0.00016290761,0.8768973,0.0035604457,0.00021549025,0.016282199],"study_design_scores_gemma":[0.0013538712,0.00016931313,0.010278454,0.000022155788,0.0000661461,0.000027807246,0.00012541305,0.007739368,0.97139436,0.006774919,0.001807544,0.00024065022],"about_ca_topic_score_codex":0.0001912651,"about_ca_topic_score_gemma":0.00087526283,"teacher_disagreement_score":0.09449711,"about_ca_system_score_codex":0.00007227528,"about_ca_system_score_gemma":0.000010670106,"threshold_uncertainty_score":0.34745786},"labels":[],"label_agreement":null},{"id":"W2031252452","doi":"10.1016/j.ultras.2006.05.214","title":"Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification","year":2006,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Wavelet; Artificial intelligence; Computer science; Feature extraction; Basis (linear algebra); Invariant (physics); Feature selection; Probabilistic logic; Wavelet transform; Mathematics","score_opus":0.01324294223632403,"score_gpt":0.26391664554250654,"score_spread":0.2506737033061825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031252452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90244305,0.0014393958,0.06502475,0.00080848153,0.00012241954,0.000387094,0.00020171398,0.00038658764,0.029186526],"genre_scores_gemma":[0.9915341,0.00021313013,0.0054282043,0.000053122512,0.00036722905,0.000061348976,0.0003864806,0.000033070755,0.0019233186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886215,0.000012171389,0.00023172118,0.00040493917,0.0001816712,0.0003073632],"domain_scores_gemma":[0.99900264,0.00047006094,0.00018255688,0.0001926587,0.0000873537,0.00006471896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014671028,0.00020672967,0.00021134099,0.00011187739,0.00027051597,0.0001276989,0.000100754194,0.00031196425,0.00027115498],"category_scores_gemma":[0.0002553243,0.00020429103,0.00009797659,0.00039273655,0.000039905848,0.0002082232,0.0000069185717,0.0002523123,0.0000059730974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011521778,0.0002671754,0.02384824,0.00018091443,0.0001378585,0.0000014645564,0.00006654011,0.000055265013,0.9370412,0.02631046,0.0109330425,0.0010425757],"study_design_scores_gemma":[0.0011948497,0.00009191831,0.04082622,0.000025643027,0.0006476438,0.00014312602,0.00055042875,0.0063697337,0.91483825,0.009001157,0.02571614,0.00059491524],"about_ca_topic_score_codex":0.00012884503,"about_ca_topic_score_gemma":0.0001049959,"teacher_disagreement_score":0.08909106,"about_ca_system_score_codex":0.00017557292,"about_ca_system_score_gemma":0.000050690123,"threshold_uncertainty_score":0.8330746},"labels":[],"label_agreement":null},{"id":"W2031452536","doi":"10.1255/jnirs.365","title":"Quantitative Analysis of Hydrogen Bonding in Poly(4-Vinylphenol) Blends Using near Infrared Spectroscopy","year":2003,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrogen bond; Ethylene oxide; Overtone; Fourier transform infrared spectroscopy; Materials science; Absorption (acoustics); Infrared spectroscopy; Analytical Chemistry (journal); Spectroscopy; Absorption spectroscopy; Polymer chemistry; Molecule; Chemistry; Spectral line; Chemical engineering; Polymer; Organic chemistry; Copolymer","score_opus":0.028261682597271423,"score_gpt":0.31899974897889666,"score_spread":0.29073806638162525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031452536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9629088,0.0044171396,0.0021645643,0.000059149173,0.00016520989,0.000102326914,0.000071130984,0.000041246618,0.030070452],"genre_scores_gemma":[0.8795839,0.00033709692,0.11845041,0.000101628786,0.00015121122,0.0000048655343,0.000023115655,0.0001108748,0.0012368942],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99473447,0.00018043462,0.002298012,0.00059712486,0.0011454951,0.0010444535],"domain_scores_gemma":[0.9957388,0.00042640438,0.0022813808,0.0007883649,0.00039824753,0.00036681854],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008889854,0.00064818043,0.0022088748,0.0023128632,0.0002867652,0.0002411187,0.0008172783,0.00038698656,0.0040781554],"category_scores_gemma":[0.00090535596,0.00064206985,0.0010962441,0.007406273,0.00041842106,0.0006959148,0.00009583726,0.0011993808,0.000013982019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006401079,0.00054434384,0.063376896,0.00011133167,0.0043074708,0.0001913949,0.0019860389,0.002508112,0.9228101,0.0030519078,0.00045084368,0.000021502774],"study_design_scores_gemma":[0.0019997822,0.0005580464,0.0013003808,0.00016152805,0.003830813,0.00009860935,0.0032941026,0.006833455,0.9757141,0.0037221438,0.0018022102,0.0006848258],"about_ca_topic_score_codex":0.0001530176,"about_ca_topic_score_gemma":0.000035217374,"teacher_disagreement_score":0.116285846,"about_ca_system_score_codex":0.0010412907,"about_ca_system_score_gemma":0.001058371,"threshold_uncertainty_score":0.99960303},"labels":[],"label_agreement":null},{"id":"W2032364504","doi":"10.1071/ar05285","title":"Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy","year":2006,"lang":"en","type":"article","venue":"Australian Journal of Agricultural Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carbon Engineering (Canada)","funders":"","keywords":"Orange (colour); Melon; Horticulture; Flesh; Cucumis; Cultivar; Near-infrared spectroscopy; Population; Mathematics; Animal science; Botany; Chemistry; Biology","score_opus":0.180865425293038,"score_gpt":0.3737644307507922,"score_spread":0.19289900545775418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032364504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992156,0.00021618912,0.000046789406,0.0022080378,0.00008712392,0.00009832036,0.000007716673,0.000008253254,0.0051715407],"genre_scores_gemma":[0.99341106,0.000024335835,0.0027353736,0.000011036791,0.00050208304,0.0000035511987,0.0000049286364,0.000011363343,0.003296239],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99677455,0.00007980176,0.0006599584,0.00017638653,0.0017481253,0.0005611872],"domain_scores_gemma":[0.9974255,0.00017648767,0.0002960844,0.00022296209,0.0016865052,0.00019247993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044843,0.0001716068,0.00033075365,0.00021896152,0.00035033104,0.00019911761,0.0005497944,0.00009864482,0.00047449677],"category_scores_gemma":[0.0004801794,0.00009668613,0.00024317842,0.0015674314,0.00014785776,0.0002478257,0.00008291417,0.0007395794,0.00002047536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009007502,0.00015739209,0.00122065,0.000036228263,0.00017890071,0.000011661091,0.0003816516,0.0033815238,0.94956666,0.00007970455,0.044824567,0.00007095847],"study_design_scores_gemma":[0.00038777728,0.00030298694,0.027722128,0.00010935927,0.0001367155,0.00010294766,0.0045383144,0.000027604106,0.96449393,0.00019580184,0.0018241341,0.00015829556],"about_ca_topic_score_codex":0.00040444665,"about_ca_topic_score_gemma":0.000071525465,"teacher_disagreement_score":0.043000434,"about_ca_system_score_codex":0.0005117989,"about_ca_system_score_gemma":0.0002470271,"threshold_uncertainty_score":0.51954067},"labels":[],"label_agreement":null},{"id":"W2032995893","doi":"10.1002/cjce.20098","title":"On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crystallinity; Chemometrics; Materials science; Polymer; Calibration; Low-density polyethylene; Biological system; Analytical Chemistry (journal); Composite material; Statistics; Mathematics; Chemistry; Chromatography","score_opus":0.024607058685512228,"score_gpt":0.2827865704456123,"score_spread":0.25817951176010007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032995893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97016305,0.0010572267,0.0283988,0.00007598717,0.00008238251,0.000019227266,0.00014142922,0.000010798465,0.000051081122],"genre_scores_gemma":[0.99794436,0.000016110278,0.0017342772,0.000016560834,0.00024223507,4.6746345e-7,0.000013727941,0.000018010895,0.000014242647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998909,0.000010811035,0.00042300083,0.0001180546,0.00021615393,0.00032296727],"domain_scores_gemma":[0.99898773,0.00021832452,0.00021723183,0.00014274186,0.00011935272,0.00031459265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030427257,0.00015742898,0.00030608894,0.00015215177,0.0001236489,0.000026661897,0.00019228096,0.000100021076,0.00009057469],"category_scores_gemma":[0.00077218685,0.00013141752,0.00012745948,0.0005686462,0.00016660325,0.00009049738,0.000019999197,0.0005122014,1.4750766e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033260174,0.000023857208,0.002900968,0.000053417163,0.00011957117,0.000032911412,0.00019471422,0.0035600504,0.9925741,0.00005277505,0.00006692097,0.00038746072],"study_design_scores_gemma":[0.000357938,0.00002001415,0.00047848577,0.000043453467,0.000099570156,0.0004505421,0.000042644104,0.034217697,0.96410793,0.00004693318,0.000024463177,0.00011029888],"about_ca_topic_score_codex":0.0012728701,"about_ca_topic_score_gemma":0.000012177975,"teacher_disagreement_score":0.030657647,"about_ca_system_score_codex":0.00027789103,"about_ca_system_score_gemma":0.00018830542,"threshold_uncertainty_score":0.53590506},"labels":[],"label_agreement":null},{"id":"W2033527146","doi":"10.1093/biomet/asp069","title":"Functional quadratic regression","year":2010,"lang":"en","type":"article","venue":"Biometrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Polynomial regression; Mathematics; Functional principal component analysis; Functional data analysis; Regression analysis; Proper linear model; Linear regression; Bayesian multivariate linear regression; Polynomial; Linear model; Scalar (mathematics); Regression; Quadratic equation; Applied mathematics; Linear form; Equivalence (formal languages); Principal component regression; Quadratic function; Statistics; Discrete mathematics; Mathematical analysis","score_opus":0.021961118702924012,"score_gpt":0.28293515133642777,"score_spread":0.26097403263350377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033527146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9400087,0.0005682544,0.00053711166,0.00028965474,0.00045105894,0.00002425655,0.000013960562,0.00018230709,0.057924695],"genre_scores_gemma":[0.983081,0.000017943627,0.0009680464,0.000069317066,0.00043734102,0.00000755837,0.000039616025,0.000014369608,0.015364849],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910325,0.0000037597981,0.0001673188,0.00023827645,0.0002792649,0.00020815704],"domain_scores_gemma":[0.9993203,0.000117674346,0.000077519784,0.00031829503,0.00005625512,0.0001099319],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009354216,0.00012725581,0.00015305134,0.00057448336,0.00011403506,0.000043087646,0.00016982488,0.00016177676,0.02690849],"category_scores_gemma":[0.000357039,0.00009932731,0.00009581497,0.0020965487,0.000070697344,0.00007615547,0.00004108663,0.00026461596,0.00046680105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013883126,0.000096571785,0.0085590985,0.00002180686,0.000038205162,0.0000036650224,0.000009312546,1.4728992e-7,0.9758577,0.0005145048,0.011262322,0.0036227826],"study_design_scores_gemma":[0.000336353,0.000014863216,0.0048280046,0.0000055544515,0.000060712566,0.000017903625,0.00006393518,0.000038163966,0.94837433,0.00032669265,0.045752678,0.00018080314],"about_ca_topic_score_codex":0.000021549899,"about_ca_topic_score_gemma":0.0000051782395,"teacher_disagreement_score":0.04307226,"about_ca_system_score_codex":0.00002923073,"about_ca_system_score_gemma":0.000037642192,"threshold_uncertainty_score":0.973981},"labels":[],"label_agreement":null},{"id":"W2033583474","doi":"10.1364/josaa.31.001445","title":"Outlier modeling for spectral data reduction","year":2014,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mahalanobis distance; Outlier; Principal component analysis; Pattern recognition (psychology); Anomaly detection; Estimator; Covariance; Computer science; Artificial intelligence; Mathematics; Basis (linear algebra); Algorithm; Statistics","score_opus":0.038680159072595366,"score_gpt":0.30665314862635784,"score_spread":0.2679729895537625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033583474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3520365,0.0006321479,0.629375,0.011834879,0.0003380822,0.00009223738,0.000029158298,0.000023928093,0.005638063],"genre_scores_gemma":[0.88621306,0.00011203427,0.11246302,0.00015467402,0.0006886071,6.3362063e-7,0.0000026683663,0.000012717767,0.00035258714],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907786,0.000008849591,0.00035900468,0.00012017097,0.00027053588,0.00016360659],"domain_scores_gemma":[0.9989904,0.000110068555,0.00034846616,0.00036394174,0.00012223594,0.000064864136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027536764,0.000085403735,0.00028750658,0.000014765677,0.00007883088,0.000017967994,0.0006172753,0.000066212386,0.00010447315],"category_scores_gemma":[0.00031984103,0.000055285345,0.00045375022,0.00019100346,0.00014992472,0.00010441238,0.000101243495,0.00025583635,9.2429053e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047359985,0.0010512564,0.00062672445,0.00042828606,0.0024263985,4.7320728e-7,0.0012534187,0.019908207,0.8729659,0.0005964598,0.079196215,0.021073084],"study_design_scores_gemma":[0.001380509,0.0002720639,0.000046879886,0.00009073047,0.0015367832,0.000091272,0.004831901,0.763115,0.21303275,0.004418661,0.0108908685,0.00029257705],"about_ca_topic_score_codex":0.000010151834,"about_ca_topic_score_gemma":5.338332e-8,"teacher_disagreement_score":0.7432068,"about_ca_system_score_codex":0.00005507489,"about_ca_system_score_gemma":0.000045107954,"threshold_uncertainty_score":0.2254471},"labels":[],"label_agreement":null},{"id":"W2034439975","doi":"10.1007/s11746-002-0555-x","title":"Multivariate model for the prediction of total phenolic acids in crude extracts of polyphenols from canola and rapessed meals: A preliminary study","year":2002,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Canola; Rapeseed; Polyphenol; Partial least squares regression; Multivariate statistics; Chemistry; Food science; Brassica; Acetone; Chromatography; Mathematics; Botany; Statistics; Organic chemistry; Biology; Antioxidant","score_opus":0.03155408841234171,"score_gpt":0.27995110012357716,"score_spread":0.24839701171123546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034439975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973684,0.0016973517,0.00034918223,0.00025659794,0.000023536837,0.00006644139,0.00008983501,0.000006853186,0.00014178915],"genre_scores_gemma":[0.99777585,0.00044329834,0.0012204547,0.000053401174,0.00011241423,0.000013736079,0.0000013022606,0.000020657377,0.00035886007],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985252,0.000027927494,0.00065435877,0.00018645173,0.00039692447,0.0002091525],"domain_scores_gemma":[0.99744993,0.0005384089,0.0014501694,0.00033780956,0.00016016107,0.000063515305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024840145,0.00018454723,0.00056747126,0.000034107034,0.00008657873,0.000016210435,0.00040236447,0.00009680924,0.00003622137],"category_scores_gemma":[0.00026637746,0.00011845194,0.00037533237,0.00043090762,0.0002979241,0.00009384819,0.00009272187,0.00046359043,5.838936e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039147402,0.0010121332,0.012232177,0.000111890455,0.0010045259,0.0000010370784,0.014019895,0.0013902937,0.9672118,7.0476756e-7,0.00039032314,0.002233786],"study_design_scores_gemma":[0.0047401274,0.0003710251,0.027273677,0.00018326566,0.0017100308,0.000031879954,0.031060176,0.1368769,0.7973847,0.000091149974,0.000016531169,0.00026054445],"about_ca_topic_score_codex":0.0004720757,"about_ca_topic_score_gemma":0.0000062968934,"teacher_disagreement_score":0.16982706,"about_ca_system_score_codex":0.00014468629,"about_ca_system_score_gemma":0.000075557924,"threshold_uncertainty_score":0.483033},"labels":[],"label_agreement":null},{"id":"W2034803951","doi":"10.1016/s0168-1699(03)00020-6","title":"Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn","year":2003,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":227,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"","keywords":"Hyperspectral imaging; Decision tree; Weed control; Weed; Artificial neural network; Growing season; Mathematics; Agronomy; Artificial intelligence; Agricultural engineering; Machine learning; Computer science; Engineering; Biology","score_opus":0.019965744341842007,"score_gpt":0.2876487232913553,"score_spread":0.2676829789495133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034803951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98465407,0.012942855,0.002120188,0.0000404185,0.000026141432,0.000074629985,0.000058736485,0.000009270655,0.00007368317],"genre_scores_gemma":[0.9977886,0.0012350635,0.00076936325,0.000015092777,0.000031324904,0.000002879264,0.00013703553,0.0000065185186,0.000014109217],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989366,0.000019964395,0.00026424893,0.00035182567,0.00012930982,0.0002980825],"domain_scores_gemma":[0.9994214,0.0001084386,0.000117046795,0.00020806472,0.000044361772,0.00010065561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008170189,0.00014484694,0.00027368605,0.000066594104,0.00005109011,0.00004664392,0.00016301037,0.00012393433,0.000007310382],"category_scores_gemma":[0.000046057652,0.00011371349,0.000022887218,0.00037691536,0.00006075778,0.000085392225,0.00008223816,0.00019904911,5.7464266e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022765495,0.00042206718,0.10439827,0.00014399369,0.00022176,0.0000040642317,0.0004566543,0.0008904165,0.8634382,0.0033776464,0.0052899234,0.02112938],"study_design_scores_gemma":[0.0049546896,0.0011158607,0.174738,0.00041641528,0.0010847165,0.000118995355,0.007792731,0.13123575,0.6654278,0.006700162,0.004373064,0.0020418],"about_ca_topic_score_codex":0.00007598727,"about_ca_topic_score_gemma":0.00019440196,"teacher_disagreement_score":0.19801036,"about_ca_system_score_codex":0.00004267951,"about_ca_system_score_gemma":0.000018386132,"threshold_uncertainty_score":0.46371016},"labels":[],"label_agreement":null},{"id":"W2034868610","doi":"10.1016/j.jfoodeng.2014.01.026","title":"Corrigendum to “A dual-view computer-vision system for volume and image texture analysis in multiple apple slices drying” [J. Food Eng. 127 (2014) 49–57]","year":2014,"lang":"en","type":"erratum","venue":"Journal of Food Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Texture (cosmology); Volume (thermodynamics); Dual (grammatical number); Computer vision; Artificial intelligence; Food science; Computer science; Image (mathematics); Computer graphics (images); Chemistry; Art; Physics; Thermodynamics","score_opus":0.010098534774843395,"score_gpt":0.23129841671734883,"score_spread":0.22119988194250542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034868610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06466074,0.08610112,0.82092816,0.00034679967,0.023144152,0.0011185609,0.0013943888,0.00045718014,0.0018489143],"genre_scores_gemma":[0.97020686,0.0007598145,0.017598808,0.00004805046,0.0060520507,0.00005101542,0.0003003222,0.00022619165,0.0047569177],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712,0.00002408853,0.0012144975,0.0005252742,0.00053392025,0.0005821745],"domain_scores_gemma":[0.9978011,0.00023870087,0.000883409,0.0004500767,0.0002786391,0.00034803737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051758223,0.00062495994,0.0019598126,0.0016744725,0.000099995166,0.00023295385,0.00051918055,0.0006343875,0.000062016166],"category_scores_gemma":[0.00024121813,0.00058401626,0.00068984664,0.0011870135,0.00002134074,0.00019783681,0.00013795636,0.001239637,0.0000056400636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042704862,0.000659817,0.0014356178,0.036256183,0.022471648,0.00028642657,0.0022125784,0.039608177,0.05790239,0.000071490955,0.82840204,0.010266572],"study_design_scores_gemma":[0.0035462906,0.002687793,0.00056157354,0.004464073,0.009296196,0.00037905443,0.0011271269,0.39344287,0.020183612,0.000008980606,0.5616949,0.00260754],"about_ca_topic_score_codex":0.000024312958,"about_ca_topic_score_gemma":0.00006784459,"teacher_disagreement_score":0.90554607,"about_ca_system_score_codex":0.00037758294,"about_ca_system_score_gemma":0.000079441605,"threshold_uncertainty_score":0.99966115},"labels":[],"label_agreement":null},{"id":"W2035137795","doi":"10.1016/j.compag.2006.12.002","title":"Combination of chemometric tools and image processing for bruise detection on apples","year":2007,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds Wetenschappelijk Onderzoek; McGill University","keywords":"Hyperspectral imaging; Bruise; Artificial intelligence; Pattern recognition (psychology); Sorting; Image processing; Computer science; Pixel; Computer vision; Chemometrics; Image (mathematics); Machine learning","score_opus":0.00907156810450279,"score_gpt":0.25134332417058186,"score_spread":0.24227175606607906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035137795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711095,0.004045299,0.023729121,0.000067384986,0.00002207904,0.00012670546,0.000005956401,0.00003073141,0.000863203],"genre_scores_gemma":[0.9981692,0.00034823717,0.0012663266,0.00002912015,0.00005540708,0.000007637946,0.000026627062,0.0000068886484,0.00009059213],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927956,0.0000033526471,0.00018117105,0.0002150479,0.0000997697,0.00022107804],"domain_scores_gemma":[0.9995788,0.00014499202,0.00011091427,0.00005924368,0.000068508096,0.000037590784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000147125,0.00012142771,0.00019019294,0.00019002527,0.00007448741,0.00004903956,0.000068388006,0.00012252486,0.0000025452389],"category_scores_gemma":[0.00004805715,0.00009514092,0.000035216464,0.0007062277,0.00003233384,0.00008913125,0.000019845274,0.00017028215,1.020227e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014319994,0.00032432418,0.0019687277,0.0006802754,0.000057622543,0.0000020303883,0.00026738844,0.00003297597,0.7169797,0.0015377344,0.0003931112,0.27761295],"study_design_scores_gemma":[0.001052496,0.00020325916,0.0061758966,0.000053659278,0.0000551924,0.000014126878,0.0002457659,0.00094798714,0.9887669,0.0009474947,0.0013379264,0.00019929622],"about_ca_topic_score_codex":0.000005327971,"about_ca_topic_score_gemma":0.000018686862,"teacher_disagreement_score":0.27741367,"about_ca_system_score_codex":0.000092062095,"about_ca_system_score_gemma":0.000011486103,"threshold_uncertainty_score":0.3879734},"labels":[],"label_agreement":null},{"id":"W2036099935","doi":"10.1007/s11517-012-1009-2","title":"Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns","year":2012,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Segmentation; Standard deviation; Pattern recognition (psychology); Binary number; Artificial intelligence; Invariant (physics); Local binary patterns; Root mean square; Hausdorff distance; Boundary (topology); Rotation (mathematics); Mathematics; Image segmentation; Computer vision; Computer science; Algorithm; Biology; Image (mathematics); Physics; Histogram; Mathematical analysis; Statistics","score_opus":0.014947693872212354,"score_gpt":0.2518443385284673,"score_spread":0.23689664465625498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036099935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73161703,0.00058107986,0.26765165,0.000009119495,0.00002898546,0.000056229743,0.0000010571694,0.00005105042,0.000003830518],"genre_scores_gemma":[0.9831441,0.000048974885,0.016687615,0.000012149243,0.00008049434,0.0000054015563,0.000010731385,0.000010200286,3.546292e-7],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891007,0.000032602467,0.00038379407,0.00020920103,0.00017056082,0.00029374976],"domain_scores_gemma":[0.9994096,0.00023295038,0.00012630217,0.000069137684,0.00001771939,0.00014426945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034298963,0.00015506879,0.00026490728,0.00016647356,0.00005145507,0.000019719468,0.0000671325,0.00017241224,0.000078416575],"category_scores_gemma":[0.00023743408,0.00013199906,0.000033472046,0.0003924201,0.00009343172,0.00006903753,0.000068780544,0.0002428736,3.2519523e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001281662,0.00013088045,0.68563527,0.00044405932,0.000047081423,0.000012617969,0.00022316682,0.0011625018,0.2235884,0.000017650238,1.7417645e-7,0.08872537],"study_design_scores_gemma":[0.000447182,0.00007751319,0.44984317,0.00021099106,0.000053145643,0.000094428746,0.00031983663,0.52786887,0.02088518,0.000010368375,0.0000012683287,0.00018807247],"about_ca_topic_score_codex":0.00013989868,"about_ca_topic_score_gemma":0.0000016609921,"teacher_disagreement_score":0.52670634,"about_ca_system_score_codex":0.00004939543,"about_ca_system_score_gemma":0.000010972921,"threshold_uncertainty_score":0.53827655},"labels":[],"label_agreement":null},{"id":"W2037478207","doi":"10.1139/v00-160","title":"IR Spectroscopy of aqueous alkali halides. Factor analysis","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chemistry; Aqueous solution; Alkali metal; Salt (chemistry); Solubility; Infrared spectroscopy; Inorganic chemistry; Halide; Attenuated total reflection; Spectral line; Analytical Chemistry (journal); Dispersion (optics); Spectroscopy; Physical chemistry; Organic chemistry","score_opus":0.013243191483456795,"score_gpt":0.24896917496177048,"score_spread":0.2357259834783137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037478207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9659737,0.0028464769,0.0004100928,0.00027471152,0.000051935906,0.000014583396,0.00009388896,0.000011547352,0.030323071],"genre_scores_gemma":[0.9958009,0.00018141455,0.0005638224,0.00006363716,0.00030318135,8.8529725e-7,0.000018695175,0.00002528669,0.0030421538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819064,0.000009386422,0.0007339721,0.00022402551,0.00034395573,0.00049803185],"domain_scores_gemma":[0.9976056,0.00007953336,0.00067844527,0.00043128026,0.00031350466,0.00089161866],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011852875,0.00025330845,0.00074856373,0.000415319,0.00008870564,0.000058158337,0.00051812484,0.00023127533,0.024426006],"category_scores_gemma":[0.00025152977,0.00024923787,0.0006190889,0.0015169472,0.00018885499,0.000114573486,0.00001492468,0.00044083982,0.0000073841766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004062635,0.00006177987,0.1033394,0.00012753974,0.0024499258,0.0007866429,0.00022019845,0.00009143596,0.8892762,0.000007029367,0.0032956458,0.0003036014],"study_design_scores_gemma":[0.00036484675,0.000020461623,0.00089576404,0.00003164718,0.0011277016,0.0003820295,0.00052362314,0.000029365956,0.99118686,0.00013662426,0.0050549433,0.00024613866],"about_ca_topic_score_codex":0.0025561564,"about_ca_topic_score_gemma":0.0017549018,"teacher_disagreement_score":0.102443635,"about_ca_system_score_codex":0.00040304015,"about_ca_system_score_gemma":0.0012631447,"threshold_uncertainty_score":0.999996},"labels":[],"label_agreement":null},{"id":"W2037683249","doi":"10.1006/jcis.2001.7652","title":"Genetic Algorithm Approach to the Determination of Particle Size Distributions from Static Light-Scattering Data","year":2001,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inversion (geology); Light scattering; Range (aeronautics); Algorithm; Scattering; Genetic algorithm; Genetic data; Particle size; Static light scattering; Particle (ecology); Particle-size distribution; Experimental data; Inverse transform sampling; Optics; Computational physics; Statistical physics; Biological system; Materials science; Computer science; Physics; Mathematics; Statistics; Chemistry; Machine learning; Geology; Population; Biology","score_opus":0.025013807583836072,"score_gpt":0.3051298649196074,"score_spread":0.28011605733577133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037683249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83259624,0.0004265702,0.16598633,0.00054413977,0.000044688175,0.000026077134,0.00003795801,0.0000030792894,0.00033488614],"genre_scores_gemma":[0.9821656,0.000085854546,0.017492257,0.0000361095,0.00005994061,0.0000010436192,6.801998e-7,0.0000028813377,0.0001556485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990954,0.000010182409,0.0002930743,0.00015356093,0.00030099892,0.00014678435],"domain_scores_gemma":[0.9991673,0.000120886194,0.00020288624,0.00027521641,0.00013872063,0.00009494522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029619737,0.000066996196,0.00014309322,0.000047163594,0.0001291497,0.00009656995,0.00070662366,0.000017714121,0.00007675082],"category_scores_gemma":[0.00039001205,0.000044477823,0.000024812238,0.00065087463,0.00015065994,0.00029996014,0.00017461865,0.000095709904,0.0000015983629],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013001645,0.000081131,0.0012614448,0.0000072221123,0.000015189995,5.443179e-7,0.00040353593,0.00006337484,0.9933376,0.000005874335,0.00021357364,0.0045975232],"study_design_scores_gemma":[0.00020579297,0.00007283838,0.003974431,0.000035341393,0.00009256991,0.000062739804,0.001673495,0.035650928,0.95698816,0.00009903188,0.0010649846,0.000079696656],"about_ca_topic_score_codex":0.00002644797,"about_ca_topic_score_gemma":0.0000030364683,"teacher_disagreement_score":0.14956932,"about_ca_system_score_codex":0.00004808944,"about_ca_system_score_gemma":0.000070001064,"threshold_uncertainty_score":0.1813753},"labels":[],"label_agreement":null},{"id":"W2037713552","doi":"10.1109/whispers.2011.6080967","title":"Infrared reflectance hyperspectral features of Athabasca oil sand ore and froth","year":2011,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Oil sands; Asphalt; Hyperspectral imaging; Reflectivity; Context (archaeology); Environmental science; Quartz; Mineralogy; Geology; Materials science; Remote sensing; Metallurgy; Composite material; Optics","score_opus":0.022136958202101122,"score_gpt":0.2611350027951395,"score_spread":0.2389980445930384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037713552","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41361222,0.0014032299,0.000018036451,0.000020068139,0.000009693141,0.000007509672,0.0000070991573,0.00004142092,0.5848807],"genre_scores_gemma":[0.94713956,0.00031856875,0.0049748723,0.000059837326,0.000050266666,0.0000044532276,0.000004318687,0.000013248999,0.047434874],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993026,0.0000046187347,0.00015330096,0.00022340272,0.000127159,0.00018889912],"domain_scores_gemma":[0.9995381,0.000035332974,0.00007485521,0.00023922579,0.000041955154,0.00007050324],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000032581862,0.00013292691,0.00023356089,0.000072911156,0.000043069656,0.00001173167,0.00014839631,0.00010425718,0.0049643116],"category_scores_gemma":[0.000052669922,0.00010771241,0.00006419972,0.00026158692,0.000105926876,0.00007428089,0.00003586499,0.00013202235,0.000004665029],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003244223,0.000280719,0.039639547,0.00031058918,0.0004091185,0.000023827984,0.0024979508,1.5637141e-7,0.9371424,0.009295878,0.007779019,0.0022964044],"study_design_scores_gemma":[0.0003790725,0.00003412931,0.0032721993,0.000013427313,0.000080004145,0.000015484136,0.0005805861,0.0000036325307,0.99336946,0.0018900027,0.00021741376,0.00014461455],"about_ca_topic_score_codex":0.00014836273,"about_ca_topic_score_gemma":0.00002292416,"teacher_disagreement_score":0.53744584,"about_ca_system_score_codex":0.000021366717,"about_ca_system_score_gemma":0.000023860059,"threshold_uncertainty_score":0.9959453},"labels":[],"label_agreement":null},{"id":"W2037895534","doi":"10.1007/s12161-011-9216-2","title":"Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk","year":2011,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hue; Partial least squares regression; Mathematics; Linear regression; Scanner; Chromatography; Food science; Chemistry; Artificial intelligence; Analytical Chemistry (journal); Statistics; Computer science","score_opus":0.06225163909780554,"score_gpt":0.36615651350870737,"score_spread":0.30390487441090186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037895534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9680257,0.00004131086,0.026050156,0.000059319234,0.000021043574,0.00023921877,0.000041579235,0.000089949,0.005431744],"genre_scores_gemma":[0.89752746,8.192307e-7,0.101877764,0.000076011376,0.000028893255,0.00009549892,0.0000050331446,0.000030711202,0.00035780005],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981516,0.00012545609,0.00046154956,0.0005724544,0.0002338094,0.00045515277],"domain_scores_gemma":[0.99837655,0.00059005944,0.00008182938,0.00049771735,0.00013091975,0.0003229236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032422668,0.00030673962,0.0005883736,0.00037149692,0.00009893119,0.000100063204,0.00026252793,0.00009461381,0.00051628833],"category_scores_gemma":[0.0009771527,0.00023991591,0.00013224647,0.001525315,0.00012339574,0.00019174242,0.00011593039,0.00030985527,0.00001895618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.073536605,0.08672164,0.47997355,0.001769792,0.03185782,0.0676479,0.07963663,0.0016741252,0.09808833,0.004831089,0.0026328883,0.07162962],"study_design_scores_gemma":[0.008711759,0.04638234,0.0031840117,0.00016343028,0.0033186194,0.0014531662,0.05506844,0.0061367815,0.8715766,0.0011376386,0.00019242498,0.0026747747],"about_ca_topic_score_codex":0.00028377472,"about_ca_topic_score_gemma":0.00044542603,"teacher_disagreement_score":0.7734883,"about_ca_system_score_codex":0.000110867906,"about_ca_system_score_gemma":0.00006496752,"threshold_uncertainty_score":0.9783487},"labels":[],"label_agreement":null},{"id":"W2038324709","doi":"10.1016/j.saa.2004.07.014","title":"On the use of wavelet filtering and correlation techniques in atmospheric condensed phase spectroscopy","year":2004,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministerio de Ciencia y Tecnología","keywords":"Aerosol; Sulfate; Spectral line; Wavelet; Sulfuric acid; Spectroscopy; Analytical Chemistry (journal); Atmosphere (unit); Wavelet transform; Phase (matter); Computational physics; Materials science; Chemistry; Remote sensing; Geology; Meteorology; Physics; Computer science; Inorganic chemistry","score_opus":0.017589166580287998,"score_gpt":0.26819569085106104,"score_spread":0.25060652427077307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038324709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97568023,0.00053593394,0.020693598,0.0010948686,0.000038201124,0.00036728216,0.000036248006,0.00011901536,0.0014345989],"genre_scores_gemma":[0.9813458,0.00051629456,0.017332513,0.0005355833,0.00005103404,0.00004996682,0.000048636575,0.0000726669,0.000047553487],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99729425,0.00007201566,0.00064096105,0.00083746517,0.00045455174,0.00070074154],"domain_scores_gemma":[0.9985725,0.0001671654,0.0003199618,0.00070842716,0.000056400804,0.00017551015],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018189981,0.00057213183,0.0006798863,0.00018104137,0.00017950736,0.00017688095,0.00030714687,0.00024649384,0.00030872694],"category_scores_gemma":[0.00021050306,0.00047286655,0.0001980199,0.0009121162,0.000427588,0.00017731143,0.00014120941,0.0005876618,0.0000043910445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034663695,0.00037857902,0.00015628066,0.00005817961,0.00014464898,0.000102727856,0.00011705369,0.000013819074,0.9902689,0.008252928,0.000095419025,0.0000648642],"study_design_scores_gemma":[0.001862531,0.00060497434,0.00007924556,0.00014284259,0.00018268301,0.00008750554,0.00008790691,0.00045961107,0.99137205,0.004231844,0.00040950018,0.00047932522],"about_ca_topic_score_codex":0.00012435729,"about_ca_topic_score_gemma":0.000025958214,"teacher_disagreement_score":0.0056654983,"about_ca_system_score_codex":0.00021162671,"about_ca_system_score_gemma":0.000093666655,"threshold_uncertainty_score":0.9997723},"labels":[],"label_agreement":null},{"id":"W2039320337","doi":"10.1155/2015/575807","title":"Mimicking Multimodal Contrast with Vertex Component Analysis of Hyperspectral CARS Images","year":2015,"lang":"en","type":"article","venue":"Journal of Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Contrast (vision); Computer science; Artificial intelligence; Computer vision; Component (thermodynamics); Vertex (graph theory); Independent component analysis; Pattern recognition (psychology); Graph; Theoretical computer science; Physics","score_opus":0.017488063817230042,"score_gpt":0.27855733323336535,"score_spread":0.2610692694161353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039320337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99049395,0.0016974814,0.002434063,0.00022425089,0.00008728483,0.00003672624,0.000034672732,0.000023386483,0.0049681985],"genre_scores_gemma":[0.986118,0.00013116444,0.0133901695,0.000040518196,0.00016754594,0.0000012063433,0.000009667469,0.000028301081,0.00011339148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99771124,0.00002782375,0.00077179674,0.00025118998,0.00081668905,0.00042123304],"domain_scores_gemma":[0.9977306,0.00014066522,0.0009689059,0.00031947944,0.0005192153,0.00032112317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030353386,0.00028894644,0.0011845808,0.000818886,0.000059468,0.000058049573,0.0004373237,0.00010748806,0.0005354646],"category_scores_gemma":[0.00011515688,0.00022173268,0.00048568545,0.0014288023,0.00020892246,0.00023199366,0.00003514605,0.00046059262,0.000003583844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007169754,0.00044262328,0.06799304,0.000029627778,0.0053502377,0.000120665274,0.0005225174,0.0009777528,0.92307013,0.0001140281,0.0005979563,0.00006447836],"study_design_scores_gemma":[0.002390428,0.00042859415,0.0069205067,0.000049977003,0.0062901047,0.00013336174,0.0029669045,0.0008280178,0.9795436,0.00009358122,0.00010226473,0.00025264127],"about_ca_topic_score_codex":0.00020074665,"about_ca_topic_score_gemma":0.000025311667,"teacher_disagreement_score":0.06107253,"about_ca_system_score_codex":0.00041634133,"about_ca_system_score_gemma":0.00023930622,"threshold_uncertainty_score":0.90419966},"labels":[],"label_agreement":null},{"id":"W2039680608","doi":"10.1021/ie050953b","title":"Rapid Development of New Polymer Blends:  The Optimal Selection of Materials and Blend Ratios","year":2006,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Raw material; Selection (genetic algorithm); Process engineering; Computer science; Process (computing); Materials science; Polymer blend; Yield (engineering); Product (mathematics); Material selection; Final product; Degrees of freedom (physics and chemistry); Polymer; Biochemical engineering; Mathematics; Composite material; Engineering; Thermodynamics; Chemistry","score_opus":0.06216214275085799,"score_gpt":0.3129911631559663,"score_spread":0.25082902040510835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039680608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99767584,0.00071876287,0.000035189532,0.00006688082,0.000029599893,0.00006652351,0.000015120036,0.000030579526,0.0013615113],"genre_scores_gemma":[0.99579126,0.000025864165,0.00014919725,7.719318e-7,0.00067758095,0.00001701245,0.000025742802,0.000022083885,0.0032904705],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843234,0.000014368039,0.00047792695,0.00023763833,0.00048036297,0.0003573607],"domain_scores_gemma":[0.9992855,0.00019765354,0.000113982715,0.00020828698,0.00011624723,0.00007832435],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048543338,0.00016337915,0.00028456326,0.000101015015,0.00009513153,0.000043462587,0.00025002754,0.0002455198,0.0016535689],"category_scores_gemma":[0.00020735734,0.00013887623,0.000043194985,0.00065734494,0.00009823128,0.00005504846,0.0000966235,0.00042241428,0.0000015493624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061365696,0.000035807258,0.00018993464,0.0001412851,0.00009723238,8.958886e-7,0.00007152907,0.00022027022,0.9978186,0.000023096758,0.0007156703,0.0006243344],"study_design_scores_gemma":[0.00053542614,0.000014106213,0.000037559563,0.000050179562,0.000035933805,0.0000086015,0.00016407647,0.00008453605,0.9970771,0.0000054502707,0.0018656256,0.00012139697],"about_ca_topic_score_codex":0.00021906162,"about_ca_topic_score_gemma":0.0000012017217,"teacher_disagreement_score":0.0019289592,"about_ca_system_score_codex":0.000094989875,"about_ca_system_score_gemma":0.0004271121,"threshold_uncertainty_score":0.99925905},"labels":[],"label_agreement":null},{"id":"W2040164510","doi":"10.1021/jf048663d","title":"Investigation of the Potential Utility of Single-Bounce Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy in the Analysis of Distilled Liquors and Wines","year":2005,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Attenuated total reflection; Fourier transform infrared spectroscopy; Chemistry; Spectroscopy; Distilled water; Analytical Chemistry (journal); Fourier transform spectroscopy; Infrared spectroscopy; Chromatography; Organic chemistry; Optics","score_opus":0.011941854378813371,"score_gpt":0.22338073980872536,"score_spread":0.211438885429912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040164510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972998,0.0012452918,0.000010768658,0.0005973437,0.00001044986,0.000035012967,0.0000375265,0.0000027310753,0.00076110923],"genre_scores_gemma":[0.9994606,0.00012486798,0.0001798035,0.000010461075,0.00008174608,0.0000010361334,0.000009479837,0.0000031166066,0.0001288914],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869394,0.000020352009,0.0006723788,0.00013084029,0.00035188152,0.00013057693],"domain_scores_gemma":[0.99878687,0.00007518287,0.00074067904,0.00013607013,0.00021197667,0.000049251386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001559554,0.00015206634,0.00047522192,0.000050597915,0.000047727193,0.000016820803,0.0002108497,0.00011063184,0.00005277021],"category_scores_gemma":[0.00009057454,0.000074353535,0.00024238678,0.0010179916,0.00025366142,0.0001295495,0.000024393677,0.00024201692,1.4457851e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010991566,0.000084172905,0.009478704,0.00015200755,0.00049971207,4.4436518e-7,0.000657156,0.000060970066,0.9888063,0.0000021712663,0.00006021706,0.00008822972],"study_design_scores_gemma":[0.0004669472,0.000062271305,0.16086835,0.00006278082,0.0009862881,0.00004184158,0.0013509603,0.00011781485,0.8358726,0.0000849326,0.0000062758654,0.00007896701],"about_ca_topic_score_codex":0.000009765435,"about_ca_topic_score_gemma":0.000014169165,"teacher_disagreement_score":0.15293372,"about_ca_system_score_codex":0.00003328426,"about_ca_system_score_gemma":0.000031903797,"threshold_uncertainty_score":0.30320492},"labels":[],"label_agreement":null},{"id":"W2040637807","doi":"10.1007/s11947-009-0265-0","title":"Prediction of Egg Freshness and Albumen Quality Using Visible/Near Infrared Spectroscopy","year":2009,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Industry Canada","keywords":"Haugh unit; Partial least squares regression; Near-infrared spectroscopy; Egg white; Chemistry; Flock; Analytical Chemistry (journal); Animal science; Correlation coefficient; Spectroscopy; Mathematics; Food science; Statistics; Biology; Chromatography; Ecology; Physics","score_opus":0.02583035584777222,"score_gpt":0.29655252128018045,"score_spread":0.27072216543240823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040637807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945964,0.0030423177,0.00043916982,0.0002062321,0.00001451771,0.000044320386,0.000055572993,0.00017688432,0.0014245686],"genre_scores_gemma":[0.99805886,0.0002358449,0.0015337538,0.000025848847,0.000037165064,0.0000034482293,0.000010967122,0.000008640076,0.00008546995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989946,0.0000048303837,0.00029913435,0.0003369138,0.00012636415,0.00023819429],"domain_scores_gemma":[0.9994532,0.000017483038,0.00017783303,0.00022924211,0.000072942465,0.00004929747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007383151,0.00016510316,0.00035085017,0.00025007504,0.0001246477,0.000030365398,0.00015494831,0.00035138623,0.000053719345],"category_scores_gemma":[0.00006941782,0.00015036932,0.000030938238,0.0008098167,0.00028952106,0.00010498241,0.000060533912,0.00019311465,4.448479e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005697035,0.000121034995,0.051933967,0.00024513758,0.00008055435,0.000001558753,0.00007686347,9.352628e-7,0.9444035,0.0011317661,0.000021145528,0.0019265595],"study_design_scores_gemma":[0.000501905,0.000355561,0.0038261896,0.00003567136,0.00009646356,0.00002796724,0.0005252326,0.00023072203,0.98234093,0.011747661,0.00016096799,0.00015073566],"about_ca_topic_score_codex":0.000023666204,"about_ca_topic_score_gemma":0.0000040507525,"teacher_disagreement_score":0.048107777,"about_ca_system_score_codex":0.000026606305,"about_ca_system_score_gemma":0.00003445318,"threshold_uncertainty_score":0.6131883},"labels":[],"label_agreement":null},{"id":"W2040934306","doi":"10.1016/s1386-1425(00)00333-4","title":"Fourier transform Raman spectroscopy of Syncrude sweet blend distillation fractions derived from Athabasca bitumen","year":2001,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada); Natural Resources Canada","funders":"","keywords":"Naphtha; Fraction (chemistry); Distillation; Raman spectroscopy; Aromaticity; Analytical Chemistry (journal); Chemistry; Oil sands; Spectroscopy; Spectral line; Asphalt; Organic chemistry; Materials science; Physics; Molecule; Optics","score_opus":0.009053312152833982,"score_gpt":0.2555139404257916,"score_spread":0.24646062827295764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040934306","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9343016,0.0021922083,0.04520188,0.0014520445,0.00015032834,0.00038176632,0.00025524906,0.0002710048,0.015793923],"genre_scores_gemma":[0.98403233,0.0019244427,0.012378479,0.00021434591,0.00043720676,0.00006067855,0.0005495232,0.00014909421,0.00025392062],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953044,0.0000867435,0.001042977,0.0014243181,0.00090283476,0.0012387328],"domain_scores_gemma":[0.9974821,0.00012729161,0.0005333657,0.001209126,0.00013746491,0.0005106419],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017607021,0.0009471405,0.0012032472,0.0005348689,0.00040038867,0.00022294268,0.0006428973,0.00045033105,0.0026918533],"category_scores_gemma":[0.000104218976,0.0009501781,0.0006553262,0.001318502,0.00046250314,0.00033682497,0.00015630099,0.00070764293,0.00003284142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043175308,0.00042041313,0.0021119316,0.00007068811,0.0008836253,0.00014549428,0.00015787616,0.0000033817596,0.9948151,0.00051951484,0.00029198462,0.00014825245],"study_design_scores_gemma":[0.0017823429,0.0003170078,0.00083901454,0.000089264286,0.0012092256,0.00012834971,0.0002670784,0.0004639409,0.98623556,0.0036858378,0.004000956,0.0009814267],"about_ca_topic_score_codex":0.0005237112,"about_ca_topic_score_gemma":0.00014338519,"teacher_disagreement_score":0.04973071,"about_ca_system_score_codex":0.0002923804,"about_ca_system_score_gemma":0.00017431486,"threshold_uncertainty_score":0.9992949},"labels":[],"label_agreement":null},{"id":"W2040962299","doi":"10.1007/s11746-004-0920-9","title":"New FTIR method for the determination of FFA in oils","year":2004,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Sultan Qaboos University","keywords":"Chemistry; Fourier transform infrared spectroscopy; Reagent; Saponification; Titration; Analytical Chemistry (journal); Calibration curve; Chromatography; Detection limit; Inorganic chemistry; Organic chemistry","score_opus":0.012861833962217128,"score_gpt":0.32388739124713994,"score_spread":0.3110255572849228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040962299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9393195,0.0020991424,0.049669735,0.006432138,0.000110253684,0.00005171049,0.0000141690425,0.000015763682,0.0022876037],"genre_scores_gemma":[0.84109664,0.0008671664,0.15530404,0.0005480521,0.00043662466,0.000006041918,7.845219e-7,0.000026824995,0.0017138366],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989349,0.000012747936,0.00043663764,0.00011529973,0.00031438807,0.00018597867],"domain_scores_gemma":[0.99787307,0.00048322522,0.0011770295,0.00026486657,0.00014307124,0.000058716694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042872966,0.00012692051,0.00036968556,0.000024343135,0.000070214584,0.00001736823,0.0005900939,0.000059208367,0.00004295461],"category_scores_gemma":[0.00038292806,0.00007523457,0.00067988655,0.0006342295,0.00015831787,0.00005992062,0.000050618775,0.00032891706,2.4976015e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062474406,0.000086866086,0.00045474106,0.00010413222,0.00017360179,8.348032e-7,0.0010849747,0.00014401815,0.9570866,0.000013828813,0.0009703855,0.039817516],"study_design_scores_gemma":[0.0008645563,0.0000300679,0.00029563202,0.000059360686,0.00024650997,0.000037483827,0.0020174,0.00014237822,0.9948749,0.0006462258,0.0006943759,0.00009109273],"about_ca_topic_score_codex":0.00021700886,"about_ca_topic_score_gemma":0.0000066358803,"teacher_disagreement_score":0.10563432,"about_ca_system_score_codex":0.0003156647,"about_ca_system_score_gemma":0.00029027837,"threshold_uncertainty_score":0.30679768},"labels":[],"label_agreement":null},{"id":"W2041369703","doi":"10.1007/s002000100081","title":"Constrained Principal Component Analysis: A Comprehensive Theory","year":2001,"lang":"en","type":"article","venue":"Applicable Algebra in Engineering Communication and Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McGill University","funders":"","keywords":"Principal component analysis; Canonical correlation; Correspondence analysis; Matrix (chemical analysis); Singular value decomposition; Canonical form; Metric (unit); Component analysis; Mathematics; Computer science; Canonical analysis; Variety (cybernetics); Applied mathematics; Algorithm; Statistics; Pure mathematics","score_opus":0.010934225052261968,"score_gpt":0.24944924275748742,"score_spread":0.23851501770522546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041369703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9287643,0.0027284434,0.06400024,0.000100465535,0.000008579933,0.00008469432,0.0000022245515,0.0002148336,0.004096251],"genre_scores_gemma":[0.9942436,0.00031730597,0.0051760995,0.00009181825,0.000022197726,0.00002031962,0.00004601722,0.000016589776,0.0000660186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989385,0.000028283252,0.00035992044,0.0002684402,0.00012144358,0.00028339837],"domain_scores_gemma":[0.99872077,0.0004930872,0.000104665094,0.0005554099,0.000041413274,0.000084646694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021663564,0.00018738153,0.00037125105,0.00028672864,0.00012628127,0.0000580834,0.00034921253,0.00008759235,0.0001613709],"category_scores_gemma":[0.00002992353,0.00020527576,0.000081223625,0.0011986464,0.00007532194,0.00005624835,0.0002241016,0.000330164,0.0000053071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001861254,0.0011597086,0.1970439,0.0007202631,0.0048767957,0.000053801105,0.0032623857,0.31176737,0.14610596,0.308227,0.000060375467,0.026536295],"study_design_scores_gemma":[0.0018573938,0.00002409538,0.025098883,0.00012095807,0.0008287973,0.00007403679,0.002560338,0.9509051,0.010694995,0.0022371213,0.0046596923,0.0009385957],"about_ca_topic_score_codex":0.00008450785,"about_ca_topic_score_gemma":0.0000065044937,"teacher_disagreement_score":0.63913774,"about_ca_system_score_codex":0.000062125284,"about_ca_system_score_gemma":0.000011333303,"threshold_uncertainty_score":0.83709025},"labels":[],"label_agreement":null},{"id":"W2041544704","doi":"10.1080/03639040500306294","title":"Artificial Neural Networks: Comparison of Two Programs for Modeling a Process of Nanoparticle Preparation","year":2005,"lang":"en","type":"article","venue":"Drug Development and Industrial Pharmacy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial neural network; Nanoparticle; Biological system; Materials science; Microporous material; Computer science; Software; Nanotechnology; Machine learning; Composite material","score_opus":0.12138356833512111,"score_gpt":0.39327659544385074,"score_spread":0.27189302710872965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041544704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981866,0.00033095744,0.00096547185,0.000075190015,0.000047497597,0.00020873481,0.0000027621797,0.000029529048,0.00015327032],"genre_scores_gemma":[0.99876845,0.0000051445536,0.0006847771,0.00000951268,0.00034622196,0.00005640366,0.000047662583,0.000010476516,0.00007133232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987948,0.0000097959655,0.00061986386,0.00019256033,0.00017035403,0.00021263707],"domain_scores_gemma":[0.99949086,0.000041851446,0.00023355299,0.000067726,0.00010262211,0.00006338801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017958616,0.00013029901,0.00028995125,0.0000632667,0.00009041096,0.000025516065,0.000103242324,0.000046192086,0.00006079568],"category_scores_gemma":[0.000035096993,0.00012299595,0.0000467844,0.00027329288,0.000039006063,0.00016805636,0.000025483598,0.00011695403,4.1492083e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035372565,0.0022022144,0.08543407,0.0003902385,0.0005603753,5.752417e-7,0.010706176,0.17304376,0.14885755,0.00016994745,0.001045964,0.57405186],"study_design_scores_gemma":[0.0008803373,0.000011210717,0.0000010105766,0.000012941036,0.00006785674,2.404467e-7,0.00031503424,0.43575788,0.56252223,0.000019502395,0.0003283329,0.000083406085],"about_ca_topic_score_codex":0.00001837804,"about_ca_topic_score_gemma":0.000011315221,"teacher_disagreement_score":0.57396847,"about_ca_system_score_codex":0.000029633931,"about_ca_system_score_gemma":0.00008946653,"threshold_uncertainty_score":0.5015629},"labels":[],"label_agreement":null},{"id":"W2041909294","doi":"10.1117/12.830332","title":"A new imaging FTIR spectroradiometer","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Spectroradiometer; Hyperspectral imaging; Remote sensing; Full spectral imaging; Optics; Detector; Spectral bands; Multispectral image; Spectral imaging; Field of view; Computer science; Physics; Geology","score_opus":0.009672630866864252,"score_gpt":0.24253928450466625,"score_spread":0.23286665363780198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041909294","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.974398,0.00035915343,0.00007219257,0.004098696,0.00009530986,0.00015316269,0.000022849526,0.00014133418,0.020659273],"genre_scores_gemma":[0.7948373,0.00017564253,0.20046802,0.00039403245,0.001449685,0.000045113793,0.000014587755,0.00008995979,0.002525652],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99760973,3.5060168e-9,0.000667616,0.000464591,0.0007253566,0.0005326805],"domain_scores_gemma":[0.9984314,0.000103512946,0.0003821991,0.00008213566,0.00080168486,0.00019907558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028258434,0.0003803656,0.0005197105,0.00019046335,0.00008530365,0.00015925734,0.0011358152,0.0001711147,0.00019460225],"category_scores_gemma":[0.0004340108,0.0003240336,0.00088530587,0.00071448804,0.00012920333,0.000518603,0.00010254837,0.0004022989,0.0000034629613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066219414,0.00010233344,0.00046953102,0.00017599895,0.00039299173,1.7830172e-7,0.000102236554,0.000008686527,0.8099874,0.17829248,0.009706895,0.0006950274],"study_design_scores_gemma":[0.0012345535,0.00016628757,0.0006719485,0.00016540101,0.00045927084,0.000038255865,0.0009749839,0.0042345887,0.98103034,0.0047716415,0.005758342,0.0004943541],"about_ca_topic_score_codex":0.000014448065,"about_ca_topic_score_gemma":5.7295274e-8,"teacher_disagreement_score":0.20039582,"about_ca_system_score_codex":0.00024967376,"about_ca_system_score_gemma":0.00004721806,"threshold_uncertainty_score":0.99992114},"labels":[],"label_agreement":null},{"id":"W2042294628","doi":"10.1007/s11947-014-1381-z","title":"Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat","year":2014,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":162,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Partial least squares regression; Principal component regression; Principal component analysis; Linear regression; Mathematics; Regression analysis; Statistics; Regression; Standard error; Coefficient of determination; Cross-validation; Hyperspectral imaging; Geography; Remote sensing","score_opus":0.0549611758418994,"score_gpt":0.35697701337955756,"score_spread":0.30201583753765815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042294628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925153,0.0033525738,0.0033464876,0.000350834,0.000022862661,0.00018550578,0.00012879081,0.00003412946,0.00006347553],"genre_scores_gemma":[0.9847796,0.000074389216,0.015032051,0.0000025782538,0.000026194315,0.000027237002,0.000015372922,0.000012771324,0.00002983033],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899733,0.00002509,0.00033923503,0.00028928308,0.00010822633,0.00024081022],"domain_scores_gemma":[0.9991541,0.00007329448,0.00032547687,0.00022557628,0.0001448613,0.00007672476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016231334,0.00017656606,0.00051520724,0.00037893368,0.00026848694,0.000020023837,0.00018380325,0.0002774037,0.000009329322],"category_scores_gemma":[0.00023552254,0.00011889761,0.000043750053,0.0005215656,0.0008442451,0.0000595184,0.000093889,0.00016956175,1.7647169e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000142334,0.00008858689,0.050341837,0.0007366346,0.0000957559,1.7444262e-7,0.0003013127,0.0000054966035,0.9435962,0.00055578887,0.0000164481,0.0041194563],"study_design_scores_gemma":[0.000566336,0.0003500176,0.0014466335,0.00027154485,0.00017109273,0.00001433546,0.002015062,0.004197411,0.98963946,0.00085766066,0.00034797794,0.00012247638],"about_ca_topic_score_codex":0.0016203574,"about_ca_topic_score_gemma":0.00035591723,"teacher_disagreement_score":0.048895206,"about_ca_system_score_codex":0.000019332123,"about_ca_system_score_gemma":0.00006129228,"threshold_uncertainty_score":0.4848504},"labels":[],"label_agreement":null},{"id":"W2042303028","doi":"10.1016/j.foodres.2007.06.008","title":"In-line colour monitoring during food extrusion: Sensitivity and correlation with product colour","year":2007,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Line (geometry); Plastics extrusion; Computer science; Calibration; Product (mathematics); Biological system; Product line; Mathematics; Artificial intelligence; Pattern recognition (psychology); Materials science; Statistics; Engineering","score_opus":0.057916957169888886,"score_gpt":0.37494283537303946,"score_spread":0.3170258782031506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042303028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934721,0.00025757347,0.0002673388,0.0005154472,0.00013240614,0.0000899207,0.000013479802,0.00003134985,0.005220409],"genre_scores_gemma":[0.99685997,0.000051911546,0.00044803967,0.0000060288944,0.00069148425,0.000013462392,0.000011718831,0.000015776342,0.0019016196],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821836,0.00003587681,0.00021294736,0.00035894633,0.00080061733,0.00037325316],"domain_scores_gemma":[0.99893457,0.00040289093,0.00006757362,0.00015018994,0.00034054273,0.00010425185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009598756,0.0001168773,0.00014110318,0.00045094287,0.00016640914,0.000066620276,0.00013309259,0.00008052311,0.000280034],"category_scores_gemma":[0.0005287911,0.00010705901,0.000025457928,0.00058574945,0.00010512864,0.00021628798,0.00014653262,0.000555123,0.000008275856],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012319005,0.0004293727,0.5566132,0.00013662777,0.00027296148,0.00027814947,0.00041156707,0.00032286844,0.43816602,0.0007534623,0.00012556236,0.0012583113],"study_design_scores_gemma":[0.0011683778,0.00026779596,0.11254649,0.000099747485,0.000012608925,0.0001311364,0.000683116,0.0005831187,0.8839065,0.00016360165,0.00027110355,0.00016643271],"about_ca_topic_score_codex":0.000047886428,"about_ca_topic_score_gemma":0.00024261544,"teacher_disagreement_score":0.44574046,"about_ca_system_score_codex":0.00032245275,"about_ca_system_score_gemma":0.00007306662,"threshold_uncertainty_score":0.43657398},"labels":[],"label_agreement":null},{"id":"W2044371403","doi":"10.1520/jai102110","title":"Automated Acid Content Determination in Lubricants by FTIR Spectroscopy as an Alternative to Acid Number Determination","year":2009,"lang":"en","type":"article","venue":"Journal of ASTM International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Absorbance; Fourier transform infrared spectroscopy; Reagent; Oleic acid; Chemistry; Analytical Chemistry (journal); Acid value; Calibration curve; Base (topology); Chromatography; Nuclear chemistry; Materials science; Detection limit; Organic chemistry; Chemical engineering; Mathematics","score_opus":0.019914975399900503,"score_gpt":0.3437764666193113,"score_spread":0.3238614912194108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044371403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98686856,0.000044310607,0.003033043,0.00134094,0.0002974752,0.00006623671,0.00003585094,0.00004818202,0.008265422],"genre_scores_gemma":[0.99106675,0.00003526327,0.00713989,0.00056215544,0.00033764465,0.0000051225124,0.00004366036,0.00001867273,0.0007908437],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974765,0.00004786115,0.0009403348,0.00029939992,0.0009470657,0.00028881914],"domain_scores_gemma":[0.9980991,0.00006945368,0.0008328068,0.00016325405,0.0006231462,0.00021226465],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035800904,0.00024376655,0.00040079368,0.0005262515,0.0000511978,0.00014790372,0.00064466044,0.00012168618,0.001987239],"category_scores_gemma":[0.00046564752,0.00022828879,0.00015831049,0.0004222843,0.00003377141,0.00079441315,0.000037716916,0.00035896403,0.000065370514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036032207,0.0006808609,0.0053704856,0.000004842927,0.000088858964,0.00011331014,0.00043478102,0.000025008001,0.98054105,0.00011291511,0.0019405199,0.010327022],"study_design_scores_gemma":[0.0015024837,0.00036905534,0.008975365,0.00007131837,0.000047884714,0.00037692816,0.00039146232,0.0052969498,0.9814809,0.00076766184,0.00047585653,0.00024412181],"about_ca_topic_score_codex":0.000043122483,"about_ca_topic_score_gemma":0.000015462798,"teacher_disagreement_score":0.0100829,"about_ca_system_score_codex":0.00079505524,"about_ca_system_score_gemma":0.00008836677,"threshold_uncertainty_score":0.9989251},"labels":[],"label_agreement":null},{"id":"W2044981414","doi":"10.1016/j.foodqual.2007.03.001","title":"Fusion of sensory and mechanical testing data to define measures of snack food texture","year":2007,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram; Texture (cosmology); Multivariate statistics; Quality (philosophy); Process (computing); Mathematics; Machine learning; Image (mathematics)","score_opus":0.49641606453639436,"score_gpt":0.3862186014129841,"score_spread":0.11019746312341028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044981414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937311,0.0013167266,0.0020254024,0.00007106789,0.000010472197,0.00004508805,0.00028533206,0.0000226328,0.0024922017],"genre_scores_gemma":[0.9963948,0.00006016276,0.0033906207,0.000048798734,0.00003430823,8.857947e-7,0.000025272213,0.000006310856,0.000038821698],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881434,0.000033146174,0.0003962053,0.00033014105,0.00025328776,0.00017290405],"domain_scores_gemma":[0.9985101,0.0006300093,0.0001709143,0.00046527648,0.00011447766,0.000109219065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007449956,0.00012421912,0.00031911724,0.000061391445,0.000053061438,0.000010514334,0.0002386802,0.00013947747,0.00006269174],"category_scores_gemma":[0.0015140988,0.00010841014,0.00002222363,0.00030377283,0.00006816931,0.000062747735,0.0002949233,0.00017023784,5.4131175e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022813599,0.00014345092,0.016506556,0.0007792271,0.00012389285,7.0265475e-7,0.0003588701,0.0000013679573,0.9614718,0.002921008,0.00006174125,0.017403226],"study_design_scores_gemma":[0.00041337856,0.00046759413,0.02184989,0.00011641354,0.00013635596,0.000008176996,0.0007388068,0.00004115577,0.97338235,0.0023859078,0.00024422162,0.00021574755],"about_ca_topic_score_codex":0.00011015962,"about_ca_topic_score_gemma":0.00022773146,"teacher_disagreement_score":0.017187478,"about_ca_system_score_codex":0.000007683079,"about_ca_system_score_gemma":0.000027215814,"threshold_uncertainty_score":0.44208372},"labels":[],"label_agreement":null},{"id":"W2045283192","doi":"10.1016/j.lwt.2007.02.022","title":"Early detection of apple bruises on different background colors using hyperspectral imaging","year":2007,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":270,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Hyperspectral imaging; Multispectral image; Bruise; Dimensionality reduction; Artificial intelligence; Wavelength; Remote sensing; Computer science; Computer vision; Pattern recognition (psychology); Optics; Physics; Geology; Medicine","score_opus":0.024561654935647933,"score_gpt":0.29154749470324093,"score_spread":0.266985839767593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045283192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99019575,0.00021826038,0.0028426694,0.000011340798,0.000057429177,0.000028699866,0.0000052399196,0.0000501182,0.006590493],"genre_scores_gemma":[0.9992274,0.000008099363,0.0002567796,0.000024807468,0.00015648022,0.0000014379558,0.0000035966232,0.0000167761,0.00030463195],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991481,0.000005012798,0.00019656833,0.00019881094,0.00019331057,0.00025820488],"domain_scores_gemma":[0.99953663,0.00009072799,0.00010514099,0.00017456012,0.0000331726,0.00005974104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003906209,0.00013096162,0.00019141384,0.00014186595,0.000080555496,0.000022891456,0.00010457306,0.00006142633,0.000351181],"category_scores_gemma":[0.00002214196,0.0001216114,0.00009886167,0.0002987838,0.000058518424,0.00005581196,0.000021244976,0.00014487773,0.0000080939935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069709335,0.000097499884,0.0153730465,0.000028141843,0.000043280696,0.0000055416513,0.000090452006,0.000010204761,0.9835134,0.00011357047,0.000011029885,0.00064409716],"study_design_scores_gemma":[0.0002798386,0.000032545908,0.0082179345,0.000014228185,0.00009051419,0.000007691717,0.0010782385,0.00018663426,0.98979235,0.000091240945,0.00008362631,0.00012517131],"about_ca_topic_score_codex":0.0003240866,"about_ca_topic_score_gemma":0.0000258293,"teacher_disagreement_score":0.009031641,"about_ca_system_score_codex":0.0001785898,"about_ca_system_score_gemma":0.0000040505893,"threshold_uncertainty_score":0.4959169},"labels":[],"label_agreement":null},{"id":"W2045288965","doi":"10.1021/ie020941f","title":"Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes","year":2003,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Principal component analysis; Computer science; Feature (linguistics); Coating; Process engineering; Product (mathematics); Control (management); Quality (philosophy); Digital imaging; Snack food; Pattern recognition (psychology); Artificial intelligence; Data mining; Image processing; Digital image; Image (mathematics); Chemistry; Engineering; Mathematics; Food science","score_opus":0.09625862212565735,"score_gpt":0.34518323634644094,"score_spread":0.2489246142207836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045288965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952718,0.0016750509,0.00018248355,0.000082422936,0.00010476852,0.0002269864,0.00041893325,0.000105315245,0.0019322112],"genre_scores_gemma":[0.99797165,0.000034281613,0.000066312205,0.0000011262287,0.0012997318,0.000061185485,0.000040160143,0.000053212712,0.00047235863],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978721,0.000010821446,0.0004595413,0.0004452301,0.00052542967,0.000686848],"domain_scores_gemma":[0.9978777,0.0010895096,0.000111215515,0.00029611817,0.00039671778,0.00022874943],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043232235,0.000269587,0.00044735172,0.00015343199,0.000110948604,0.00015844393,0.00031735084,0.00036582202,0.00011126382],"category_scores_gemma":[0.008227282,0.00028399169,0.00009528215,0.00089819846,0.00013929559,0.0001871648,0.00006755861,0.00096326874,6.685639e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015269645,0.00018908753,0.021101559,0.0006414671,0.00025955023,0.0000056280683,0.00003130923,0.00020827953,0.9749271,0.000023241277,0.00017097949,0.0022890475],"study_design_scores_gemma":[0.0032144948,0.000053559972,0.0000066426132,0.00017199991,0.00006951384,0.000015539557,0.00040830902,0.00019510245,0.99008995,0.00003440364,0.0054872613,0.00025323982],"about_ca_topic_score_codex":0.0000075181374,"about_ca_topic_score_gemma":1.2697528e-7,"teacher_disagreement_score":0.021094916,"about_ca_system_score_codex":0.00014871394,"about_ca_system_score_gemma":0.00036331595,"threshold_uncertainty_score":0.9999612},"labels":[],"label_agreement":null},{"id":"W2045325565","doi":"10.1366/000370202760354713","title":"In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis","year":2002,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Partial least squares regression; Principal component analysis; Principal component regression; Spectral line; Biological system; Multiplicative function; Analytical Chemistry (journal); Spectrometer; Chemistry; Mathematics; Materials science; Optics; Statistics; Physics; Chromatography; Mathematical analysis","score_opus":0.04079423446206292,"score_gpt":0.3083071632250463,"score_spread":0.2675129287629834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045325565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91495335,0.0062626833,0.0014842653,0.00030195195,0.00006025235,0.00009795418,0.00007142705,0.00015125964,0.07661682],"genre_scores_gemma":[0.99232364,0.00031380344,0.004222317,0.000043109947,0.00041494129,0.00001701451,0.000063135136,0.000039681905,0.0025623653],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99745697,0.0000074250147,0.0006323557,0.0008193321,0.0004612612,0.0006226698],"domain_scores_gemma":[0.9981555,0.000057073557,0.0002902259,0.0013426946,0.00003197297,0.00012255667],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014130083,0.00033625745,0.0007788051,0.0006309098,0.00013402698,0.00004362567,0.0010146174,0.00016909234,0.006881127],"category_scores_gemma":[0.0000282259,0.00033725475,0.0001462477,0.0035439,0.00013429485,0.00022493466,0.0002700334,0.00040898705,0.000038033882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006399564,0.0006970252,0.035216264,0.00009941908,0.0006627635,0.000015225214,0.00033847964,0.00006727688,0.96120554,0.00065572007,0.00044158034,0.00053668284],"study_design_scores_gemma":[0.00058781187,0.000031132586,0.0007142738,0.000012840145,0.0010618133,0.0000027191004,0.00031249397,0.0017677344,0.99476135,0.0002333432,0.00018855384,0.00032596206],"about_ca_topic_score_codex":0.00013889352,"about_ca_topic_score_gemma":0.000023495682,"teacher_disagreement_score":0.07737025,"about_ca_system_score_codex":0.00013327293,"about_ca_system_score_gemma":0.000031858483,"threshold_uncertainty_score":0.999908},"labels":[],"label_agreement":null},{"id":"W2045342133","doi":"10.1017/s1743921309991256","title":"Measuring the GC luminosity function up to <i>z</i> ~ 0.2","year":2009,"lang":"en","type":"article","venue":"Proceedings of the International Astronomical Union","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dominion Astrophysical Observatory; Herzberg Institute of Astrophysics","funders":"","keywords":"Physics; Globular cluster; Surface brightness; Photometry (optics); Astrophysics; Galaxy; Metallicity; Redshift; Brightness; Luminosity; Elliptical galaxy; Luminosity function; Astronomy; Stars","score_opus":0.01607899033036189,"score_gpt":0.22999390512724663,"score_spread":0.21391491479688474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045342133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97699326,0.000013739293,0.00024264338,0.005488877,0.00017370463,0.000044779572,0.0000041351536,0.000026996075,0.017011868],"genre_scores_gemma":[0.9972781,0.0000014347823,0.00015940149,0.00022839035,0.0002750913,0.0000031627774,0.000002137184,0.0000055375576,0.0020467557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922323,0.0000017505454,0.00019713126,0.00017499624,0.00026961585,0.00013325886],"domain_scores_gemma":[0.9995649,0.000027237666,0.00013648515,0.00008320167,0.00014382091,0.00004436496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014380031,0.00009841981,0.00010363391,0.000053288724,0.00008832201,0.00005329478,0.0006926294,0.000041780284,0.00015860757],"category_scores_gemma":[0.00011869515,0.00006335147,0.00011695907,0.00016766907,0.000035271038,0.00011421826,0.00012853886,0.00017269597,0.000012657282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017149805,0.00008636455,0.06414051,0.0000086225655,0.000105819905,1.9075843e-8,0.000112113994,0.00015877088,0.92249864,0.005482027,0.004184953,0.0030506335],"study_design_scores_gemma":[0.00020998908,0.000031619988,0.06788569,0.000026550617,0.00006687794,0.0000020462373,0.00025360798,0.00028764713,0.9249883,0.00094544847,0.005210047,0.00009216514],"about_ca_topic_score_codex":0.000022654423,"about_ca_topic_score_gemma":0.0000011714653,"teacher_disagreement_score":0.020284832,"about_ca_system_score_codex":0.00016414188,"about_ca_system_score_gemma":0.000013255703,"threshold_uncertainty_score":0.25833982},"labels":[],"label_agreement":null},{"id":"W2045838956","doi":"10.1007/s002160051565","title":"Infrared and Raman imaging of biological and biomimetic samples","year":2000,"lang":"en","type":"review","venue":"Fresenius Journal of Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Chemical imaging; Raman spectroscopy; Infrared; Microscopy; Positron emission tomography; Nuclear magnetic resonance; Biological specimen; Materials science; Biological system; Nanotechnology; Optics; Analytical Chemistry (journal); Computer science; Chemistry; Artificial intelligence; Physics; Hyperspectral imaging; Environmental chemistry; Biology","score_opus":0.05582864295364131,"score_gpt":0.34133676272981955,"score_spread":0.28550811977617824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045838956","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038453978,0.987098,0.000018752222,0.000027965169,0.000014268338,0.00003300123,0.000085270054,0.00000986172,0.008867477],"genre_scores_gemma":[0.003826982,0.9940655,0.00038626255,0.000007905278,0.00033324657,0.0000014679274,0.000031756656,0.00003372901,0.0013131564],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973362,0.000039338007,0.0014750705,0.00040039644,0.00037117672,0.0003778054],"domain_scores_gemma":[0.9973287,0.0008155605,0.0009847356,0.00034064046,0.00014068483,0.0003896939],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027224285,0.0005011925,0.0027022292,0.00025663275,0.00006183962,0.00008087731,0.00047292776,0.00050919224,0.003792807],"category_scores_gemma":[0.0007555248,0.00036171428,0.0007048595,0.0005515571,0.00073478994,0.00007416709,0.00015895886,0.0008968466,0.000002817723],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005229426,0.0015566164,0.0078031234,0.105084926,0.011857629,0.0018019106,0.00015057417,0.0000026805842,0.007026986,0.0001808191,0.0087318905,0.8552799],"study_design_scores_gemma":[0.0013896489,0.000108301356,0.000028320199,0.008159795,0.011607849,0.005015154,0.00028255171,0.00008495036,0.013141465,0.0019740069,0.9569374,0.0012705887],"about_ca_topic_score_codex":0.000004737008,"about_ca_topic_score_gemma":7.9975536e-8,"teacher_disagreement_score":0.9482055,"about_ca_system_score_codex":0.00008043901,"about_ca_system_score_gemma":0.00020316435,"threshold_uncertainty_score":0.9998835},"labels":[],"label_agreement":null},{"id":"W2045909750","doi":"10.1007/s11746-000-0005-9","title":"Discrimination of edible oil products and quantitative determination of their iodine value by Fourier transform near‐infrared spectroscopy","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Calibration; Iodine value; Linear discriminant analysis; Near-infrared spectroscopy; Analytical Chemistry (journal); Fourier transform infrared spectroscopy; Chemometrics; Chromatography; Mathematics; Spectroscopy; Chemistry; Artificial intelligence; Statistics; Computer science; Optics; Physics; Food science","score_opus":0.010253248108409156,"score_gpt":0.26846901561186076,"score_spread":0.2582157675034516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045909750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911782,0.0012179859,0.00015769237,0.0010584147,0.000023450157,0.000016783704,0.0000505507,0.000008315806,0.006288604],"genre_scores_gemma":[0.97045964,0.0042890813,0.020715524,0.00009322333,0.000116425676,0.000003478287,0.00000997186,0.000028731716,0.004283935],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855864,0.000025781525,0.0005574892,0.00018639679,0.00046801983,0.0002036452],"domain_scores_gemma":[0.99805874,0.00015058428,0.0011651105,0.00025117377,0.00030558178,0.000068832334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030623865,0.00019668254,0.00054088753,0.000029881763,0.0001069508,0.000025677202,0.000323492,0.000076912635,0.00014514924],"category_scores_gemma":[0.00016502853,0.00013721194,0.00034817797,0.0006680959,0.00073268433,0.00017690561,0.000023754614,0.00033401966,2.6101276e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018103045,0.00021728483,0.00036083828,0.00044111453,0.00021104205,4.4065845e-7,0.0033003006,0.000009580362,0.9720201,0.0000062628,0.00178147,0.021470556],"study_design_scores_gemma":[0.0006251199,0.00013125778,0.0001981144,0.00010997167,0.00030200326,0.00002435295,0.0028290278,0.0006709222,0.99410313,0.00018111194,0.00068643515,0.00013857805],"about_ca_topic_score_codex":0.00005450613,"about_ca_topic_score_gemma":9.886832e-7,"teacher_disagreement_score":0.022083025,"about_ca_system_score_codex":0.00014622124,"about_ca_system_score_gemma":0.00014186709,"threshold_uncertainty_score":0.5595341},"labels":[],"label_agreement":null},{"id":"W2046103546","doi":"10.1016/j.foodres.2008.04.011","title":"Magnetic resonance measurements of French fries to determine spatially resolved oil and water content","year":2008,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Foods and Materials Network","keywords":"French fries; Relaxation (psychology); Absorption (acoustics); Palatability; Texture (cosmology); Chemistry; Food science; Materials science; Computer science; Composite material; Artificial intelligence; Image (mathematics)","score_opus":0.1863224999895442,"score_gpt":0.34754185398462834,"score_spread":0.16121935399508414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046103546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9693993,0.0014373466,0.000036245397,0.0008728682,0.000061931634,0.00003065496,0.00004896746,0.000016394984,0.02809633],"genre_scores_gemma":[0.9772955,0.00015224521,0.00086064794,0.0000389823,0.00013150238,0.00003002857,0.000020868662,0.000012116814,0.021458063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99795043,0.000029248497,0.00027773375,0.000281091,0.0011434755,0.00031801863],"domain_scores_gemma":[0.9988646,0.00009894881,0.00003297863,0.0002107842,0.00066842523,0.00012428778],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003043105,0.000107559994,0.000171481,0.00028370036,0.00010750013,0.000029417464,0.00038657678,0.000061518855,0.0022665672],"category_scores_gemma":[0.0005780378,0.000084381085,0.000047258014,0.0001885347,0.00022031054,0.00006924653,0.00023218685,0.00019588332,0.00003209432],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024019547,0.00022611035,0.05969337,0.00006678285,0.00013746812,0.000025956884,0.0006754532,0.0000035342973,0.9266193,0.00006373314,0.002311785,0.009936311],"study_design_scores_gemma":[0.0006806655,0.00031238384,0.008516282,0.00004813182,0.00000971381,0.00001616904,0.00006136324,0.000055044235,0.97943616,0.000097417906,0.010657662,0.00010902653],"about_ca_topic_score_codex":0.0003197522,"about_ca_topic_score_gemma":0.00013514391,"teacher_disagreement_score":0.05281684,"about_ca_system_score_codex":0.0000917289,"about_ca_system_score_gemma":0.000057728666,"threshold_uncertainty_score":0.9986455},"labels":[],"label_agreement":null},{"id":"W2046284106","doi":"10.1007/s11694-012-9126-z","title":"Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging","year":2012,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Fusarium; VNIR; False positive paradox; Linear discriminant analysis; Partial least squares regression; Infrared; Near-infrared spectroscopy; Kernel (algebra); Mathematics; Pattern recognition (psychology); Artificial intelligence; Biology; Horticulture; Computer science; Physics; Optics; Statistics","score_opus":0.03366940748179755,"score_gpt":0.2577262295239047,"score_spread":0.22405682204210717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046284106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997087,0.0004208496,0.001513687,0.00006384663,0.00022181061,0.000048156086,0.000010166885,0.000007935134,0.0006265617],"genre_scores_gemma":[0.9993874,0.000031618285,0.00020291693,0.0000319334,0.00029494058,0.000001132378,0.000015147715,0.000019661731,0.000015263497],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983202,0.000035072775,0.000587496,0.00010624597,0.000531142,0.0004197996],"domain_scores_gemma":[0.99881613,0.000009038331,0.00043281144,0.00013878907,0.00038739727,0.00021580789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069889094,0.00014758788,0.0002962038,0.00056385976,0.00008567544,0.000051674248,0.00013087626,0.00008251119,0.00020377828],"category_scores_gemma":[0.0001371788,0.0001367209,0.000108617205,0.00047145417,0.000025407719,0.00071971444,0.000011948655,0.00023165232,0.0000013121386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004351243,0.00010464118,0.06842688,0.00005521786,0.00007060441,0.0000023602101,0.00040665618,0.000021403692,0.9304545,0.0000047898325,0.0000014671849,0.0004079947],"study_design_scores_gemma":[0.00047810556,0.000044314555,0.09636634,0.000067215035,0.00015483245,0.00003184566,0.00024801548,0.00047910083,0.9018264,0.000011118347,0.00016941507,0.00012333952],"about_ca_topic_score_codex":0.0017352415,"about_ca_topic_score_gemma":0.0010877375,"teacher_disagreement_score":0.02862812,"about_ca_system_score_codex":0.0011768292,"about_ca_system_score_gemma":0.00026112268,"threshold_uncertainty_score":0.55753165},"labels":[],"label_agreement":null},{"id":"W2046733527","doi":"10.1002/jsfa.3215","title":"Unsupervised classification methods in food sciences: discussion and outlook","year":2008,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principal component analysis; Computer science; Artificial intelligence; Heuristic; Probabilistic logic; Cluster (spacecraft); Machine learning; Principal (computer security); Basis (linear algebra); Similarity (geometry); Component (thermodynamics); Multivariate statistics; Data mining; Mathematics; Image (mathematics)","score_opus":0.0422237410408119,"score_gpt":0.31655500420890675,"score_spread":0.27433126316809486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046733527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99616456,0.0012749766,0.000058783866,0.0011816805,0.000040560084,0.00002249138,0.0000022098013,0.0000022712875,0.001252452],"genre_scores_gemma":[0.9968386,0.00030622128,0.0026476055,0.000017367245,0.00004007818,3.651569e-7,9.760413e-8,0.0000013261251,0.00014829074],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991086,0.000024402898,0.00025833686,0.00013436086,0.00034799258,0.00012630537],"domain_scores_gemma":[0.99942654,0.000046670317,0.0002836128,0.00008219198,0.00009585816,0.000065108354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217145,0.00007611353,0.0001773665,0.00011848494,0.00028712078,0.00003141632,0.00036569408,0.00005010022,0.000011651659],"category_scores_gemma":[0.00021516987,0.0000278337,0.000059356575,0.0012424206,0.00067207456,0.00027414504,0.00006406657,0.00017679969,5.4427556e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066881566,0.00004421184,0.0026076038,0.000015270376,0.000008237418,3.4879926e-7,0.0004349965,0.000013081035,0.9958432,0.00021466849,0.00005860778,0.0007530971],"study_design_scores_gemma":[0.00030632748,0.00017995885,0.029602291,0.00008310795,0.000031334563,0.00016138608,0.0030601034,0.00007706255,0.9654538,0.00089548365,0.000073640396,0.000075493976],"about_ca_topic_score_codex":0.00000210132,"about_ca_topic_score_gemma":0.0000022901477,"teacher_disagreement_score":0.03038938,"about_ca_system_score_codex":0.000030424433,"about_ca_system_score_gemma":0.000085450425,"threshold_uncertainty_score":0.24762867},"labels":[],"label_agreement":null},{"id":"W2046928803","doi":"10.1111/j.1365-2621.2000.tb10262.x","title":"An Ultrasonic Method for Assessing the Residence Time Distribution of Particulate Foods During Ohmic Heating","year":2000,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Ultrasonic sensor; Sonication; Gravimetric analysis; Particulates; Residence time distribution; Starch; Materials science; Copper; Chemistry; Analytical Chemistry (journal); Chromatography; Mineralogy; Metallurgy; Acoustics; Food science","score_opus":0.021134811064746403,"score_gpt":0.359346418500973,"score_spread":0.3382116074362266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046928803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914831,0.00026008787,0.007831103,0.00013111535,0.00002017332,0.000031715823,0.0000060378698,0.000008550377,0.00022810066],"genre_scores_gemma":[0.9937443,0.000019145999,0.0060622767,0.000011232772,0.000096028656,0.0000016390934,7.1540654e-7,0.0000053264694,0.000059324906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985959,0.00003462003,0.00045383247,0.00016531193,0.00046755385,0.00028278792],"domain_scores_gemma":[0.9987099,0.00030868134,0.00044189644,0.00021911606,0.000226888,0.00009347157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015300964,0.00009122697,0.00021745168,0.00006980327,0.000402327,0.00013478384,0.000538885,0.000038935625,0.00015992885],"category_scores_gemma":[0.0006097951,0.00006172798,0.00012046335,0.00089334825,0.0002020093,0.000674905,0.0000151477825,0.0001964103,8.3992603e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023840457,0.000042209304,0.001266639,0.000025427007,0.000020513704,0.0000010793584,0.00013273422,0.0019489769,0.9945742,0.000035834746,0.0000053549343,0.0019232216],"study_design_scores_gemma":[0.0002226283,0.00015207524,0.0071706213,0.00008813326,0.00010033637,0.00011134765,0.00029464078,0.008346553,0.9828777,0.00055080745,0.0000078114945,0.00007740162],"about_ca_topic_score_codex":0.000007696561,"about_ca_topic_score_gemma":7.3147004e-7,"teacher_disagreement_score":0.011696526,"about_ca_system_score_codex":0.00011796532,"about_ca_system_score_gemma":0.00020582603,"threshold_uncertainty_score":0.30944145},"labels":[],"label_agreement":null},{"id":"W2047078731","doi":"10.1016/j.jala.2005.11.002","title":"Automated FTIR Analysis of Free Fatty Acids or Moisture in Edible Oils","year":2006,"lang":"en","type":"article","venue":"JALA Journal of the Association for Laboratory Automation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Sultan Qaboos University","keywords":"Moisture; Solvent; Fourier transform infrared spectroscopy; Methanol; Chromatography; Chemistry; Extraction (chemistry); Infrared spectroscopy; Vegetable oil; Analytical Chemistry (journal); Materials science; Chemical engineering; Organic chemistry","score_opus":0.00721180495917211,"score_gpt":0.2653553000322713,"score_spread":0.2581434950730992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047078731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977633,0.00018586736,0.00035204084,0.00044025475,0.0001783574,0.000072450275,0.0002407115,0.000070514965,0.0006965329],"genre_scores_gemma":[0.9977645,0.000014261072,0.0013614664,0.000047830858,0.0001829391,0.0000072213006,0.00003605134,0.000013158822,0.00057254726],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99818337,0.00007689006,0.00088420283,0.00011689405,0.0005591125,0.00017953143],"domain_scores_gemma":[0.9960418,0.00036762407,0.0024377783,0.0002592518,0.000862128,0.000031436484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009070864,0.00013272498,0.0004979045,0.0005561366,0.000090089045,0.0000472068,0.00037532067,0.00023719827,0.00011516209],"category_scores_gemma":[0.0017033217,0.00009441144,0.000326955,0.0038119685,0.000020211084,0.00030119246,0.000028791566,0.00020012172,9.782701e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015335475,0.00043679905,0.5905998,0.00015120403,0.0023779152,0.0000022444954,0.00029756216,0.009383959,0.3792587,0.00049483887,0.016781086,0.00006254577],"study_design_scores_gemma":[0.0016855886,0.00003805945,0.45008084,0.000050389594,0.002756493,0.0000011514686,0.00021977724,0.011430647,0.5321594,0.0005785828,0.0008404962,0.00015861914],"about_ca_topic_score_codex":0.00003898972,"about_ca_topic_score_gemma":0.00016098634,"teacher_disagreement_score":0.15290067,"about_ca_system_score_codex":0.0008148891,"about_ca_system_score_gemma":0.00020412014,"threshold_uncertainty_score":0.3849987},"labels":[],"label_agreement":null},{"id":"W2047522351","doi":"10.1007/s11746-008-1232-z","title":"Determination of Free Fatty Acids in Edible Oils with the Use of a Variable Filter Array IR Spectrometer","year":2008,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Methanol; Spectrometer; Chemistry; Biodiesel; Absorbance; Fourier transform infrared spectroscopy; Extraction (chemistry); Chromatography; Solvent; Analytical Chemistry (journal); Infrared spectroscopy; Absorption (acoustics); Materials science; Organic chemistry; Catalysis; Chemical engineering","score_opus":0.021921032826834365,"score_gpt":0.24567467595042405,"score_spread":0.2237536431235897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047522351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964209,0.00012579578,0.0012428393,0.0004282293,0.000019362435,0.00001729805,0.000016767524,0.0000058804912,0.0017229113],"genre_scores_gemma":[0.9611923,0.0003514105,0.036974557,0.00025262396,0.00014321116,0.0000037447403,0.0000012810463,0.000022072829,0.0010587655],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984921,0.000030955976,0.00050637405,0.00015475851,0.00056905986,0.0002467005],"domain_scores_gemma":[0.99724066,0.00033900168,0.0015458836,0.00056309724,0.000254067,0.0000572986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002464574,0.00018205584,0.00054809946,0.000050130217,0.000079223304,0.000016033386,0.00072482316,0.00007060063,0.00013223432],"category_scores_gemma":[0.00025947508,0.00010164312,0.0004259503,0.0011567039,0.0007070282,0.00016383857,0.00008361144,0.0005030194,2.6569825e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011760926,0.00019558042,0.021094741,0.00010950339,0.00022902091,0.0000041141634,0.0010633334,0.000045150973,0.97464037,0.0000022333816,0.0022539203,0.00024440346],"study_design_scores_gemma":[0.0006930168,0.00008073944,0.0026566284,0.000081842256,0.00019924156,0.00014619519,0.00093058293,0.00006337346,0.9945894,0.000034556222,0.00039751382,0.00012693592],"about_ca_topic_score_codex":0.00012266419,"about_ca_topic_score_gemma":0.00000400963,"teacher_disagreement_score":0.035731718,"about_ca_system_score_codex":0.00019601398,"about_ca_system_score_gemma":0.00017973164,"threshold_uncertainty_score":0.4144886},"labels":[],"label_agreement":null},{"id":"W2047796313","doi":"10.1016/j.powtec.2006.12.007","title":"An experimental investigation of effusivity as an indicator of powder blend uniformity","year":2006,"lang":"en","type":"article","venue":"Powder Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal effusivity; Nephelometry; Materials science; Sensitivity (control systems); Mixing (physics); Work (physics); Composite material; Process engineering; Biological system; Chromatography; Chemistry; Thermodynamics; Physics; Engineering","score_opus":0.009155235172346243,"score_gpt":0.27643743796612275,"score_spread":0.2672822027937765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047796313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964272,0.00035221517,0.000034730616,0.00011451061,0.000016315407,0.000049492348,0.000020627618,0.00016977615,0.0028151714],"genre_scores_gemma":[0.9992405,0.0000056471326,0.00052233716,0.000023308681,0.000032900083,0.00001282463,0.000060273243,0.00001743105,0.00008478504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989311,0.000014979353,0.0003351188,0.0003069568,0.00018700102,0.0002248057],"domain_scores_gemma":[0.99903965,0.000022689139,0.00028383388,0.0005366061,0.000058289857,0.00005890376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007899956,0.00015758109,0.00031267546,0.00043704,0.00005391917,0.0000064507876,0.0003587063,0.00037475728,0.0006743246],"category_scores_gemma":[0.000033455817,0.00015881319,0.000055470675,0.00069001457,0.0005131296,0.00015574643,0.00006338757,0.00019509191,0.0000073665524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014678526,0.00033081556,0.19613543,0.000030172758,0.000027387678,0.0000019005494,0.000062214254,0.0000016246851,0.79851687,0.0047262353,0.000033333225,0.000119364384],"study_design_scores_gemma":[0.0003806984,0.00015783928,0.0069892285,0.000006288878,0.000055835906,0.000013938513,0.00094803644,0.000027712433,0.9849577,0.0062719975,0.00004766104,0.00014304025],"about_ca_topic_score_codex":0.0004921663,"about_ca_topic_score_gemma":0.00002867622,"teacher_disagreement_score":0.1891462,"about_ca_system_score_codex":0.000061274244,"about_ca_system_score_gemma":0.00006587701,"threshold_uncertainty_score":0.7383382},"labels":[],"label_agreement":null},{"id":"W2048547897","doi":"10.1177/2211068214551825","title":"Automated Acid and Base Number Determination of Mineral-Based Lubricants by Fourier Transform Infrared Spectroscopy: Commercial Laboratory Evaluation","year":2014,"lang":"en","type":"article","venue":"SLAS TECHNOLOGY","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Infrared; Mineral; Fourier transform spectroscopy; Fourier transform; Base (topology); Analytical Chemistry (journal); Infrared spectroscopy; Spectroscopy; Materials science; Chemistry; Mathematics; Engineering; Optics; Chromatography; Metallurgy; Chemical engineering; Physics; Organic chemistry","score_opus":0.008260113513837522,"score_gpt":0.2851406433079059,"score_spread":0.27688052979406835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048547897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98876405,0.00020055228,0.0038030725,0.0006470852,0.000034762554,0.00011913258,0.00007858319,0.00045442538,0.005898311],"genre_scores_gemma":[0.9950086,0.000019129247,0.0046015624,0.000076742195,0.000024605075,0.000050382147,0.0000924395,0.000023179244,0.00010338761],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883246,0.000033523633,0.00031532152,0.00029436234,0.00026606774,0.00025825188],"domain_scores_gemma":[0.9991778,0.000071297494,0.00019396422,0.0003083967,0.00019657236,0.00005198064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022744927,0.00018826166,0.00033755423,0.00028506533,0.000075266675,0.000013471869,0.0001769422,0.0003760085,0.00057111296],"category_scores_gemma":[0.0002745986,0.00019329414,0.00004523643,0.0008806279,0.00025643836,0.00008583473,0.0000231754,0.00019943109,0.000008389702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006347294,0.00017903069,0.020869598,0.00009551976,0.000052360287,0.0000017434746,0.000040367086,0.0000016898728,0.9511038,0.00014972866,0.0036797565,0.023762947],"study_design_scores_gemma":[0.001363638,0.00007739142,0.0004383263,0.000021432588,0.00019680354,0.0000045680968,0.00005953643,0.033998117,0.9613794,0.0013312896,0.00094277627,0.00018669222],"about_ca_topic_score_codex":0.000021814845,"about_ca_topic_score_gemma":0.000024171459,"teacher_disagreement_score":0.03399643,"about_ca_system_score_codex":0.00010735775,"about_ca_system_score_gemma":0.00008460937,"threshold_uncertainty_score":0.7882306},"labels":[],"label_agreement":null},{"id":"W2048676560","doi":"10.1007/s11004-006-9053-9","title":"Spectral Corrected Semivariogram Models","year":2006,"lang":"en","type":"article","venue":"Mathematical Geology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Variogram; Interpolation (computer graphics); Covariance; Mathematics; Geostatistics; Covariance function; Applied mathematics; Consistency (knowledge bases); Statistics; Kriging; Statistical physics; Computer science; Physics; Geometry; Spatial variability","score_opus":0.01416797251932832,"score_gpt":0.2490305476955081,"score_spread":0.23486257517617976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048676560","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37167132,0.00020279443,0.03869535,0.0002704732,0.00004109313,0.00004724735,0.000004698644,0.0003418044,0.5887252],"genre_scores_gemma":[0.973244,0.000005066081,0.0068573034,0.00009160373,0.00018235188,0.000019736783,0.000031125408,0.000021824299,0.019546948],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99875,0.00001151746,0.00032899954,0.0002855214,0.00016120133,0.0004627878],"domain_scores_gemma":[0.9992706,0.00021741526,0.00007415867,0.00031915942,0.00004113608,0.00007754098],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000063211206,0.00018554734,0.00038272844,0.00010200512,0.00006559014,0.000024404208,0.00022744067,0.0002174572,0.016744182],"category_scores_gemma":[0.00007885469,0.00016071039,0.00013655033,0.00036662706,0.00017054754,0.00004795665,0.00006624902,0.00023600881,0.00042210057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062716936,0.0015046436,0.0069778007,0.000299623,0.0002602492,0.0001232642,0.00016299353,0.0008202728,0.03346824,0.94265664,0.013201341,0.0004622248],"study_design_scores_gemma":[0.00047343053,0.000034085828,0.00025706357,0.000009528083,0.00018214127,0.00012572149,0.00009769947,0.029654786,0.057872634,0.9100873,0.0008757327,0.00032989142],"about_ca_topic_score_codex":0.00012081265,"about_ca_topic_score_gemma":0.0000143307125,"teacher_disagreement_score":0.60157275,"about_ca_system_score_codex":0.00004353399,"about_ca_system_score_gemma":0.000020364832,"threshold_uncertainty_score":0.98415464},"labels":[],"label_agreement":null},{"id":"W2049016269","doi":"10.1366/000370208784909661","title":"Effects of Preprocessing of Ultraviolet-Induced Fluorescence Spectra in Plant Fingerprinting Applications","year":2008,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Preprocessor; Computer science; Database normalization; Multiplicative function; Data pre-processing; Linear discriminant analysis; Pattern recognition (psychology); Biological system; Artificial intelligence; Mathematics","score_opus":0.010888726676974488,"score_gpt":0.24880526165928207,"score_spread":0.23791653498230758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049016269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9794694,0.0003322485,0.0035146738,0.000012160925,0.0000134299535,0.00024212278,0.000008630441,0.00008069752,0.016326588],"genre_scores_gemma":[0.9893904,0.00017558335,0.010131223,0.000014601771,0.00008050638,0.00012203774,0.00001369644,0.000030799714,0.000041151863],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99809206,0.000009618253,0.00062110566,0.0005179255,0.00032300377,0.00043630594],"domain_scores_gemma":[0.9987129,0.00029679356,0.00041338138,0.00045873536,0.000042094478,0.00007607677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011375861,0.0002589821,0.0006021842,0.00026780472,0.000101896636,0.000009441407,0.00041926734,0.00015906896,0.00012290473],"category_scores_gemma":[0.00006908287,0.00026937944,0.00010411945,0.0010311352,0.00017938307,0.00006081083,0.00005949786,0.0003685244,0.000009409483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060904655,0.00035545585,0.010566101,0.00058622466,0.000041815583,0.0000081265325,0.00036630637,0.000009917045,0.9862858,0.0013283191,0.000011807556,0.00037923426],"study_design_scores_gemma":[0.0006148549,0.000027275093,0.0060342886,0.000089125024,0.00006942087,0.000011106265,0.00018677488,0.00006155973,0.9920406,0.00062692125,0.000012381501,0.00022570697],"about_ca_topic_score_codex":0.00010724157,"about_ca_topic_score_gemma":0.0000062731474,"teacher_disagreement_score":0.016285436,"about_ca_system_score_codex":0.00011150676,"about_ca_system_score_gemma":0.00012178748,"threshold_uncertainty_score":0.99997586},"labels":[],"label_agreement":null},{"id":"W2049086159","doi":"10.5539/ijc.v6n4","title":"Identification of Adulteration of Olive Oil with Other Edible Oils by LED-induced Fluorescence and Multivariate Calibration","year":2014,"lang":"en","type":"article","venue":"International Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Chemometrics; Edible oil; Olive oil; Chemistry; Partial least squares regression; Canola; Sunflower oil; Sunflower; Food science; Principal component analysis; Vegetable oil; Calibration; Multivariate statistics; Chromatography; Mathematics; Statistics","score_opus":0.006182612110380636,"score_gpt":0.26065933410198106,"score_spread":0.25447672199160043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049086159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917317,0.000113821014,0.0065393117,0.00012586638,0.000049825845,0.000009094108,0.000028646939,0.0000060601874,0.0013956977],"genre_scores_gemma":[0.99858594,0.000083394596,0.00072549115,0.000017763408,0.00019235133,0.0000016013428,0.000015680715,0.000010003997,0.00036774663],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988094,0.000011951182,0.0005457315,0.00012350903,0.00043463462,0.00007475485],"domain_scores_gemma":[0.9981165,0.00008434095,0.0010285407,0.00010896204,0.00061207445,0.0000495706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016128474,0.00010328423,0.00020032945,0.00006570193,0.000019390987,0.000035243167,0.00023019544,0.0000828161,0.00019429934],"category_scores_gemma":[0.00021682472,0.00008780247,0.000055050496,0.000100533536,0.0000625705,0.00023729028,0.000020718016,0.00012906053,3.945212e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015305616,0.00010939661,0.0020801558,0.00016395737,0.00016727367,8.5324194e-7,0.00013761823,0.000036487585,0.9958083,0.000036922236,0.000084872176,0.0012211299],"study_design_scores_gemma":[0.00079595484,0.000027562857,0.000082870574,0.00018224765,0.00007229521,0.000031040672,0.00011813476,0.0019325443,0.996555,0.000076603916,0.000046203706,0.00007956756],"about_ca_topic_score_codex":0.00003654584,"about_ca_topic_score_gemma":0.0000011092739,"teacher_disagreement_score":0.006854291,"about_ca_system_score_codex":0.000046376208,"about_ca_system_score_gemma":0.000050976,"threshold_uncertainty_score":0.3580481},"labels":[],"label_agreement":null},{"id":"W2049154504","doi":"10.1002/cem.700","title":"An investigation of orthogonal signal correction algorithms and their characteristics","year":2002,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":165,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Orthogonalization; Algorithm; Calibration; Computer science; Component (thermodynamics); Point (geometry); Orthographic projection; Group (periodic table); SIGNAL (programming language); Interpretation (philosophy); Orthogonal transformation; Component analysis; Principal component analysis; Orthogonal matrix; Projection (relational algebra); Orthogonal array; Mathematics; Artificial intelligence; Orthogonal basis; Statistics; Machine learning; Taguchi methods","score_opus":0.0240845813132987,"score_gpt":0.2507610509566038,"score_spread":0.22667646964330507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049154504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945279,0.0012159512,0.0033881364,0.00007592406,0.00016189193,0.000017333967,0.000016199128,0.00001332979,0.00058335596],"genre_scores_gemma":[0.9974394,0.00061508856,0.0012067339,0.00004601466,0.00039507324,5.606507e-7,0.000008401852,0.000016396958,0.0002723627],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986753,0.000018908353,0.0006384563,0.00013633016,0.0003696267,0.00016136697],"domain_scores_gemma":[0.9980576,0.00022912519,0.0009396319,0.00014035126,0.00044619487,0.00018711777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031274327,0.00015949277,0.00042298317,0.0008887994,0.000057024576,0.000043803426,0.00018458422,0.00015446897,0.00081485516],"category_scores_gemma":[0.00032747298,0.00013149506,0.000117002346,0.0018790573,0.00010618295,0.00029194885,0.000020710693,0.0003558201,0.000001541406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008501249,0.00053819566,0.24594507,0.0002140851,0.00035466772,0.0000214572,0.0011821777,0.000026804939,0.643661,0.000034017616,0.0016424168,0.1062951],"study_design_scores_gemma":[0.0007561635,0.0003924567,0.018617928,0.00005069951,0.00024581817,0.0003774226,0.00078908174,0.010870279,0.96696365,0.00025479702,0.00042943374,0.00025224293],"about_ca_topic_score_codex":0.0000028746824,"about_ca_topic_score_gemma":2.3944258e-7,"teacher_disagreement_score":0.3233027,"about_ca_system_score_codex":0.00007517248,"about_ca_system_score_gemma":0.000035453657,"threshold_uncertainty_score":0.8922093},"labels":[],"label_agreement":null},{"id":"W2049634071","doi":"10.1007/s11694-010-9099-8","title":"Sprouting detection at early stages in individual CWAD and CWRS wheat kernels using SWIR spectroscopy","year":2010,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sprouting; Linear discriminant analysis; Germination; Amylase; Logistic regression; Mathematics; Chemistry; Food science; Biology; Botany; Statistics; Enzyme; Biochemistry","score_opus":0.05289556866494902,"score_gpt":0.3374625773336971,"score_spread":0.28456700866874807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049634071","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983026,0.00010755683,0.0008943237,0.00017078212,0.00007814497,0.000114499846,0.00007945596,0.000037686917,0.00021499317],"genre_scores_gemma":[0.99666125,0.00006907654,0.0030335158,0.000056732555,0.00007524411,0.0000014900728,0.000033982855,0.000014732971,0.000053975964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988152,0.000034138313,0.00039143895,0.0003573891,0.00015104233,0.00025080476],"domain_scores_gemma":[0.9994639,0.00013649547,0.00017469036,0.00010632428,0.00003396397,0.000084624626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041247124,0.0001730987,0.000258214,0.00012598648,0.00044756854,0.00012365305,0.000032300493,0.00015838306,0.000018352144],"category_scores_gemma":[0.00009101652,0.00017969243,0.000040195857,0.00016470607,0.00012479891,0.00022820783,0.000050512223,0.00023475257,2.6648505e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027688374,0.000028454117,0.07065558,0.00018803032,0.00008091665,6.0465516e-7,0.002089159,0.0000035836708,0.9002746,0.0002851406,0.0000012694635,0.026115779],"study_design_scores_gemma":[0.001954147,0.000155459,0.048861325,0.000043948992,0.00012356874,0.000033780194,0.0034527164,0.0010400992,0.94250065,0.0014454032,0.00007607843,0.00031284618],"about_ca_topic_score_codex":0.00071853795,"about_ca_topic_score_gemma":0.0017030394,"teacher_disagreement_score":0.042226028,"about_ca_system_score_codex":0.00011158025,"about_ca_system_score_gemma":0.000018683257,"threshold_uncertainty_score":0.7327645},"labels":[],"label_agreement":null},{"id":"W2049642972","doi":"10.1016/j.laa.2011.01.002","title":"A new family of constrained principal component analysis (CPCA)","year":2011,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Singular value decomposition; Principal component analysis; Mathematics; Column (typography); Orthogonal matrix; Singular value; Decomposition; Matrix (chemical analysis); Component (thermodynamics); Ridge; Space (punctuation); Orthogonal transformation; Eigenvalues and eigenvectors; Applied mathematics; Pure mathematics; Algorithm; Orthogonal basis; Statistics; Geometry; Computer science; Chemistry","score_opus":0.03129435247461975,"score_gpt":0.27175355949390845,"score_spread":0.2404592070192887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049642972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8947763,0.0015085115,0.044438772,0.00010486412,0.000009031296,0.00016605665,0.00011326104,0.00008178783,0.05880142],"genre_scores_gemma":[0.9916903,0.00012697012,0.006527255,0.000034461493,0.00005797912,0.000030411267,0.000055050194,0.000008838718,0.001468746],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923503,0.000004631854,0.00026940252,0.00023258415,0.00011726251,0.00014111015],"domain_scores_gemma":[0.9993584,0.000047093323,0.00012486114,0.0002674942,0.00006638917,0.000135746],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000039102975,0.00011526478,0.00026744942,0.00015035665,0.00006733347,0.000008025899,0.00016046545,0.000077155295,0.0022312405],"category_scores_gemma":[0.000013961329,0.00010722853,0.000116731375,0.00085867743,0.000060603317,0.00003687825,0.000044703942,0.00009131179,0.000028256849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088959365,0.0010243749,0.028961843,0.00024670138,0.005001749,0.0000034457255,0.0014750003,0.000024580528,0.743355,0.210797,0.0004579318,0.008563426],"study_design_scores_gemma":[0.0012607377,0.000072516945,0.023776054,0.000014341224,0.005834293,0.0000091593565,0.00114411,0.0053247483,0.94557333,0.0027096416,0.013639664,0.00064139353],"about_ca_topic_score_codex":0.00011515945,"about_ca_topic_score_gemma":0.0000086036625,"teacher_disagreement_score":0.20808735,"about_ca_system_score_codex":0.000009900383,"about_ca_system_score_gemma":0.00004263281,"threshold_uncertainty_score":0.99868083},"labels":[],"label_agreement":null},{"id":"W2051565742","doi":"10.1016/j.foodchem.2012.05.008","title":"Improving the determination of moisture in edible oils by FTIR spectroscopy using acetonitrile extraction","year":2012,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Division of Chemistry; Natural Sciences and Engineering Research Council of Canada","keywords":"Acetonitrile; Fourier transform infrared spectroscopy; Chemistry; Extraction (chemistry); Solvent; Moisture; Analytical Chemistry (journal); Chromatography; Dilution; Infrared spectroscopy; Organic chemistry; Chemical engineering","score_opus":0.016203448622786096,"score_gpt":0.28429802775566243,"score_spread":0.2680945791328763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051565742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99028295,0.0022083698,0.00097141386,0.000030304564,0.000062530555,0.0000443794,0.000033936485,0.00003914473,0.00632699],"genre_scores_gemma":[0.99712986,0.00003676935,0.0019223908,0.000014867575,0.00037928624,0.000014892046,0.000038351987,0.0000249435,0.0004386574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987581,0.000010172287,0.00033050662,0.00022847872,0.00026372663,0.00040904043],"domain_scores_gemma":[0.9991215,0.000096315474,0.0003100312,0.0003462257,0.000049093313,0.00007685149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016945739,0.00019820814,0.00024174101,0.000047077723,0.00009353012,0.000028874132,0.00024401552,0.00023873887,0.00067163375],"category_scores_gemma":[0.00013929882,0.0001724293,0.000098912424,0.000471619,0.00007156836,0.00022744585,0.000056859015,0.00039645738,0.0000028016182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019907035,0.00015450486,0.002961187,0.00022883729,0.000023064746,5.448069e-7,0.00010499675,0.0000026543694,0.9960084,0.0000025524532,0.00017312026,0.00032021044],"study_design_scores_gemma":[0.0003064621,0.000012810779,0.00006661175,0.000026672873,0.00010314961,0.000019873474,0.0006895025,0.00029452262,0.9980597,0.000029509081,0.00021698663,0.00017421445],"about_ca_topic_score_codex":0.00010885661,"about_ca_topic_score_gemma":0.000003913243,"teacher_disagreement_score":0.006846911,"about_ca_system_score_codex":0.00026671658,"about_ca_system_score_gemma":0.000052890366,"threshold_uncertainty_score":0.73539186},"labels":[],"label_agreement":null},{"id":"W2052548655","doi":"10.1051/matecconf/20152001004","title":"Spectrum construction for non stationary vibration: Application to a moving flexible robot","year":2015,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Vibration; Autoregressive model; Modal; Computation; Computer science; Autoregressive–moving-average model; Block (permutation group theory); Robot; Representation (politics); Sliding window protocol; Trajectory; Modal analysis; Series (stratigraphy); Algorithm; Mathematics; Artificial intelligence; Acoustics; Window (computing); Physics","score_opus":0.02966475830888653,"score_gpt":0.29513458557972594,"score_spread":0.2654698272708394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052548655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40725565,0.00013187181,0.5050097,0.0016082327,0.00014622138,0.00035329722,0.00008303643,0.0001920003,0.085219994],"genre_scores_gemma":[0.9885773,0.000007902943,0.010484908,0.00003872066,0.00010285252,0.000089521694,0.00007276519,0.000007931344,0.0006180794],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992384,0.00000484253,0.00024377267,0.00019629259,0.00018216623,0.0001345556],"domain_scores_gemma":[0.99939275,0.00005652229,0.00015422441,0.00015141917,0.00016895852,0.00007613517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009632208,0.00010069394,0.00018612617,0.00013986496,0.000053616823,0.00004204153,0.0001407373,0.00006581177,0.0004249766],"category_scores_gemma":[0.000057458074,0.00009847016,0.000038116992,0.00030793398,0.00005054947,0.000120670345,0.000023132798,0.00004426583,0.000017552815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005007781,0.00024126712,0.04273615,0.0005842603,0.00022872598,9.1989364e-7,0.00065423606,0.0028579542,0.8402295,0.09577666,0.0031986134,0.012990941],"study_design_scores_gemma":[0.0004412,0.00009724495,0.0007376382,0.000022173368,0.000060845476,0.0000035802789,0.0020902206,0.007297065,0.97446907,0.011056626,0.0035596143,0.00016472013],"about_ca_topic_score_codex":0.00010389212,"about_ca_topic_score_gemma":0.000030256699,"teacher_disagreement_score":0.58132166,"about_ca_system_score_codex":0.000044406985,"about_ca_system_score_gemma":0.00042049494,"threshold_uncertainty_score":0.46531957},"labels":[],"label_agreement":null},{"id":"W2053315081","doi":"10.1016/j.biosystemseng.2005.02.002","title":"Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision and Neural Networks","year":2005,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of British Columbia","funders":"","keywords":"Artificial neural network; Quality (philosophy); Product (mathematics); Artificial intelligence; Machine vision; Sample (material); Computer science; Computer vision; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.006528161532311914,"score_gpt":0.2276679812311526,"score_spread":0.22113981969884067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053315081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9805423,0.004866956,0.013888661,0.00011641396,0.00009046941,0.00006210755,0.000030044415,0.000110430774,0.00029262333],"genre_scores_gemma":[0.9987297,0.000042109357,0.0004961461,0.000013247702,0.00019030228,0.0000063400885,0.000026119333,0.000022885726,0.00047314796],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992302,0.00000414331,0.0002505916,0.00019398182,0.00013619868,0.0001848808],"domain_scores_gemma":[0.9996176,0.000038086677,0.000067253786,0.00016031813,0.000024540308,0.000092206814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074872674,0.00015016746,0.00023117238,0.00010917349,0.000027972352,0.00002816326,0.0001011833,0.00008503528,0.00005060227],"category_scores_gemma":[0.000038548107,0.0001357878,0.000046973757,0.00024586052,0.000008300647,0.0000918302,0.000036925972,0.00009658066,0.0000019202705],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011487906,0.000039628267,0.0033975756,0.00033188766,0.00005752682,0.0000012188617,0.000093588395,0.012622472,0.9799269,0.00006720776,0.0006369142,0.0028135984],"study_design_scores_gemma":[0.00032104526,0.00003675327,0.0012659925,0.000115187744,0.00007960785,0.0000141092205,0.00006591914,0.49860427,0.4972772,6.293041e-7,0.001958798,0.00026049034],"about_ca_topic_score_codex":0.00006421134,"about_ca_topic_score_gemma":0.00000413862,"teacher_disagreement_score":0.4859818,"about_ca_system_score_codex":0.000054384618,"about_ca_system_score_gemma":0.0000024070252,"threshold_uncertainty_score":0.5537266},"labels":[],"label_agreement":null},{"id":"W2053322928","doi":"10.1016/j.snb.2012.05.011","title":"Identifying the production region of single-malt Scotch whiskies using optical spectroscopy and pattern recognition techniques","year":2012,"lang":"en","type":"article","venue":"Sensors and Actuators B Chemical","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal College of Physicians and Surgeons of Canada","funders":"Centre National de la Recherche Scientifique","keywords":"Cluster (spacecraft); Spectroscopy; Multivariate statistics; Absorption (acoustics); Pattern recognition (psychology); Materials science; Optics; Analytical Chemistry (journal); Artificial intelligence; Computer science; Mathematics; Chemistry; Physics; Statistics; Chromatography","score_opus":0.04602007039643522,"score_gpt":0.28459780843639565,"score_spread":0.23857773803996044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053322928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99777895,0.00060357887,0.0007039767,0.00017962424,0.000043653647,0.00006653814,0.000004016272,0.000058201927,0.00056147145],"genre_scores_gemma":[0.9962455,0.0002496638,0.0029916512,0.000026415695,0.0004177975,0.000004440489,0.000007811765,0.000021960668,0.000034737517],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894446,0.000016807657,0.0002719077,0.00027395,0.00019184714,0.00030103553],"domain_scores_gemma":[0.9993928,0.00011185066,0.00015814396,0.00018510399,0.00005575729,0.000096358395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013783525,0.00018702616,0.00025758566,0.000085152125,0.00011925259,0.000047614813,0.00007154824,0.0001590379,0.00006142831],"category_scores_gemma":[0.00023747343,0.00014136001,0.00006297496,0.00022037783,0.00030494347,0.00021810134,0.00006802349,0.00023647219,0.0000011643647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026441852,0.000088605855,0.007989573,0.0001427279,0.000039750554,0.0000012203592,0.00030610565,4.7246193e-8,0.9843257,0.000016170465,0.000059517028,0.0070041236],"study_design_scores_gemma":[0.000080516824,0.00001555728,0.00013290119,0.0001057008,0.0001857282,0.00017707274,0.0008948863,0.00003192159,0.9978682,0.00027853253,0.00005892699,0.0001700732],"about_ca_topic_score_codex":0.000033219458,"about_ca_topic_score_gemma":7.595984e-7,"teacher_disagreement_score":0.013542466,"about_ca_system_score_codex":0.0000616585,"about_ca_system_score_gemma":0.000010526496,"threshold_uncertainty_score":0.5764494},"labels":[],"label_agreement":null},{"id":"W2053795316","doi":"10.1007/s11694-009-9087-z","title":"Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels","year":2009,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Amylase; Sprouting; Germination; Kernel (algebra); Alpha-amylase; Food science; Environmental science; Remote sensing; Chemistry; Biological system; Horticulture; Agronomy; Mathematics; Biology; Enzyme; Biochemistry; Geology","score_opus":0.057330787547892156,"score_gpt":0.3327567328606009,"score_spread":0.27542594531270875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053795316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99697495,0.00009078789,0.00093724456,0.00047484617,0.00004770169,0.0001895836,0.0003332396,0.00005280564,0.00089885783],"genre_scores_gemma":[0.9979425,0.00001741016,0.0014708112,0.00038075625,0.000071434726,0.0000019251754,0.00007502013,0.000013184873,0.000026976819],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99856174,0.000051566632,0.00039666993,0.00040205638,0.00019253475,0.00039544216],"domain_scores_gemma":[0.9993729,0.00007380927,0.00008436591,0.00014591757,0.000047976482,0.00027503064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036185439,0.0002112246,0.00034501668,0.00026315646,0.00032653104,0.00014244174,0.00006015755,0.00010674144,0.000007199304],"category_scores_gemma":[0.00005259908,0.00022921112,0.0000588101,0.0002798493,0.00004998839,0.00028235366,0.000020634778,0.00016322847,3.2273726e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024413865,0.00038541958,0.20149007,0.0014115648,0.0007939822,0.000049650283,0.018985247,0.00032392042,0.2004783,0.0023782372,0.00010199885,0.5711602],"study_design_scores_gemma":[0.009675702,0.00096420705,0.4327516,0.0010332171,0.0010001076,0.00040123795,0.06371151,0.05394028,0.43220776,0.001151334,0.00055709376,0.0026059505],"about_ca_topic_score_codex":0.007969877,"about_ca_topic_score_gemma":0.0075875646,"teacher_disagreement_score":0.5685543,"about_ca_system_score_codex":0.0004215769,"about_ca_system_score_gemma":0.000112877184,"threshold_uncertainty_score":0.9986361},"labels":[],"label_agreement":null},{"id":"W2054486851","doi":"10.1021/jf072182n","title":"Multivariate Approach to the Measurement of Tomato Maturity and Gustatory Attributes and Their Rapid Assessment by Vis−NIR Spectroscopy","year":2008,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut National d'Optique; Agriculture and Agri-Food Canada","funders":"","keywords":"Lycopene; Titratable acid; Taste; Mathematics; Multivariate statistics; Brix; Chemistry; Repeatability; Food science; Maturity (psychological); Statistics; Analytical Chemistry (journal); Chromatography; Sugar; Carotenoid","score_opus":0.020645688260323574,"score_gpt":0.23382117287045787,"score_spread":0.2131754846101343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054486851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890026,0.008368004,0.00010998888,0.0005068945,0.00001814329,0.000057755293,0.000060442693,0.000011600924,0.0018645807],"genre_scores_gemma":[0.9978081,0.00095054816,0.0009368605,0.000037135196,0.00015872571,0.0000048222523,0.000009214685,0.000007823647,0.00008677489],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998698,0.000016252936,0.00044095187,0.00022778241,0.0003863923,0.00023063802],"domain_scores_gemma":[0.9989623,0.000061323924,0.00038976892,0.00013327376,0.00024191511,0.00021142847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023200156,0.00025979482,0.00047157914,0.000024703759,0.00018749262,0.000046934685,0.00019638673,0.000118749864,0.000034975368],"category_scores_gemma":[0.000045295903,0.0001280419,0.00011141461,0.00019211957,0.00012931031,0.00010952155,0.00009985544,0.0003687122,1.3222039e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044434943,0.00020121643,0.0014523761,0.0002288221,0.0003911883,0.000001134566,0.00043545075,0.0000041085227,0.9924593,0.00000726299,0.004676423,0.00009825653],"study_design_scores_gemma":[0.00067396375,0.00012995108,0.02222007,0.00005031001,0.00014506873,0.00044387858,0.003244682,0.000015475225,0.97230023,0.000025248437,0.00055523723,0.00019590644],"about_ca_topic_score_codex":0.000009037906,"about_ca_topic_score_gemma":5.1509767e-7,"teacher_disagreement_score":0.020767694,"about_ca_system_score_codex":0.00008807416,"about_ca_system_score_gemma":0.000042236403,"threshold_uncertainty_score":0.52213967},"labels":[],"label_agreement":null},{"id":"W2054859756","doi":"10.1007/s00216-012-6364-1","title":"Evaluating the validity of spectral calibration models for quantitative analysis following signal preprocessing","year":2012,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Higher Education Discipline Innovation Project","keywords":"Preprocessor; Calibration; Computer science; Context (archaeology); Interference (communication); SIGNAL (programming language); Data mining; Mean squared error; Artificial intelligence; Statistics; Mathematics","score_opus":0.1436442549495707,"score_gpt":0.4020590210066163,"score_spread":0.2584147660570456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054859756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9306735,0.00087387266,0.06301443,0.0003334517,0.000014230429,0.0000835425,0.000050980765,0.00004565724,0.004910335],"genre_scores_gemma":[0.9961083,0.000015696163,0.0027631551,0.000048495785,0.00019619182,0.000015885531,0.000056121924,0.000016324917,0.0007798153],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786085,0.000030756884,0.0006125751,0.00045521418,0.0004974833,0.0005431407],"domain_scores_gemma":[0.9978847,0.0011997548,0.0002186758,0.00031183474,0.00013250255,0.00025253114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008353238,0.0002704233,0.00065159943,0.00008320181,0.00023522868,0.00008120303,0.00023838597,0.00020192076,0.00062645326],"category_scores_gemma":[0.0006374286,0.00018650734,0.00069888216,0.0011736403,0.0003262893,0.0002819407,0.00008680873,0.00026113458,9.080325e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011120716,0.002520218,0.25626957,0.0031959028,0.024940155,0.000009383664,0.0014698178,0.007749198,0.66740584,0.032480896,0.00028396095,0.0025629879],"study_design_scores_gemma":[0.0003211701,0.00005274083,0.0003080082,0.000019334348,0.009841136,0.0000032567104,0.0006181405,0.7700539,0.2150626,0.003444504,0.0000116085785,0.00026354936],"about_ca_topic_score_codex":0.000030819217,"about_ca_topic_score_gemma":0.0000020612372,"teacher_disagreement_score":0.7623047,"about_ca_system_score_codex":0.000060966948,"about_ca_system_score_gemma":0.0000664936,"threshold_uncertainty_score":0.7605549},"labels":[],"label_agreement":null},{"id":"W2055086496","doi":"10.1016/j.polymer.2003.11.016","title":"Off-line monitoring of styrene/butyl acrylate copolymerizations in toluene using ATR-FTIR spectroscopy","year":2003,"lang":"en","type":"article","venue":"Polymer","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fourier transform infrared spectroscopy; Monomer; Styrene; Toluene; Attenuated total reflection; Copolymer; Butyl acrylate; Acrylate; Chemistry; Analytical Chemistry (journal); Spectroscopy; Gravimetric analysis; Materials science; Polymer; Infrared spectroscopy; Polymer chemistry; Chromatography; Organic chemistry; Chemical engineering","score_opus":0.029981366763923552,"score_gpt":0.3132369733990544,"score_spread":0.28325560663513083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055086496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9805123,0.010323976,0.00053660205,0.000033826687,0.00013531238,0.000041305215,0.000024552852,0.000052482996,0.008339646],"genre_scores_gemma":[0.9956267,0.00035705877,0.0017527217,0.000022168962,0.00015145712,0.000007163126,0.000011049211,0.000043410713,0.002028263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842787,0.000025228195,0.00049420405,0.00033487688,0.0002591865,0.00045865471],"domain_scores_gemma":[0.99916756,0.000078432204,0.00020483766,0.00039837015,0.000043950313,0.000106822175],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000082791084,0.00024292764,0.00042258145,0.00028517522,0.00008778733,0.000024836072,0.00020092398,0.00014295452,0.001931256],"category_scores_gemma":[0.00009378865,0.00025736744,0.00012206274,0.0012212241,0.00009463541,0.00012883914,0.000039949897,0.00021369815,0.00001201764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016940585,0.00013965422,0.09907271,0.000036988233,0.00007221892,0.0000056065487,0.00010545336,0.00011135746,0.9000314,0.00023290188,0.000007873071,0.0001669022],"study_design_scores_gemma":[0.0005198599,0.000018504212,0.00058219075,0.000045608598,0.00012415908,0.000007572211,0.00035651994,0.0002564184,0.9976888,0.00007257725,0.00008795019,0.00023989633],"about_ca_topic_score_codex":0.000416424,"about_ca_topic_score_gemma":0.000011896469,"teacher_disagreement_score":0.09849052,"about_ca_system_score_codex":0.00012369032,"about_ca_system_score_gemma":0.000107908265,"threshold_uncertainty_score":0.99998784},"labels":[],"label_agreement":null},{"id":"W2056091790","doi":"10.1139/x03-189","title":"Application of near-infrared spectroscopy for the detection of internal insect infestation in<i>Picea</i><i>abies</i>seed lots","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Sveriges Lantbruksuniversitet; Kempe Foundation","keywords":"Partial least squares regression; Near-infrared spectroscopy; Horticulture; Picea abies; Spectroscopy; Infestation; Botany; Biology; Spectrometer; Analytical Chemistry (journal); Chemistry; Mathematics; Chromatography; Physics; Optics; Statistics","score_opus":0.031013388608092225,"score_gpt":0.3272058220617999,"score_spread":0.29619243345370766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056091790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97983325,0.00085705623,0.017942242,0.00020865288,0.000053735348,0.00017082167,0.000023718023,0.0000028921995,0.000907634],"genre_scores_gemma":[0.99917305,0.00006358975,0.000503205,0.000018073104,0.00012338364,0.000019102303,0.000005208649,0.000016650542,0.00007776473],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985191,0.000032973338,0.0005713545,0.00013390128,0.0003718261,0.0003708284],"domain_scores_gemma":[0.9980007,0.0004082629,0.0003765718,0.00024271457,0.00076941116,0.00020232849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090911955,0.000104569495,0.0002561971,0.00070397346,0.00015470032,0.000052486135,0.0004681719,0.00011298886,0.000047873702],"category_scores_gemma":[0.00087178056,0.000085988504,0.00013442693,0.0011399837,0.00033942418,0.00015721312,0.000016163258,0.0005299089,0.0000018051795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064764376,0.000130559,0.3968558,0.00034173473,0.00022083383,0.000019426087,0.0015628483,0.0047576986,0.59193724,0.0012330835,0.00012735883,0.0021657518],"study_design_scores_gemma":[0.001504517,0.0004127426,0.12474008,0.00011475529,0.00005991715,0.000044140645,0.00087428116,0.00045380177,0.8618514,0.008943774,0.0008988729,0.000101738035],"about_ca_topic_score_codex":0.03220552,"about_ca_topic_score_gemma":0.13476858,"teacher_disagreement_score":0.27211574,"about_ca_system_score_codex":0.00061310414,"about_ca_system_score_gemma":0.0015439856,"threshold_uncertainty_score":0.9742391},"labels":[],"label_agreement":null},{"id":"W2056786935","doi":"10.1021/cc0200944","title":"High-Throughput Analysis in Catalysis Research Using Novel Approaches to Transmission Infrared Spectroscopy","year":2003,"lang":"en","type":"article","venue":"Journal of Combinatorial Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Materials science; Comonomer; Polymer; Infrared spectroscopy; Throughput; Crystallinity; Fourier transform infrared spectroscopy; Wafer; Chemical engineering; Nanotechnology; Organic chemistry; Copolymer; Computer science; Chemistry; Composite material","score_opus":0.11274059314051911,"score_gpt":0.33828339251575923,"score_spread":0.22554279937524013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056786935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98269093,0.00067783386,0.0061844545,0.00014276258,0.0002000096,0.00006059052,0.000009095481,0.00001840832,0.010015885],"genre_scores_gemma":[0.9856522,0.000052550367,0.013179746,0.000011295728,0.00046140145,0.000005000776,0.000013057166,0.000037365455,0.0005874018],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996381,0.0000822428,0.0011350118,0.00047150932,0.0012656227,0.00066464767],"domain_scores_gemma":[0.99791783,0.00029167935,0.00046992532,0.00054552144,0.0003569343,0.00041810912],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016303232,0.00033178052,0.0011351766,0.00087079,0.00016924714,0.00012907904,0.0006782507,0.00038372446,0.0014403404],"category_scores_gemma":[0.0007673121,0.00031764063,0.00051912334,0.0062603885,0.0001120807,0.00025085907,0.0000682695,0.0012287833,0.0000034808265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029148348,0.0010653209,0.0036076386,0.00015285659,0.0011161786,0.000043121705,0.00031389948,0.0024471711,0.9899816,0.0007268131,0.00022418342,0.00002975726],"study_design_scores_gemma":[0.0019390859,0.000055084864,0.0000665387,0.00006027504,0.00106375,0.000036264188,0.0007569753,0.0001639172,0.9897139,0.004520397,0.0013041396,0.00031966993],"about_ca_topic_score_codex":0.000111473426,"about_ca_topic_score_gemma":0.000001923729,"teacher_disagreement_score":0.009428483,"about_ca_system_score_codex":0.0009323477,"about_ca_system_score_gemma":0.00046827216,"threshold_uncertainty_score":0.9999276},"labels":[],"label_agreement":null},{"id":"W2057685604","doi":"10.1016/j.jmr.2003.12.002","title":"Accurate relaxation parameters for large proteins","year":2003,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Robustness (evolution); Relaxation (psychology); Biological system; Physics; Millisecond; Algorithm; Spectral line; Computer science; Nuclear magnetic resonance; Chemistry; Biology","score_opus":0.019956737063353942,"score_gpt":0.27934226005142454,"score_spread":0.2593855229880706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057685604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9114766,0.047129076,0.020473111,0.000596223,0.00020767255,0.00025389172,0.00003176903,0.000029329587,0.0198023],"genre_scores_gemma":[0.9715955,0.00065349444,0.021737758,0.000120321056,0.000090818576,0.00001964394,0.0000014850059,0.000019191619,0.0057617575],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989966,0.000019180152,0.00040746992,0.00012059844,0.00022637704,0.00022978515],"domain_scores_gemma":[0.99901253,0.00015114783,0.00044526038,0.00015634832,0.00016421457,0.00007052136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032270554,0.00010257378,0.00022831357,0.000084477324,0.00006630332,0.000033566335,0.00016623993,0.00007934077,0.00063050946],"category_scores_gemma":[0.001132083,0.00008874434,0.0001505687,0.00025286392,0.000022586079,0.000111131456,0.0000074353193,0.00017811287,0.000006026413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003619296,0.0042713415,0.05002094,0.0020189655,0.00078931765,0.0003542754,0.0020977354,0.00071368326,0.6248821,0.05791675,0.073235415,0.18008019],"study_design_scores_gemma":[0.003594374,0.00069035776,0.003596315,0.0001189945,0.00027991063,0.00015900302,0.00042150286,0.00038883585,0.44958845,0.004407488,0.53641164,0.00034310913],"about_ca_topic_score_codex":0.0000018533938,"about_ca_topic_score_gemma":0.0000014671249,"teacher_disagreement_score":0.46317622,"about_ca_system_score_codex":0.000058717233,"about_ca_system_score_gemma":0.00007493346,"threshold_uncertainty_score":0.69036365},"labels":[],"label_agreement":null},{"id":"W2057895172","doi":"10.1094/cfw-57-5-0230","title":"AACCI Approved Methods Technical Committee Report: Collaborative Study on a Method for Determining Firmness of Cooked Pulses (AACCI Method 56-36.01)","year":2012,"lang":"en","type":"article","venue":"Cereal Foods World","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Agriculture Food and Rural Development; Canadian International Grains Institute","funders":"","keywords":"Chemistry; Computer science; Materials science; Mathematics","score_opus":0.05818033457340128,"score_gpt":0.44789783440101655,"score_spread":0.3897174998276153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057895172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1076036,0.0020651082,0.75091743,0.0005729181,0.0008646435,0.0032042055,0.00039215892,0.0008776637,0.13350224],"genre_scores_gemma":[0.54576474,0.0000051419606,0.45021465,0.00009176994,0.0003546984,0.00054073456,0.000054671415,0.00009051736,0.0028830771],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99584645,0.00056430814,0.0013168682,0.00088062417,0.00051291863,0.00087880937],"domain_scores_gemma":[0.99172455,0.0053236606,0.0010242113,0.0012445429,0.00036641705,0.0003166374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003053238,0.0006202003,0.0016084104,0.0005233393,0.0002767346,0.00007021432,0.00068328,0.00031026822,0.00050290534],"category_scores_gemma":[0.0022909197,0.00055310735,0.0005059636,0.0024892562,0.000118735974,0.00021207581,0.0002611716,0.00062500406,0.000004159059],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004517407,0.011639743,0.091763206,0.0013862291,0.0064517483,0.00011964242,0.0062274747,0.000089518726,0.7592937,0.004244816,0.012259834,0.10200668],"study_design_scores_gemma":[0.0024931408,0.00067972764,0.0051406957,0.00007834159,0.0020116167,0.0000596097,0.0058389837,0.00036345836,0.97349393,0.000790276,0.008262047,0.0007881727],"about_ca_topic_score_codex":0.00011628679,"about_ca_topic_score_gemma":0.00017012528,"teacher_disagreement_score":0.43816113,"about_ca_system_score_codex":0.00024359714,"about_ca_system_score_gemma":0.00015741843,"threshold_uncertainty_score":0.999692},"labels":[],"label_agreement":null},{"id":"W2057996605","doi":"10.1094/cchem.2003.80.3.285","title":"Color Calibration of Scanners for Scanner‐Independent Grain Grading","year":2003,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Saskatchewan Pulse Growers","keywords":"Scanner; Artificial intelligence; Computer vision; RGB color model; Histogram; Computer science; Calibration; Color correction; Grayscale; Color balance; Pattern recognition (psychology); Color image; Image processing; Mathematics; Pixel; Statistics; Image (mathematics)","score_opus":0.01620229563003978,"score_gpt":0.26580273002665694,"score_spread":0.24960043439661717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057996605","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89422715,0.00034725986,0.0014273977,0.000075586715,0.000051000872,0.00008806054,0.00006837577,0.00008066186,0.10363453],"genre_scores_gemma":[0.9894777,0.000016323973,0.0005381906,0.000026115478,0.000115278825,0.000045665085,0.000112913214,0.000029592613,0.0096382415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987798,0.000007057162,0.00033536556,0.0003355127,0.0002169874,0.000325302],"domain_scores_gemma":[0.9992088,0.000080404076,0.00020523282,0.00028249048,0.00008762386,0.00013542768],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001271152,0.0001930239,0.00030443372,0.00004219501,0.0000973508,0.000026464559,0.00020426353,0.00021283232,0.0014803653],"category_scores_gemma":[0.0002851626,0.00020342173,0.00018405485,0.00029425044,0.00009173414,0.00007043937,0.000020485651,0.00014037229,0.0000014358237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049710146,0.00011311942,0.003376163,0.00055453886,0.00012430431,0.0000025179102,0.00007394251,0.00006162643,0.9912135,0.00058656133,0.0037508304,0.00009317104],"study_design_scores_gemma":[0.0006576242,0.000014117743,0.000026994714,0.000020519283,0.000135177,0.0000138343885,0.0007526579,0.00047489422,0.9953361,0.00074636244,0.0016089497,0.00021275775],"about_ca_topic_score_codex":0.000043950902,"about_ca_topic_score_gemma":0.000004132897,"teacher_disagreement_score":0.09525055,"about_ca_system_score_codex":0.00015433326,"about_ca_system_score_gemma":0.00014482376,"threshold_uncertainty_score":0.99943244},"labels":[],"label_agreement":null},{"id":"W2058037240","doi":"10.1021/jf801563z","title":"Measurement of Condensed Tannins and Dry Matter in Red Grape Homogenates Using Near Infrared Spectroscopy and Partial Least Squares","year":2008,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Water Research Institute","keywords":"Partial least squares regression; Chemistry; Dry matter; Near-infrared spectroscopy; Proanthocyanidin; Wine; Calibration; Spectroscopy; Analytical Chemistry (journal); Grape wine; Standard error; Correlation coefficient; Coefficient of determination; Chromatography; Food science; Mathematics; Botany; Polyphenol; Biochemistry; Optics","score_opus":0.02039556716461102,"score_gpt":0.22638335081948355,"score_spread":0.20598778365487252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058037240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99322504,0.0060810335,0.000005210501,0.00012662442,0.000015212108,0.00002631239,0.000010730449,0.0000059407394,0.00050387206],"genre_scores_gemma":[0.99892426,0.00042537777,0.00041807283,0.000015591437,0.00011608635,0.0000010306408,0.0000028898867,0.000007799625,0.000088909204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987688,0.000010798582,0.0004999084,0.00018516846,0.0003200464,0.00021529646],"domain_scores_gemma":[0.9991888,0.000025251067,0.00038992052,0.00008087896,0.00017655756,0.00013861783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008700985,0.0002075866,0.00045442604,0.000045122248,0.000107441905,0.000043110165,0.00009370281,0.00013556248,0.00012716911],"category_scores_gemma":[0.000041908876,0.00014155201,0.000090390335,0.00021027388,0.00021434065,0.00014492655,0.000046682424,0.0002783292,1.7091948e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080250684,0.000079203615,0.10472517,0.00020528388,0.0001595472,0.000014186002,0.0002632652,0.000008849077,0.89427614,5.5386465e-7,0.00017469875,0.000012872376],"study_design_scores_gemma":[0.0011364341,0.000095280964,0.10852002,0.00014331246,0.00015112448,0.00063177326,0.0011878656,0.000022799988,0.8878734,0.000029741526,0.000027906308,0.0001803054],"about_ca_topic_score_codex":0.0000168086,"about_ca_topic_score_gemma":0.0000022517816,"teacher_disagreement_score":0.0064026895,"about_ca_system_score_codex":0.000060203845,"about_ca_system_score_gemma":0.000044164713,"threshold_uncertainty_score":0.57723236},"labels":[],"label_agreement":null},{"id":"W2058087661","doi":"10.1016/j.ejps.2003.11.011","title":"Determination of the thickness of plastic sheets used in blister packaging by near infrared spectroscopy: development and validation of the method","year":2004,"lang":"en","type":"article","venue":"European Journal of Pharmaceutical Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Repeatability; Near-infrared spectroscopy; Materials science; Chemometrics; Polyvinyl chloride; Partial least squares regression; Calibration; Chromatography; Analytical Chemistry (journal); Computer science; Chemistry; Mathematics; Composite material; Optics; Statistics; Machine learning","score_opus":0.045688058790000166,"score_gpt":0.3511156731780752,"score_spread":0.30542761438807503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058087661","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916404,0.00018539949,0.006404986,0.00020655477,0.000061594656,0.000030031415,0.0000021614312,0.0000017705204,0.0014671136],"genre_scores_gemma":[0.99460196,0.000015670686,0.005316267,0.000028919483,0.000015255059,1.9358755e-7,1.7033939e-7,0.0000043289065,0.000017234881],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985964,0.0001819051,0.0005133995,0.000105185485,0.00047722782,0.00012590864],"domain_scores_gemma":[0.9990295,0.00025935375,0.0005158595,0.000076148084,0.000074246585,0.000044884055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001413996,0.000080700185,0.00017953177,0.00008174459,0.0000863877,0.000030524126,0.0004156882,0.000017641312,0.00007498272],"category_scores_gemma":[0.00040984616,0.000045040164,0.00006588949,0.0006672709,0.00042963063,0.00014136452,0.00008974681,0.00019501308,2.498542e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003618362,0.00012554508,0.037148397,0.00009676279,0.00002224904,0.000002455543,0.0017187714,0.00085052714,0.95817673,0.00003733105,0.000011903748,0.0017731602],"study_design_scores_gemma":[0.0005774696,0.000032016946,0.015740924,0.0001414326,0.000057524092,0.00001248363,0.00035093687,0.00043396652,0.98240286,0.00010891879,0.00009093333,0.000050534327],"about_ca_topic_score_codex":0.0000028898398,"about_ca_topic_score_gemma":7.386579e-7,"teacher_disagreement_score":0.024226148,"about_ca_system_score_codex":0.00004601631,"about_ca_system_score_gemma":0.00012787219,"threshold_uncertainty_score":0.18366846},"labels":[],"label_agreement":null},{"id":"W2059481799","doi":"10.1021/jf060266a","title":"Heterospectral Two-Dimensional Correlation Spectroscopy of Mid-Infrared and Fourier Self-Deconvolved Near-Infrared Spectra of Sugar Solutions","year":2006,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Chemistry; Analytical Chemistry (journal); Infrared spectroscopy; Fructose; Spectroscopy; Aqueous solution; Sugar; Infrared; Fourier transform infrared spectroscopy; Absorption (acoustics); Chromatography; Optics; Physical chemistry; Physics; Organic chemistry","score_opus":0.007700412539208302,"score_gpt":0.210422794255089,"score_spread":0.2027223817158807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059481799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925013,0.0032259703,0.000041190022,0.00010032571,0.000054072014,0.000042121195,0.00004885591,0.000024710513,0.003961439],"genre_scores_gemma":[0.9941692,0.00010857489,0.004480709,0.0000075436387,0.00035711392,0.0000014884328,0.000039587863,0.0000126530795,0.0008231718],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981753,0.000012904774,0.0008531027,0.00023790928,0.00040176776,0.0003190098],"domain_scores_gemma":[0.998345,0.000101419646,0.0009380891,0.0001537483,0.0003051423,0.00015657436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119208984,0.00028268874,0.0006127673,0.00005579879,0.00014709223,0.000051612544,0.00016449623,0.00019624192,0.0003397689],"category_scores_gemma":[0.000042178977,0.00020514443,0.0002711734,0.0003112143,0.00020162876,0.00021857551,0.00005865539,0.0003842486,5.972166e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067370536,0.00025349506,0.0104682585,0.00019349025,0.00035287315,0.0000042636943,0.000113789625,0.00015423376,0.98735535,0.00003677223,0.0009884287,0.000011677978],"study_design_scores_gemma":[0.0016991819,0.00020046097,0.040284418,0.00008285369,0.00046512397,0.00037405553,0.00035297344,0.00013447803,0.9551321,0.0009931457,0.000033585355,0.00024762002],"about_ca_topic_score_codex":0.000021165517,"about_ca_topic_score_gemma":0.0000031858117,"teacher_disagreement_score":0.032223243,"about_ca_system_score_codex":0.00009730167,"about_ca_system_score_gemma":0.00007680981,"threshold_uncertainty_score":0.8365547},"labels":[],"label_agreement":null},{"id":"W2059840627","doi":"10.1007/s11694-008-9068-7","title":"Use of spectroscopic data for automation in food processing industry","year":2009,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canada Research Chairs","keywords":"Automation; Chemometrics; Spectroscopy; Food industry; Data processing; Food processing; Computer science; Process engineering; Chemistry; Analytical Chemistry (journal); Biological system; Environmental science; Engineering; Food science; Environmental chemistry; Physics; Machine learning; Mechanical engineering; Database","score_opus":0.14514272514292387,"score_gpt":0.37989336688483316,"score_spread":0.2347506417419093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059840627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912709,0.00016242266,0.0073787817,0.0005444605,0.000018335111,0.00014529696,0.00029383652,0.000027740805,0.00015821733],"genre_scores_gemma":[0.9900867,0.000041781455,0.00929048,0.0001248616,0.000034356122,0.00000135579,0.00038236467,0.0000061218702,0.000032011165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905586,0.000017807879,0.0004207294,0.00026349453,0.0000959301,0.00014615047],"domain_scores_gemma":[0.9994,0.0001332581,0.00021314708,0.00016648519,0.000049343536,0.000037747457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025278056,0.000106253174,0.00023202272,0.000090668196,0.00013357258,0.000059099428,0.000050008483,0.00014930191,0.0000053123736],"category_scores_gemma":[0.00019033266,0.00010863417,0.000025615682,0.00017449893,0.000049839484,0.00038708656,0.000018991179,0.00010949966,3.9028546e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015049012,0.00044564443,0.016589852,0.002819425,0.00028273521,3.8538542e-7,0.0027475518,0.00006429521,0.18274269,0.012228702,0.00015835214,0.7804155],"study_design_scores_gemma":[0.0084067965,0.0017277994,0.073274635,0.0005759859,0.000410106,0.00001639807,0.008787256,0.04860834,0.8336203,0.02202423,0.0016527195,0.0008954284],"about_ca_topic_score_codex":0.000030895466,"about_ca_topic_score_gemma":0.00005939209,"teacher_disagreement_score":0.77952003,"about_ca_system_score_codex":0.000036560603,"about_ca_system_score_gemma":0.00003828016,"threshold_uncertainty_score":0.44299728},"labels":[],"label_agreement":null},{"id":"W2059843984","doi":"10.1080/10837450802390232","title":"Quality Control of Multi-Component, Intact Pharmaceutical Tablets with Three Different Near-Infrared Apparatuses","year":2008,"lang":"en","type":"article","venue":"Pharmaceutical Development and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"Université de Montréal","keywords":"Active ingredient; Chemometrics; Calibration; Partial least squares regression; Process analytical technology; Quality by Design; Chromatography; Dosage form; Near-infrared spectroscopy; Pharmaceutical formulation; Biological system; Sample preparation; Chemistry; Process engineering; Computer science; Mathematics; Machine learning; Particle size; Statistics","score_opus":0.06942402700722748,"score_gpt":0.34278708341849107,"score_spread":0.27336305641126357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059843984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870048,0.0024738668,0.008423837,0.00044032745,0.00003845989,0.00016914122,0.000016733213,0.0002736013,0.0011592426],"genre_scores_gemma":[0.9960896,0.0003147818,0.0031175814,0.000114921204,0.000021521544,0.0000683453,0.00001782395,0.000026067652,0.0002293884],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997946,0.000024440531,0.00058719754,0.00051055854,0.00033763045,0.00059413776],"domain_scores_gemma":[0.998981,0.00023116141,0.00016706357,0.0002701326,0.00011793121,0.00023271488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000117330295,0.00037399272,0.0007693153,0.00023358887,0.000261922,0.000020300831,0.00031872562,0.00027996872,0.00054503785],"category_scores_gemma":[0.00009945765,0.00028763307,0.0000618714,0.00061258505,0.0009682763,0.00007913222,0.000164974,0.00060173665,0.000015734166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007197402,0.0013362535,0.7314471,0.0003164775,0.0010643525,0.00009562829,0.00015153261,0.0000029162015,0.2508427,0.0019867704,0.000080888145,0.011955642],"study_design_scores_gemma":[0.005788699,0.000052279018,0.019393183,0.000031134867,0.00022515755,0.00010692269,0.00008718373,0.0017775096,0.9610541,0.00018516627,0.010819197,0.0004794821],"about_ca_topic_score_codex":0.000007864255,"about_ca_topic_score_gemma":0.000006141097,"teacher_disagreement_score":0.71205395,"about_ca_system_score_codex":0.000083184816,"about_ca_system_score_gemma":0.00012390284,"threshold_uncertainty_score":0.99995756},"labels":[],"label_agreement":null},{"id":"W2060547382","doi":"10.1021/jf401279w","title":"Authentication of the Botanical and Geographical Origin of Distillers Dried Grains and Solubles (DDGS) by FT-IR Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Food science; Linear discriminant analysis; European union; Chemistry; Raw material; Quadratic classifier; Mathematics; Horticulture; Biology; Statistics; Computer science; Support vector machine; Organic chemistry; Business; Artificial intelligence","score_opus":0.006561036727899464,"score_gpt":0.2130288926047697,"score_spread":0.20646785587687025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060547382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968089,0.0017812487,0.000010678351,0.000900269,0.00001760689,0.000033848573,0.000021387088,0.000005316378,0.00042075393],"genre_scores_gemma":[0.9986998,0.000714531,0.00021783223,0.000011819697,0.00007818912,0.0000018733684,0.0000053141434,0.0000051951315,0.00026544675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897796,0.000009255466,0.0004455134,0.00015447313,0.00025034882,0.00016242932],"domain_scores_gemma":[0.9990394,0.000080281905,0.00048538813,0.00010984921,0.00014382672,0.00014123092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006529866,0.00016054239,0.00035244777,0.000018553856,0.00007682506,0.000040121882,0.00015041737,0.00014268472,0.00011835406],"category_scores_gemma":[0.00007249469,0.00008489509,0.00013269609,0.00020777354,0.00034888505,0.00010769329,0.000069730224,0.00027403567,1.0865443e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016319953,0.000091482616,0.01556368,0.00016144336,0.00018856338,2.0515567e-7,0.00007834883,2.5468515e-7,0.98247516,0.000052500134,0.0013148003,0.000057233392],"study_design_scores_gemma":[0.00053867075,0.00008287403,0.060148764,0.00006529736,0.0002708524,0.00012671844,0.0012421987,0.00000732916,0.93695915,0.00032919788,0.000103292805,0.00012563319],"about_ca_topic_score_codex":0.000016057227,"about_ca_topic_score_gemma":0.0000016035115,"teacher_disagreement_score":0.045515995,"about_ca_system_score_codex":0.000019565146,"about_ca_system_score_gemma":0.000019453,"threshold_uncertainty_score":0.34619212},"labels":[],"label_agreement":null},{"id":"W2060659712","doi":"10.1007/s00226-013-0590-3","title":"Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy","year":2013,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intertek (Canada)","funders":"Natural Science Foundation of Shandong Province; Donghua University; National Natural Science Foundation of China; Auburn University; China Scholarship Council; U.S. Department of Energy","keywords":"Lignin; Near-infrared spectroscopy; Cellulose; Particle size; Chemistry; Spectroscopy; Calibration; Range (aeronautics); Biomass (ecology); Infrared spectroscopy; Particle (ecology); Analytical Chemistry (journal); Materials science; Chromatography; Organic chemistry; Optics; Geology; Mathematics; Composite material","score_opus":0.007437736403827461,"score_gpt":0.25571821910533626,"score_spread":0.2482804827015088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060659712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97061354,0.00051622983,0.000074911295,0.00064902555,0.000017997103,0.0000821163,0.0000035887142,0.0001421334,0.027900456],"genre_scores_gemma":[0.99498117,0.000067632245,0.0046368293,0.000031700285,0.000011695833,0.00003630089,0.0000017490822,0.000009406044,0.00022354243],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998611,0.000003958765,0.00020863033,0.0004231779,0.00033095182,0.00042231017],"domain_scores_gemma":[0.9990311,0.000038301376,0.00014193605,0.00042528982,0.0002763873,0.000086987166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012755615,0.0001623476,0.00030200084,0.00044737215,0.00022313253,0.000098697325,0.0004848454,0.00014478642,0.0003293452],"category_scores_gemma":[0.000058751233,0.00012279059,0.000022031827,0.0030193029,0.002361292,0.00020728784,0.00013696995,0.00018347758,0.000008537084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000224473,0.00006728494,0.019968992,0.000023779623,0.0000368849,0.0000042601164,0.000025866615,2.2469965e-7,0.9773084,0.0016733896,0.00013489854,0.0007538016],"study_design_scores_gemma":[0.00032148158,0.00027942352,0.0012096151,0.000013353964,0.00004530289,0.000027301356,0.00077839155,0.00048732222,0.99470484,0.0017954006,0.00018067744,0.00015688332],"about_ca_topic_score_codex":0.00008463718,"about_ca_topic_score_gemma":0.0000042165366,"teacher_disagreement_score":0.027676914,"about_ca_system_score_codex":0.000063487,"about_ca_system_score_gemma":0.00032299643,"threshold_uncertainty_score":0.87002784},"labels":[],"label_agreement":null},{"id":"W2060847820","doi":"10.1117/12.908524","title":"Analysis of independent components obtained from functional near infrared data","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Infrared; Remote sensing; Optics; Physics","score_opus":0.026600529665886295,"score_gpt":0.25542667229047633,"score_spread":0.22882614262459003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060847820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99532986,0.00027964037,0.00007651305,0.00028783607,0.00012787835,0.00012491662,0.0005719626,0.000056874196,0.0031445408],"genre_scores_gemma":[0.9697716,0.000061008945,0.028949926,0.00005051421,0.00040556715,0.000031465923,0.00038189886,0.000041906384,0.00030611176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972889,1.3616763e-8,0.00083697226,0.00042907117,0.0010311456,0.00041393877],"domain_scores_gemma":[0.99770683,0.00025138707,0.0006299159,0.00016371801,0.0010795044,0.00016865028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048406833,0.0003124692,0.00067807216,0.00020287056,0.00008494898,0.00008016746,0.0014645667,0.0002457101,0.0003777667],"category_scores_gemma":[0.00067224004,0.00027181834,0.0007624094,0.000995032,0.00023561041,0.00071238633,0.00045325374,0.0003326541,0.0000020173986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018024122,0.00045872107,0.04370758,0.000299746,0.009097481,4.7820276e-8,0.00022689879,0.00018777643,0.9042517,0.0389755,0.0025405595,0.00007375739],"study_design_scores_gemma":[0.0027464894,0.00013666633,0.06976784,0.00021806144,0.01006335,0.000007217904,0.0039409935,0.13972434,0.76682055,0.0008571474,0.0047787055,0.00093865034],"about_ca_topic_score_codex":0.00006619153,"about_ca_topic_score_gemma":2.5517005e-7,"teacher_disagreement_score":0.13953656,"about_ca_system_score_codex":0.00017353943,"about_ca_system_score_gemma":0.000036359706,"threshold_uncertainty_score":0.9999734},"labels":[],"label_agreement":null},{"id":"W2060863427","doi":"10.1016/s0893-9659(03)80051-4","title":"The inversion of correlation matrix for MA(1) process","year":2003,"lang":"en","type":"article","venue":"Applied Mathematics Letters","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Inversion (geology); Inverse; Correlation; Matrix (chemical analysis); Covariance matrix; Context (archaeology); Process (computing); Applied mathematics; Mathematical analysis; Statistics; Geometry; Geology; Computer science","score_opus":0.0116422760518698,"score_gpt":0.26651822379502793,"score_spread":0.25487594774315814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060863427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7361515,0.00026148788,0.19346882,0.00084084546,0.000099732184,0.0005495763,0.000015875665,0.0001257603,0.06848637],"genre_scores_gemma":[0.98374826,0.000017449225,0.015271485,0.0001436152,0.000029329993,0.000078402496,0.0000109512275,0.000025112951,0.0006753789],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925137,0.000002314393,0.0002618974,0.00012925676,0.00018045954,0.00017468892],"domain_scores_gemma":[0.9990646,0.00039584227,0.00023167646,0.00024410203,0.000035729932,0.000028047587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016026669,0.00011077794,0.00016860606,0.000043950604,0.00013556176,0.000019826293,0.0001704192,0.00006294653,0.000091088514],"category_scores_gemma":[0.00011545506,0.000081767284,0.00007155756,0.00021458474,0.000069013266,0.000027080672,0.000011409834,0.00008159109,0.000011905339],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004828208,0.00014169348,0.00019117743,0.0014656127,0.00020697228,4.859066e-7,0.0010317665,0.00060151675,0.81897336,0.16778524,0.0092420885,0.00031179626],"study_design_scores_gemma":[0.00053534505,0.000008308228,0.0000016528571,0.000019005594,0.00021953009,0.000002494062,0.0018725696,0.0024206047,0.9698613,0.023096137,0.0017996993,0.00016334797],"about_ca_topic_score_codex":8.4631864e-7,"about_ca_topic_score_gemma":3.0340772e-7,"teacher_disagreement_score":0.24759676,"about_ca_system_score_codex":0.000045628774,"about_ca_system_score_gemma":0.000020355847,"threshold_uncertainty_score":0.3334373},"labels":[],"label_agreement":null},{"id":"W2060986454","doi":"10.1016/j.saa.2014.03.009","title":"Characterization of protein and carbohydrate mid-IR spectral features in crop residues","year":2014,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Crop; Crop residue; Vine; Chemistry; Residue (chemistry); Agronomy; Carbohydrate; Fourier transform infrared spectroscopy; Food science; Biology; Horticulture; Agriculture; Biochemistry","score_opus":0.004958675427485639,"score_gpt":0.23165947716390534,"score_spread":0.2267008017364197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060986454","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923935,0.0010604796,0.0012393025,0.0007229415,0.000037760736,0.0002643808,0.00003122398,0.000092435,0.0041579683],"genre_scores_gemma":[0.99736077,0.00030882785,0.0016522611,0.00014329914,0.00012565605,0.000039454982,0.00010913135,0.000071364266,0.00018922606],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971976,0.00010453108,0.0006078873,0.00092384545,0.000412473,0.0007536534],"domain_scores_gemma":[0.9987798,0.000032593496,0.00031120382,0.0005875788,0.00006096945,0.00022785102],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021193607,0.000551619,0.0007927186,0.00050124427,0.00012464084,0.00014293297,0.00032171913,0.0002799174,0.00015468185],"category_scores_gemma":[0.000110758985,0.00053607934,0.00017213673,0.0008341167,0.0003369156,0.00015273104,0.00014778285,0.00044312616,0.0000028478132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019126046,0.0001597611,0.0016001905,0.00018177443,0.0001287571,0.000041977528,0.00012264145,6.343447e-7,0.99480534,0.002701892,0.000011539493,0.000054226904],"study_design_scores_gemma":[0.0010180478,0.00024653494,0.004007224,0.00010979857,0.00017717264,0.000045515564,0.000050110524,0.00008680902,0.99159986,0.0018140405,0.0003043967,0.00054051937],"about_ca_topic_score_codex":0.00012940945,"about_ca_topic_score_gemma":0.00008511272,"teacher_disagreement_score":0.0049672714,"about_ca_system_score_codex":0.00008771763,"about_ca_system_score_gemma":0.00006686113,"threshold_uncertainty_score":0.99970907},"labels":[],"label_agreement":null},{"id":"W2061644785","doi":"10.1016/j.jms.2010.09.002","title":"Special Issue of Journal of Molecular Spectroscopy in honor of Philip R. Bunker and Robert McKellar","year":2010,"lang":"en","type":"article","venue":"Journal of Molecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Honor; Bunker; Molecular spectroscopy; Spectroscopy; Physics; Computer science; Astronomy; History; Archaeology","score_opus":0.004360216701548382,"score_gpt":0.25721414832428857,"score_spread":0.25285393162274017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061644785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98469883,0.0033771612,0.001136495,0.0003975701,0.0005694964,0.00009482503,0.000016654352,0.0000066044818,0.009702362],"genre_scores_gemma":[0.97676307,0.0013948033,0.019065544,0.000104769264,0.0023998294,0.0000013568725,0.0000028563045,0.00007936677,0.00018841554],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955753,0.00008927408,0.0022170765,0.0003539506,0.0011821744,0.00058218604],"domain_scores_gemma":[0.9955741,0.00015370655,0.0027817686,0.0005904388,0.00056434184,0.00033561836],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00092299486,0.0004838956,0.0016929762,0.0011065466,0.000048882925,0.000055529606,0.00090453937,0.00039133464,0.0031743553],"category_scores_gemma":[0.0005871579,0.0004448696,0.0007036651,0.001026301,0.00041083922,0.0002723444,0.00012651908,0.0016550177,0.0000046958708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061710656,0.0006251012,0.008495498,0.00019622086,0.0004901482,0.0005229756,0.00021015298,0.000062400024,0.9863011,0.00042049563,0.0018703542,0.00018842988],"study_design_scores_gemma":[0.0025653122,0.0006658316,0.00072231796,0.0001717717,0.0006005301,0.00068407407,0.00024881586,0.000019158098,0.9886331,0.001810617,0.0035456924,0.0003327848],"about_ca_topic_score_codex":0.00004472342,"about_ca_topic_score_gemma":0.000023074721,"teacher_disagreement_score":0.01792905,"about_ca_system_score_codex":0.00014486129,"about_ca_system_score_gemma":0.0004392525,"threshold_uncertainty_score":0.9998003},"labels":[],"label_agreement":null},{"id":"W2062012811","doi":"10.2202/1542-6580.1108","title":"Analysis of the Microscopic Flow Structure of a CFB Downer Reactor Using Solids Concentration Signals","year":2003,"lang":"en","type":"article","venue":"International Journal of Chemical Reactor Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Dar es Salaam","keywords":"Volumetric flow rate; Sauter mean diameter; Flux (metallurgy); Analytical Chemistry (journal); Materials science; Mechanics; Chemistry; Chromatography; Thermodynamics; Physics","score_opus":0.009648056134205583,"score_gpt":0.26433670576137047,"score_spread":0.25468864962716486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062012811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99743736,0.00027081813,0.0018624895,0.000022835628,0.00019497314,0.00001760177,0.000053965,0.0000051990264,0.00013474915],"genre_scores_gemma":[0.9980635,0.000018220673,0.0017388726,0.000014403036,0.00013688048,2.524813e-7,0.000010163664,0.000012572505,0.000005117],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985171,0.000010426071,0.0006611167,0.00011328571,0.00055729196,0.00014076721],"domain_scores_gemma":[0.99853307,0.0001833057,0.0006324129,0.00015284041,0.0004322325,0.00006616144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008254638,0.00014428707,0.00041675425,0.00020223108,0.000004759814,0.000021939599,0.0003956882,0.000121427736,0.00054567313],"category_scores_gemma":[0.00074107177,0.00010470545,0.00039484847,0.00054535706,0.000046147186,0.00012074444,0.000028176672,0.0002785885,1.1776984e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023396677,0.000047979313,0.0067065293,0.000025509302,0.0018748975,0.000002507137,0.00008705353,0.0030402318,0.98809385,0.000050609324,0.000009624126,0.000037794933],"study_design_scores_gemma":[0.00030778398,0.0000062439203,0.00020630522,0.00008212053,0.0008828775,0.000024568604,0.000023926012,0.005083254,0.9931494,0.000030625844,0.00011265174,0.00009023853],"about_ca_topic_score_codex":0.000011775197,"about_ca_topic_score_gemma":3.5208976e-7,"teacher_disagreement_score":0.0065002237,"about_ca_system_score_codex":0.00020193221,"about_ca_system_score_gemma":0.00008645597,"threshold_uncertainty_score":0.5974738},"labels":[],"label_agreement":null},{"id":"W2063061758","doi":"10.1021/jf1003545","title":"Using DRIFT Molecular Spectroscopy with Uni- and Multivariate Spectral Techniques To Detect Protein Molecular Structure Differences among Different Genotypes of Barley","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Institute of Biomedical Imaging and Bioengineering; Centers for Disease Control and Prevention; University of Saskatchewan","keywords":"Multivariate statistics; Multivariate analysis; Genotype; Spectroscopy; Biology; Chemistry; Genetics; Gene; Mathematics; Statistics; Physics","score_opus":0.006028330775068966,"score_gpt":0.22092348282302535,"score_spread":0.2148951520479564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063061758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99881583,0.00045098926,0.00031714872,0.000065596796,0.000014588932,0.00009316488,0.000023612887,0.000021127773,0.00019795385],"genre_scores_gemma":[0.9878213,0.000021575448,0.011953166,0.0000092984,0.00013712393,0.0000021985434,0.0000035293976,0.00001651849,0.000035308105],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865824,0.000011653429,0.0004160574,0.0002811267,0.0003439733,0.0002889308],"domain_scores_gemma":[0.99892324,0.00002419176,0.00046388444,0.00015916195,0.00017343566,0.00025610134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045848064,0.00035588973,0.000567751,0.00006709406,0.00008233512,0.00008487974,0.0002474682,0.00022456319,0.00010514401],"category_scores_gemma":[0.00004537103,0.00019864706,0.00011806001,0.0002142768,0.00016531043,0.00011646706,0.000081069906,0.00065477967,3.9501717e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000711729,0.00004220843,0.0068487856,0.00018735218,0.00028213797,0.0000142055715,0.000097741315,0.000006021904,0.9923472,0.000017568389,0.0000024588953,0.000083183564],"study_design_scores_gemma":[0.00037753757,0.0002883701,0.027817266,0.00015588326,0.00030065182,0.0002996304,0.00031124413,0.000005841204,0.969865,0.00027764076,0.0000030258711,0.00029790672],"about_ca_topic_score_codex":0.000027973792,"about_ca_topic_score_gemma":0.000018144481,"teacher_disagreement_score":0.02248216,"about_ca_system_score_codex":0.00003720686,"about_ca_system_score_gemma":0.000031848653,"threshold_uncertainty_score":0.81005925},"labels":[],"label_agreement":null},{"id":"W2065084513","doi":"10.1016/j.lwt.2003.10.013","title":"Identification of animal meat muscles by visible and near infrared reflectance spectroscopy","year":2004,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":295,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Indigenous and Northern Affairs Canada","keywords":"Principal component analysis; Near infrared reflectance spectroscopy; Reflectivity; Monochromator; Partial least squares regression; Near-infrared spectroscopy; Spectroscopy; Infrared; Chemistry; Analytical Chemistry (journal); Materials science; Biology; Mathematics; Optics; Optoelectronics; Chromatography; Computer science; Artificial intelligence; Physics","score_opus":0.010300243140570656,"score_gpt":0.281160705641072,"score_spread":0.27086046250050133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065084513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988534,0.0019203369,0.0005234528,0.00013499912,0.000015438842,0.000029570476,0.000022576747,0.00005317133,0.008766493],"genre_scores_gemma":[0.99744266,0.00022799696,0.001218005,0.00003330803,0.000037954702,0.00000455309,0.000015644571,0.000013524214,0.0010063645],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991923,0.0000045154848,0.00023802329,0.00023560275,0.00016247602,0.00016709353],"domain_scores_gemma":[0.99951404,0.000023613657,0.00013324102,0.00023949513,0.000039295985,0.00005028791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000028582146,0.00010921907,0.00018165275,0.000049582923,0.00007885203,0.000049445505,0.0001323851,0.00008109688,0.00027875553],"category_scores_gemma":[0.000056188455,0.00011094652,0.000042103955,0.00036757952,0.00010171272,0.000120122786,0.000026485055,0.00009677542,0.000013225602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037259513,0.000058183457,0.0013817418,0.00005603204,0.000029608389,0.0000011842797,0.00012768745,0.000003289043,0.9971759,0.00053710176,0.0005463391,0.000045697878],"study_design_scores_gemma":[0.00036441,0.000044850873,0.0007900228,0.000014237495,0.00005247959,0.0000033427002,0.00013264095,0.000036968548,0.9960939,0.0018927621,0.00046634584,0.00010803094],"about_ca_topic_score_codex":0.00004590024,"about_ca_topic_score_gemma":0.000005987372,"teacher_disagreement_score":0.008908688,"about_ca_system_score_codex":0.0000505027,"about_ca_system_score_gemma":0.0000123628115,"threshold_uncertainty_score":0.4524268},"labels":[],"label_agreement":null},{"id":"W2066502489","doi":"10.5430/air.v2n4p87","title":"Effective classification of Chinese tea samples in hyperspectral imaging","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Classifier (UML); Feature extraction; Computer science; Robustness (evolution); Pixel; Imaging spectroscopy; Contextual image classification; Computer vision; Image (mathematics); Chemistry","score_opus":0.1107078367755154,"score_gpt":0.43108876330174806,"score_spread":0.3203809265262326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066502489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810194,0.0003578448,0.0009856607,0.00050402136,0.000018735376,0.00019247137,0.000004609543,0.000030199355,0.016887052],"genre_scores_gemma":[0.99920136,0.00006111531,0.0003365746,0.0000068682925,0.00011025565,0.00011986023,0.000008161581,0.000014970135,0.00014086122],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99816775,0.00008744292,0.00040298994,0.00036341377,0.0004720171,0.00050637167],"domain_scores_gemma":[0.9981841,0.0009619685,0.00006938187,0.0003410549,0.00035504744,0.000088448025],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067751866,0.00012943872,0.00023641369,0.0005488584,0.00010310357,0.00007329314,0.0003803816,0.00008034853,0.0027860212],"category_scores_gemma":[0.001385309,0.00011242757,0.00007987992,0.0019098547,0.00037746318,0.00020241708,0.0000801756,0.00049525744,0.0002962029],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003171919,0.00024095623,0.06877165,0.00004661896,0.000019139077,0.0000028241325,0.0006004283,0.000029154618,0.88654524,0.00776205,0.00004350804,0.035906687],"study_design_scores_gemma":[0.000027280437,0.00002727142,0.01861748,0.000021127404,0.00000605486,0.0000019927563,0.008846063,0.006637884,0.90534425,0.06033495,0.00001296304,0.00012270731],"about_ca_topic_score_codex":0.0038985726,"about_ca_topic_score_gemma":0.00017924112,"teacher_disagreement_score":0.0525729,"about_ca_system_score_codex":0.00020566309,"about_ca_system_score_gemma":0.0000659323,"threshold_uncertainty_score":0.99812555},"labels":[],"label_agreement":null},{"id":"W2067145094","doi":"10.1081/css-200043187","title":"Compositional Analysis of Cattle Manure During Composting Using a Field‐Portable Near‐Infrared Spectrometer","year":2005,"lang":"en","type":"article","venue":"Communications in Soil Science and Plant Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":129,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Manure; Compost; Stockpile; Environmental science; Feedlot; Raw material; Straw; Nutrient; Pulp and paper industry; Composition (language); Biogas; Waste management; Chemistry; Agronomy; Animal science; Engineering","score_opus":0.03277086102974354,"score_gpt":0.3158700235043078,"score_spread":0.2830991624745643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067145094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919002,0.0007016278,0.00022981789,0.00019611054,0.0000039153074,0.000021262378,0.000071681745,0.000016071199,0.006859278],"genre_scores_gemma":[0.9903936,0.00022395686,0.009034797,0.00004429695,0.000012047801,0.000004028965,0.00015842082,0.0000036113518,0.00012527089],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986571,0.000023595829,0.00042273715,0.00028410624,0.00035953367,0.00025292428],"domain_scores_gemma":[0.9983888,0.0003062721,0.00021704343,0.0008678696,0.00014582765,0.00007413499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004208862,0.000118788754,0.0004380612,0.001563938,0.0005586238,0.00010267592,0.0007741799,0.00006152338,0.00055859867],"category_scores_gemma":[0.0000965612,0.0001189294,0.0001620513,0.009992503,0.0004496207,0.000253052,0.00025411329,0.0001978593,0.0000013651902],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024799388,0.0002846492,0.8025844,0.00003259958,0.002684045,0.0000026066048,0.0011136081,0.023390785,0.16913266,0.00029513848,0.000026272004,0.00042841435],"study_design_scores_gemma":[0.00032218403,0.000012480123,0.1268174,0.000035415535,0.009157309,0.0000123661375,0.0011504438,0.7521986,0.10973384,0.00007247352,0.00013404955,0.0003534717],"about_ca_topic_score_codex":0.0011414713,"about_ca_topic_score_gemma":0.0016543764,"teacher_disagreement_score":0.7288078,"about_ca_system_score_codex":0.0001487793,"about_ca_system_score_gemma":0.000093656876,"threshold_uncertainty_score":0.6116264},"labels":[],"label_agreement":null},{"id":"W2067852711","doi":"10.1139/s07-049","title":"Petroleum contamination characterization and quantification using fluorescence emission-excitation matrices (EEMs) and parallel factor analysis (PARAFAC)","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Petroleum; Gasoline; Diesel fuel; Petroleum product; Hydrocarbon; Environmental science; Chemistry; Contamination; Fluorescence; Environmental chemistry; Organic chemistry; Ecology","score_opus":0.013894544701176065,"score_gpt":0.2309492630159886,"score_spread":0.21705471831481252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067852711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9775005,0.0005075922,0.021927003,0.000016649265,0.000018360122,0.00001360655,0.000006040976,0.0000053966537,0.0000048653496],"genre_scores_gemma":[0.9967416,0.0014954478,0.0016785708,0.0000031903378,0.000023140628,4.2428692e-7,0.0000041837575,0.0000038937287,0.00004955908],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992783,0.0000043884575,0.00021525088,0.00014866464,0.00025174752,0.00010166617],"domain_scores_gemma":[0.9995927,0.00003488777,0.00020686991,0.000056385812,0.000015217823,0.00009393568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012625285,0.00008570519,0.00015013093,0.00032787278,0.0001653972,0.000048983788,0.000065489476,0.000035142868,0.000024414954],"category_scores_gemma":[0.000046857684,0.00007645141,0.000026007123,0.00032635918,0.00014159842,0.00048197265,0.00002062486,0.000081084734,1.6539548e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006282806,0.000017639479,0.07044455,0.000013848002,0.000026137255,0.0000020350856,0.00021092116,0.0010288091,0.92779034,0.000005307647,1.9968498e-7,0.00045392796],"study_design_scores_gemma":[0.00018621045,0.000027148828,0.7214767,0.00001604887,0.00015776507,0.00009813929,0.00018848201,0.2038151,0.07390545,0.0000021263147,0.00001990637,0.00010696839],"about_ca_topic_score_codex":0.0000041076764,"about_ca_topic_score_gemma":7.835114e-8,"teacher_disagreement_score":0.8538849,"about_ca_system_score_codex":0.00006807859,"about_ca_system_score_gemma":0.000012610104,"threshold_uncertainty_score":0.31175983},"labels":[],"label_agreement":null},{"id":"W2068155108","doi":"10.1016/s0024-3795(01)00357-3","title":"The asymptotic variance of the univariate PLS estimator","year":2002,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Univariate; Applied mathematics; Covariance matrix; Estimator; Covariance; Estimation of covariance matrices; Statistics; Multivariate statistics","score_opus":0.013110823697026226,"score_gpt":0.2406520807225539,"score_spread":0.22754125702552766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068155108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.622291,0.04266188,0.031054832,0.022851303,0.0002537439,0.0016457292,0.00037238956,0.0005480641,0.27832106],"genre_scores_gemma":[0.99111444,0.00047919442,0.00025179322,0.00007640089,0.00008972471,0.000053635675,0.0000027076978,0.000010263553,0.007921847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994519,0.000007268573,0.00016700648,0.00014299054,0.00010339926,0.00012743902],"domain_scores_gemma":[0.99925065,0.00019066251,0.00010076835,0.00036938066,0.000047407717,0.00004112065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004261033,0.000081469545,0.00009998272,0.000015795355,0.0003596088,0.000023063738,0.00028340204,0.000051493116,0.0003926536],"category_scores_gemma":[0.00006985967,0.000048349095,0.00004784994,0.0004186086,0.00009053927,0.00003294799,0.00005544215,0.00011393261,0.00003995483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009683511,0.0003185351,0.0014801823,0.00014820506,0.0002965194,4.2557238e-7,0.00030032807,0.00007116645,0.09257879,0.8956021,0.0025928249,0.006601255],"study_design_scores_gemma":[0.0014331405,0.000041004907,0.007311052,0.000070355374,0.0011124581,0.00004193981,0.0005934396,0.07426357,0.57154423,0.03219841,0.3105942,0.00079620804],"about_ca_topic_score_codex":0.000006106065,"about_ca_topic_score_gemma":0.0000012973521,"teacher_disagreement_score":0.8634037,"about_ca_system_score_codex":0.000010339456,"about_ca_system_score_gemma":0.000012099526,"threshold_uncertainty_score":0.42992815},"labels":[],"label_agreement":null},{"id":"W2069041784","doi":"10.2202/1934-2659.1292","title":"Correlation of the Transport Properties for the Ethanol-Water System Using Neural Networks","year":2008,"lang":"en","type":"article","venue":"Chemical Product and Process Modeling","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; A priori and a posteriori; Computer science; Biological system; Diffusion; Experimental data; Viscosity; Binary number; Artificial intelligence; Materials science; Mathematics; Thermodynamics; Statistics","score_opus":0.05351836946342361,"score_gpt":0.2557921175554866,"score_spread":0.20227374809206297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069041784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752295,0.0036333387,0.020695109,0.00012901245,0.000055237786,0.00014120634,0.0000018997132,0.000032334945,0.00008237232],"genre_scores_gemma":[0.99951094,0.000029076215,0.000063882864,0.000016911994,0.0002573394,0.000023944109,0.000006101903,0.00001650823,0.00007529859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917346,0.0000043999285,0.00025600853,0.00023066896,0.0001426606,0.00019278862],"domain_scores_gemma":[0.9995618,0.000026440048,0.000070275375,0.00018349041,0.00013329605,0.000024702234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099651596,0.00012753345,0.00018689744,0.00001655569,0.0002670488,0.00000918179,0.00017441106,0.00008015882,0.0000061332657],"category_scores_gemma":[0.000037762173,0.000060490554,0.00007966562,0.00014276819,0.00012437701,0.000080243866,0.000022685277,0.00018893277,6.738094e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014294859,0.000029084636,0.0025407826,0.0012119795,0.00007145375,2.8968282e-7,0.0014151715,0.55233866,0.44204798,0.000008997989,0.000004523971,0.0001881194],"study_design_scores_gemma":[0.00008721583,0.0000016915443,0.000001161015,0.00003064262,0.000107803135,0.000014247866,0.00017325037,0.53658885,0.46291944,0.000018224982,0.0000016233388,0.000055844346],"about_ca_topic_score_codex":0.000019525754,"about_ca_topic_score_gemma":2.2064235e-7,"teacher_disagreement_score":0.024281448,"about_ca_system_score_codex":0.000021455424,"about_ca_system_score_gemma":0.000023792834,"threshold_uncertainty_score":0.24667332},"labels":[],"label_agreement":null},{"id":"W2069379834","doi":"10.1016/j.talanta.2013.11.015","title":"Rapid and non-invasive quantification of intramuscular fat content of intact pork cuts","year":2013,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Intramuscular fat; Chemistry; Content (measure theory); Pattern recognition (psychology); Filter (signal processing); Artificial intelligence; Linear regression; Texture (cosmology); Wavelength; Mathematics; Computer vision; Computer science; Statistics; Optics; Image (mathematics); Food science; Physics","score_opus":0.028381361553771255,"score_gpt":0.2530480526644746,"score_spread":0.22466669111070336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069379834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955684,0.00074617396,0.00011100426,0.000054865093,0.000017989016,0.00006427377,0.000015345777,0.000012994799,0.0034089482],"genre_scores_gemma":[0.9988482,0.00054273417,0.00007618024,0.000016685957,0.000029548417,0.000008619512,0.000038073045,0.000008326171,0.00043165035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934185,0.000006550661,0.00025357542,0.00015698996,0.0001270186,0.00011399216],"domain_scores_gemma":[0.9993343,0.00008182005,0.00019238761,0.00025013712,0.00009368422,0.00004771967],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004277439,0.0000958462,0.00024851988,0.00007488779,0.00002039172,0.000010788219,0.00011562614,0.000073583265,0.0009275892],"category_scores_gemma":[0.000081230355,0.000081553124,0.000063652034,0.0001684113,0.00009368122,0.00006908397,0.000033135508,0.000070856644,0.000016017853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012424616,0.000054495777,0.016225103,0.00018746073,0.00011156911,8.9845224e-7,0.00016474567,7.6840377e-7,0.98192185,0.00009905186,0.00047995523,0.0007416661],"study_design_scores_gemma":[0.00026022265,0.000033328728,0.012193253,0.000040320418,0.00009471182,0.000004479727,0.0010425783,0.00005684151,0.98605275,0.000088045286,0.000050287912,0.00008317744],"about_ca_topic_score_codex":0.000530631,"about_ca_topic_score_gemma":0.0000056150866,"teacher_disagreement_score":0.004130892,"about_ca_system_score_codex":0.000020887941,"about_ca_system_score_gemma":0.000019130184,"threshold_uncertainty_score":0.9999857},"labels":[],"label_agreement":null},{"id":"W2070707236","doi":"10.1366/0003702001950661","title":"Investigation of the Temperature Behavior of the Bands Due to the Methylene Stretching Vibrations of Phospholipid Acyl Chains by Two-Dimensional Infrared Correlation Spectroscopy","year":2000,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Dipalmitoylphosphatidylcholine; Infrared spectroscopy; Phase (matter); Infrared; Chemistry; Spectroscopy; Phase transition; Methylene; Spectral line; Molecular physics; Intramolecular force; Analytical Chemistry (journal); Atmospheric temperature range; Crystallography; Materials science; Phospholipid; Optics; Stereochemistry; Thermodynamics; Physics; Organic chemistry; Membrane","score_opus":0.007050330823497078,"score_gpt":0.23612599799063907,"score_spread":0.229075667167142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070707236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99332887,0.00026807864,0.00015362342,0.00067027967,0.000113532784,0.00048965035,0.0002356976,0.000037648777,0.004702634],"genre_scores_gemma":[0.9965754,0.000020561149,0.0017132302,0.00031887632,0.00014348485,0.00009842742,0.00009103096,0.000040444404,0.0009985467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976288,0.00006745696,0.00076080766,0.000429689,0.00075794035,0.00035528434],"domain_scores_gemma":[0.9979592,0.00021545407,0.0005567355,0.0010604492,0.00011506311,0.000093077026],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002655163,0.00034285587,0.0005170855,0.00009827548,0.00039049308,0.000037001388,0.0008183851,0.0002065213,0.0016888396],"category_scores_gemma":[0.00007013428,0.00020931258,0.00025702122,0.0017589342,0.00034135053,0.000105697945,0.00012346933,0.0006164188,0.0000073824085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008390906,0.000109368746,0.004409256,0.00002807778,0.000093092734,1.9585543e-7,0.0008266078,0.0014642307,0.9889857,0.0012765985,0.0026715016,0.00005145549],"study_design_scores_gemma":[0.000626538,0.000057489702,0.005315741,0.000050220413,0.0003573369,0.000004430005,0.00033470997,0.00029548953,0.9918404,0.0008406565,0.000080508384,0.00019644012],"about_ca_topic_score_codex":0.00017337622,"about_ca_topic_score_gemma":0.00005941776,"teacher_disagreement_score":0.0037040876,"about_ca_system_score_codex":0.00014282024,"about_ca_system_score_gemma":0.00021122288,"threshold_uncertainty_score":0.99922377},"labels":[],"label_agreement":null},{"id":"W2070766575","doi":"10.1117/12.818290","title":"A new fast infrared imaging spectroradiometer","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Spectroradiometer; Hyperspectral imaging; Remote sensing; Full spectral imaging; Detector; Optics; Spectral bands; Infrared; Computer science; Multispectral image; Field of view; Spectral imaging; Moderate-resolution imaging spectroradiometer; Physics; Satellite; Geology","score_opus":0.008939274665649954,"score_gpt":0.23782106446418239,"score_spread":0.22888178979853244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070766575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96663535,0.00032336253,0.0001417776,0.0027738204,0.00009224915,0.00015996347,0.000028425595,0.00014426048,0.02970079],"genre_scores_gemma":[0.75505775,0.00018659003,0.23750474,0.00046200995,0.0016464811,0.00005936031,0.000022686736,0.000111868176,0.004948538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99758786,3.9707455e-9,0.00069086824,0.000461002,0.0007276703,0.0005326105],"domain_scores_gemma":[0.9983732,0.00009765001,0.0003999933,0.000085096595,0.0008412317,0.00020284428],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025649535,0.00038789376,0.0005276133,0.0002049399,0.00008670991,0.00017574154,0.001143364,0.00017724799,0.00019648233],"category_scores_gemma":[0.00044995433,0.00033475008,0.00085036387,0.0007514502,0.00013159962,0.0005563376,0.00011109052,0.0004100418,0.0000032280745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007869278,0.00011194532,0.00072444946,0.00020069955,0.0004543573,2.0475268e-7,0.0001517728,0.000013738121,0.8400205,0.1471579,0.010163147,0.0009226464],"study_design_scores_gemma":[0.0015923006,0.00019925293,0.0010253264,0.0002163927,0.0005173641,0.000043151784,0.0015489551,0.0064269453,0.97484106,0.0072824867,0.005700336,0.0006064394],"about_ca_topic_score_codex":0.00001174082,"about_ca_topic_score_gemma":6.0904554e-8,"teacher_disagreement_score":0.23736295,"about_ca_system_score_codex":0.00025196804,"about_ca_system_score_gemma":0.00005057674,"threshold_uncertainty_score":0.9999105},"labels":[],"label_agreement":null},{"id":"W2070958859","doi":"10.1016/j.laa.2005.10.022","title":"Nonnegative determinant of a rectangular matrix: Its definition and applications to multivariate analysis","year":2005,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Square matrix; Matrix (chemical analysis); Canonical correlation; Multivariate statistics; Canonical form; Nonnegative matrix; Square (algebra); Combinatorics; Block matrix; Applied mathematics; Statistics; Symmetric matrix; Pure mathematics; Geometry","score_opus":0.018266089778259213,"score_gpt":0.30773354171548567,"score_spread":0.28946745193722645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070958859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83857447,0.00575918,0.14436838,0.0010907003,0.0000072974935,0.0010639206,0.0007263445,0.00018502145,0.008224711],"genre_scores_gemma":[0.9866964,0.00040227608,0.011360771,0.00007762871,0.00013792378,0.0005819294,0.000092388094,0.000016272223,0.00063436356],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989878,0.000010180722,0.00032798722,0.00037474892,0.00012485264,0.00017445236],"domain_scores_gemma":[0.9991357,0.00014798003,0.00015251331,0.00027503958,0.00014341682,0.00014538702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007155887,0.00015508247,0.00029807087,0.00027747368,0.0001757189,0.000023481922,0.00013060888,0.00010081924,0.00024757395],"category_scores_gemma":[0.000048630416,0.00014951114,0.000083533945,0.0014423631,0.000042465337,0.000087706496,0.00006406489,0.00010378972,0.000044153283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000809371,0.0009350353,0.002974451,0.00045023445,0.0018993707,0.0000013418446,0.0016423862,0.00030126466,0.8609546,0.09909949,0.00010147326,0.031559445],"study_design_scores_gemma":[0.00080341566,0.00006439497,0.0033332293,0.000027339407,0.0037264724,0.000013652259,0.00068884756,0.015861277,0.95195144,0.002119329,0.020770889,0.0006397082],"about_ca_topic_score_codex":0.000023555831,"about_ca_topic_score_gemma":0.000023461564,"teacher_disagreement_score":0.148122,"about_ca_system_score_codex":0.000027697639,"about_ca_system_score_gemma":0.000019440851,"threshold_uncertainty_score":0.6096887},"labels":[],"label_agreement":null},{"id":"W2071090551","doi":"10.1002/cem.680","title":"Interpretation of regression coefficients under a latent variable regression model","year":2001,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Mathematics; Statistics; Regression diagnostic; Latent variable; Regression analysis; Linear regression; Segmented regression; Proper linear model; Partial least squares regression; Standardized coefficient; Linear predictor function; Polynomial regression","score_opus":0.02813926701004112,"score_gpt":0.3085553559105928,"score_spread":0.2804160889005517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071090551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7310254,0.0027803136,0.2546346,0.0001809357,0.00017971448,0.000038905026,0.000007814877,0.000023338624,0.01112901],"genre_scores_gemma":[0.9904236,0.0008099848,0.0058559845,0.00007296516,0.000093319584,8.4328536e-7,0.000006457489,0.000023686356,0.002713132],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978271,0.000017336622,0.00085642986,0.00018094112,0.0008658607,0.00025234482],"domain_scores_gemma":[0.9974112,0.00019109758,0.0012965618,0.00027014673,0.00067760964,0.00015336648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000436315,0.00019926434,0.00054251286,0.0013182571,0.00006103647,0.00003335037,0.0003849631,0.00021777373,0.0005015789],"category_scores_gemma":[0.0008672216,0.00014362001,0.00023432981,0.0036927948,0.000053597632,0.00024078507,0.00007811222,0.00040098425,0.0000037276677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001696245,0.0025912337,0.023973536,0.00057260983,0.0006762357,0.00007284152,0.0006474644,0.08797537,0.849968,0.00051127846,0.00727738,0.024037769],"study_design_scores_gemma":[0.0024953375,0.00021540848,0.00033352707,0.00069345435,0.00062021625,0.00023520291,0.0005354823,0.12205369,0.86894804,0.0026793191,0.000824849,0.00036549062],"about_ca_topic_score_codex":0.0000044719077,"about_ca_topic_score_gemma":1.5880327e-7,"teacher_disagreement_score":0.25939825,"about_ca_system_score_codex":0.00024591235,"about_ca_system_score_gemma":0.00013230226,"threshold_uncertainty_score":0.5856654},"labels":[],"label_agreement":null},{"id":"W2071247413","doi":"10.1007/s00216-011-5036-x","title":"A new direct Fourier transform infrared analysis of free fatty acids in edible oils using spectral reconstitution","year":2011,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; Northwest A and F University","keywords":"Absorbance; Fourier transform infrared spectroscopy; Chemistry; Analytical Chemistry (journal); Canola; Absorption (acoustics); Infrared spectroscopy; Infrared; Near-infrared spectroscopy; Saponification; Spectroscopy; Fourier transform; Calibration; Materials science; Chromatography; Organic chemistry; Optics; Mathematics","score_opus":0.036527329534720536,"score_gpt":0.270716833641115,"score_spread":0.23418950410639444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071247413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68514323,0.00057160703,0.005901119,0.00015858306,0.000022083057,0.00006347347,0.00011813256,0.00007399247,0.30794775],"genre_scores_gemma":[0.9913855,0.00013840887,0.006244713,0.00003883347,0.00012102546,0.0000029789812,0.00003849554,0.000017369879,0.0020126721],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99748915,0.0000127973035,0.0008418228,0.0006569211,0.0004067777,0.0005925516],"domain_scores_gemma":[0.99857116,0.00014080683,0.00014797742,0.0005744016,0.00008385898,0.00048177058],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021750959,0.00036952415,0.0010891996,0.00040940748,0.000067260116,0.000039649494,0.0003849083,0.0004188751,0.008740082],"category_scores_gemma":[0.0003298357,0.0003241953,0.0005503137,0.003094366,0.000498884,0.00017368763,0.00010181316,0.00042787218,0.0000032565893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014771167,0.0022053136,0.7171828,0.0012900135,0.016173001,0.00033624022,0.0007156838,0.000103104554,0.25011757,0.005231528,0.0006361469,0.0045314915],"study_design_scores_gemma":[0.0014235529,0.000049318336,0.0055495347,0.00007565762,0.011400491,0.00002394125,0.00036411724,0.042983312,0.93320054,0.0040666973,0.00014841497,0.0007144121],"about_ca_topic_score_codex":0.0007780013,"about_ca_topic_score_gemma":0.0000844903,"teacher_disagreement_score":0.71163327,"about_ca_system_score_codex":0.00014997981,"about_ca_system_score_gemma":0.00018579754,"threshold_uncertainty_score":0.999921},"labels":[],"label_agreement":null},{"id":"W2071758994","doi":"10.1002/cjce.20002","title":"Determination of ternary solutions concentration in liquid–liquid extraction by the use of attenuated total reflectance‐Fourier transform infrared spectroscopy and multivariate data analysis","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Instituto Politécnico Nacional","keywords":"Raffinate; Attenuated total reflection; Extraction (chemistry); Fourier transform infrared spectroscopy; Partial least squares regression; Chemistry; Ternary operation; Chromatography; Analytical Chemistry (journal); Fourier transform; Infrared spectroscopy; Solvent; Correlation coefficient; Mathematics; Optics; Organic chemistry","score_opus":0.04124193150425126,"score_gpt":0.27231178366375847,"score_spread":0.2310698521595072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071758994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878582,0.00055400364,0.011146751,0.0002497774,0.000022111544,0.000039503906,0.000073476425,0.000004220463,0.00005196364],"genre_scores_gemma":[0.9982941,0.000054538534,0.0015275799,0.000009447538,0.000041923362,0.000001416451,0.00003582161,0.000009051531,0.000026089392],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905217,0.000014211023,0.00045740203,0.000107329346,0.00017502664,0.00019385676],"domain_scores_gemma":[0.9991687,0.00016665203,0.00023972709,0.0002264343,0.00008959903,0.00010889001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017319836,0.000106383086,0.00025757242,0.00016516891,0.00006171506,0.000016155016,0.00023028476,0.00008795585,0.000050422903],"category_scores_gemma":[0.00026428717,0.00008049203,0.00007412552,0.000641846,0.00011968667,0.00030081076,0.000013345775,0.00030814254,7.4904776e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010708171,0.000017975322,0.00030108308,0.000017688297,0.00018947753,0.000008280888,0.00036877114,0.0024628048,0.99638134,0.000009571235,0.000062034014,0.00007386147],"study_design_scores_gemma":[0.00034226326,0.00003939986,0.00026366473,0.000037309655,0.0003930806,0.0000959426,0.0000339978,0.10507358,0.8935561,0.00000959753,0.00006460633,0.00009044969],"about_ca_topic_score_codex":0.0018721039,"about_ca_topic_score_gemma":0.00035436946,"teacher_disagreement_score":0.10282526,"about_ca_system_score_codex":0.00018279388,"about_ca_system_score_gemma":0.00016390493,"threshold_uncertainty_score":0.32823697},"labels":[],"label_agreement":null},{"id":"W2071929021","doi":"10.1016/j.chemolab.2005.03.004","title":"Automatic masking in multivariate image analysis using support vector machines","year":2005,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Multivariate statistics; Masking (illustration); Pattern recognition (psychology); Computer science; Support vector machine; Artificial intelligence; Multivariate analysis; Image (mathematics); Data mining; Machine learning","score_opus":0.023083380619398006,"score_gpt":0.3015311663790972,"score_spread":0.2784477857596992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071929021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9851257,0.0063180723,0.0060415748,0.000030138446,0.00014553391,0.0001304132,0.00009932526,0.00013167538,0.0019775368],"genre_scores_gemma":[0.99708474,0.00025680297,0.0017095241,0.000051004925,0.00023993733,0.000018812594,0.000040081926,0.00004481772,0.0005542921],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749124,0.000043289903,0.0009175267,0.0005940764,0.0004353887,0.00051848026],"domain_scores_gemma":[0.99847597,0.0002255595,0.00038189517,0.0004603838,0.00023526253,0.00022092715],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005956628,0.0003836472,0.0008739351,0.0027548021,0.00012511219,0.0002786065,0.0003022847,0.00023631255,0.0017502796],"category_scores_gemma":[0.00043316025,0.00036146105,0.00017385303,0.011209809,0.00007839099,0.0003136059,0.00010358146,0.0003175598,0.00004648828],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066213426,0.0015690298,0.4984663,0.003222235,0.005418834,0.00019549402,0.0029893506,0.007238357,0.46522698,0.0007997937,0.00072533026,0.014082058],"study_design_scores_gemma":[0.0007225977,0.000048460563,0.003534241,0.00009575405,0.0016782351,0.00001885371,0.0017781745,0.7208498,0.26431206,0.000011306364,0.0059131496,0.0010373491],"about_ca_topic_score_codex":0.00032886723,"about_ca_topic_score_gemma":0.00001684222,"teacher_disagreement_score":0.7136115,"about_ca_system_score_codex":0.00046517883,"about_ca_system_score_gemma":0.00008592923,"threshold_uncertainty_score":0.9998837},"labels":[],"label_agreement":null},{"id":"W2073153283","doi":"10.1021/jf200078h","title":"Rapid Determination of Carbohydrates, Ash, and Extractives Contents of Straw Using Attenuated Total Reflectance Fourier Transform Mid-Infrared Spectroscopy","year":2011,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Xylan; Galactan; Partial least squares regression; Chemistry; Attenuated total reflection; Glucan; Triticale; Straw; Mannan; Biomass (ecology); Food science; Glycan; Hydrolysate; Chromatography; Polysaccharide; Infrared spectroscopy; Botany; Agronomy; Hydrolysis; Biochemistry; Mathematics; Organic chemistry; Biology","score_opus":0.02973080798121507,"score_gpt":0.25116979547256457,"score_spread":0.2214389874913495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073153283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928407,0.0027588871,0.000049165126,0.000018095703,0.000025909161,0.000039431612,0.000024950556,0.0000066625576,0.0042361952],"genre_scores_gemma":[0.9978021,0.00041201263,0.0014893299,0.0000025788677,0.000083984974,0.0000010790249,0.000005804307,0.000008214232,0.00019490367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988578,0.0000084016565,0.00057335966,0.00014459543,0.00023275345,0.00018309195],"domain_scores_gemma":[0.9987043,0.000039922765,0.0007681405,0.00008525475,0.0002891109,0.00011326295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006117965,0.00020304628,0.0004666354,0.000047149017,0.000052774998,0.000016239856,0.000119686156,0.0001600515,0.00013564434],"category_scores_gemma":[0.00003877107,0.00013910932,0.0001443417,0.00022185003,0.00013884829,0.00026631745,0.000018493969,0.00024625045,4.7833268e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023973969,0.00013901896,0.0009840644,0.00026528942,0.00023778134,0.0000037927884,0.00061716745,5.5489255e-7,0.99717337,0.0000015483969,0.000007618724,0.00033003883],"study_design_scores_gemma":[0.0013861508,0.00023262606,0.004660673,0.0001399042,0.000365196,0.00032875466,0.0026902768,0.000019733827,0.98989755,0.00011005578,0.0000025508837,0.00016652855],"about_ca_topic_score_codex":0.000010860153,"about_ca_topic_score_gemma":0.0000011285563,"teacher_disagreement_score":0.0072758356,"about_ca_system_score_codex":0.000050687526,"about_ca_system_score_gemma":0.000029443709,"threshold_uncertainty_score":0.56727135},"labels":[],"label_agreement":null},{"id":"W2073994361","doi":"10.1039/c0an00345j","title":"Detecting influential observations by cluster analysis and Monte Carlo cross-validation","year":2010,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Principal component analysis; Monte Carlo method; Partial least squares regression; Computer science; Cross-validation; Component (thermodynamics); Cluster (spacecraft); Series (stratigraphy); Sample (material); Data mining; Statistics; Artificial intelligence; Mathematics; Machine learning; Chemistry; Physics","score_opus":0.013363306546627306,"score_gpt":0.28784867486334825,"score_spread":0.27448536831672093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073994361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972049,0.0001436353,0.0012056328,0.00027585498,0.000026085618,0.00002809924,0.000027522789,0.000052071962,0.0010361912],"genre_scores_gemma":[0.99652374,0.000020621063,0.00021813245,0.00014527932,0.00013165567,0.00001062968,0.000040995772,0.000012178268,0.002896751],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990194,0.000018896231,0.00028218704,0.0002633087,0.00021125408,0.00020495796],"domain_scores_gemma":[0.9990318,0.00015132222,0.00017591567,0.0004640946,0.000110028726,0.00006685538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024954433,0.0001420218,0.00023267296,0.00017430785,0.00041562872,0.00024118809,0.0002496856,0.00011151821,0.0005988465],"category_scores_gemma":[0.0001653826,0.0001067481,0.00017862611,0.0015125406,0.00012075711,0.00018251047,0.00007835724,0.00031915284,0.00000936425],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001693819,0.00003253305,0.50816363,0.000012866365,0.0016144319,7.120489e-7,0.00022598616,0.0006778912,0.48841143,0.00003073022,0.00037952643,0.00043334623],"study_design_scores_gemma":[0.0006618496,0.000013189555,0.08503698,0.0000041363533,0.009524231,0.000007290045,0.0005525152,0.019432718,0.8800468,0.00015640848,0.004115807,0.0004481078],"about_ca_topic_score_codex":0.0016098154,"about_ca_topic_score_gemma":0.0009886017,"teacher_disagreement_score":0.42312664,"about_ca_system_score_codex":0.000022774337,"about_ca_system_score_gemma":0.000013643786,"threshold_uncertainty_score":0.65569496},"labels":[],"label_agreement":null},{"id":"W2074955526","doi":"10.2307/3316051","title":"Simple and accurate one‐sided inference from signed roots of likelihood ratios","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistic; Simple (philosophy); Inference; Mathematics; Statistics; Nuisance parameter; Scalar (mathematics); Null hypothesis; Statistical hypothesis testing; Statistical inference; Computer science; Applied mathematics; Algorithm; Estimator; Artificial intelligence","score_opus":0.02637955835057473,"score_gpt":0.270371991212576,"score_spread":0.24399243286200126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074955526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.939452,0.0011498812,0.056224898,0.00015376849,0.000062012594,0.0000325385,0.0012629029,0.000005541934,0.0016564454],"genre_scores_gemma":[0.9945302,0.00017413912,0.0049627908,0.00007129472,0.00010255489,4.9407663e-7,0.00004281735,0.000012141866,0.00010357726],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990225,0.000014248472,0.00045397395,0.00010427772,0.00016516123,0.00023985213],"domain_scores_gemma":[0.9982229,0.0004308181,0.0004080603,0.00013458477,0.0003081639,0.0004954668],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000073169176,0.000119181524,0.0003213534,0.0001772804,0.000074781376,0.000042333937,0.00017673199,0.000078799305,0.0027836366],"category_scores_gemma":[0.0010695638,0.00012040902,0.000035747482,0.00024092784,0.00009684651,0.00010341497,0.000010007767,0.00019210565,0.0000035763992],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027325164,0.00024791397,0.63009024,0.0003069371,0.0019482976,0.0019654173,0.002241415,0.00045108664,0.30697164,0.0048161335,0.02540509,0.025282549],"study_design_scores_gemma":[0.007988542,0.0011062073,0.2632198,0.0007510724,0.0037509573,0.0003468147,0.0072164917,0.0041405004,0.51289463,0.1856273,0.010748145,0.002209503],"about_ca_topic_score_codex":0.008045722,"about_ca_topic_score_gemma":0.026054798,"teacher_disagreement_score":0.36687046,"about_ca_system_score_codex":0.000086032735,"about_ca_system_score_gemma":0.0008007668,"threshold_uncertainty_score":0.9985598},"labels":[],"label_agreement":null},{"id":"W2075961729","doi":"10.1016/j.aam.2010.11.005","title":"Cubature formulae for orthogonal polynomials in terms of elements of finite order of compact simple Lie groups","year":2010,"lang":"en","type":"article","venue":"Advances in Applied Mathematics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Victoria","funders":"","keywords":"Mathematics; Simple Lie group; Simple (philosophy); Lie theory; Pure mathematics; Representation of a Lie group; Orthogonal polynomials; Lie algebra; Fundamental representation; Combinatorics; Discrete mathematics","score_opus":0.011154301761226138,"score_gpt":0.30851998913190243,"score_spread":0.2973656873706763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075961729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9759179,0.00020638599,0.0065058717,0.000010256078,0.000033378827,0.0002676235,0.0001960872,0.00001258076,0.016849969],"genre_scores_gemma":[0.9570702,0.00007358638,0.042692043,0.0000100627985,0.0000269225,0.000026764827,0.000044473793,0.000020045538,0.000035934157],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985196,0.0000022412278,0.00086982426,0.00016849296,0.00020838267,0.0002314643],"domain_scores_gemma":[0.99830246,0.00063621195,0.0006683105,0.00031901657,0.000041866897,0.000032119646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019454883,0.0001708151,0.000648448,0.00018246863,0.000014856642,0.0000038117305,0.00028454122,0.00013960603,0.00024895443],"category_scores_gemma":[0.00027617865,0.00015034385,0.00008565215,0.0004776285,0.00010573077,0.000071607,0.000042958985,0.0001984788,6.9886545e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023222913,0.0015006678,0.039809715,0.005677197,0.00011018186,9.097932e-7,0.00080202986,0.00049134926,0.8698197,0.07789557,0.000052256524,0.0036082128],"study_design_scores_gemma":[0.0015307581,0.000041562274,0.00029065966,0.00008393942,0.000070042435,9.0161797e-7,0.0005310539,0.0011429929,0.8702764,0.12538607,0.00045709717,0.0001885445],"about_ca_topic_score_codex":0.000004450403,"about_ca_topic_score_gemma":0.00008399429,"teacher_disagreement_score":0.047490504,"about_ca_system_score_codex":0.000020320258,"about_ca_system_score_gemma":0.000026068108,"threshold_uncertainty_score":0.61308444},"labels":[],"label_agreement":null},{"id":"W2076066879","doi":"10.1007/s11746-000-0109-2","title":"Monitoring peroxide value in fatliquor manufacture by Fourier transform infrared spectroscopy","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Sichuan University","keywords":"Attenuated total reflection; Fourier transform infrared spectroscopy; Peroxide value; Peroxide; Chemistry; Chloroform; Infrared spectroscopy; Spectrometer; Analytical Chemistry (journal); Materials science; Chromatography; Organic chemistry; Chemical engineering; Optics","score_opus":0.005394985631294994,"score_gpt":0.2473626962291488,"score_spread":0.2419677105978538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076066879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9815542,0.00132269,0.00002314449,0.0012175344,0.000048521688,0.000018040899,0.000015535707,0.000025106194,0.015775222],"genre_scores_gemma":[0.9707046,0.0042377105,0.0037094736,0.00047908092,0.00059130794,0.0000056848794,0.0000024359426,0.000055042296,0.020214655],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99799246,0.00002288868,0.0006074615,0.0002555119,0.00063184864,0.0004898162],"domain_scores_gemma":[0.9986561,0.000115600415,0.0005927188,0.00040003005,0.00006540166,0.00017018372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023116147,0.000313152,0.00063006853,0.00003487966,0.00013944613,0.00006517325,0.000805972,0.00014265731,0.0008782824],"category_scores_gemma":[0.000059379145,0.0002296332,0.0008340109,0.00072101975,0.0002954552,0.00016591631,0.000033809996,0.0011579199,0.0000039729116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019319693,0.0002467299,0.006612861,0.00010990274,0.0004280727,0.000012555645,0.0018472351,0.00016245643,0.9659614,7.096025e-7,0.014533451,0.009891433],"study_design_scores_gemma":[0.00076152047,0.00003490285,0.00060987315,0.000093766845,0.00017421751,0.00006711865,0.0023210216,0.00005030414,0.9840252,0.00011867458,0.0114837475,0.00025964392],"about_ca_topic_score_codex":0.00012459864,"about_ca_topic_score_gemma":9.877701e-7,"teacher_disagreement_score":0.018063813,"about_ca_system_score_codex":0.00070155074,"about_ca_system_score_gemma":0.00013179776,"threshold_uncertainty_score":0.96165764},"labels":[],"label_agreement":null},{"id":"W2076390469","doi":"10.1016/j.talanta.2011.09.039","title":"Wavelets and genetic algorithms applied to search prefilters for spectral library matching in forensics","year":2011,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"","keywords":"Wavelet; Fourier transform; Pattern recognition (psychology); Matching pursuit; Fourier transform infrared spectroscopy; Wavelet transform; Identification (biology); Matching (statistics); Algorithm; Artificial intelligence; Computer science; Chemistry; Mathematics; Optics; Compressed sensing; Physics; Statistics","score_opus":0.031010500690048928,"score_gpt":0.2591901410322038,"score_spread":0.22817964034215485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076390469","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814952,0.00010892934,0.0009140501,0.000048779002,0.000017543107,0.00012150945,0.0000324376,0.000054320746,0.017207207],"genre_scores_gemma":[0.9801291,0.000049832932,0.018723097,0.000121074365,0.00010045838,0.00003126069,0.000027577651,0.000031878742,0.0007856696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913937,0.0000033527806,0.00015790241,0.00027572588,0.00009896517,0.00032468006],"domain_scores_gemma":[0.99964184,0.000046782494,0.000025915082,0.00017317521,0.0000059995423,0.00010627763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033599714,0.00013025243,0.00018862193,0.00012536206,0.000040078397,0.00003064636,0.0001572636,0.00007037492,0.00031220823],"category_scores_gemma":[0.0000055102396,0.00012512913,0.0000359682,0.00021159372,0.000032900403,0.00006306577,0.000090734466,0.000118019954,0.000010528615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011197905,0.0004719782,0.08356881,0.0012551507,0.00044089145,0.00025134152,0.014315934,0.000059350714,0.8394016,0.0040615806,0.004164715,0.050888833],"study_design_scores_gemma":[0.00063420646,0.000052133535,0.011637242,0.000021587464,0.000047549634,0.000029671271,0.0007506053,0.00025270958,0.98268366,0.0034545518,0.00016514554,0.00027094988],"about_ca_topic_score_codex":0.000066942375,"about_ca_topic_score_gemma":0.000011020228,"teacher_disagreement_score":0.14328203,"about_ca_system_score_codex":0.00002329776,"about_ca_system_score_gemma":0.000020435837,"threshold_uncertainty_score":0.5102618},"labels":[],"label_agreement":null},{"id":"W2076714610","doi":"10.1366/000370208786049132","title":"How to Deal with Some Spurious Fringes in Fourier Transform Infrared Spectrometers","year":2008,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Montréal","funders":"","keywords":"Spurious relationship; Spectrometer; Infrared; Fourier transform; Optics; Fourier transform spectroscopy; Fourier transform infrared spectroscopy; Interferometry; Spectrum (functional analysis); Physics; Materials science; Computer science","score_opus":0.01142272966917424,"score_gpt":0.2268777929150203,"score_spread":0.21545506324584607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076714610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9077337,0.0003134548,0.002052857,0.0012147378,0.000023239303,0.00031855598,0.000019777046,0.00029037322,0.0880333],"genre_scores_gemma":[0.95294225,0.00024692825,0.041195992,0.000428745,0.0003251865,0.00019236989,0.000027204558,0.00010259411,0.004538707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996991,0.000008388756,0.000376669,0.00086080935,0.00062306534,0.0011400777],"domain_scores_gemma":[0.99879074,0.0000932357,0.000121040764,0.00061361206,0.000032246968,0.00034909826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000090601185,0.0005543228,0.0007562533,0.00061274035,0.00021514733,0.00011422002,0.0005356608,0.00021494036,0.0006043622],"category_scores_gemma":[0.000013138058,0.00050609605,0.00013708445,0.0014482697,0.0001821764,0.00024729944,0.000057145546,0.00059351587,0.00007642057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015162299,0.00073539454,0.0078859925,0.00016104431,0.00046894012,0.0005417791,0.0023347873,0.000057556143,0.9753668,0.0064585865,0.0031226045,0.0013503392],"study_design_scores_gemma":[0.001962978,0.00029268576,0.0013249803,0.00002156137,0.000103571096,0.00008109411,0.00071623706,0.000013816779,0.9849867,0.0026917213,0.0070345574,0.0007700655],"about_ca_topic_score_codex":0.000075976925,"about_ca_topic_score_gemma":0.0001053327,"teacher_disagreement_score":0.083494596,"about_ca_system_score_codex":0.00048232227,"about_ca_system_score_gemma":0.00013666671,"threshold_uncertainty_score":0.99973905},"labels":[],"label_agreement":null},{"id":"W2076918326","doi":"10.1002/cjce.5450780615","title":"Methods for measuring the concentrations of SO<sub>2</sub>, and of gaseous reduced sulphur compounds in the combustion chamber of a circulating fluidized bed boiler","year":2000,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Combustion; Sorbent; Sulfur; Chemistry; Fluidized bed combustion; Boiler (water heating); Combustion chamber; Coal; Analytical Chemistry (journal); Waste management; Environmental chemistry; Adsorption; Organic chemistry","score_opus":0.019640856766208442,"score_gpt":0.2627832734628925,"score_spread":0.24314241669668404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076918326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99815434,0.0010506887,0.00035148094,0.0002620453,0.000017777063,0.00006775451,0.000010659575,0.0000025123713,0.000082759456],"genre_scores_gemma":[0.9990071,0.000018857922,0.00087695895,0.000019382478,0.000057717767,0.000004233268,0.0000025940922,0.000011459898,0.000001668631],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905807,0.000033455595,0.0004982468,0.00006991215,0.00015224553,0.00018809139],"domain_scores_gemma":[0.99875605,0.0006825919,0.00021407867,0.0001486549,0.00011607693,0.000082548155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007220546,0.00010301022,0.0003066685,0.00008277848,0.0000633274,0.000020548645,0.00021273902,0.00006847667,0.000027977816],"category_scores_gemma":[0.00047289967,0.000066862536,0.00011924018,0.0003205678,0.00014103037,0.000042291293,0.000006631069,0.0002623887,3.5341532e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000244552,0.00001105567,0.00008937824,0.00007685951,0.000088363966,0.0000014894639,0.0009144693,0.0023155939,0.99570656,0.000084766296,0.000015793905,0.0006712223],"study_design_scores_gemma":[0.00047976914,0.000012077207,0.00007394568,0.00011112466,0.00014980712,0.00008858714,0.00017094496,0.005523813,0.99320424,0.00010343383,0.000018283039,0.00006399787],"about_ca_topic_score_codex":0.0004206001,"about_ca_topic_score_gemma":0.00005402072,"teacher_disagreement_score":0.0032082193,"about_ca_system_score_codex":0.00008468461,"about_ca_system_score_gemma":0.00017911378,"threshold_uncertainty_score":0.2726575},"labels":[],"label_agreement":null},{"id":"W2076946603","doi":"10.13031/2013.36288","title":"Identification of Classes and Moisture Contents for Location-specific and Crop year-specific Wheat Samples Using the Near-Infrared (NIR) Hyperspectral Imaging","year":2010,"lang":"en","type":"article","venue":"ASABE/CSBE North Central Intersectional Meeting","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Near-infrared spectroscopy; Principal component analysis; Moisture; Crop; Linear discriminant analysis; Water content; Vegetation (pathology); Remote sensing; Quadratic classifier; Environmental science; Materials science; Mathematics; Agronomy; Geography; Artificial intelligence; Computer science; Geology; Optics","score_opus":0.023463385336957722,"score_gpt":0.2679651580755395,"score_spread":0.24450177273858179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076946603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99429905,0.0013680626,0.0031051007,0.00019616768,0.0003997818,0.00015500843,0.00007494137,0.000042115364,0.00035975696],"genre_scores_gemma":[0.99721134,0.0001503616,0.0019314749,0.000039263043,0.00040497116,0.000017134304,0.000057141162,0.00002959772,0.00015873276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848616,0.000020311054,0.00044913884,0.00043671002,0.00024830454,0.00035939005],"domain_scores_gemma":[0.9987137,0.00031173654,0.00030538486,0.00023431014,0.0003388173,0.000096060765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015470349,0.00022001448,0.00024915734,0.00009188582,0.0004875093,0.00023871854,0.00018351992,0.00009309083,0.00011712275],"category_scores_gemma":[0.00020632744,0.00019091571,0.00010842158,0.00031019465,0.00038721118,0.00023376236,0.000055284803,0.00033827798,0.0000010767538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012550522,0.000069797265,0.30070585,0.00010532458,0.00013892789,0.0000010461965,0.00074240915,0.00017544656,0.695784,0.00093540316,0.0005099397,0.0007063628],"study_design_scores_gemma":[0.0023634532,0.000066646004,0.3524121,0.00023082369,0.0004845673,0.00019425739,0.0209572,0.014525134,0.6006148,0.0010985892,0.0060815956,0.00097086123],"about_ca_topic_score_codex":0.00016976902,"about_ca_topic_score_gemma":0.00016245533,"teacher_disagreement_score":0.09516922,"about_ca_system_score_codex":0.00009924219,"about_ca_system_score_gemma":0.000043155967,"threshold_uncertainty_score":0.7785317},"labels":[],"label_agreement":null},{"id":"W2077903554","doi":"10.1016/j.postharvbio.2008.11.008","title":"Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks","year":2009,"lang":"en","type":"article","venue":"Postharvest Biology and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":247,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Hyperspectral imaging; Mathematics; Artificial intelligence; Robustness (evolution); Artificial neural network; Pattern recognition (psychology); Horticulture; Biological system; Computer science; Chemistry; Biology","score_opus":0.00843239040086062,"score_gpt":0.27104455264810556,"score_spread":0.26261216224724493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077903554","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99204785,0.0059059095,0.00058540906,0.0009962063,0.000029596034,0.000047282752,0.000004455573,0.00018039157,0.00020292294],"genre_scores_gemma":[0.99868494,0.00029404543,0.0007478721,0.00016114679,0.00007638057,0.0000039295155,0.000010952996,0.000012634216,0.000008116757],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876887,0.000016866936,0.000258392,0.00044053796,0.000029654477,0.00048566365],"domain_scores_gemma":[0.9995862,0.000054306507,0.00010353165,0.00017918184,0.000024774305,0.00005199552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010239655,0.0002059586,0.0003368535,0.00045925225,0.00017860146,0.00002440234,0.00014576173,0.0003793433,0.00001953591],"category_scores_gemma":[0.00011209392,0.0002031104,0.000029621646,0.000652695,0.0002699887,0.00008272673,0.000081281934,0.0005858167,3.0280646e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045990633,0.000042668846,0.60278517,0.0000100515135,0.0000296956,0.00003201567,0.000042884873,0.00004769383,0.37036687,0.0011272068,0.000002524672,0.0254672],"study_design_scores_gemma":[0.0039982814,0.00075038866,0.057430778,0.00013433018,0.0004763122,0.0039303945,0.003307568,0.27843764,0.61949897,0.029399995,0.000274459,0.0023609132],"about_ca_topic_score_codex":0.000105434556,"about_ca_topic_score_gemma":0.000041716794,"teacher_disagreement_score":0.5453544,"about_ca_system_score_codex":0.000041022267,"about_ca_system_score_gemma":0.000010431255,"threshold_uncertainty_score":0.8282602},"labels":[],"label_agreement":null},{"id":"W2078314228","doi":"10.1366/0003702041389382","title":"Maximum Likelihood Principal Components Regression on Wavelet-Compressed Data","year":2004,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Principal component analysis; Covariance matrix; Wavelet transform; Covariance; Discrete wavelet transform; Algorithm; Mathematics; Principal component regression; Computer science; Data compression; Matrix (chemical analysis); Statistics; Pattern recognition (psychology); Artificial intelligence; Chemistry","score_opus":0.03697518002245617,"score_gpt":0.2959830664171526,"score_spread":0.2590078863946964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078314228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79942393,0.00036643154,0.0050973063,0.0007053764,0.00020033421,0.0003038634,0.00018595377,0.00070203986,0.19301479],"genre_scores_gemma":[0.9874943,0.000121483325,0.010127883,0.00046881635,0.0005005837,0.00004150054,0.00083422393,0.00009257066,0.00031866436],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965549,0.000011705785,0.0005026248,0.001236282,0.000761976,0.0009324849],"domain_scores_gemma":[0.99690354,0.00009012004,0.00026496837,0.0024073108,0.00003595404,0.00029813696],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015801904,0.0005536478,0.0006237526,0.00020958093,0.00032256293,0.00011889984,0.0016310122,0.00027638057,0.0017887041],"category_scores_gemma":[0.000042227806,0.00048413084,0.00011764577,0.0005860624,0.00013949732,0.00014205345,0.0005027784,0.0007296518,0.0007312106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003471142,0.0007790676,0.00023702394,0.00006987725,0.00016051794,0.00003724355,0.000074890035,0.00003121655,0.99208087,0.0038877265,0.0017450277,0.0005494377],"study_design_scores_gemma":[0.0025479202,0.000073057825,0.00039639501,0.00006814503,0.00015732147,0.000010685862,0.00011699069,0.00009394509,0.9808941,0.010009348,0.005091425,0.0005406753],"about_ca_topic_score_codex":0.00009991812,"about_ca_topic_score_gemma":0.000012357423,"teacher_disagreement_score":0.19269612,"about_ca_system_score_codex":0.00036135636,"about_ca_system_score_gemma":0.00011128131,"threshold_uncertainty_score":0.99976104},"labels":[],"label_agreement":null},{"id":"W2078808897","doi":"10.13031/2013.39041","title":"Investigation of the absorption and scattering properties of onion scales at 632.8 nm","year":2011,"lang":"en","type":"article","venue":"2011 Louisville, Kentucky, August 7 - August 10, 2011","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Flesh; Scattering; Penetration (warfare); Penetration depth; Transmittance; Absorption (acoustics); Integrating sphere; Light scattering; Chemistry; Materials science; Wavelength; Optics; Analytical Chemistry (journal); Mathematics; Food science; Composite material; Chromatography; Optoelectronics; Physics","score_opus":0.04333897639978133,"score_gpt":0.22057727829337456,"score_spread":0.17723830189359324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078808897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97558635,0.0027236065,0.000017766402,0.000095985306,0.00017779034,0.00020336277,0.000034728353,0.00009414045,0.021066284],"genre_scores_gemma":[0.982675,0.0005063371,0.0002862272,0.00006196927,0.00012736027,0.00003270295,0.000025920306,0.0000642312,0.01622026],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976799,0.00006738871,0.000796077,0.0005508228,0.00048004356,0.0004257562],"domain_scores_gemma":[0.9979058,0.000055348835,0.00077530515,0.0008495249,0.00022166065,0.00019234038],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030211208,0.00039558514,0.00054954056,0.00024364698,0.00020870537,0.000023922783,0.0005497947,0.00031974047,0.012478057],"category_scores_gemma":[0.00014382873,0.00029699347,0.00021558281,0.0003070254,0.0008761157,0.0003198598,0.00043757918,0.00026449244,0.00015167185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027649166,0.00015557732,0.17215246,0.0009845428,0.00022358933,0.0000016666319,0.0010847559,0.0000034277627,0.8030886,0.00033744902,0.02134174,0.00034970208],"study_design_scores_gemma":[0.00062729634,0.00008191906,0.047705755,0.00025703412,0.0002937785,0.00002326034,0.0005299791,0.000048557078,0.94871235,0.0003108425,0.001031402,0.00037785224],"about_ca_topic_score_codex":0.0017672472,"about_ca_topic_score_gemma":0.00013547695,"teacher_disagreement_score":0.14562373,"about_ca_system_score_codex":0.00012541607,"about_ca_system_score_gemma":0.000079156656,"threshold_uncertainty_score":0.9999482},"labels":[],"label_agreement":null},{"id":"W2079016633","doi":"10.1111/j.1472-765x.2008.02544.x","title":"An image analysis technique to estimate the cell density and biomass concentration of<i>Trichoderma reesei</i>","year":2009,"lang":"en","type":"article","venue":"Letters in Applied Microbiology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trichoderma reesei; Library science; Computer science; Biology","score_opus":0.004319709073061276,"score_gpt":0.2654759573601416,"score_spread":0.26115624828708034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079016633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9585145,0.00006369897,0.03903772,0.0010492888,0.00000648712,0.00013387106,0.000013780598,0.000034409215,0.0011462482],"genre_scores_gemma":[0.9874326,0.000008381021,0.010491209,0.0019487189,0.00001933947,0.000016105389,0.00006643147,0.000006200072,0.000011038945],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991052,0.000026240878,0.00025699346,0.00033133244,0.000035369874,0.00024482285],"domain_scores_gemma":[0.9994234,0.00006872822,0.00011695105,0.00033271263,0.00001796742,0.000040245086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015102791,0.00014696522,0.0003240904,0.00018307549,0.000053636086,0.00001834531,0.00023225616,0.00013349719,0.000036664802],"category_scores_gemma":[0.0000098546125,0.00011853183,0.00005432342,0.00096903206,0.00013124142,0.000029888293,0.00003251498,0.00015897317,0.0000024259593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036994323,0.00005416799,0.0036643988,0.000014046253,0.000047484955,0.0000046131745,0.00010232068,0.000044894812,0.9955593,0.0000650189,0.0002490558,0.00015775875],"study_design_scores_gemma":[0.00020594784,0.000019802465,0.0010145402,0.000002055721,0.00021313236,0.000008505567,0.000044073724,0.000067126064,0.99810356,0.00009289908,0.000099006946,0.00012933303],"about_ca_topic_score_codex":0.00003729453,"about_ca_topic_score_gemma":0.00001647786,"teacher_disagreement_score":0.028918078,"about_ca_system_score_codex":0.000047718233,"about_ca_system_score_gemma":0.000008870697,"threshold_uncertainty_score":0.4833588},"labels":[],"label_agreement":null},{"id":"W2079039395","doi":"10.1017/s1431927610057466","title":"Microfluidic High-Throughput Imaging and Automated Lineage Tracking of Yeast","year":2010,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Throughput; Lineage (genetic); Microanalysis; Microscopy; Tracking (education); Microfluidics; Yeast; Nanotechnology; Materials science; Computer science; Biology; Chemistry; Optics; Physics; Genetics; Operating system; Gene","score_opus":0.006789275452017963,"score_gpt":0.2671123862693511,"score_spread":0.2603231108173331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079039395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846025,0.01404685,0.00045844866,0.00010523817,0.00005889382,0.000039633807,0.0001078442,0.00016793537,0.00041262002],"genre_scores_gemma":[0.9938727,0.0021662023,0.0030529618,0.00009731831,0.00009346328,0.000004389335,0.0000665997,0.000036735513,0.00060964486],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983813,0.000017030081,0.0005105326,0.0005673919,0.00013877879,0.00038492391],"domain_scores_gemma":[0.99898344,0.000080008365,0.00025878398,0.00042402753,0.000116509495,0.00013721321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001940135,0.0003148071,0.00063922093,0.00033374623,0.000215767,0.00012581183,0.00022411645,0.00017770984,0.000802064],"category_scores_gemma":[0.000071656534,0.00030263115,0.00016462417,0.000684486,0.00041003549,0.0001729067,0.00013159472,0.00037720456,0.000008259797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021932246,0.00008273227,0.021268224,0.00013756733,0.0003135308,0.000008637389,0.0002446188,2.9453096e-7,0.9756413,0.00003432149,0.0009978975,0.0012489242],"study_design_scores_gemma":[0.0005842796,0.000009807509,0.0022755568,0.000029195242,0.0011209337,0.000056602134,0.00035814403,0.00024469453,0.99250233,0.000032212734,0.0024835158,0.00030273147],"about_ca_topic_score_codex":0.0007743396,"about_ca_topic_score_gemma":0.000052065538,"teacher_disagreement_score":0.018992668,"about_ca_system_score_codex":0.00002323196,"about_ca_system_score_gemma":0.000038610015,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W2079201387","doi":"10.1007/s11760-009-0145-0","title":"A criterion for measuring the separability of clusters and its applications to principal component analysis","year":2009,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Principal component analysis; Curse of dimensionality; Dimensionality reduction; Dimension (graph theory); Pattern recognition (psychology); Computer science; Set (abstract data type); Feature (linguistics); Simple (philosophy); Feature vector; Data set; Intrinsic dimension; Space (punctuation); Artificial intelligence; Data mining; Mathematics","score_opus":0.030001899944127396,"score_gpt":0.33085499045635375,"score_spread":0.30085309051222636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079201387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88652885,0.0031427115,0.108890355,0.00061215094,0.0000022123597,0.00020718104,0.000017036204,0.000030545387,0.0005689489],"genre_scores_gemma":[0.9978932,0.000021940648,0.0018276939,0.00011621513,0.000036099027,0.00004191667,0.0000061796827,0.0000047584203,0.000051993706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992567,0.000010602385,0.0002194414,0.0002464382,0.000119466014,0.00014739961],"domain_scores_gemma":[0.99948287,0.00011260849,0.000096710275,0.00011431837,0.00012514328,0.00006833472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023978745,0.00010212045,0.00022030767,0.000100214296,0.000218985,0.00008331271,0.000097881304,0.00003526617,0.000021068738],"category_scores_gemma":[0.0000638478,0.00007718411,0.00007035424,0.0004927078,0.000048113587,0.00012254911,0.00003283126,0.0000668399,2.5244825e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087427,0.00008336776,0.0009740661,0.00044473828,0.00013100322,3.5732126e-7,0.00065813964,0.000069667214,0.9718397,0.000023074972,0.000009702482,0.025678722],"study_design_scores_gemma":[0.00034678355,0.000054924374,0.0031234254,0.000053695712,0.0013955508,0.0000048773695,0.0006486162,0.020884506,0.9724361,0.00052016333,0.00032201086,0.00020936852],"about_ca_topic_score_codex":0.0000090547255,"about_ca_topic_score_gemma":0.0000030874687,"teacher_disagreement_score":0.11136434,"about_ca_system_score_codex":0.000020703535,"about_ca_system_score_gemma":0.000019516387,"threshold_uncertainty_score":0.31474766},"labels":[],"label_agreement":null},{"id":"W2079291942","doi":"10.1366/000370206778062147","title":"Semi-Parametric Estimation in the Compositional Modeling of Multicomponent Systems from Raman Spectroscopic Data","year":2006,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"National Research Council Canada; Genomic Health","keywords":"Parametric statistics; Parametric model; Covariate; Semiparametric model; Biological system; Component (thermodynamics); Linear model; Term (time); Omitted-variable bias; Mathematics; Computer science; Econometrics; Statistics; Applied mathematics; Thermodynamics; Physics","score_opus":0.027227169075133666,"score_gpt":0.28025130230404727,"score_spread":0.2530241332289136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079291942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8978344,0.0013144416,0.08241908,0.00011429328,0.000065740394,0.0002785622,0.00019798125,0.000091970076,0.017683504],"genre_scores_gemma":[0.9869397,0.000033318764,0.010578261,0.000040237406,0.00026132833,0.000055186785,0.0020256932,0.000032378317,0.00003386171],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973141,0.000037566788,0.00080963114,0.000677156,0.0007009354,0.00046064204],"domain_scores_gemma":[0.9979222,0.000415585,0.0003087838,0.0012657368,0.000037335554,0.00005034698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030336194,0.000328529,0.00057567714,0.0003349253,0.00013750921,0.00012710213,0.001159792,0.00016398213,0.00030180748],"category_scores_gemma":[0.00003080852,0.0002760615,0.000079351885,0.0010778499,0.00010196118,0.00014420971,0.00014600826,0.00044210127,0.000042175467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109620785,0.0007723032,0.0038029258,0.00011137993,0.00011289622,0.0000088232555,0.00009786936,0.07702909,0.9050326,0.0123910345,0.0005115684,0.000019902443],"study_design_scores_gemma":[0.0011597519,0.00002222949,0.0012005477,0.000046137753,0.00023468205,0.0000052624596,0.0004895565,0.65132946,0.34032854,0.0048250747,0.000041142084,0.00031762215],"about_ca_topic_score_codex":0.0038305304,"about_ca_topic_score_gemma":0.000045409757,"teacher_disagreement_score":0.57430035,"about_ca_system_score_codex":0.0002408224,"about_ca_system_score_gemma":0.00005993234,"threshold_uncertainty_score":0.9999692},"labels":[],"label_agreement":null},{"id":"W2079307108","doi":"10.1002/aic.12210","title":"Simultaneous quantitative measurement of gaseous species composition and solids volume fraction in a gas/solid flow","year":2010,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Absorbance; Mole fraction; Analytical Chemistry (journal); Chemistry; Fraction (chemistry); TRACER; Volume fraction; Alkane; Bubble; Volume (thermodynamics); Nitrogen; Fiber; Gas composition; Thermodynamics; Chromatography; Hydrocarbon; Mechanics; Physical chemistry; Organic chemistry","score_opus":0.0199949369292814,"score_gpt":0.28795234315966123,"score_spread":0.26795740623037984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079307108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947077,0.0004655979,0.0023105957,0.00022792333,0.00008711192,0.000029817867,0.000008427897,0.000013789081,0.0021490473],"genre_scores_gemma":[0.99645495,0.00018226178,0.0029645548,0.000029737488,0.00016587578,0.000001673196,0.0000042124298,0.000011780841,0.00018496728],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998867,0.000025054896,0.00035988403,0.00014472617,0.00039928485,0.00020406459],"domain_scores_gemma":[0.9991278,0.00013625804,0.0002615095,0.00011792671,0.00026416278,0.00009232589],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032562093,0.00013316257,0.00027881295,0.00019255366,0.000104879895,0.000052219275,0.00010196788,0.00011345578,0.0011668142],"category_scores_gemma":[0.00037786106,0.00012314391,0.000071809955,0.00018560223,0.000089715046,0.00013727833,0.000021477634,0.000676848,0.000007589381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012610284,0.00019435011,0.009304572,0.00003739129,0.00009101731,0.000047412548,0.00033218143,0.00042167006,0.98842514,0.000021881582,0.00025082257,0.00074748875],"study_design_scores_gemma":[0.0011193379,0.00019640809,0.0047969497,0.000094556824,0.00022703722,0.0006242423,0.0014180657,0.0195133,0.9702535,0.0005764833,0.00091478374,0.00026528517],"about_ca_topic_score_codex":0.000058262107,"about_ca_topic_score_gemma":0.00012046468,"teacher_disagreement_score":0.01909163,"about_ca_system_score_codex":0.00012956923,"about_ca_system_score_gemma":0.000055726403,"threshold_uncertainty_score":0.99974626},"labels":[],"label_agreement":null},{"id":"W2080190894","doi":"10.1515/hf.2008.071","title":"Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy","year":2008,"lang":"en","type":"article","venue":"Holzforschung","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; Sveriges Lantbruksuniversitet; University of Manitoba","keywords":"Pellets; Water content; Pellet; Analytical Chemistry (journal); Chemistry; Absorbance; Materials science; Thermodynamics; Composite material; Chromatography","score_opus":0.01746654513032899,"score_gpt":0.24202837128873952,"score_spread":0.22456182615841053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080190894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9497336,0.00040802118,0.0035414281,0.0000827212,0.00005637569,0.00010124767,0.00002484471,0.00016324788,0.045888495],"genre_scores_gemma":[0.9885872,0.00016320427,0.002237463,0.00008987848,0.000089949404,0.000025169256,0.00019963107,0.00006527537,0.008542253],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979617,0.000026166437,0.00044958186,0.00052719243,0.0003295237,0.0007058511],"domain_scores_gemma":[0.9991928,0.00005440922,0.00011068305,0.00046518096,0.00004861704,0.00012834891],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011519712,0.00034435012,0.00043413177,0.00015526499,0.00021667806,0.00006196821,0.0003114573,0.00027511967,0.0023678388],"category_scores_gemma":[0.000038110422,0.00029219646,0.00016093503,0.00040096397,0.000117693555,0.0002501205,0.00008753757,0.0004268945,0.0001949835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115618604,0.0003916659,0.0013827514,0.000031255182,0.00006378708,0.000038387294,0.0010236792,0.0008061574,0.9955241,0.000029610372,0.00053450617,0.00005849189],"study_design_scores_gemma":[0.001737672,0.000047916197,0.00033891498,0.000021320113,0.000060138416,0.000029594288,0.00018187276,0.038647346,0.9570425,0.00058655743,0.00082611816,0.00048002848],"about_ca_topic_score_codex":0.00025185247,"about_ca_topic_score_gemma":0.000018934308,"teacher_disagreement_score":0.03885356,"about_ca_system_score_codex":0.00029784848,"about_ca_system_score_gemma":0.00004041869,"threshold_uncertainty_score":0.99995303},"labels":[],"label_agreement":null},{"id":"W2080366100","doi":"10.3168/jds.s0022-0302(02)74286-0","title":"Statistical Data Validation Methods for Large Cheese Plant Database","year":2002,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Database; Computer science; Statistics; Mathematics","score_opus":0.11690391940527264,"score_gpt":0.4295730212893258,"score_spread":0.31266910188405317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080366100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06349947,0.0012349916,0.9292675,0.00051361043,0.00035167945,0.00006305484,0.001989978,0.000023358922,0.0030562999],"genre_scores_gemma":[0.5529529,0.00014913465,0.44574922,0.00012474775,0.00034719886,0.0000015166128,0.00011480991,0.000010310129,0.0005501652],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986411,0.000020428528,0.0003742652,0.00024343409,0.00043629316,0.0002845024],"domain_scores_gemma":[0.9984794,0.000383237,0.00030963364,0.0004967492,0.00015406661,0.00017686277],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002107174,0.0000889209,0.00020351552,0.00018827281,0.00018384757,0.000096068055,0.0011353933,0.00003504898,0.001755501],"category_scores_gemma":[0.0032057797,0.00006932975,0.000040853356,0.00060049514,0.00013486824,0.00090896856,0.00017835498,0.00019013956,0.000007670495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080032136,0.0005411879,0.0023335551,0.00011475699,0.0000851071,0.00003731035,0.00022087607,0.00001208619,0.9360904,0.0022207904,0.047085267,0.011178648],"study_design_scores_gemma":[0.0017573739,0.00017443228,0.0007783469,0.00006126274,0.0004565614,0.00038544077,0.000695089,0.08133515,0.84163785,0.00094460684,0.07141509,0.00035879717],"about_ca_topic_score_codex":0.000002765725,"about_ca_topic_score_gemma":5.9151887e-7,"teacher_disagreement_score":0.4894534,"about_ca_system_score_codex":0.000065746724,"about_ca_system_score_gemma":0.00010834757,"threshold_uncertainty_score":0.999157},"labels":[],"label_agreement":null},{"id":"W2080712240","doi":"10.1007/s11336-009-9119-y","title":"Regularized Generalized Structured Component Analysis","year":2009,"lang":"en","type":"article","venue":"Psychometrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Collinearity; Structural equation modeling; Component (thermodynamics); Component analysis; Regularization (linguistics); Mathematics; Computer science; Econometrics; Monte Carlo method; Applied mathematics; Mathematical optimization; Algorithm; Statistics; Artificial intelligence","score_opus":0.0160125939493809,"score_gpt":0.2922551709638825,"score_spread":0.2762425770145016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080712240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96343637,0.0026267406,0.0039414,0.0006852277,0.00011849303,0.00007289674,0.00004111388,0.00032369213,0.028754061],"genre_scores_gemma":[0.9888927,0.00017973284,0.0056939432,0.00046216638,0.00020190855,0.0000066993184,0.00016440933,0.000017853072,0.004380618],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99799526,0.000023878196,0.00045308706,0.0005649446,0.00050288683,0.00045992102],"domain_scores_gemma":[0.9985545,0.00010663545,0.00020469728,0.00082831783,0.00008792515,0.00021792571],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014201229,0.00029603727,0.000717347,0.0016829268,0.00013644365,0.00009834044,0.0004730327,0.00019368323,0.011048985],"category_scores_gemma":[0.00016498103,0.00026751863,0.00056219834,0.009030761,0.00004991761,0.000081818354,0.00002322201,0.00024611404,0.000049939936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044196472,0.000780994,0.036623716,0.00004067472,0.0060378863,0.000024436076,0.00017776206,0.00017715637,0.93170184,0.0025607503,0.0076042293,0.013828598],"study_design_scores_gemma":[0.007983876,0.0001688829,0.18636793,0.000014257863,0.010404635,0.000026231834,0.0002523172,0.0020086574,0.7503499,0.007944227,0.032605335,0.0018737994],"about_ca_topic_score_codex":0.000040596835,"about_ca_topic_score_gemma":0.000005848861,"teacher_disagreement_score":0.18135197,"about_ca_system_score_codex":0.00011421492,"about_ca_system_score_gemma":0.000017738705,"threshold_uncertainty_score":0.9999777},"labels":[],"label_agreement":null},{"id":"W2080754835","doi":"10.1177/2211068211431108","title":"Analysis of Base Content in In-Service Oils by Fourier Transform Infrared Spectroscopy","year":2012,"lang":"en","type":"article","venue":"SLAS TECHNOLOGY","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thermal-Lube (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fourier transform infrared spectroscopy; Heptane; Analytical Chemistry (journal); Chemistry; Alkalinity; Infrared spectroscopy; Base (topology); Solvent; Chromatography; Organic chemistry; Chemical engineering; Mathematics","score_opus":0.019027676531450002,"score_gpt":0.2705720131682981,"score_spread":0.2515443366368481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080754835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767632,0.0019132613,0.0003219437,0.0008950351,0.000022218059,0.000061209816,0.000047239668,0.000086923916,0.019888971],"genre_scores_gemma":[0.99806976,0.00014759557,0.0008031027,0.00011825508,0.000014346792,0.000042017764,0.00004279325,0.0000165862,0.0007455118],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984882,0.000010167995,0.0005002258,0.00027774533,0.00016833069,0.00055532565],"domain_scores_gemma":[0.999182,0.00007215643,0.00014140435,0.00048525105,0.000051413208,0.00006778301],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015095317,0.00019732902,0.0006660509,0.0017183014,0.000021142896,0.000005749898,0.00035950067,0.0004129963,0.001539367],"category_scores_gemma":[0.000102645376,0.00019625183,0.00013486171,0.005866963,0.00011585821,0.00009646383,0.000049885555,0.00038215297,0.000013064589],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054835185,0.0004667642,0.48810592,0.000058827314,0.0005182495,0.000006310721,0.00021155464,0.0000061946034,0.50898296,0.0006545787,0.00024436114,0.0006894469],"study_design_scores_gemma":[0.00079296756,0.00003005016,0.003282118,0.000013162105,0.00056732167,0.000002348362,0.0011663802,0.00025996694,0.99179035,0.00047292255,0.0014249918,0.00019740837],"about_ca_topic_score_codex":0.0005014993,"about_ca_topic_score_gemma":0.00073592085,"teacher_disagreement_score":0.48482382,"about_ca_system_score_codex":0.00018026364,"about_ca_system_score_gemma":0.0000305569,"threshold_uncertainty_score":0.9993734},"labels":[],"label_agreement":null},{"id":"W2080896155","doi":"10.1007/s11814-008-0107-1","title":"Fed-batch optimization of recombinant α-amylase production by Bacillus subtilis using a modified Markov chain Monte Carlo technique","year":2008,"lang":"en","type":"article","venue":"Korean Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Amylase; Mathematical optimization; Monte Carlo method; Bacillus subtilis; Markov chain; Computer science; Mathematics; Biological system; Chemistry; Statistics; Biology; Bacteria; Biochemistry","score_opus":0.01238503107385573,"score_gpt":0.2174097687956498,"score_spread":0.20502473772179408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080896155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94097733,0.00065320544,0.057940014,0.000057944486,0.0001074948,0.00006253511,0.0000070168026,0.00004338338,0.00015106802],"genre_scores_gemma":[0.9808913,0.00015238236,0.018630998,0.0000037358295,0.00015472154,0.0000043476216,0.0000070884885,0.000038577327,0.000116843024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985634,0.000009768472,0.00066089764,0.00019345545,0.00032028006,0.0002521825],"domain_scores_gemma":[0.9989948,0.000050003844,0.00039844683,0.00020089967,0.00021463924,0.00014119313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020210215,0.00021678327,0.00047337345,0.0002141602,0.000037368092,0.000011022167,0.00020824093,0.00016870686,0.0000764601],"category_scores_gemma":[0.00040628403,0.00020803144,0.00018936394,0.000523328,0.000057452293,0.0001758102,0.000032022483,0.00040448573,1.5097277e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086239525,0.000103057995,0.00009726911,0.00012402653,0.00009101782,0.000021110254,0.000081393315,0.12871632,0.87025744,0.0000018846403,0.00030207427,0.000118175914],"study_design_scores_gemma":[0.00039453665,0.000034309567,0.0000019197648,0.00012945637,0.00010191205,0.0005201775,0.000038352402,0.092982456,0.9055889,0.0000055547753,0.000023865694,0.00017854573],"about_ca_topic_score_codex":0.000027394117,"about_ca_topic_score_gemma":5.9245664e-8,"teacher_disagreement_score":0.039913967,"about_ca_system_score_codex":0.00021604815,"about_ca_system_score_gemma":0.00005884508,"threshold_uncertainty_score":0.8483276},"labels":[],"label_agreement":null},{"id":"W2080935832","doi":"10.1117/12.689487","title":"Performance of the FIRST: a long-wave infrared hyperspectral imaging sensor","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defence Science and Technology Laboratory; Ministère de la Défense Nationale; Johns Hopkins University","keywords":"Hyperspectral imaging; Remote sensing; Spectrometer; Spectral resolution; Imaging spectrometer; Chemical imaging; Full spectral imaging; Image resolution; Pixel; Radiometric calibration; Calibration; Computer science; Optics; Imaging spectroscopy; Sensitivity (control systems); Spectral imaging; Physics; Artificial intelligence; Spectral line; Geology","score_opus":0.00827821069393372,"score_gpt":0.21323073927536035,"score_spread":0.20495252858142662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080935832","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98440635,0.00020031827,0.000005587197,0.0013479589,0.00009189756,0.00017508038,0.000040291958,0.00006311758,0.013669415],"genre_scores_gemma":[0.9832119,0.00006041088,0.015015121,0.000063278094,0.000411009,0.000048452122,0.0000053969466,0.000050623832,0.0011338525],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997859,6.5694596e-9,0.0006805752,0.00034071118,0.0007057209,0.0004139696],"domain_scores_gemma":[0.998166,0.00011019123,0.0005039874,0.00009205478,0.0010641927,0.00006357103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024310831,0.00031155042,0.0004233352,0.00009551357,0.00012216381,0.00007032315,0.00090826454,0.0001534304,0.00006162409],"category_scores_gemma":[0.00030221578,0.00023146636,0.0007940248,0.00057312334,0.00034555088,0.0003472254,0.0001840425,0.00035305243,8.767746e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097275224,0.00019075173,0.02873177,0.001021812,0.00045599366,1.4695705e-7,0.00014951969,0.00017769555,0.90757895,0.058806326,0.0027326958,0.000057050856],"study_design_scores_gemma":[0.0008549312,0.000067040295,0.006946391,0.00025737498,0.00035182497,0.000032872696,0.0009934341,0.023437792,0.96543574,0.00046741017,0.0008417673,0.00031341473],"about_ca_topic_score_codex":0.000025073306,"about_ca_topic_score_gemma":2.4792635e-7,"teacher_disagreement_score":0.058338914,"about_ca_system_score_codex":0.0002082003,"about_ca_system_score_gemma":0.000035883113,"threshold_uncertainty_score":0.9438924},"labels":[],"label_agreement":null},{"id":"W2081907822","doi":"10.1366/000370206777887134","title":"Automated Estimation of White Gaussian Noise Level in a Spectrum with or without Spike Noise Using a Spectral Shifting Technique","year":2006,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Noise (video); Gaussian noise; Noise reduction; Additive white Gaussian noise; White noise; Gradient noise; SIGNAL (programming language); Value noise; Noise spectral density; Standard deviation; Spectrum (functional analysis); Algorithm; Noise floor; Signal-to-noise ratio (imaging); Computer science; Spectral line; Spike (software development); Acoustics; Noise measurement; Mathematics; Physics; Optics; Statistics; Artificial intelligence; Telecommunications; Noise figure","score_opus":0.017113809630517517,"score_gpt":0.2836976257063103,"score_spread":0.2665838160757928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081907822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9169923,0.000054862292,0.058634903,0.000081451544,0.000019683248,0.00053089275,0.000038369817,0.0006945294,0.022952992],"genre_scores_gemma":[0.8678045,0.0000063933444,0.13145806,0.000027457147,0.00014338037,0.00010247052,0.000057894416,0.00010632112,0.00029350875],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966987,0.000021904045,0.0009296476,0.00082446827,0.00056485384,0.0009604464],"domain_scores_gemma":[0.9984747,0.00007202952,0.0006067259,0.0006739225,0.00004219123,0.00013041873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025166717,0.0006211008,0.0010066308,0.0007058306,0.00022826165,0.00008109861,0.00041655335,0.0003030124,0.00079203735],"category_scores_gemma":[0.00002875859,0.0005309581,0.00012474551,0.002387222,0.00023024471,0.00020604287,0.000102458194,0.0005704322,0.0000125965735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075088633,0.0004390404,0.037503358,0.0002586352,0.0000751404,0.00006245269,0.00023792413,0.004697333,0.9544701,0.001438237,0.00004395984,0.000022913722],"study_design_scores_gemma":[0.001777497,0.00012303372,0.0053900746,0.00018923252,0.00023109239,0.000099142955,0.0002893231,0.0325932,0.9575313,0.0011151122,0.000008230943,0.000652767],"about_ca_topic_score_codex":0.00142006,"about_ca_topic_score_gemma":0.0008041611,"teacher_disagreement_score":0.07282316,"about_ca_system_score_codex":0.0006257388,"about_ca_system_score_gemma":0.0002980887,"threshold_uncertainty_score":0.9997142},"labels":[],"label_agreement":null},{"id":"W2082697267","doi":"10.1016/j.jfoodeng.2006.10.016","title":"Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry","year":2006,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":482,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Partial least squares regression; Hyperspectral imaging; Ripeness; Linear regression; Correlation coefficient; Calibration; Analytical Chemistry (journal); Mathematics; Multivariate statistics; Matrix (chemical analysis); Linear correlation; Chemistry; Artificial intelligence; Statistics; Chromatography; Computer science","score_opus":0.01663876045963265,"score_gpt":0.2806159680231453,"score_spread":0.2639772075635127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082697267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83885247,0.0011672763,0.1595972,0.000064222055,0.00009800987,0.000048649545,0.000064607484,0.00001669133,0.000090881214],"genre_scores_gemma":[0.9641313,0.000006833944,0.03533247,0.0000030163271,0.0004739284,0.000004712508,0.0000056485906,0.000016154767,0.000025947644],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912626,0.0000023770808,0.00045800858,0.00008828546,0.00014361518,0.00018148085],"domain_scores_gemma":[0.99908215,0.00024589783,0.0003497124,0.000074143034,0.0002117259,0.000036368256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016636538,0.00011048817,0.00030135814,0.00016952063,0.000029313866,0.000017847358,0.00011942538,0.00005032101,0.000010643027],"category_scores_gemma":[0.00020015133,0.00010884629,0.00024819747,0.00012668998,0.000015981816,0.00022831537,0.000007506125,0.000106270854,5.344942e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055328517,0.00006431077,0.00087509875,0.00033953966,0.000112175614,0.0000011820276,0.00004541262,0.0018402642,0.99311703,0.00282988,0.000042488486,0.00067729887],"study_design_scores_gemma":[0.00081907806,0.0001114205,0.001121207,0.000036828293,0.00017129912,0.000032596643,0.00020318868,0.0034280494,0.990768,0.0031257633,0.000056077035,0.00012647008],"about_ca_topic_score_codex":0.0000056878985,"about_ca_topic_score_gemma":8.911637e-7,"teacher_disagreement_score":0.12527883,"about_ca_system_score_codex":0.00012348733,"about_ca_system_score_gemma":0.00003118491,"threshold_uncertainty_score":0.4438623},"labels":[],"label_agreement":null},{"id":"W2083220141","doi":"10.1094/cchem-86-3-0256","title":"Detection of Sprouted and Midge‐Damaged Wheat Kernels Using Near‐Infrared Hyperspectral Imaging","year":2009,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hyperspectral imaging; Principal component analysis; Linear discriminant analysis; Mahalanobis distance; Pattern recognition (psychology); Artificial intelligence; Chemistry; Computer science","score_opus":0.012091734996636136,"score_gpt":0.2561032003250591,"score_spread":0.244011465328423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083220141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96890575,0.0009058329,0.00015728727,0.000045920566,0.00001436245,0.000032198022,0.000012291142,0.000105432766,0.029820947],"genre_scores_gemma":[0.9980946,0.000037914804,0.0008166328,0.000032699238,0.00015197771,0.0000017599162,0.000019568814,0.000021176373,0.0008236484],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878365,0.000006958032,0.00029850568,0.0003768181,0.00019867951,0.000335366],"domain_scores_gemma":[0.999267,0.000033494765,0.00015965168,0.00032204436,0.000078726735,0.00013908073],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000063021434,0.00023703245,0.000332977,0.000044098066,0.00014362019,0.00006669732,0.00016208718,0.00016349093,0.00049604126],"category_scores_gemma":[0.000082207116,0.000253717,0.00011476926,0.0003535414,0.00018665627,0.00013004775,0.000036118192,0.0002943114,0.0000016199749],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005317612,0.00006370284,0.0034080613,0.00014110937,0.000043216336,0.00001075522,0.00010627552,0.000011214218,0.99479556,0.000002613151,0.000028804416,0.001335526],"study_design_scores_gemma":[0.00060391787,0.000011103247,0.002453187,0.0000357616,0.00018085421,0.00008859175,0.0005376138,0.0025815293,0.9929785,0.00023293094,0.00005046611,0.00024551115],"about_ca_topic_score_codex":0.00009617117,"about_ca_topic_score_gemma":8.002331e-7,"teacher_disagreement_score":0.029188894,"about_ca_system_score_codex":0.00015659082,"about_ca_system_score_gemma":0.00005683483,"threshold_uncertainty_score":0.9999915},"labels":[],"label_agreement":null},{"id":"W2083511353","doi":"10.1118/1.2244653","title":"Po‐Thur Eve General‐26: Tracking intraprostatic volumes for determining non‐linear warping algorithm variability","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Image warping; Prostate; Electromagnetic coil; Magnetic resonance imaging; Algorithm; Data set; Prostate cancer; Computer science; Volume (thermodynamics); Nuclear medicine; Artificial intelligence; Medicine; Physics; Radiology; Cancer","score_opus":0.01525206622351301,"score_gpt":0.2915434451894032,"score_spread":0.27629137896589023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083511353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32218635,0.00011303748,0.67489004,0.0002427472,0.00014139786,0.00013466041,0.000035508747,0.00023165534,0.0020246021],"genre_scores_gemma":[0.9829567,0.000010409009,0.012938293,0.00032471414,0.00280998,0.00006292279,0.000106144806,0.000066165274,0.00072467985],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978393,0.000020033165,0.0005118093,0.0004923876,0.0005815184,0.00055497617],"domain_scores_gemma":[0.9986135,0.00057606597,0.00017049584,0.00033212744,0.00012597172,0.0001818612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038190247,0.00026981128,0.00047824308,0.000043150885,0.00020411966,0.00005072999,0.00031882076,0.00022465098,0.0004243949],"category_scores_gemma":[0.00058955065,0.00025512298,0.00022278122,0.00040249806,0.00017180572,0.00014079729,0.00006894998,0.00039925854,0.000014708791],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007896869,0.002079345,0.040851012,0.0019831539,0.0006522232,0.000108117136,0.0007433949,0.00024050909,0.050237965,0.0005439682,0.008313868,0.8941675],"study_design_scores_gemma":[0.0016223283,0.00006230469,0.0008643498,0.00012704996,0.0004296992,0.000013062204,0.00008859388,0.22574812,0.7506536,0.018796207,0.00089392456,0.00070071843],"about_ca_topic_score_codex":0.00008113598,"about_ca_topic_score_gemma":0.0000045568804,"teacher_disagreement_score":0.8934668,"about_ca_system_score_codex":0.00013085431,"about_ca_system_score_gemma":0.00015855806,"threshold_uncertainty_score":0.9999901},"labels":[],"label_agreement":null},{"id":"W2083517703","doi":"10.1016/j.idairyj.2004.12.005","title":"Prediction of moisture in cheese of commercial production using neural networks","year":2005,"lang":"en","type":"article","venue":"International Dairy Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cheesemaking; Moisture; Water content; Mathematics; Artificial neural network; Statistics; Predictive modelling; Production (economics); Mean absolute percentage error; Environmental science; Agricultural engineering; Food science; Computer science; Mean squared error; Machine learning; Engineering; Chemistry","score_opus":0.03255032437278204,"score_gpt":0.2980107050089664,"score_spread":0.2654603806361844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083517703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961537,0.00029175167,0.0007599401,0.00038532887,0.00039324895,0.000017734603,0.0000143908655,0.0000075733674,0.001976348],"genre_scores_gemma":[0.9975919,0.00007529853,0.0005057767,0.000021379881,0.0015518724,8.1649074e-7,0.000014889644,0.000007726051,0.00023033083],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902415,0.000014360237,0.00045095803,0.000104292885,0.00029787925,0.0001083577],"domain_scores_gemma":[0.9993467,0.000018825942,0.0003312894,0.00008200263,0.00018759523,0.00003361001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016053546,0.000081789025,0.00015568691,0.0002272233,0.000034984547,0.000014089248,0.00018721553,0.000080802194,0.0006875668],"category_scores_gemma":[0.00011337212,0.00007829793,0.00009111448,0.00022639665,0.00004893315,0.00024129445,0.000028639473,0.0003553203,4.0796021e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038968076,0.0006334915,0.3951131,0.00003862125,0.00031218282,0.000010516355,0.00045646538,0.28442314,0.30176324,0.00009380055,0.0014351597,0.015330602],"study_design_scores_gemma":[0.0025074757,0.000087854394,0.2359588,0.00025750286,0.000250083,0.0010210675,0.000771817,0.44775182,0.30872658,0.00034289292,0.0020172053,0.00030689006],"about_ca_topic_score_codex":0.0000522036,"about_ca_topic_score_gemma":0.000026752494,"teacher_disagreement_score":0.16332868,"about_ca_system_score_codex":0.00018194686,"about_ca_system_score_gemma":0.00002991027,"threshold_uncertainty_score":0.7528375},"labels":[],"label_agreement":null},{"id":"W2083768548","doi":"10.13031/2013.25093","title":"Protein and Oil Contents Determination in Wheat Using Near-infrared (NIR) Hyperspectral Imaging","year":2008,"lang":"en","type":"article","venue":"2008 Providence, Rhode Island, June 29 - July 2, 2008","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Partial least squares regression; Hyperspectral imaging; Absorbance; Near-infrared spectroscopy; Materials science; Chemistry; Remote sensing; Mathematics; Chromatography; Optics; Geology","score_opus":0.024259262779827522,"score_gpt":0.2686702913800802,"score_spread":0.24441102860025268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083768548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9824097,0.0047378386,0.00055236905,0.00024960955,0.000120099285,0.0002438718,0.000047694542,0.0001548702,0.011483927],"genre_scores_gemma":[0.9689661,0.00073323224,0.0078037656,0.00014984133,0.00023180203,0.000079897145,0.000040313636,0.00007789086,0.021917114],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968728,0.00006371355,0.0007250875,0.0008612956,0.0005970226,0.00088011165],"domain_scores_gemma":[0.99870557,0.000107323904,0.00030514286,0.00049247645,0.00017574607,0.0002137558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019573681,0.0005075038,0.00066891237,0.00043920198,0.0004830198,0.00016837401,0.0003767728,0.0002533959,0.00038053287],"category_scores_gemma":[0.00036223477,0.00050902145,0.00015252254,0.0009838103,0.00043867633,0.0008962181,0.000118631935,0.0005968499,0.00005143132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009432256,0.0011725159,0.32988468,0.001023352,0.00025183003,0.0026061155,0.00508053,0.00020104289,0.63656765,0.000059615348,0.010889743,0.011319721],"study_design_scores_gemma":[0.016234357,0.00030043416,0.015640868,0.0018768574,0.0007516084,0.005355416,0.0027217094,0.13296539,0.8051202,0.0012603428,0.012833727,0.004939065],"about_ca_topic_score_codex":0.0010364181,"about_ca_topic_score_gemma":0.00031119876,"teacher_disagreement_score":0.3142438,"about_ca_system_score_codex":0.00036385166,"about_ca_system_score_gemma":0.0002854244,"threshold_uncertainty_score":0.99973613},"labels":[],"label_agreement":null},{"id":"W2084167732","doi":"10.1139/x05-046","title":"NIR spectral information used to predict water content of pine seeds from multivariate calibration","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vetenskapsrådet","keywords":"Calibration; Multivariate statistics; Water content; Partial least squares regression; Content (measure theory); Spectral line; Chemistry; Degree (music); Canopy; Biological system; Mathematics; Botany; Analytical Chemistry (journal); Horticulture; Environmental chemistry; Statistics; Biology; Physics; Geology","score_opus":0.06731719712950365,"score_gpt":0.31953795021208226,"score_spread":0.25222075308257863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084167732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954008,0.00009342024,0.0007075884,0.0017877334,0.000043681077,0.00006917415,0.00011026669,0.0000048411694,0.0017825293],"genre_scores_gemma":[0.9984584,0.0000074400755,0.0005014333,0.00005948472,0.00038322536,0.0000030118824,0.000061251536,0.000010000316,0.0005157491],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983446,0.00003459212,0.0005404491,0.00008951391,0.00053243357,0.00045843466],"domain_scores_gemma":[0.99838585,0.00008517976,0.0001211221,0.00018667946,0.00054501044,0.00067613984],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004973357,0.0001019278,0.00023742473,0.0008603747,0.00010898447,0.00009993423,0.00034418298,0.00010487031,0.0018335037],"category_scores_gemma":[0.00036463895,0.00007669445,0.00009401585,0.00041811785,0.0000904181,0.00056291104,0.00002199599,0.00042312642,0.00003008487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046754323,0.00011958421,0.45527738,0.00010910221,0.0005363217,0.00007221257,0.007235724,0.0034810095,0.519769,0.0005413564,0.010454036,0.0019367017],"study_design_scores_gemma":[0.001175021,0.00020730017,0.06944955,0.00007929934,0.000048639846,0.00001825721,0.0010018412,0.0012000998,0.92000234,0.00023206351,0.0064480314,0.00013753156],"about_ca_topic_score_codex":0.026819747,"about_ca_topic_score_gemma":0.042156603,"teacher_disagreement_score":0.40023333,"about_ca_system_score_codex":0.0004610149,"about_ca_system_score_gemma":0.0006828667,"threshold_uncertainty_score":0.9990789},"labels":[],"label_agreement":null},{"id":"W2084537152","doi":"10.1117/12.631347","title":"Determination of pork quality attributes using hyperspectral imaging technique","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Repeatability; Grading (engineering); Computer science; Correlation coefficient; Artificial neural network; Quality (philosophy); Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Machine learning","score_opus":0.019416491544001026,"score_gpt":0.28297949738657424,"score_spread":0.2635630058425732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084537152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99502003,0.00023400079,0.0005231402,0.00097245345,0.000044502,0.00020061122,0.000058320158,0.00008259207,0.0028643298],"genre_scores_gemma":[0.731043,0.000049496244,0.26828888,0.0000350635,0.00035512538,0.000051080995,0.000008288893,0.000042343825,0.00012671395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977578,1.2754289e-8,0.0008623049,0.00036073575,0.00064515043,0.00037402447],"domain_scores_gemma":[0.9975442,0.00016299491,0.00066375325,0.00007144127,0.0014668464,0.00009074628],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005931883,0.00029557198,0.0005035836,0.00015310106,0.00007980189,0.00006224197,0.0007747122,0.00018970284,0.00006485569],"category_scores_gemma":[0.00073690177,0.0002675194,0.00069120445,0.0004976441,0.0002469439,0.0005314976,0.00013254589,0.00031164032,5.248751e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004735163,0.00014746755,0.0021161581,0.0005342555,0.0002166286,5.047277e-8,0.00010912396,0.00004444824,0.90996873,0.08631625,0.00019612952,0.000303414],"study_design_scores_gemma":[0.0005005143,0.000045527195,0.00030329698,0.00016918329,0.00025567718,0.000018957753,0.0012985808,0.017322397,0.97867674,0.000821065,0.00031782722,0.00027024283],"about_ca_topic_score_codex":0.000025730946,"about_ca_topic_score_gemma":1.3893228e-7,"teacher_disagreement_score":0.26776576,"about_ca_system_score_codex":0.00034541351,"about_ca_system_score_gemma":0.000040101,"threshold_uncertainty_score":0.9999777},"labels":[],"label_agreement":null},{"id":"W2084626691","doi":"10.3109/10837450.2013.867448","title":"The critical role of NIR spectroscopy and statistical process control (SPC) strategy towards captopril tablets (25 mg) manufacturing process understanding: a case study","year":2013,"lang":"en","type":"article","venue":"Pharmaceutical Development and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Statistical process control; Process (computing); Captopril; Process analytical technology; Process engineering; Critical quality attributes; Manufacturing process; Process capability; Process control; Materials science; Chemistry; Computer science; Chemical engineering; Process optimization; Work in process; Engineering; Medicine; Particle size; Internal medicine; Operations management","score_opus":0.035612128498280445,"score_gpt":0.3528920355578456,"score_spread":0.31727990705956516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084626691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940537,0.001352478,0.0020647105,0.00075962825,0.000022896553,0.00032619794,0.0000091442,0.00012594133,0.0012853082],"genre_scores_gemma":[0.99927545,0.00008442575,0.00034896575,0.000030616633,0.000023842076,0.00017041677,0.0000025324537,0.0000224031,0.000041354877],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803084,0.000024233736,0.00049006916,0.00049471075,0.00030868838,0.00065146724],"domain_scores_gemma":[0.9990085,0.0003799814,0.00009114799,0.0001751664,0.00012534851,0.0002198583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019214145,0.0003064989,0.00045584043,0.00018808716,0.00043676078,0.00012230204,0.00023571498,0.0002070426,0.0003371903],"category_scores_gemma":[0.00019221853,0.00022636901,0.000021816952,0.0003365744,0.00078451657,0.00012278212,0.00012165547,0.00056225364,0.0000040361692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012480478,0.007586608,0.42832398,0.005851549,0.0064349463,0.0067532756,0.011708801,0.000017645274,0.23317192,0.107823566,0.00029667185,0.19078298],"study_design_scores_gemma":[0.002746018,0.00022192983,0.00072551373,0.00003371861,0.0004956403,0.0014251963,0.08455535,0.0022708683,0.87052786,0.036139093,0.00028385327,0.0005749345],"about_ca_topic_score_codex":0.0000701751,"about_ca_topic_score_gemma":0.000022978298,"teacher_disagreement_score":0.637356,"about_ca_system_score_codex":0.000106547275,"about_ca_system_score_gemma":0.00017003945,"threshold_uncertainty_score":0.923106},"labels":[],"label_agreement":null},{"id":"W2084662124","doi":"10.1255/jnirs.679","title":"Analysis of Grapes and Wine by near Infrared Spectroscopy","year":2006,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National Du Cancer; Australian Government; Alberta Water Research Institute","keywords":"Wine; Spectroscopy; Near-infrared spectroscopy; Infrared spectroscopy; Food science; Environmental science; Chemistry; Optics","score_opus":0.006504162218372033,"score_gpt":0.25269440341534005,"score_spread":0.24619024119696803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084662124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9681855,0.0059745307,0.0015417249,0.00027838637,0.00010193727,0.00007897363,0.00018111536,0.000062327505,0.0235955],"genre_scores_gemma":[0.9647619,0.0010655217,0.028009014,0.00018604864,0.00070570153,0.0000060522734,0.00012966621,0.00010436092,0.0050317193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99616855,0.000071758994,0.0016473961,0.00047561352,0.0009480421,0.0006886103],"domain_scores_gemma":[0.99677026,0.0002977838,0.0016458841,0.000615939,0.00036733545,0.00030277335],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004399709,0.0005293526,0.001709209,0.00090304675,0.0003062092,0.00030466347,0.0006327605,0.0003244142,0.0029376715],"category_scores_gemma":[0.00022681095,0.00048181257,0.0007595435,0.0031668192,0.0005383881,0.0004609939,0.00010220476,0.00089874904,0.0000091535985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004250862,0.00052756944,0.07780128,0.00010187331,0.0031034334,0.00005609863,0.00024382626,0.00026475146,0.87431455,0.00013753881,0.042977434,0.00004658078],"study_design_scores_gemma":[0.0019285416,0.0005037801,0.013990056,0.0000618832,0.005245268,0.00007519985,0.00043988728,0.001060803,0.9562553,0.0023018988,0.017556932,0.0005804077],"about_ca_topic_score_codex":0.00019186347,"about_ca_topic_score_gemma":0.000017093878,"teacher_disagreement_score":0.081940815,"about_ca_system_score_codex":0.00024387988,"about_ca_system_score_gemma":0.00024131149,"threshold_uncertainty_score":0.99976337},"labels":[],"label_agreement":null},{"id":"W2084730277","doi":"10.1115/dscc2011-6024","title":"Development of Vibration Loading Profiles for Accelerated Durability Tests of Ground Vehicles","year":2011,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Durability; Cluster analysis; Acceleration; Computer science; Vibration; Process (computing); Wavelet; SIGNAL (programming language); Identification (biology); Wavelet transform; Simulation; Reliability engineering; Engineering; Real-time computing; Artificial intelligence; Acoustics","score_opus":0.09431294251260541,"score_gpt":0.3082972483995478,"score_spread":0.2139843058869424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084730277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889762,0.000036099504,0.0020793069,0.000006208199,0.000008758663,0.00007238325,0.000005379476,0.000032100837,0.008783576],"genre_scores_gemma":[0.9533631,0.0000020093826,0.046301942,0.0000051147663,0.000011046517,0.000021855341,0.000020578858,0.000006851621,0.00026749386],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992729,0.0000036234246,0.000360962,0.0001487714,0.00009978234,0.000114000795],"domain_scores_gemma":[0.9994194,0.00007478224,0.00016468413,0.00013303642,0.00018074561,0.000027362992],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010244076,0.0000851334,0.00018525093,0.000059832088,0.000043666063,0.0000059704066,0.000108912776,0.00006789728,0.0010792922],"category_scores_gemma":[0.00012582903,0.000071543094,0.000042707376,0.0002827122,0.00004338508,0.00009781662,0.00002275994,0.000034287033,0.0000016058866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047254933,0.00017054997,0.021460008,0.0002756864,0.000068075344,6.380554e-8,0.0005431212,3.4506687e-7,0.9759663,0.00038741317,0.000024509925,0.0010566753],"study_design_scores_gemma":[0.00020045505,0.00001698579,0.02734053,0.000012218492,0.0000365118,2.9503315e-7,0.00065574853,0.00007506105,0.97126263,0.000301132,0.000020215812,0.0000782302],"about_ca_topic_score_codex":0.000047700214,"about_ca_topic_score_gemma":0.000025195966,"teacher_disagreement_score":0.044222634,"about_ca_system_score_codex":0.000038320104,"about_ca_system_score_gemma":0.000077524004,"threshold_uncertainty_score":0.9998339},"labels":[],"label_agreement":null},{"id":"W2084979585","doi":"10.1080/09500340.2013.871587","title":"Symmetry studies of refraction data","year":2014,"lang":"en","type":"article","venue":"Journal of Modern Optics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Refraction; Multiplicative function; Basis (linear algebra); Representation (politics); Torus; Symmetry (geometry); Mathematics; Pure mathematics; Optics; Mathematical analysis; Physics; Geometry","score_opus":0.0945699784734568,"score_gpt":0.37282574335539764,"score_spread":0.2782557648819408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084979585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9541556,0.008633113,0.02705862,0.0003690918,0.0002262792,0.00001408096,0.000016924287,0.000014122392,0.009512138],"genre_scores_gemma":[0.9907247,0.0012046259,0.0071757277,0.000033519296,0.00036075927,1.16515984e-7,0.0000037132918,0.000010835195,0.0004860462],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990104,0.000012589467,0.0004557237,0.0000982299,0.00031604606,0.0001070292],"domain_scores_gemma":[0.9983389,0.0002445006,0.00065970694,0.00041390344,0.00029327322,0.00004972272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038317437,0.00008771578,0.00034870187,0.00012257346,0.000034404402,0.000012734958,0.00040191325,0.00007114702,0.000053755906],"category_scores_gemma":[0.000987254,0.00006980383,0.00007475213,0.00015781303,0.000051207473,0.00019431973,0.000104916624,0.00023997783,0.0000021965104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059150194,0.0016790237,0.019199487,0.0019213075,0.007315436,0.000055045733,0.0015248739,0.002803589,0.8445102,0.0027540007,0.020978054,0.09666749],"study_design_scores_gemma":[0.0016319451,0.00034014593,0.00035666456,0.0002603048,0.0024421175,0.00024182496,0.002546562,0.03350547,0.9354064,0.017284576,0.005599672,0.00038432964],"about_ca_topic_score_codex":0.0000024681478,"about_ca_topic_score_gemma":0.0000012279472,"teacher_disagreement_score":0.09628316,"about_ca_system_score_codex":0.000059111735,"about_ca_system_score_gemma":0.000035238776,"threshold_uncertainty_score":0.28465176},"labels":[],"label_agreement":null},{"id":"W2085088276","doi":"10.1533/9780857095770.3.400","title":"Grain quality evaluation by computer vision","year":2012,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hyperspectral imaging; Kernel (algebra); Starch; Water content; Agricultural engineering; Quality (philosophy); Computer vision; Grain yield; Artificial intelligence; Environmental science; Computer science; Food science; Mathematics; Horticulture; Biology; Engineering; Physics; Geotechnical engineering","score_opus":0.032595036345451786,"score_gpt":0.33036784773925176,"score_spread":0.29777281139379996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085088276","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030165768,0.008540228,0.000048234495,0.000038718325,0.0001970383,0.00017318747,0.0001038407,0.00011117237,0.9904859],"genre_scores_gemma":[0.006720132,0.0001495082,0.00031285154,0.00026083874,0.0009803558,0.00002887669,0.0005246798,0.0000927771,0.99092996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974472,0.000038322793,0.00063067744,0.00058353494,0.0009218104,0.00037845373],"domain_scores_gemma":[0.9982598,0.000115815215,0.00049405865,0.0007586285,0.00018701889,0.00018470344],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007396368,0.0005089962,0.0006886467,0.0001427539,0.00012879416,0.000064186555,0.0003372833,0.0006570555,0.012082635],"category_scores_gemma":[0.000029916153,0.00048162942,0.0003738986,0.000025571442,0.00012660526,0.00006343593,0.00013866133,0.00059662486,0.0004361738],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016122945,0.000030099553,0.000022295873,0.00016277947,0.00021663075,0.0000016598295,0.00006428571,2.1193439e-7,0.0031466403,0.0008983217,0.0048544537,0.9905865],"study_design_scores_gemma":[0.00042224774,0.00003002994,0.000015564949,0.00013105034,0.0008767022,0.0000066950074,0.0000070559386,0.000040508483,0.0066676885,0.0044229776,0.98673445,0.0006450041],"about_ca_topic_score_codex":0.0000021600135,"about_ca_topic_score_gemma":0.000004804255,"teacher_disagreement_score":0.9899415,"about_ca_system_score_codex":0.0003240019,"about_ca_system_score_gemma":0.00007817639,"threshold_uncertainty_score":0.99976355},"labels":[],"label_agreement":null},{"id":"W2085370119","doi":"10.1021/jf061528b","title":"Geographic Classification of Spanish and Australian Tempranillo Red Wines by Visible and Near-Infrared Spectroscopy Combined with Multivariate Analysis","year":2006,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Water Research Institute","keywords":"Linear discriminant analysis; Principal component analysis; Chemometrics; Partial least squares regression; Multivariate statistics; Wine; Near-infrared spectroscopy; Multivariate analysis; Mathematics; Statistics; Pattern recognition (psychology); Chemistry; Artificial intelligence; Chromatography; Computer science; Food science; Biology","score_opus":0.006758843992660313,"score_gpt":0.2167204217088794,"score_spread":0.2099615777162191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085370119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99744093,0.0011791053,0.000017120332,0.00036591006,0.0000069463795,0.000032983586,0.000042781652,0.0000150948235,0.00089914503],"genre_scores_gemma":[0.99766624,0.00024811353,0.00077245105,0.0000052848036,0.00008564794,0.0000018469634,0.00006522373,0.000008371445,0.0011468014],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987752,0.000009799047,0.0005034404,0.00025005694,0.00025765444,0.00020385292],"domain_scores_gemma":[0.9987956,0.00004869908,0.00068241695,0.00012140429,0.00021090992,0.00014095682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075534364,0.00024406066,0.0005657482,0.00006882332,0.00012390217,0.00011959331,0.0001179137,0.00016128541,0.00008785262],"category_scores_gemma":[0.00001938826,0.00015104386,0.00013055182,0.00072050135,0.00019436779,0.00018388797,0.00002734098,0.00024022492,6.755835e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013053618,0.00012199808,0.07075636,0.00016647778,0.0009024179,0.0000019090453,0.00003716967,0.000008771696,0.92726153,0.000007089424,0.0005898062,0.000015948353],"study_design_scores_gemma":[0.001192539,0.00022614904,0.35122046,0.000042716983,0.0015062831,0.00008355882,0.0006456142,0.000024612484,0.6447097,0.000068395966,0.00008639636,0.00019354104],"about_ca_topic_score_codex":0.000079659934,"about_ca_topic_score_gemma":0.000008218629,"teacher_disagreement_score":0.2825518,"about_ca_system_score_codex":0.000025820531,"about_ca_system_score_gemma":0.000017577062,"threshold_uncertainty_score":0.61593896},"labels":[],"label_agreement":null},{"id":"W2085586961","doi":"10.13031/2013.23392","title":"Near-Infrared Hyperspectral Imaging to Differentiate Wheat Classes","year":2007,"lang":"en","type":"article","venue":"2007 Minneapolis, Minnesota, June 17-20, 2007","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Winter wheat; Linear discriminant analysis; Pattern recognition (psychology); Reflectivity; Artificial intelligence; Near-infrared spectroscopy; Mathematics; Environmental science; Remote sensing; Agronomy; Computer science; Geography; Biology","score_opus":0.012143841446875843,"score_gpt":0.27436570352608175,"score_spread":0.2622218620792059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085586961","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68906105,0.009181548,0.0033325418,0.0031484351,0.0022107374,0.00051720225,0.00063239905,0.0010706232,0.29084545],"genre_scores_gemma":[0.47939578,0.00054717076,0.016451698,0.0057641207,0.007920926,0.00010901087,0.0013500423,0.0006783927,0.48778287],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928255,0.00004153486,0.0014105502,0.0015917151,0.0011258076,0.0030048802],"domain_scores_gemma":[0.99601585,0.00040467654,0.00041408563,0.001553366,0.0003930299,0.0012190016],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005961254,0.0011962943,0.0012749981,0.0009266641,0.00064755994,0.00045745264,0.0012453806,0.00054373295,0.018973399],"category_scores_gemma":[0.0002995329,0.0012210483,0.00062868,0.0022784187,0.00041842533,0.0005749998,0.00033624435,0.000975685,0.0016109421],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004177384,0.00081782934,0.026863243,0.00018842847,0.0004352634,0.00043173096,0.00085771043,0.000040825824,0.17292577,0.00035894703,0.79519874,0.0014637827],"study_design_scores_gemma":[0.0019670015,0.00007722983,0.0028715592,0.00011004948,0.0006323857,0.00020625362,0.0024279554,0.0003563931,0.35549158,0.00026374124,0.633816,0.001779828],"about_ca_topic_score_codex":0.0003507016,"about_ca_topic_score_gemma":0.0004640551,"teacher_disagreement_score":0.20966528,"about_ca_system_score_codex":0.0007557553,"about_ca_system_score_gemma":0.00021254944,"threshold_uncertainty_score":0.9991664},"labels":[],"label_agreement":null},{"id":"W2086110133","doi":"10.1021/ac020311n","title":"Principal Component Analysis of TOF-SIMS Images of Organic Monolayers","year":2002,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Principal component analysis; Chemistry; Analytical Chemistry (journal); Monolayer; Image resolution; Mass spectrometry; Biological system; Pattern recognition (psychology); Mass spectrum; Secondary ion mass spectrometry; Artificial intelligence; Chromatography; Computer science","score_opus":0.020878250723882615,"score_gpt":0.26573457948802154,"score_spread":0.24485632876413893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086110133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9379604,0.00058443367,0.00023824642,0.00008783274,0.000007137546,0.000018759521,0.000054367047,0.000045072633,0.06100374],"genre_scores_gemma":[0.9948247,0.00011364833,0.00016298337,0.000021355305,0.000048446454,0.000002758908,0.000046781683,0.000019062107,0.004760284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980927,0.000008904957,0.00067782303,0.00041146894,0.0004632671,0.000345838],"domain_scores_gemma":[0.9984984,0.00019191038,0.00029399956,0.00067841524,0.00014955593,0.00018773966],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000096719756,0.00024796277,0.00087420945,0.00017778593,0.00003965119,0.000015313137,0.00044719919,0.00021079756,0.032806072],"category_scores_gemma":[0.00023089042,0.00023660966,0.00057423837,0.0021431171,0.00025597613,0.000053327993,0.00011598948,0.00026255377,0.000018457933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023560584,0.00055381766,0.04301518,0.0002939535,0.0040522367,0.000012870158,0.00006201806,0.00014574423,0.951119,0.00004383397,0.0005780226,0.000099734956],"study_design_scores_gemma":[0.0003141681,0.000012940701,0.0015169518,0.0000132444575,0.005131671,0.0000032529085,0.00023507868,0.013063831,0.97925115,0.000025704376,0.00019452396,0.00023749011],"about_ca_topic_score_codex":0.000038470567,"about_ca_topic_score_gemma":0.0000021672488,"teacher_disagreement_score":0.05686427,"about_ca_system_score_codex":0.00009474537,"about_ca_system_score_gemma":0.00002239327,"threshold_uncertainty_score":0.9680781},"labels":[],"label_agreement":null},{"id":"W2086821265","doi":"10.1117/12.579428","title":"Online prediction of organileptic data for snack food using color images","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; RGB color model; Feature (linguistics); Partial least squares regression; Computer science; Wavelet transform; Pattern recognition (psychology); Computer vision; Feature extraction; Mathematics; Wavelet; Statistics","score_opus":0.02765815119825089,"score_gpt":0.26740940085746495,"score_spread":0.23975124965921407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086821265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996437,0.0001865709,0.00031884015,0.00069926895,0.0001020951,0.00030813628,0.0013842327,0.00007671055,0.00048715423],"genre_scores_gemma":[0.7897893,0.00011109888,0.20900898,0.000043818105,0.00061393785,0.000052518713,0.00016082314,0.000082786144,0.0001367237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997828,5.5249134e-9,0.0007827085,0.0004598957,0.0005702913,0.00035909205],"domain_scores_gemma":[0.9974711,0.00014540703,0.0005522362,0.00012392906,0.0016088404,0.00009848039],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032970062,0.00030120186,0.00051440176,0.00011994192,0.00008330351,0.000061191175,0.0013174092,0.00022766535,0.000041381365],"category_scores_gemma":[0.0011610308,0.0002638691,0.00046518882,0.0004902289,0.00024067916,0.000555003,0.00027252993,0.00025095232,3.3467845e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009577954,0.00038067024,0.000314164,0.0012651891,0.00095287745,3.4282394e-8,0.00007417655,0.00023275848,0.93580204,0.06000506,0.0008396945,0.000037523754],"study_design_scores_gemma":[0.0017819698,0.00037609547,0.00021132297,0.00028037422,0.0008502029,0.000014887647,0.0017879694,0.019070366,0.97351295,0.0013315668,0.00051905616,0.00026321778],"about_ca_topic_score_codex":0.000014746979,"about_ca_topic_score_gemma":2.6474655e-7,"teacher_disagreement_score":0.20869014,"about_ca_system_score_codex":0.00025691502,"about_ca_system_score_gemma":0.0000793531,"threshold_uncertainty_score":0.99998134},"labels":[],"label_agreement":null},{"id":"W2087915685","doi":"10.1117/12.417179","title":"Texture analysis of images using principal component analysis","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Grayscale; Artificial intelligence; Principal component analysis; Pixel; Pattern recognition (psychology); Computer vision; Computer science; Image texture; Mathematics; Image processing; Image (mathematics)","score_opus":0.01519038048420737,"score_gpt":0.2631665965378018,"score_spread":0.2479762160535944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087915685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99532145,0.0002492266,0.0002570681,0.00037071522,0.000041297764,0.00012858467,0.00012367331,0.000059636273,0.0034483254],"genre_scores_gemma":[0.96139616,0.00015637718,0.03776702,0.00003398837,0.00019447599,0.00002846469,0.0000348531,0.00003947486,0.00034917492],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997239,1.265764e-8,0.00095300947,0.00048017097,0.00090243964,0.0004253915],"domain_scores_gemma":[0.99719733,0.00016561606,0.00075837754,0.00010866299,0.0016319687,0.00013803673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041594447,0.00036679796,0.0010083779,0.00060542894,0.00008821302,0.000082815364,0.0010265242,0.00024650223,0.0002269813],"category_scores_gemma":[0.0003874213,0.00030970696,0.0021565715,0.0033950342,0.00027153376,0.0003228033,0.00019223864,0.0003269237,4.9964746e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010358683,0.00027803713,0.024729194,0.00038159586,0.019623468,2.3126393e-7,0.00014986831,0.0025354223,0.9127737,0.039112538,0.00024746184,0.00006486089],"study_design_scores_gemma":[0.0009832736,0.00011050556,0.009426301,0.00011954101,0.02583415,0.000011890452,0.0023107824,0.20916262,0.75032014,0.00029530813,0.00081245037,0.00061303977],"about_ca_topic_score_codex":0.00006662369,"about_ca_topic_score_gemma":6.52395e-7,"teacher_disagreement_score":0.2066272,"about_ca_system_score_codex":0.00023149469,"about_ca_system_score_gemma":0.000029578086,"threshold_uncertainty_score":0.9999355},"labels":[],"label_agreement":null},{"id":"W2088131560","doi":"10.1016/j.aca.2012.02.041","title":"Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—A review","year":2012,"lang":"en","type":"review","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":152,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Commission","keywords":"Chemometrics; Chemistry; Olive oil; Animal fat; Authentication (law); Food science; Fish <Actinopterygii>; Vegetable oil; Composition (language); Characterization (materials science); Chromatography; Computer science; Biology","score_opus":0.094979487430254,"score_gpt":0.3537560121442814,"score_spread":0.25877652471402735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088131560","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078214734,0.99641097,0.00027311398,0.0002512159,0.000026881211,0.00043678842,0.001717825,0.000022823913,0.00007824745],"genre_scores_gemma":[0.006945846,0.98504865,0.0005385721,0.00008092533,0.00014815856,0.00005436985,0.007120692,0.000038760194,0.000024008501],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981303,0.00004284024,0.00078369735,0.0005554073,0.00025205643,0.00023567448],"domain_scores_gemma":[0.99635714,0.0017433777,0.0009149186,0.0007723095,0.00008499923,0.0001272392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036978623,0.0003764138,0.0015603692,0.00030811058,0.0001640623,0.00009053695,0.00045264346,0.00023654776,0.00014526726],"category_scores_gemma":[0.00030640818,0.0002726814,0.00020295718,0.0009188741,0.0002231757,0.00019976651,0.0002692961,0.00025557994,0.0000010545273],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010982035,0.00084579515,0.0008966197,0.20559672,0.011447286,0.0000012726691,0.00023003378,3.503338e-9,0.035859954,0.0011636743,0.0012327002,0.7426161],"study_design_scores_gemma":[0.0026213399,0.00012526146,0.0014694862,0.034544293,0.14307715,0.00013298343,0.00009560578,0.0015445786,0.004853591,0.0003089361,0.8091095,0.0021172755],"about_ca_topic_score_codex":0.000007662054,"about_ca_topic_score_gemma":5.175359e-7,"teacher_disagreement_score":0.8078768,"about_ca_system_score_codex":0.000024816787,"about_ca_system_score_gemma":0.000055777928,"threshold_uncertainty_score":0.9999725},"labels":[],"label_agreement":null},{"id":"W2089707262","doi":"10.1002/cem.808","title":"Shifted factor analysis—Part I: Models and properties","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Factor analysis; Uniqueness; Factor (programming language); Representation (politics); Set (abstract data type); Sequence (biology); Position (finance); Principal component analysis; Computer science; Mathematics; Algorithm; Econometrics; Artificial intelligence; Mathematical analysis","score_opus":0.0586618537725024,"score_gpt":0.2713634106343817,"score_spread":0.21270155686187928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089707262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97244257,0.009522874,0.0034982422,0.00010728641,0.00007138751,0.000023799048,0.000009126224,0.000022282706,0.014302413],"genre_scores_gemma":[0.9950246,0.0008681659,0.0015179772,0.00006537371,0.00010403153,0.0000010371818,0.0000014746188,0.000018431556,0.0023988981],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831396,0.000021303355,0.0006285037,0.00018986563,0.0005699529,0.00027638327],"domain_scores_gemma":[0.99852574,0.00013379932,0.000533677,0.00022920119,0.0003546339,0.00022294097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029920178,0.00020324453,0.0006495954,0.0019182956,0.00007471406,0.000102682905,0.00023164014,0.00015667325,0.0010074397],"category_scores_gemma":[0.0010710313,0.00015544535,0.00031344744,0.0060098334,0.000072021496,0.0003318314,0.000030303036,0.00037489616,0.000003007824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080161967,0.0041272445,0.47496378,0.0018090643,0.038500044,0.0004530633,0.0037312985,0.0045518694,0.440057,0.0076417145,0.01036216,0.013001125],"study_design_scores_gemma":[0.001532917,0.00013537999,0.0013058165,0.00003773892,0.004530867,0.00018568005,0.0010376233,0.0010631349,0.9761975,0.0020173618,0.011339957,0.0006160656],"about_ca_topic_score_codex":0.0000036465553,"about_ca_topic_score_gemma":6.8280804e-7,"teacher_disagreement_score":0.53614044,"about_ca_system_score_codex":0.00011092178,"about_ca_system_score_gemma":0.000090982496,"threshold_uncertainty_score":0.99990577},"labels":[],"label_agreement":null},{"id":"W2090082692","doi":"10.1515/hf.2010.014","title":"Detection of wet-pockets on the surface of Tsuga heterophylla (Raf.) Sarg. by near infrared (NIR) spectroscopy","year":2009,"lang":"en","type":"article","venue":"Holzforschung","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tsuga; Near-infrared spectroscopy; Partial least squares regression; Water content; Root mean square; Analytical Chemistry (journal); Mean squared error; Moisture; Coefficient of determination; Materials science; Spectroscopy; Chemistry; Mathematics; Chromatography; Botany; Optics; Statistics; Composite material; Physics; Geology","score_opus":0.010060063194929397,"score_gpt":0.24675761839901553,"score_spread":0.23669755520408614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090082692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789048,0.00093007216,0.0003552475,0.00016107202,0.00005357011,0.00009538288,0.00004138813,0.00006704114,0.019391442],"genre_scores_gemma":[0.9977032,0.00014185286,0.00049276784,0.00012619706,0.00006823151,0.000003741031,0.000015066507,0.000025281888,0.0014236744],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983232,0.00003643211,0.0004778828,0.00033793907,0.00045282027,0.00037172058],"domain_scores_gemma":[0.9986242,0.00018544352,0.00036450117,0.00065181573,0.00009069019,0.00008332415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001717287,0.00027135867,0.00043885122,0.00008332768,0.0001525924,0.000038390208,0.00039646757,0.00018980344,0.00071884465],"category_scores_gemma":[0.0001547535,0.00021244034,0.00022933069,0.00073286076,0.0001502293,0.00010113299,0.00003772057,0.00035721814,0.000026781809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014945971,0.00017428029,0.00087478315,0.00004442336,0.000080186364,0.000001943275,0.00013921622,0.000077168996,0.9968413,0.00007698802,0.0010876146,0.00045263133],"study_design_scores_gemma":[0.00041414142,0.00027855096,0.0012716054,0.000037382808,0.000092611066,0.0000040532204,0.00021476565,0.00022898227,0.99593985,0.00046288504,0.00085705635,0.00019810653],"about_ca_topic_score_codex":0.00008388623,"about_ca_topic_score_gemma":0.0000053027748,"teacher_disagreement_score":0.018798402,"about_ca_system_score_codex":0.000093582625,"about_ca_system_score_gemma":0.000039755734,"threshold_uncertainty_score":0.86630654},"labels":[],"label_agreement":null},{"id":"W2090137458","doi":"10.1364/oe.20.023887","title":"Scattering through fruits during ripening: laser speckle technique correlated to biochemical and fluorescence measurements","year":2012,"lang":"en","type":"article","venue":"Optics Express","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Speckle pattern; Ripening; Optics; Rayleigh scattering; Materials science; Chlorophyll fluorescence; Scattering; Polarization (electrochemistry); Speckle noise; Fluorescence; Biology; Food science; Chemistry; Physics","score_opus":0.03313449326826555,"score_gpt":0.28263665782085096,"score_spread":0.2495021645525854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090137458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891688,0.00039764325,0.0017099555,0.000045056076,0.00006914575,0.000109722096,0.000009341012,0.00012227459,0.008368015],"genre_scores_gemma":[0.97861683,0.00003303259,0.02017179,0.000040194274,0.00016990973,0.000048586437,0.000008704561,0.000035204735,0.00087574264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986371,0.000008937106,0.00023710846,0.00032403783,0.00028644918,0.0005064015],"domain_scores_gemma":[0.9992548,0.000036997975,0.00007611415,0.0003506257,0.000057839225,0.00022361116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097346725,0.00022405996,0.00023699571,0.00007299436,0.00013101987,0.00006146418,0.00025093326,0.00016883382,0.00032029007],"category_scores_gemma":[0.00012523039,0.00022870892,0.000048251935,0.00029892736,0.0000665777,0.00021041182,0.00019105632,0.00024780107,0.0000349889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024749326,0.00008335163,0.013377376,0.00008842348,0.00004701126,0.0000036062738,0.00030842662,0.000009539423,0.9857171,0.000016813216,0.00030240454,0.000021175101],"study_design_scores_gemma":[0.0002620245,0.000008519463,0.0005754035,0.000106311505,0.00007579731,0.00002493591,0.00012700458,0.000024580402,0.998248,0.000018581148,0.0002492104,0.00027963382],"about_ca_topic_score_codex":0.000018352473,"about_ca_topic_score_gemma":2.4047608e-7,"teacher_disagreement_score":0.018461833,"about_ca_system_score_codex":0.00009188593,"about_ca_system_score_gemma":0.000010027835,"threshold_uncertainty_score":0.9326479},"labels":[],"label_agreement":null},{"id":"W2090308073","doi":"10.1007/s11814-011-0086-5","title":"Wavelet texture analysis in process industries","year":2011,"lang":"en","type":"article","venue":"Korean Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; McMaster University","keywords":"Wavelet; Process (computing); Texture (cosmology); Computer science; Artificial intelligence; Image (mathematics)","score_opus":0.013943743427512588,"score_gpt":0.2301873376009443,"score_spread":0.2162435941734317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090308073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960076,0.0003639221,0.0011398696,0.000027831951,0.000019429024,0.000010242907,0.0000025299937,0.00003292129,0.0023956313],"genre_scores_gemma":[0.9986884,0.000010954594,0.0011023509,0.000011634114,0.0001285186,0.0000011890647,0.0000022999834,0.000020241905,0.00003443249],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989714,0.000002715969,0.00044740597,0.00012279412,0.00022030863,0.00023536223],"domain_scores_gemma":[0.9994177,0.00004924961,0.00017716699,0.00013024788,0.000091688176,0.00013391105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011009634,0.00015637015,0.00043428235,0.00045205437,0.000008676306,0.0000147066685,0.0002931665,0.0001597714,0.0007550739],"category_scores_gemma":[0.0002873277,0.00013442204,0.00017308381,0.001653157,0.0000295919,0.00013168548,0.000022034437,0.000610459,0.000001669525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000264966,0.0006679841,0.107444294,0.00048100165,0.0044456427,0.00057893735,0.0036801894,0.0042563425,0.8737328,0.00035490285,0.00041363807,0.0036793086],"study_design_scores_gemma":[0.00031900234,0.000012384615,0.00056315167,0.000042335014,0.0005287766,0.00005057973,0.00021941247,0.0012025135,0.9967545,0.0000952612,0.000047215646,0.00016484808],"about_ca_topic_score_codex":0.0000064825,"about_ca_topic_score_gemma":3.0396828e-7,"teacher_disagreement_score":0.12302172,"about_ca_system_score_codex":0.00007612777,"about_ca_system_score_gemma":0.000039242914,"threshold_uncertainty_score":0.826753},"labels":[],"label_agreement":null},{"id":"W2090675660","doi":"10.1016/j.foodchem.2009.04.088","title":"High-power gradient diffusion NMR spectroscopy for the rapid assessment of extra-virgin olive oil adulteration","year":2009,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Regione Campania; McGill University","keywords":"Olive oil; Chemistry; Chromatography; Food science; Diffusion; Linear discriminant analysis; Mathematics; Sunflower oil; Statistics; Physics","score_opus":0.01069128120325925,"score_gpt":0.270871093832374,"score_spread":0.26017981262911477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090675660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708128,0.0019120511,0.0031316704,0.00054121466,0.00009790751,0.00010124895,0.0001478624,0.000097361735,0.023157887],"genre_scores_gemma":[0.9948323,0.0005074461,0.0022676976,0.0001032795,0.00033897033,0.0000614225,0.00014905687,0.000025066083,0.0017148069],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982963,0.0000089800005,0.00046260312,0.00044258987,0.0003892494,0.00040022942],"domain_scores_gemma":[0.99863267,0.00018513987,0.00032243808,0.00062078563,0.00013604293,0.000102906146],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012605925,0.00031906972,0.00039619964,0.000042346033,0.00020153822,0.000057048434,0.0004151894,0.00019587335,0.002267168],"category_scores_gemma":[0.00008156016,0.00024040033,0.0002787922,0.00028910342,0.00008858936,0.0000769058,0.000041236915,0.00029160877,0.0000025769032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079376005,0.00039220697,0.00009942881,0.0002463106,0.00015755866,0.0000012925109,0.00010892232,0.000043422402,0.9960633,0.00037330916,0.0012560388,0.0011788248],"study_design_scores_gemma":[0.0010479424,0.00023334385,0.00024493766,0.00006496897,0.00024885213,0.000006838093,0.00039957397,0.0005527754,0.9954275,0.00036099757,0.0011501652,0.00026211195],"about_ca_topic_score_codex":0.000009733807,"about_ca_topic_score_gemma":0.0000015727426,"teacher_disagreement_score":0.02401946,"about_ca_system_score_codex":0.00019185092,"about_ca_system_score_gemma":0.00007785226,"threshold_uncertainty_score":0.9986449},"labels":[],"label_agreement":null},{"id":"W2091356921","doi":"10.1016/j.saa.2011.05.078","title":"Study the sensitivity of molecular functional groups to bioethanol processing in lipid biopolymer of co-products using DRIFT molecular spectroscopy","year":2011,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Biopolymer; Chemistry; Spectroscopy; Biofuel; Antisymmetric relation; Molecule; Food science; Organic chemistry; Polymer; Mathematics; Biology; Physics","score_opus":0.03265736747197472,"score_gpt":0.2911824930385474,"score_spread":0.25852512556657264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091356921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9785359,0.0015944338,0.016081944,0.00028639584,0.00010942501,0.00088205666,0.00005808013,0.000099467004,0.0023523322],"genre_scores_gemma":[0.99195814,0.00006641692,0.007330946,0.00024968447,0.00011794073,0.00006124995,0.000041862175,0.0001543293,0.000019446363],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9940339,0.0003708951,0.001358871,0.0017057667,0.0012072475,0.0013233458],"domain_scores_gemma":[0.99705535,0.00006897383,0.0007339335,0.0015225369,0.00026902123,0.00035019944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085744704,0.0010274156,0.0014442322,0.0010426977,0.0002390403,0.00010418576,0.00074222975,0.00033413878,0.00027225318],"category_scores_gemma":[0.00024101339,0.0009075291,0.00045222524,0.0031388456,0.0007663021,0.00021353786,0.00039089972,0.0007672746,0.0000067920614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063206855,0.001594035,0.0041302363,0.00017931864,0.00052096695,0.0004547454,0.0009426862,0.000014861884,0.99058867,0.00087189686,0.00002410406,0.00004639362],"study_design_scores_gemma":[0.00143249,0.00069952937,0.0011499156,0.0001162032,0.00086310785,0.00018047697,0.0015516076,0.00014935872,0.99252313,0.00042449316,0.000026692494,0.0008829865],"about_ca_topic_score_codex":0.0005226118,"about_ca_topic_score_gemma":0.00005971721,"teacher_disagreement_score":0.013422259,"about_ca_system_score_codex":0.00021784,"about_ca_system_score_gemma":0.00035671477,"threshold_uncertainty_score":0.99933755},"labels":[],"label_agreement":null},{"id":"W2092171609","doi":"10.1021/jf801299r","title":"Nondestructive Measurement of Fresh Tomato Lycopene Content and Other Physicochemical Characteristics Using Visible−NIR Spectroscopy","year":2008,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut National d'Optique; Agriculture and Agri-Food Canada","funders":"","keywords":"Lycopene; Titratable acid; Cultivar; Chemometrics; Chemistry; Context (archaeology); Partial least squares regression; Food science; Mathematics; Analytical Chemistry (journal); Horticulture; Carotenoid; Chromatography; Statistics; Biology","score_opus":0.04128164521015109,"score_gpt":0.24300781955942088,"score_spread":0.2017261743492698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092171609","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970931,0.0018250606,0.000032324097,0.000045564793,0.000027179827,0.000029315168,0.000037192753,0.000009693226,0.0009005829],"genre_scores_gemma":[0.9982931,0.00022024635,0.0010145727,0.000021628899,0.0003683164,0.000001102444,0.0000033688016,0.000011703027,0.000065958324],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854946,0.0000073794617,0.0005590654,0.00020061505,0.0004597586,0.00022371889],"domain_scores_gemma":[0.9985946,0.00003171384,0.0006827218,0.00011173973,0.0003995458,0.00017972432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073141906,0.00025193152,0.0005980084,0.00003079434,0.00009503948,0.000029219342,0.00015681077,0.00013834293,0.00014824088],"category_scores_gemma":[0.00006247743,0.00016609073,0.00016062698,0.00017714738,0.0001857446,0.00013199473,0.00006193117,0.00029950816,3.1946195e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072527124,0.00014354689,0.0077312742,0.00020832154,0.00038287198,0.000006497721,0.0001383353,0.0000010679549,0.9911632,0.00000697185,0.00011969221,0.00002567834],"study_design_scores_gemma":[0.00080790516,0.000102369544,0.013610895,0.00015457168,0.00028807845,0.0012710487,0.001104513,0.0000061595324,0.9823797,0.000035421628,0.000026785976,0.00021255783],"about_ca_topic_score_codex":0.000012068341,"about_ca_topic_score_gemma":5.2481045e-7,"teacher_disagreement_score":0.008783521,"about_ca_system_score_codex":0.00012388246,"about_ca_system_score_gemma":0.00005520505,"threshold_uncertainty_score":0.67729837},"labels":[],"label_agreement":null},{"id":"W2092505368","doi":"10.1080/00085030.2010.10757617","title":"Forensic Fibre Analysis by UV-Visible Microspectrophotometry","year":2010,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Health Sciences Centre","funders":"University of Toronto Mississauga","keywords":"Materials science; Polyester; Absorbance; Polyvinylpyrrolidone; Visible spectrum; Textile; Quartz; Spectrophotometry; Polymer; Ultraviolet; Analytical Chemistry (journal); Composite material; Chemistry; Polymer chemistry; Chromatography; Optoelectronics","score_opus":0.007185275630390517,"score_gpt":0.25392788268858235,"score_spread":0.24674260705819184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092505368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914211,0.0005719179,0.0002930325,0.00062376657,0.00023406252,0.000033620017,0.00010694572,0.000023186549,0.0066923816],"genre_scores_gemma":[0.9796005,0.00010284324,0.017996933,0.0005210437,0.00024021545,0.0000015839943,0.000016954902,0.000019607114,0.0015003432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971333,0.000004800634,0.00047988544,0.00047161386,0.0008499622,0.0010604041],"domain_scores_gemma":[0.99729246,0.000059175432,0.00037549812,0.0005598157,0.00047654225,0.0012365214],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068905525,0.00025521778,0.0005197856,0.0008288876,0.00092920655,0.00028850572,0.001090853,0.00022909672,0.0052968403],"category_scores_gemma":[0.00015655748,0.00023844464,0.0008325709,0.0075340816,0.0019982934,0.00039127466,0.000057430996,0.0009837894,0.000011260146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041385924,0.000028627675,0.054086212,0.000019457137,0.0005363528,0.0000074092586,0.00032977262,0.000011041311,0.87277526,0.000078827135,0.06918242,0.0029405],"study_design_scores_gemma":[0.00032395808,0.000033113676,0.0036751141,0.000010491656,0.0006334372,0.00018804143,0.0016471427,0.0005650481,0.98836195,0.00048213085,0.0037543976,0.0003251457],"about_ca_topic_score_codex":0.009305679,"about_ca_topic_score_gemma":0.010281578,"teacher_disagreement_score":0.11558674,"about_ca_system_score_codex":0.0005687735,"about_ca_system_score_gemma":0.0016841531,"threshold_uncertainty_score":0.99729145},"labels":[],"label_agreement":null},{"id":"W2093183583","doi":"10.1016/j.biortech.2012.01.089","title":"Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques","year":2012,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Principal component regression; Calibration; Multivariate statistics; Chemometrics; Cellulose; Lignocellulosic biomass; Regression analysis; Linear regression; Biological system; Mathematics; Chemistry; Analytical Chemistry (journal); Statistics; Chromatography; Organic chemistry","score_opus":0.026159898482868534,"score_gpt":0.2954329998016348,"score_spread":0.26927310131876625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093183583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958075,0.0013021845,0.0021338705,0.00008514274,0.00001731892,0.000057256788,0.00002872863,0.00027010022,0.00029784307],"genre_scores_gemma":[0.9957173,0.00005215235,0.0040706233,0.000011803298,0.000040245784,0.000008727853,0.000018366954,0.000022143164,0.00005862048],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991609,0.000014231109,0.00024250508,0.0002043509,0.00009216945,0.00028584883],"domain_scores_gemma":[0.99936134,0.000049284554,0.00025429105,0.000226022,0.00006003622,0.00004900236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096224554,0.00016673912,0.00028769023,0.0005085379,0.00007087251,0.000009521676,0.00015785989,0.00034410888,0.0000725287],"category_scores_gemma":[0.000105805346,0.00015294334,0.00004393686,0.0007488337,0.00031506913,0.00008294907,0.00011386221,0.00013443014,0.000002724105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019596342,0.00007680597,0.07916187,0.00006434553,0.00006122811,9.795723e-7,0.0001323651,3.8133003e-8,0.9189027,0.00091944257,0.0000024791123,0.00065812987],"study_design_scores_gemma":[0.00014100963,0.000040196006,0.0022796178,0.000026640446,0.00010971105,0.00001348101,0.00029177938,0.00014259308,0.99624115,0.00012449283,0.0004336067,0.00015574195],"about_ca_topic_score_codex":0.00006880738,"about_ca_topic_score_gemma":7.3791836e-7,"teacher_disagreement_score":0.077338405,"about_ca_system_score_codex":0.000035119712,"about_ca_system_score_gemma":0.000010785324,"threshold_uncertainty_score":0.6236848},"labels":[],"label_agreement":null},{"id":"W2093641577","doi":"10.1002/adv.21402","title":"Neural Network: A Potential Approach for Error Reduction in Color Values of Polycarbonate","year":2013,"lang":"en","type":"article","venue":"Advances in Polymer Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovative Medicines Canada; Ontario Tech University","funders":"","keywords":"Sigmoid function; Artificial neural network; Backpropagation; Mean squared error; Gradient descent; Reduction (mathematics); Computer science; Polycarbonate; Conjugate gradient method; Algorithm; Approximation error; Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematics; Materials science","score_opus":0.008179730074589905,"score_gpt":0.2743029122731423,"score_spread":0.2661231821985524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093641577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95417994,0.041031815,0.0012653234,0.00041684543,0.00014071772,0.00021115335,0.0000072707385,0.00012471408,0.0026222179],"genre_scores_gemma":[0.9953778,0.00026336542,0.0034782146,0.000017050394,0.000081488724,0.00029317843,0.000014615614,0.00001905609,0.0004552342],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987358,0.000009886336,0.00038193498,0.0003405852,0.00009403998,0.000437784],"domain_scores_gemma":[0.99946684,0.00003626307,0.00018022962,0.0002582794,0.00003464321,0.000023724318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056505352,0.00015950199,0.00036654586,0.00045154573,0.000034592762,0.0000060389407,0.00029877937,0.00027745898,0.000094262614],"category_scores_gemma":[0.0000470727,0.0001561019,0.00007050301,0.0011333498,0.000297235,0.00020332346,0.00006972846,0.0002405409,0.000001191876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046060095,0.0009266737,0.051346816,0.00045828556,0.00016953745,0.00000934594,0.00016529049,0.009107248,0.8617537,0.0075015146,0.00022380026,0.067877196],"study_design_scores_gemma":[0.0011346716,0.000111986876,0.00027771964,0.000019917743,0.000061247265,0.000034753444,0.0015509288,0.02354394,0.96006227,0.012834552,0.00010233851,0.0002656908],"about_ca_topic_score_codex":0.00012516371,"about_ca_topic_score_gemma":0.0000088095985,"teacher_disagreement_score":0.09830856,"about_ca_system_score_codex":0.000054844226,"about_ca_system_score_gemma":0.000018411956,"threshold_uncertainty_score":0.6365651},"labels":[],"label_agreement":null},{"id":"W2093829185","doi":"10.1002/cjs.5550350305","title":"A new estimation procedure for a partially nonlinear model via a mixed‐effects approach","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Nonlinear system; Parametric statistics; Applied mathematics; Component (thermodynamics); Gaussian; Semiparametric model; Mathematics; Mixed model; Nonlinear regression; Estimation; Nonparametric statistics; Econometrics; Statistics; Regression analysis; Physics","score_opus":0.01685110463664221,"score_gpt":0.2645094865367687,"score_spread":0.24765838190012648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093829185","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005098752,0.00034846162,0.99347335,0.00005440873,0.00006775047,0.000075834534,0.00016539809,0.000007424133,0.0007086026],"genre_scores_gemma":[0.3108952,0.000004830359,0.68798697,0.000089567955,0.00027027802,0.0000022774268,0.000046527948,0.00002377195,0.00068059616],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889946,0.000003942639,0.00040985306,0.00012580746,0.00018397173,0.00037695302],"domain_scores_gemma":[0.9983714,0.0001698915,0.0002924273,0.000110676345,0.00027366943,0.0007819252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020591858,0.00014587262,0.0002657425,0.00024165546,0.00010886655,0.000055346405,0.00018703863,0.00011825962,0.00007012234],"category_scores_gemma":[0.0006958744,0.00014108827,0.00007960201,0.0002532728,0.000039759427,0.00008426312,0.000004400164,0.00021356772,0.0000023645882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012030036,0.00077915593,0.0098857125,0.0077907997,0.0029156571,0.0014537004,0.0069587403,0.16775343,0.041471817,0.042764213,0.4171396,0.2998842],"study_design_scores_gemma":[0.0019751706,0.00025599077,0.0003342929,0.00010486217,0.001053142,0.0003562052,0.00032861618,0.9205363,0.055385847,0.01733999,0.0018256203,0.00050396525],"about_ca_topic_score_codex":0.0004669011,"about_ca_topic_score_gemma":0.0035886092,"teacher_disagreement_score":0.7527829,"about_ca_system_score_codex":0.00022575045,"about_ca_system_score_gemma":0.0018189457,"threshold_uncertainty_score":0.5753413},"labels":[],"label_agreement":null},{"id":"W2093976534","doi":"10.1016/j.talanta.2013.10.066","title":"Development of search prefilters for infrared library searching of clear coat paint smears","year":2013,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"National Institute of Justice; Office of Justice Programs; U.S. Department of Justice","keywords":"Spectrometer; Fourier transform infrared spectroscopy; Chemistry; Infrared; Infrared spectroscopy; Analytical Chemistry (journal); Fourier transform; Optics; Remote sensing; Chromatography; Physics","score_opus":0.029081637386412913,"score_gpt":0.2755210707520188,"score_spread":0.2464394333656059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093976534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875752,0.00009008883,0.00018729887,0.000043230786,0.00000995895,0.00011062566,0.00003093188,0.000038275473,0.011914413],"genre_scores_gemma":[0.98782444,0.000028209739,0.008275697,0.000026981936,0.000028921733,0.000025853213,0.00007258488,0.000024345014,0.0036929743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904954,0.000011299909,0.0003060846,0.00016791686,0.00021987573,0.00024529715],"domain_scores_gemma":[0.99940664,0.00015875405,0.00009590034,0.00021853551,0.000043537686,0.00007663086],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010642723,0.00010381668,0.0002358908,0.00012285254,0.0000493009,0.000021173362,0.0002622074,0.00006661346,0.0018741314],"category_scores_gemma":[0.000042015923,0.00009338056,0.00008126521,0.00021670373,0.000069706446,0.00013894626,0.0001328787,0.00011406312,0.00001870899],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011507888,0.00015032421,0.00973477,0.00091947924,0.00023949439,0.0000010754566,0.0020537998,0.00001176831,0.9772841,0.00015253005,0.0038296657,0.0055079563],"study_design_scores_gemma":[0.00031846666,0.000031392305,0.0014106141,0.000052116713,0.000022602893,0.000001311424,0.0013844214,0.00027510236,0.9949478,0.00014368838,0.0013065862,0.00010592898],"about_ca_topic_score_codex":0.00003855069,"about_ca_topic_score_gemma":9.059268e-7,"teacher_disagreement_score":0.017663712,"about_ca_system_score_codex":0.000026652304,"about_ca_system_score_gemma":0.00010797319,"threshold_uncertainty_score":0.9990383},"labels":[],"label_agreement":null},{"id":"W2094043751","doi":"10.1016/j.biosystemseng.2011.01.002","title":"Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western Red Spring (CWRS) wheat kernels","year":2011,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Absorbance; Partial least squares regression; Near-infrared spectroscopy; Spectrometer; Wavelength; Remote sensing; Imaging spectrometer; Spectral imaging; Infrared; Environmental science; Chemistry; Materials science; Optics; Mathematics; Physics; Geology; Statistics","score_opus":0.035496666893831554,"score_gpt":0.2627854839425816,"score_spread":0.22728881704875006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094043751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968367,0.0004023379,0.00064441026,0.000004023961,0.00013812835,0.00033121923,0.00013182349,0.00024446019,0.001266879],"genre_scores_gemma":[0.99704224,0.00000470882,0.0024268173,0.000003391123,0.00023577045,0.00010043162,0.00003178849,0.00010955253,0.000045274915],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99744964,0.000021263306,0.0007503481,0.0005593356,0.0003532363,0.0008661519],"domain_scores_gemma":[0.9988101,0.00010033571,0.00020768298,0.00048339862,0.000070690934,0.00032778064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027432916,0.00045758902,0.00087375497,0.0009375848,0.00009892495,0.00009471084,0.00041575165,0.00018019923,0.000023428987],"category_scores_gemma":[0.00004227014,0.00046169458,0.00013270708,0.0005643713,0.00003968898,0.0004062003,0.00004711508,0.00034218578,8.7134595e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013689358,0.00013342929,0.635357,0.0020167627,0.00048263828,0.000082406266,0.0050172484,0.00036664226,0.35616928,0.00009218882,0.0000080613645,0.00013745179],"study_design_scores_gemma":[0.0012046301,0.00021282547,0.022074632,0.0010220197,0.00031421203,0.000096863536,0.018133523,0.037576344,0.91846144,0.0000011072204,0.00005098254,0.00085144205],"about_ca_topic_score_codex":0.020905418,"about_ca_topic_score_gemma":0.0077069267,"teacher_disagreement_score":0.6132824,"about_ca_system_score_codex":0.00076236686,"about_ca_system_score_gemma":0.00013639314,"threshold_uncertainty_score":0.99978346},"labels":[],"label_agreement":null},{"id":"W2094659500","doi":"10.1142/9789812772435_0014","title":"COMPARATIVE QSAR ANALYSIS OF BACTERIAL, FUNGAL PLANT AND HUMAN METABOLITES","year":2006,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Quantitative structure–activity relationship; Computer science; Biochemical engineering; Computational biology; Biology; Engineering; Machine learning","score_opus":0.02196721615600408,"score_gpt":0.29087769593175095,"score_spread":0.2689104797757469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094659500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93630886,0.00063131406,0.00005258472,0.000008381826,0.0000055964606,0.000013004771,0.00012702924,0.000027463922,0.062825784],"genre_scores_gemma":[0.9968801,0.000013222069,0.0002630687,0.000009260991,0.000054479762,0.0000023357122,0.00026719866,0.000004205103,0.0025061127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992983,0.000009567757,0.00025160355,0.00018605537,0.00011992244,0.0001345942],"domain_scores_gemma":[0.9995734,0.00008115907,0.00011105913,0.00015955337,0.00003871813,0.00003610326],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000047928686,0.00011712598,0.0005351904,0.0002554873,0.00005731774,0.000026910275,0.000085303065,0.00005379948,0.0071058],"category_scores_gemma":[0.0000062923764,0.000094521216,0.00011649133,0.0007080338,0.00008491553,0.000050988747,0.000028453374,0.000053385407,0.00000258144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015751291,0.00011995389,0.089611955,0.000019390349,0.0017605405,0.0000016086351,0.000066556735,0.0000102771055,0.8999105,0.007525081,0.000952811,0.0000055347164],"study_design_scores_gemma":[0.00020117464,0.000011281402,0.03601996,0.0000017245868,0.00308108,0.0000010768184,0.0003327224,0.00013521663,0.9589349,0.00012011533,0.0010370028,0.00012373572],"about_ca_topic_score_codex":0.00088354276,"about_ca_topic_score_gemma":0.00025599095,"teacher_disagreement_score":0.060571272,"about_ca_system_score_codex":0.000012093306,"about_ca_system_score_gemma":0.000007824705,"threshold_uncertainty_score":0.99380183},"labels":[],"label_agreement":null},{"id":"W2095027494","doi":"10.1007/s11947-008-0093-7","title":"Wavelet Analysis of Signals in Agriculture and Food Quality Inspection","year":2008,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wavelet; Hyperspectral imaging; Signal processing; Computer science; Quality (philosophy); Image processing; Artificial intelligence; Food quality; Food processing; Food safety; Wavelet transform; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Food industry; Computer vision; Digital signal processing; Image (mathematics); Computer hardware; Food science","score_opus":0.019558070976424972,"score_gpt":0.2706871827605886,"score_spread":0.25112911178416364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095027494","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99559367,0.0033124904,0.000067990724,0.00023963874,0.000004184204,0.000025580022,0.000035619676,0.00010395996,0.00061686087],"genre_scores_gemma":[0.99918556,0.0005996623,0.00008967896,0.000013806571,0.000012704398,0.000009713276,0.000015972548,0.0000043346004,0.00006858602],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991746,0.0000049258447,0.00026853397,0.00029354394,0.00009834134,0.0001600384],"domain_scores_gemma":[0.99960023,0.000022197957,0.00014581211,0.00013589888,0.00006608534,0.00002978916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052510037,0.00012628165,0.00043585166,0.00073864876,0.00007067884,0.0000059000395,0.00010599251,0.0003306552,0.000034352575],"category_scores_gemma":[0.000066850385,0.000100054116,0.00005053733,0.0033542365,0.00024229997,0.000049508868,0.00005227637,0.00017584367,2.6261642e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003705227,0.0003248839,0.5310926,0.00030061835,0.0013426052,0.0000050684775,0.00034247973,0.0000060012853,0.46321929,0.0017243935,0.00004002088,0.0015650345],"study_design_scores_gemma":[0.00039905467,0.00024609987,0.047450535,0.0000115583825,0.0004010641,0.000028380357,0.0015737434,0.00005833747,0.9484062,0.0011491245,0.000084834166,0.0001910362],"about_ca_topic_score_codex":0.000058262023,"about_ca_topic_score_gemma":0.00026126398,"teacher_disagreement_score":0.48518696,"about_ca_system_score_codex":0.000018780718,"about_ca_system_score_gemma":0.000012465092,"threshold_uncertainty_score":0.40800884},"labels":[],"label_agreement":null},{"id":"W2095486851","doi":"10.1002/cjce.20691","title":"In‐line near infrared spectroscopy monitoring of pharmaceutical powder moisture in a fluidised bed dryer: An efficient methodology for chemometric model development","year":2011,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"Université de Sherbrooke; Pfizer","keywords":"Chemometrics; Moisture; Calibration; Process analytical technology; Process engineering; Near-infrared spectroscopy; Water content; Materials science; Analytical Chemistry (journal); Chromatography; Environmental science; Chemistry; Chemical engineering; Mathematics; Engineering; Composite material","score_opus":0.07504867671237031,"score_gpt":0.31416217592541834,"score_spread":0.23911349921304803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095486851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99512887,0.00090665644,0.0036162473,0.00006970234,0.00008150387,0.00007382967,0.0000058133446,0.000008204801,0.000109173794],"genre_scores_gemma":[0.94010454,0.000006625081,0.059697237,0.000024484812,0.00010779076,0.0000099258605,0.0000023348039,0.000029102059,0.000017958804],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984211,0.00001530056,0.00068005687,0.00017503854,0.00020064329,0.00050790585],"domain_scores_gemma":[0.99893904,0.00020729454,0.00016197537,0.000183558,0.00011837881,0.00038973163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059654965,0.00020738473,0.00050527876,0.0005990514,0.000037876733,0.000018763787,0.00047410763,0.00018554505,0.00010372603],"category_scores_gemma":[0.0006282241,0.0001772367,0.00012051383,0.00092692534,0.000077186865,0.00007511973,0.00002408805,0.00060031726,4.0686362e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017562238,0.000104308565,0.0025142618,0.00013438257,0.0001175441,0.000028663968,0.002601875,0.021664891,0.9724377,0.00009689605,0.00001565052,0.00010824715],"study_design_scores_gemma":[0.0009707133,0.000025961244,0.00022371837,0.000052946634,0.000071981936,0.000024162919,0.0000912441,0.039768554,0.9583872,0.0001834704,0.00003088848,0.00016915216],"about_ca_topic_score_codex":0.00033574316,"about_ca_topic_score_gemma":0.00003918072,"teacher_disagreement_score":0.05608099,"about_ca_system_score_codex":0.00057566684,"about_ca_system_score_gemma":0.00060211436,"threshold_uncertainty_score":0.72275025},"labels":[],"label_agreement":null},{"id":"W2097652629","doi":"10.5539/jfr.v1n1p3","title":"Evaluation of Surface Enhanced Raman Spectroscopy (SERS) for Detection of Acetone in the Context of Food Safety and Quality Application","year":2012,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Cooperative State Research, Education, and Extension Service; North Dakota State University; U.S. Department of Agriculture","keywords":"Raman spectroscopy; Acetone; Detection limit; Chemistry; Context (archaeology); Food spoilage; Analytical Chemistry (journal); Surface-enhanced Raman spectroscopy; Nuclear chemistry; Chromatography; Physics; Organic chemistry; Raman scattering","score_opus":0.15502226303215735,"score_gpt":0.4619575262517937,"score_spread":0.3069352632196364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097652629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913784,0.0026661411,0.005080789,0.00010390872,0.000016925016,0.00024200926,0.00001699816,0.0000014283029,0.0004933694],"genre_scores_gemma":[0.9993592,0.00024660327,0.0002891254,0.0000029661237,0.00007418422,0.000009105233,0.0000022359027,0.0000070488577,0.000009568173],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973534,0.0003400407,0.00070700963,0.000102778315,0.001283316,0.0002134993],"domain_scores_gemma":[0.9967856,0.00087799534,0.000697276,0.00021951798,0.0013709859,0.0000486565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011523255,0.000077002005,0.00034779252,0.0002081937,0.000055739652,0.0000077127725,0.00021892798,0.000101926555,0.000033634802],"category_scores_gemma":[0.001335986,0.00005715541,0.00010338317,0.000650096,0.00014046565,0.00013702773,0.000026557942,0.00032572905,1.960726e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007834218,0.00029072215,0.0014655185,0.0002223946,0.00014779657,1.3347797e-8,0.0010896662,0.000025764335,0.9927702,0.00030023034,0.000006385338,0.0028978747],"study_design_scores_gemma":[0.0015237961,0.000773389,0.0047453963,0.000034546774,0.00013204599,0.0000023623197,0.0053447103,0.00016957683,0.98646337,0.000747177,0.000017701095,0.000045941724],"about_ca_topic_score_codex":0.00007238799,"about_ca_topic_score_gemma":0.00009277394,"teacher_disagreement_score":0.010187269,"about_ca_system_score_codex":0.00017908907,"about_ca_system_score_gemma":0.00013547284,"threshold_uncertainty_score":0.39937535},"labels":[],"label_agreement":null},{"id":"W2097761429","doi":"10.3109/10520290903081369","title":"Effect of granule size on the interference colors of starch in polarized light","year":2010,"lang":"en","type":"article","venue":"Biotechnic & Histochemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Birefringence; Starch; Materials science; Polarized light microscopy; Optics; Wavelength; Absorbance; Chemistry; Optoelectronics; Physics; Food science","score_opus":0.005958807309138089,"score_gpt":0.2579551797943012,"score_spread":0.2519963724851631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097761429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98486817,0.00022793212,0.000020585896,0.00023859688,0.000041554555,0.00010703811,0.0000264056,0.00004711464,0.014422603],"genre_scores_gemma":[0.99886835,0.000018344661,0.00010505691,0.000020120076,0.000034103694,0.000036659934,0.000006656568,0.000018197241,0.0008925359],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986781,0.000023425851,0.00043162282,0.00032516915,0.0002839017,0.00025776346],"domain_scores_gemma":[0.997936,0.0008641289,0.0002753462,0.00081503397,0.00005173611,0.000057752848],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041704893,0.0002336076,0.00046160197,0.00008768726,0.000045479082,0.0000074117393,0.0007414807,0.00043140363,0.001850439],"category_scores_gemma":[0.0012754964,0.00016902055,0.00018345106,0.0005889359,0.00032685613,0.00002302152,0.0001036406,0.0009602084,0.0000057778916],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037439438,0.00012782824,0.00073978375,0.00028984644,0.000030150139,0.0000026642708,0.000046100246,1.6013527e-7,0.9978863,0.000095300136,0.00024441522,0.00016308068],"study_design_scores_gemma":[0.00053218246,0.00018180742,0.00007178953,0.000081028105,0.00006253369,0.0000045035276,0.000044439992,0.00001029841,0.9984181,0.00006472579,0.0003789101,0.00014969161],"about_ca_topic_score_codex":0.00014220299,"about_ca_topic_score_gemma":0.000012723202,"teacher_disagreement_score":0.014000156,"about_ca_system_score_codex":0.00012360772,"about_ca_system_score_gemma":0.00008966863,"threshold_uncertainty_score":0.999062},"labels":[],"label_agreement":null},{"id":"W2098120634","doi":"10.1002/masy.200850503","title":"Planar Array Transient Infrared Spectroscopy: A New Tool for the Time‐Resolved Analysis of Polymers","year":2008,"lang":"en","type":"article","venue":"Macromolecular Symposia","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Planar; Infrared spectroscopy; Infrared; Transient (computer programming); Spectroscopy; Materials science; Spectral line; Polymer; Planar array; Analytical Chemistry (journal); Optoelectronics; Optics; Chemistry; Computer science; Physics; Organic chemistry; Composite material","score_opus":0.009545817118076205,"score_gpt":0.23969624432256253,"score_spread":0.23015042720448634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098120634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5842125,0.009214914,0.37524623,0.0011352312,0.00010562989,0.0005985298,0.00043763604,0.00024388616,0.028805476],"genre_scores_gemma":[0.98237365,0.00023248272,0.0048179324,0.00043288484,0.00013984178,0.00006186676,0.00027280088,0.00007228632,0.01159624],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998157,0.000022163422,0.00048030453,0.00044866404,0.00042216334,0.00046968763],"domain_scores_gemma":[0.99862206,0.0002103482,0.00020808355,0.000759005,0.00006476176,0.00013571604],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010255858,0.00030680373,0.00065356866,0.00032874127,0.00022820086,0.00003467066,0.0005489358,0.00014953443,0.0024371615],"category_scores_gemma":[0.000059289785,0.00024756213,0.0008570735,0.0018157838,0.00016285373,0.000051927338,0.000021071413,0.00016366532,0.000014038575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001733689,0.00011285829,0.0012621664,0.00003161908,0.004948663,0.000025504947,0.0005360504,0.00014639836,0.9896068,0.00024443032,0.0028229048,0.00008920866],"study_design_scores_gemma":[0.0008961059,0.00007953609,0.0010038317,0.000010743514,0.006076735,0.000020466377,0.00009681589,0.0008959171,0.9830214,0.000079301644,0.0074890354,0.00033013956],"about_ca_topic_score_codex":0.0002171358,"about_ca_topic_score_gemma":0.000011177351,"teacher_disagreement_score":0.3981612,"about_ca_system_score_codex":0.000082443155,"about_ca_system_score_gemma":0.00015059634,"threshold_uncertainty_score":0.9999977},"labels":[],"label_agreement":null},{"id":"W2098540087","doi":"10.1139/x11-089","title":"High-throughput prediction of physical and mechanical properties of paper from Raman chemometric analysis of pulp fibres<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"FPInnovations; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pulp (tooth); Chemometrics; Partial least squares regression; Kraft paper; Kraft process; Pulp and paper industry; Process engineering; Raw material; Raman spectroscopy; Papermaking; Lignin; Softwood; Computer science; Biological system; Materials science; Mathematics; Pattern recognition (psychology); Environmental science; Artificial intelligence; Chemistry; Machine learning; Engineering","score_opus":0.03585869998277379,"score_gpt":0.26511311703508295,"score_spread":0.22925441705230915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098540087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964605,0.0013835226,0.0000052131277,0.0017968983,0.0000064946876,0.00015123977,0.00011764159,0.0000012256342,0.00007729581],"genre_scores_gemma":[0.9997102,0.00006873289,0.000040940868,0.000018848394,0.00009699736,0.000005869596,0.00000439675,0.000007864086,0.000046138706],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984436,0.00012915702,0.00052317313,0.00017771672,0.000433523,0.0002928056],"domain_scores_gemma":[0.99839485,0.0002639274,0.00026491718,0.00035787062,0.0005246755,0.00019378818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009559522,0.00011577837,0.00045077308,0.00069034577,0.0001020269,0.00003311035,0.0003975587,0.00014462668,0.00017801457],"category_scores_gemma":[0.001205956,0.00006377151,0.00010549112,0.0027461015,0.0005401946,0.00018605797,0.000054883243,0.00055970426,4.184958e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008105841,0.00027402432,0.8324665,0.00016589352,0.002185556,0.000013599972,0.023835246,0.00074356893,0.1346041,0.0027640169,0.0007890069,0.001347938],"study_design_scores_gemma":[0.0004659675,0.00041853404,0.17201741,0.00008657228,0.0005404896,0.000014297165,0.008836427,0.0008770694,0.81486195,0.0015806112,0.00022679087,0.000073869436],"about_ca_topic_score_codex":0.03690523,"about_ca_topic_score_gemma":0.011716036,"teacher_disagreement_score":0.68025786,"about_ca_system_score_codex":0.00007980317,"about_ca_system_score_gemma":0.0004816176,"threshold_uncertainty_score":0.9695081},"labels":[],"label_agreement":null},{"id":"W2099067734","doi":"10.1021/ac035382g","title":"Transfer of Multivariate Calibrations between Four Near-Infrared Spectrometers Using Orthogonal Signal Correction","year":2004,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Partenariat Canadien Contre Le Cancer","keywords":"Chemistry; Partial least squares regression; Transferability; Calibration; Spectrometer; Multivariate statistics; Impulse response; SIGNAL (programming language); Near-infrared spectroscopy; Analytical Chemistry (journal); Biological system; Statistics; Optics; Chromatography; Computer science; Mathematics; Physics","score_opus":0.03678350694455628,"score_gpt":0.2884119578267584,"score_spread":0.2516284508822021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099067734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9549433,0.00004136214,0.03058275,0.000100977806,0.000028067207,0.000036875204,0.000068904126,0.00008633687,0.014111431],"genre_scores_gemma":[0.99611247,0.000004645544,0.0016524501,0.00003169774,0.00023752719,0.0000038099186,0.00010101592,0.000031384254,0.0018250194],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998224,0.000010159999,0.0005459786,0.0004201647,0.00039737718,0.00040227931],"domain_scores_gemma":[0.9991069,0.00016089261,0.00009911656,0.00030202023,0.00010186794,0.00022921893],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008753666,0.00027022586,0.0004883614,0.00006804861,0.00015261209,0.000055418775,0.00023346332,0.0002831307,0.0052699572],"category_scores_gemma":[0.0001222528,0.00027517354,0.00034302793,0.0008061519,0.0002403013,0.00018826488,0.000037493355,0.00042877742,0.000011042676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006498864,0.00024204423,0.006549197,0.00015563803,0.0006049278,0.000023830906,0.00007113355,0.002403511,0.9895326,0.00017791624,0.0000741156,0.00010009384],"study_design_scores_gemma":[0.0008788577,0.000024547262,0.00058466394,0.000042816628,0.000809587,0.00002707385,0.000121638484,0.008218419,0.98820907,0.0006491346,0.00011726043,0.0003169553],"about_ca_topic_score_codex":0.00013560712,"about_ca_topic_score_gemma":0.000002147944,"teacher_disagreement_score":0.041169155,"about_ca_system_score_codex":0.00023074413,"about_ca_system_score_gemma":0.00025263822,"threshold_uncertainty_score":0.99997},"labels":[],"label_agreement":null},{"id":"W2099396514","doi":"10.1109/icassp.2011.5946665","title":"Robust hyperspectral signal unmixing in the presence of correlated noise","year":2011,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hyperspectral imaging; Gaussian noise; Noise (video); Gradient noise; Pattern recognition (psychology); Robustness (evolution); Computer science; Value noise; Additive white Gaussian noise; Curse of dimensionality; Dimensionality reduction; Artificial intelligence; Algorithm; White noise; Mathematics; Noise measurement; Noise reduction; Statistics; Noise floor; Image (mathematics)","score_opus":0.051082048474867865,"score_gpt":0.24914973546665042,"score_spread":0.19806768699178257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099396514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6979565,0.00023751384,0.00079390797,0.000041207393,0.000014138499,0.000030912102,0.0000019997462,0.000031580068,0.30089223],"genre_scores_gemma":[0.9968146,0.000018863706,0.0014775735,0.00003751572,0.000022273902,0.0000041055077,0.0000021075937,0.0000064713877,0.0016165009],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928224,0.000013723017,0.00019494788,0.00015428785,0.00016320888,0.00019159484],"domain_scores_gemma":[0.9995278,0.00012828529,0.00006507777,0.00022095889,0.000030013724,0.000027840468],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011434879,0.00009239856,0.00014309303,0.000077601835,0.000029910218,0.000008456396,0.00034265997,0.000073446834,0.007815174],"category_scores_gemma":[0.000060859154,0.00006144633,0.00006354717,0.0005731668,0.00008001487,0.00006600868,0.000027778886,0.00019952685,0.000014914843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003015783,0.0017071826,0.22730221,0.00016795486,0.00030982832,0.00012529071,0.013916293,0.000573587,0.7385361,0.013818264,0.0025573012,0.0006844049],"study_design_scores_gemma":[0.00035938204,0.000033439672,0.0042868727,0.000017480092,0.00008412005,0.000014761185,0.0066777826,0.0019135029,0.9859903,0.0004556658,0.000023336564,0.0001433418],"about_ca_topic_score_codex":0.00063514325,"about_ca_topic_score_gemma":0.000027217759,"teacher_disagreement_score":0.29927573,"about_ca_system_score_codex":0.00002328635,"about_ca_system_score_gemma":0.00002355684,"threshold_uncertainty_score":0.9930918},"labels":[],"label_agreement":null},{"id":"W2101047548","doi":"10.1093/mnras/stv1608","title":"A hybrid ensemble learning approach to star–galaxy classification","year":2015,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Institut national des sciences de l'Univers; Natural Sciences and Engineering Research Council of Canada; Office of Science; University of Illinois at Urbana-Champaign; Centre National de la Recherche Scientifique; Canadian Space Agency; Alfred P. Sloan Foundation; U.S. Department of Energy; National Science Foundation","keywords":"Random forest; Machine learning; Artificial intelligence; Bayesian probability; Galaxy; Ensemble learning; Classifier (UML); Supervised learning; Physics; Pattern recognition (psychology); Computer science; Artificial neural network; Astrophysics","score_opus":0.025646374180644662,"score_gpt":0.24188916262170657,"score_spread":0.2162427884410619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101047548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821512,0.00010816385,0.0006025165,0.00026221364,0.0000450455,0.00007546884,0.000014742873,0.00005133838,0.016689299],"genre_scores_gemma":[0.9871033,9.264202e-8,0.010184583,0.000034653305,0.00015713622,0.000017494604,0.000021134934,0.000019979263,0.0024616404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987429,0.00003060383,0.00029788594,0.00032918376,0.00028387608,0.0003155455],"domain_scores_gemma":[0.99908495,0.00007517319,0.0002125199,0.00035727237,0.000074444695,0.00019566852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023142423,0.00017430983,0.00028844338,0.000017813498,0.000120012715,0.000046255027,0.000544109,0.00009084598,0.00007846384],"category_scores_gemma":[0.00014635707,0.0001343843,0.000331679,0.00016225399,0.00010803351,0.00006191878,0.00022003551,0.0003457084,0.00002529744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007990921,0.00027601072,0.031656798,0.000054235657,0.00022684441,4.780346e-8,0.00075194606,0.9569239,0.0027369906,0.000057721572,0.006721097,0.00051448826],"study_design_scores_gemma":[0.00078525086,0.00007486458,0.017208673,0.000018639785,0.0003255556,3.7051503e-8,0.004163017,0.89808506,0.07449728,0.00004093913,0.004451642,0.0003490141],"about_ca_topic_score_codex":0.00019652852,"about_ca_topic_score_gemma":0.0000021545152,"teacher_disagreement_score":0.0717603,"about_ca_system_score_codex":0.00026417375,"about_ca_system_score_gemma":0.00006937073,"threshold_uncertainty_score":0.54800326},"labels":[],"label_agreement":null},{"id":"W2101409616","doi":"10.1039/c5ay00377f","title":"Application of vibrational spectroscopy techniques to non-destructively monitor plant health and development","year":2015,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"European Regional Development Fund; Centre for Global Eco-Innovation","keywords":"Spectroscopy; Analytical Chemistry (journal); Plant growth; Materials science; Chemistry; Environmental chemistry; Physics; Botany; Biology; Astronomy","score_opus":0.054396488045437105,"score_gpt":0.41815252081121707,"score_spread":0.36375603276577995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101409616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023204098,0.00012012343,0.9733053,0.00044841712,0.00001370436,0.00009049342,0.000011990226,0.000047447393,0.0027584345],"genre_scores_gemma":[0.07050803,0.000012550609,0.92911685,0.00016838642,0.00008889066,0.000032067597,0.000022089982,0.000011614369,0.000039531908],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988709,0.000036307138,0.0003619627,0.00029059936,0.00023527093,0.00020497412],"domain_scores_gemma":[0.9990649,0.00016084373,0.00012274523,0.00018145355,0.000098791636,0.00037125734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046537176,0.00013706,0.00034886328,0.00015692004,0.00005196594,0.000018477898,0.00013267214,0.00008148689,0.000042362117],"category_scores_gemma":[0.0002562665,0.00012283528,0.000032942007,0.0004057804,0.00006828353,0.00005357814,0.00006652106,0.000120924706,0.0000055387345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000433922,0.0005210994,0.036043793,0.00038471504,0.00051212875,0.000003537247,0.0016778883,0.00001958086,0.80460817,0.016864432,0.0023569432,0.13657378],"study_design_scores_gemma":[0.00012980244,0.0000975245,0.0012475263,0.000013181151,0.000030539788,0.0000058936935,0.00038401643,0.001306168,0.99347997,0.0014663242,0.0017016709,0.00013741227],"about_ca_topic_score_codex":0.0000762624,"about_ca_topic_score_gemma":0.0000022920417,"teacher_disagreement_score":0.18887176,"about_ca_system_score_codex":0.00016626257,"about_ca_system_score_gemma":0.00023973119,"threshold_uncertainty_score":0.5009077},"labels":[],"label_agreement":null},{"id":"W2101702257","doi":"10.1016/j.saa.2012.09.064","title":"Univariate and multivariate molecular spectral analyses of lipid related molecular structural components in relation to nutrient profile in feed and food mixtures","year":2012,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Sciences and Engineering Research Council of Canada; Beef Cattle Research Council","keywords":"Degree of unsaturation; Chemistry; Fourier transform infrared spectroscopy; Spectroscopy; Principal component analysis; Molecule; Hordeum vulgare; Analytical Chemistry (journal); Crystallography; Food science; Chromatography; Organic chemistry; Botany; Poaceae; Biology; Chemical engineering; Mathematics","score_opus":0.016675982402549375,"score_gpt":0.2865328735042769,"score_spread":0.26985689110172756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101702257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99118143,0.0044035264,0.0018777093,0.00047260046,0.00007370352,0.00058830675,0.0000558133,0.00007533845,0.0012715415],"genre_scores_gemma":[0.9926662,0.00022374051,0.006626225,0.00014097236,0.000048571248,0.000044513377,0.00013501823,0.000094779236,0.000020022017],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99573237,0.00021413834,0.0010634534,0.0011414831,0.00058524986,0.0012632954],"domain_scores_gemma":[0.99830097,0.0000752742,0.00039216806,0.00062643335,0.00007078369,0.0005343453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003336719,0.00080531265,0.0011466115,0.0011445208,0.00012325826,0.000115241906,0.0003362834,0.0004140734,0.00012853176],"category_scores_gemma":[0.00017667304,0.0008024136,0.00021836627,0.0017848837,0.00028032076,0.00027723945,0.00033139114,0.00068257994,0.0000029943149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032094138,0.00029597717,0.010734967,0.00015338768,0.00040724815,0.00008799116,0.0005063228,0.000021425223,0.9814553,0.005989062,0.0000057941406,0.000021582317],"study_design_scores_gemma":[0.0026844973,0.00036423415,0.025653055,0.00014769565,0.0003673718,0.00010563122,0.00017922447,0.0005924599,0.96678686,0.002293099,0.000028272922,0.000797607],"about_ca_topic_score_codex":0.00029601628,"about_ca_topic_score_gemma":0.00002069996,"teacher_disagreement_score":0.014918087,"about_ca_system_score_codex":0.0002312516,"about_ca_system_score_gemma":0.00006633966,"threshold_uncertainty_score":0.9994427},"labels":[],"label_agreement":null},{"id":"W2101872957","doi":"10.2307/3316072","title":"Dimensionality reduction approach to multivariate prediction","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Principal component analysis; Dimensionality reduction; Principal component regression; Multivariate statistics; Statistics; Rank (graph theory); Regression; Mathematics; Curse of dimensionality; Regression analysis; Latent variable; Bayesian multivariate linear regression; Computer science; Artificial intelligence","score_opus":0.02999788938464854,"score_gpt":0.2599478739927343,"score_spread":0.22994998460808574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101872957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.429167,0.000743949,0.51367706,0.00071762566,0.0010768528,0.00012056102,0.0016061782,0.000036272635,0.052854512],"genre_scores_gemma":[0.9655102,0.0000231052,0.032550216,0.000056450106,0.00036773385,0.0000012782402,0.000039671686,0.000013234262,0.0014381018],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99914724,0.00001269734,0.00032357106,0.00011414699,0.0001846139,0.00021775374],"domain_scores_gemma":[0.9987898,0.000035301215,0.00016207369,0.00011978713,0.00028722326,0.0006058294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014482836,0.000094759926,0.00017232011,0.00023796958,0.00013907341,0.00003760114,0.0001243792,0.00006828493,0.00074972596],"category_scores_gemma":[0.00029627056,0.00009154945,0.000042300344,0.0003550356,0.00004666353,0.00007816024,0.000005472487,0.00020772564,0.000011491995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007468903,0.0011424854,0.12162443,0.00050798565,0.0024669983,0.0013816374,0.0058919857,0.019485766,0.18505414,0.06266615,0.56179297,0.037238557],"study_design_scores_gemma":[0.010059794,0.0015147448,0.2481848,0.0005944147,0.005502707,0.01729355,0.018973606,0.018786045,0.1370872,0.053383544,0.48458284,0.0040367586],"about_ca_topic_score_codex":0.0028928574,"about_ca_topic_score_gemma":0.00038475668,"teacher_disagreement_score":0.5363432,"about_ca_system_score_codex":0.0003106735,"about_ca_system_score_gemma":0.0004174296,"threshold_uncertainty_score":0.82089734},"labels":[],"label_agreement":null},{"id":"W2102731131","doi":"10.1115/imece2007-42323","title":"Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion","year":2007,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Fast Fourier transform; Artificial neural network; Fault (geology); Accelerometer; Feature (linguistics); Computer vision; Feature extraction; Support vector machine; Pattern recognition (psychology); Fuzzy logic; Frequency domain; Sensor fusion; Algorithm","score_opus":0.03850630179644528,"score_gpt":0.30950733734848607,"score_spread":0.2710010355520408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102731131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9170469,0.00012666073,0.00030009582,0.00015096995,0.00006827003,0.00003938281,0.00002578709,0.00009488162,0.08214703],"genre_scores_gemma":[0.99577177,0.00008845996,0.0013470773,0.00024199263,0.00034633288,0.00000348848,0.000044014796,0.000020886293,0.0021359972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987661,0.0000117018035,0.0003611549,0.00028445284,0.00029439977,0.0002821389],"domain_scores_gemma":[0.999041,0.00031103255,0.00013635347,0.00035264177,0.000057075333,0.00010188891],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013484905,0.00016905878,0.00027894473,0.000098256125,0.000067106375,0.000016549453,0.00021364362,0.00022435725,0.0075876093],"category_scores_gemma":[0.00028417358,0.0001408866,0.000111501,0.0005988551,0.00005001727,0.00012993554,0.00006322202,0.0002602999,0.000019152303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047295855,0.0016586649,0.3575131,0.000098003875,0.00024291003,0.0001045804,0.0003476166,0.000040386305,0.6129784,0.0010283923,0.0076739504,0.017841028],"study_design_scores_gemma":[0.00075520796,0.00007595535,0.0009203919,0.0000070475976,0.00011113915,0.00000867455,0.00041311723,0.00004795088,0.9927475,0.00014811002,0.0045966157,0.00016828543],"about_ca_topic_score_codex":0.0012960568,"about_ca_topic_score_gemma":0.0000783538,"teacher_disagreement_score":0.3797691,"about_ca_system_score_codex":0.000038856208,"about_ca_system_score_gemma":0.000021738813,"threshold_uncertainty_score":0.9933196},"labels":[],"label_agreement":null},{"id":"W2104964414","doi":"10.1109/icpr.1992.202153","title":"VLSI architecture for parallel concentration-contour approach","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Very-large-scale integration; Diagonal; Uniprocessor system; Computer science; Transformation (genetics); Methods of contour integration; Projection (relational algebra); Artificial intelligence; Object (grammar); Pattern recognition (psychology); Algorithm; Mathematics; Parallel computing; Geometry; Mathematical analysis; Chemistry","score_opus":0.018868262459072093,"score_gpt":0.2656773273040242,"score_spread":0.24680906484495213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104964414","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011772551,0.00054207747,0.3189181,0.00014748365,0.000026238125,0.00010751836,0.000010791022,0.000105549676,0.66836965],"genre_scores_gemma":[0.9226288,0.000014237699,0.049093574,0.00030116475,0.000108773136,0.000046424324,0.00004808403,0.000016006492,0.027742935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992656,0.0000061448936,0.00014801872,0.0002304242,0.00010344695,0.0002463604],"domain_scores_gemma":[0.99956805,0.00008304186,0.000046698264,0.00019081209,0.000038922157,0.000072505456],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000053252556,0.00012425805,0.00016733358,0.000024425235,0.00008286245,0.00003455013,0.00011480815,0.00009485203,0.0024820063],"category_scores_gemma":[0.00011968976,0.000100689984,0.00010873732,0.00015633291,0.00003221522,0.000035505975,0.0000056780705,0.000095956355,0.000010444592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028064995,0.0015911619,0.021443576,0.0008875424,0.0015515474,0.000007710016,0.001180326,0.002820044,0.34179968,0.5288298,0.09528867,0.004319293],"study_design_scores_gemma":[0.0020603982,0.000034800836,0.000058072656,0.0000043447867,0.0002117287,0.000021126914,0.0011790368,0.0011273496,0.8934307,0.00770363,0.09368974,0.0004790365],"about_ca_topic_score_codex":0.000010995007,"about_ca_topic_score_gemma":0.0000040783575,"teacher_disagreement_score":0.91085625,"about_ca_system_score_codex":0.00002933306,"about_ca_system_score_gemma":0.000038748105,"threshold_uncertainty_score":0.99842983},"labels":[],"label_agreement":null},{"id":"W2105354819","doi":"","title":"Extreme Components Analysis","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Principal component analysis; Probabilistic logic; Minor (academic); Computer science; Statistical model; Regular polygon; Algorithm; Mathematics; Artificial intelligence","score_opus":0.04715183877837796,"score_gpt":0.2753054889149741,"score_spread":0.2281536501365961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105354819","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36632973,0.00021970717,0.0037560463,0.000026478363,0.000011294429,0.000006782384,0.0000022753527,0.00007801785,0.62956965],"genre_scores_gemma":[0.96334267,0.000014072472,0.0011098121,0.00008116309,0.000013869011,0.0000021975175,0.000014808753,0.0000057653747,0.035415642],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993754,0.0000062483314,0.00012988748,0.00017894027,0.00014060015,0.0001689257],"domain_scores_gemma":[0.99958664,0.000035356974,0.00003552692,0.00025066707,0.000023444656,0.000068392255],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000044232784,0.0000878681,0.00018512463,0.00016610751,0.000050876773,0.0000207631,0.000104328574,0.000049243175,0.058814522],"category_scores_gemma":[0.000042730575,0.000075115626,0.00016338442,0.0012165664,0.000020903573,0.00003467335,0.000010824292,0.00006426864,0.00014713993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010829639,0.00042396388,0.5932005,0.000029024817,0.004525048,0.000016645452,0.000056283894,0.00008461298,0.38411328,0.013780873,0.0034880566,0.00027083483],"study_design_scores_gemma":[0.00035124688,0.0000051013685,0.004204745,0.0000012141841,0.0023622692,0.000003857119,0.00026849553,0.00047520798,0.970319,0.0005668177,0.021162752,0.0002793063],"about_ca_topic_score_codex":0.00005805902,"about_ca_topic_score_gemma":0.000010433984,"teacher_disagreement_score":0.59701294,"about_ca_system_score_codex":0.000031269796,"about_ca_system_score_gemma":0.000007647128,"threshold_uncertainty_score":0.94204587},"labels":[],"label_agreement":null},{"id":"W2105490013","doi":"10.1016/j.patcog.2006.07.007","title":"Real-time detection of steam in video images","year":2006,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Image processing; Complex wavelet transform; Support vector machine; Frame (networking); Wavelet transform; Wavelet; Image (mathematics); Discrete wavelet transform","score_opus":0.010838163529640545,"score_gpt":0.2419944651734681,"score_spread":0.23115630164382756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105490013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97346485,0.00006882417,0.00062786747,0.000016207327,0.00001907118,0.000032212913,0.0000414533,0.00005420873,0.025675286],"genre_scores_gemma":[0.9990891,0.00007347451,0.00008265619,0.000009197356,0.00011381982,0.00001622556,0.0001256399,0.000013546322,0.0004763431],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992778,0.000014646958,0.0002608363,0.00017806955,0.000123909,0.00014478067],"domain_scores_gemma":[0.9996221,0.000064616026,0.00013081526,0.000113595896,0.000050204693,0.000018662007],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000073974305,0.000096279044,0.00016444163,0.00018050605,0.000022027107,0.000011161355,0.000057261077,0.000087005195,0.0020863393],"category_scores_gemma":[0.000027698718,0.00010238838,0.0000616944,0.00028731095,0.000024563049,0.00009444577,0.000013104283,0.000092100476,0.00015665923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015466925,0.00010538747,0.012484614,0.00006907583,0.000011363708,0.0000032150037,0.00001171113,0.0000024205715,0.9476635,1.1960478e-7,0.000073938536,0.039559178],"study_design_scores_gemma":[0.00035502386,0.000018959949,0.013248044,0.000038023045,0.000051085834,0.00000467096,0.00006650211,0.00011080121,0.9854581,0.0005190394,0.00001718889,0.000112606285],"about_ca_topic_score_codex":0.0017874824,"about_ca_topic_score_gemma":0.00011037255,"teacher_disagreement_score":0.039446574,"about_ca_system_score_codex":0.000069494134,"about_ca_system_score_gemma":0.000007327936,"threshold_uncertainty_score":0.9988259},"labels":[],"label_agreement":null},{"id":"W2105565638","doi":"10.1016/j.aca.2014.06.035","title":"Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil","year":2014,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Council for Scientific Research; Université Libanaise; Agence Universitaire de la Francophonie","keywords":"Olive oil; Chemistry; Fluorescence spectroscopy; Sorption; Polypropylene; Plastic packaging; Food packaging; Fluorescence; Food science; Organic chemistry; Composite material; Adsorption; Materials science","score_opus":0.017316313875292876,"score_gpt":0.2692857453523473,"score_spread":0.2519694314770544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105565638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966833,0.000012010162,0.0011465291,0.00038279206,0.000024012068,0.000102325204,0.000018985067,0.00007608057,0.0015539894],"genre_scores_gemma":[0.9990139,0.000032065116,0.00039342605,0.0000894578,0.00015006245,0.000022341199,0.000040298808,0.00003467208,0.00022374821],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99764216,0.0000742709,0.0004371876,0.0006871158,0.0006403381,0.00051890896],"domain_scores_gemma":[0.9978335,0.000754488,0.00026409503,0.0007750278,0.00009534708,0.00027757697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002593622,0.00040135506,0.00072450575,0.00042101863,0.0003464326,0.0002378551,0.0005747486,0.000089144974,0.00040678077],"category_scores_gemma":[0.00024691242,0.00028159982,0.00013639499,0.0012753876,0.000114230585,0.00015479598,0.00022407819,0.0005156163,0.000030716004],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047641536,0.00090502953,0.8561516,0.00010113774,0.014832146,0.000008188535,0.0028334246,0.00013864716,0.124055386,0.000030918214,0.00014599906,0.00032112605],"study_design_scores_gemma":[0.0055779107,0.0024581007,0.59042156,0.0002549697,0.05075013,0.00004303665,0.02102516,0.121393815,0.20513234,0.00013559005,0.00026449928,0.0025428913],"about_ca_topic_score_codex":0.00015664911,"about_ca_topic_score_gemma":0.0002457946,"teacher_disagreement_score":0.26573002,"about_ca_system_score_codex":0.00017178603,"about_ca_system_score_gemma":0.000028619881,"threshold_uncertainty_score":0.99996364},"labels":[],"label_agreement":null},{"id":"W2106567082","doi":"10.11606/t.3.2008.tde-30092008-150619","title":"Oxicorte: estudo da transferência de calor e modelamento por redes neurais artificiais de variáveis do processo.","year":2008,"lang":"pt","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Humanities; Philosophy","score_opus":0.02811432431700946,"score_gpt":0.308009561005875,"score_spread":0.27989523668886557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106567082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9217549,0.0044675185,0.015352694,0.00032002415,0.0002379849,0.0006067403,0.00027416658,0.00049868354,0.056487292],"genre_scores_gemma":[0.89210194,0.0029255175,0.0009564179,0.00034939058,0.00034575508,0.0002872944,0.0014622483,0.00025089167,0.101320565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99332225,0.00008456419,0.0016528425,0.0018717747,0.0012178972,0.0018506568],"domain_scores_gemma":[0.99668896,0.00028204368,0.00059860863,0.001097362,0.0005722845,0.0007607148],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00031940895,0.0015040182,0.001675381,0.00068013114,0.00078469847,0.00050588144,0.0011992394,0.0018660491,0.0066194707],"category_scores_gemma":[0.0003778728,0.0015472929,0.0009190833,0.0022739633,0.00019665573,0.00033950375,0.000055291595,0.001605828,0.00016715721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028765136,0.009211265,0.028376414,0.008488836,0.0069145686,0.0013541264,0.039373364,0.002540411,0.8671745,0.0015505472,0.019974925,0.012164524],"study_design_scores_gemma":[0.0011872229,0.00025447895,0.0010881207,0.00028368877,0.003774034,0.00017700659,0.016864272,0.012521597,0.96014994,0.0008222487,0.0006225019,0.0022548882],"about_ca_topic_score_codex":0.0022856786,"about_ca_topic_score_gemma":0.00044057466,"teacher_disagreement_score":0.09297543,"about_ca_system_score_codex":0.001013969,"about_ca_system_score_gemma":0.001683562,"threshold_uncertainty_score":0.9997709},"labels":[],"label_agreement":null},{"id":"W2107842421","doi":"10.1016/j.aca.2005.09.041","title":"Approaching the direct exponential curve resolution algorithm from a maximum likelihood perspective","year":2005,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dow Chemical Company","keywords":"Exponential function; Chemometrics; Algorithm; Chemistry; Bilinear interpolation; Resolution (logic); Statistics; Computer science; Mathematics; Artificial intelligence; Chromatography","score_opus":0.014115148569166146,"score_gpt":0.25990313394966863,"score_spread":0.24578798538050248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107842421","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45542058,0.0017208834,0.0044937874,0.018308694,0.00023354066,0.00041652605,0.00051077496,0.0009160575,0.51797915],"genre_scores_gemma":[0.9929604,0.00017350973,0.003930932,0.00028933346,0.0014486647,0.000022823519,0.00010335244,0.000045808967,0.0010251574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977579,0.000060234055,0.0003839822,0.00070975436,0.00047573764,0.00061240717],"domain_scores_gemma":[0.9984728,0.00027814147,0.00020236183,0.0007771272,0.00010066722,0.00016887984],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022152228,0.00034401147,0.00042455687,0.00016064913,0.00040107637,0.00016699344,0.0006521059,0.00019429575,0.0024020905],"category_scores_gemma":[0.00025532462,0.00026128654,0.0003632071,0.00066056807,0.0001687978,0.00024564067,0.00017995002,0.00063398364,0.00011836688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092957146,0.005790104,0.004335203,0.00010634309,0.013591965,0.00006859134,0.015101057,0.000054828473,0.8448584,0.0040412676,0.060354855,0.050767817],"study_design_scores_gemma":[0.004071518,0.00017308655,0.0031024767,0.0001255735,0.007190643,0.000073148374,0.018330358,0.24379238,0.6464172,0.021287236,0.052482434,0.0029539468],"about_ca_topic_score_codex":0.0005722314,"about_ca_topic_score_gemma":0.000069725655,"teacher_disagreement_score":0.53753984,"about_ca_system_score_codex":0.00043157514,"about_ca_system_score_gemma":0.000082426704,"threshold_uncertainty_score":0.9999839},"labels":[],"label_agreement":null},{"id":"W2107995835","doi":"10.1109/tbme.2006.872813","title":"Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-Means analysis","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Humber River Regional Hospital; University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Wavelet; Artificial intelligence; Fuzzy logic; Computer science; Matrix (chemical analysis); Materials science","score_opus":0.01321386461102538,"score_gpt":0.25501126922920103,"score_spread":0.24179740461817564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107995835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47462663,0.00005489762,0.52493954,0.000013773063,0.000030405283,0.000050639766,0.00014405072,0.00009433463,0.00004570521],"genre_scores_gemma":[0.995922,0.000029590203,0.0038921149,0.000005025943,0.000054319622,0.000017381068,0.000025439871,0.000022290924,0.000031824362],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873203,0.000007950935,0.00038978425,0.0002774784,0.00026063054,0.00033213577],"domain_scores_gemma":[0.99947447,0.00019494796,0.000038403243,0.00015039944,0.00003895888,0.00010281491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098064294,0.00018151152,0.00039871287,0.0009930371,0.000050242616,0.000016496817,0.00009362625,0.0001706907,0.00013303792],"category_scores_gemma":[0.000009250049,0.00019041845,0.00016385119,0.001530528,0.00007495179,0.000063676656,0.000001653275,0.00022395562,6.321924e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010409159,0.0005641817,0.0004023917,0.00034899655,0.0009195086,0.000029292312,0.00015641298,0.25815946,0.7382056,0.0000662796,0.000007722341,0.0010360308],"study_design_scores_gemma":[0.00070101034,0.000057931895,0.000467856,0.000058877675,0.00037445172,0.00000649093,0.000099055695,0.64406055,0.35399333,0.0000107316555,0.0000074137783,0.00016232047],"about_ca_topic_score_codex":0.000346718,"about_ca_topic_score_gemma":0.000036687674,"teacher_disagreement_score":0.52129537,"about_ca_system_score_codex":0.0001363375,"about_ca_system_score_gemma":0.00003847863,"threshold_uncertainty_score":0.7765039},"labels":[],"label_agreement":null},{"id":"W2108721029","doi":"10.1109/nafips.1999.781826","title":"Fuzzy methods for automated inspection of food products","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Visual inspection; Fuzzy logic; Artificial intelligence; Machine vision; Automation; Quality (philosophy); Grading (engineering); Fuzzy control system; Machine learning; Engineering","score_opus":0.03481461617247557,"score_gpt":0.3672893132210652,"score_spread":0.3324746970485896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108721029","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23426686,0.0011529463,0.05471838,0.00015899837,0.00020317265,0.00024026085,0.000016035972,0.0027192628,0.7065241],"genre_scores_gemma":[0.8234018,0.000010933411,0.17449278,0.000017728951,0.00003365333,0.000020167217,0.000007039618,0.00001259196,0.0020033137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993853,0.000014783475,0.00019259841,0.00019413784,0.00006651325,0.00014669237],"domain_scores_gemma":[0.99944323,0.00009851665,0.000088980094,0.00020958834,0.00012984108,0.00002985554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017154138,0.00009038614,0.00018644733,0.00009103142,0.00004680317,0.000008364116,0.00007278338,0.00007632353,0.0003763298],"category_scores_gemma":[0.0007223215,0.00007821018,0.000067139874,0.00059708493,0.000029379898,0.000046491612,0.000007703291,0.000050353225,0.0000030539786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014855149,0.00014505208,0.0003594792,0.0001915885,0.00016829744,7.981089e-8,0.000043403576,0.0000075588982,0.9754903,0.020007554,0.0029398375,0.00063204917],"study_design_scores_gemma":[0.00028500467,0.00005851986,0.00003365201,0.0000024222963,0.00009771322,0.000003556275,0.00016976228,0.00035832846,0.9933669,0.0022615418,0.003270537,0.00009206616],"about_ca_topic_score_codex":0.000008842486,"about_ca_topic_score_gemma":0.0000016278206,"teacher_disagreement_score":0.70452076,"about_ca_system_score_codex":0.000038028556,"about_ca_system_score_gemma":0.000040717034,"threshold_uncertainty_score":0.41205475},"labels":[],"label_agreement":null},{"id":"W2110190618","doi":"10.1139/x08-099","title":"On the selection of samples for multivariate regression analysis: application to near-infrared (NIR) calibration models for the prediction of pulp yield in <i>Eucalyptus nitens</i>","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eucalyptus nitens; Calibration; Cross-validation; Model selection; Selection (genetic algorithm); Mathematics; Near-infrared spectroscopy; Computer science; Statistics; Artificial intelligence; Eucalyptus; Biology","score_opus":0.10167961916634134,"score_gpt":0.3395049496663587,"score_spread":0.23782533050001736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110190618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85113466,0.00025027824,0.14652576,0.0010375232,0.000029359873,0.00054306537,0.00020713928,0.0000041707813,0.00026806994],"genre_scores_gemma":[0.9988532,0.000041545052,0.00064466684,0.00003532458,0.00009589321,0.00010319242,0.000028516275,0.000013118011,0.0001845509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985432,0.000056742665,0.0005220936,0.00016345877,0.00041205253,0.00030245568],"domain_scores_gemma":[0.9967258,0.0016708958,0.00029361073,0.00025608743,0.0008788139,0.00017477619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010125393,0.000099817276,0.00027064813,0.00080577785,0.00039041636,0.000034311262,0.00034398073,0.00012392773,0.0000611989],"category_scores_gemma":[0.0016631199,0.000062509025,0.00019322128,0.0020963652,0.00014112768,0.00012860287,0.000011193096,0.000322993,2.3944293e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00320494,0.00037993418,0.29651135,0.00035159884,0.0019249611,0.00000684727,0.0045379424,0.512275,0.1478757,0.009842727,0.020352999,0.00273601],"study_design_scores_gemma":[0.0014257272,0.0008639789,0.05661056,0.00027313808,0.00062889187,0.000020090458,0.0019660168,0.5963913,0.3269567,0.013537171,0.0011148095,0.0002115877],"about_ca_topic_score_codex":0.010532601,"about_ca_topic_score_gemma":0.023129916,"teacher_disagreement_score":0.23990078,"about_ca_system_score_codex":0.00024282829,"about_ca_system_score_gemma":0.000770948,"threshold_uncertainty_score":0.9960563},"labels":[],"label_agreement":null},{"id":"W2112344487","doi":"10.1109/igarss.2003.1295496","title":"Spectroradiometric analysis in a hyperspectral use perspective to discriminate between forest species","year":2004,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Balsam; Hyperspectral imaging; Forestry; Vegetation (pathology); Herbaceous plant; Black spruce; Remote sensing; Environmental science; Geography; Taiga; Ecology; Botany; Biology","score_opus":0.03932799914010005,"score_gpt":0.302710228450966,"score_spread":0.26338222931086597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112344487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93366766,0.00020673175,0.0066290284,0.001251831,0.000015684818,0.000104313054,0.000057100384,0.00015934445,0.0579083],"genre_scores_gemma":[0.9888676,0.000048692305,0.0051800865,0.000106893676,0.00020368805,0.000019681615,0.000039940704,0.000035006495,0.005498445],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997382,0.000015106886,0.00046139036,0.00083361554,0.00049227226,0.00081560086],"domain_scores_gemma":[0.99862885,0.00020472026,0.0001021001,0.00061121106,0.00012860398,0.00032452538],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013307434,0.00039365925,0.0008502142,0.0045779925,0.00010770982,0.00020219809,0.0005007058,0.00015354862,0.0019421304],"category_scores_gemma":[0.00046619476,0.00035711812,0.00047172862,0.01793603,0.00014202666,0.0003448922,0.000116209325,0.00037921488,0.000108286156],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007060691,0.0004805654,0.91511846,0.00002373886,0.0020342537,0.000120220975,0.001604881,0.0025373984,0.016243743,0.06159968,0.00011133016,0.000055131037],"study_design_scores_gemma":[0.0015800269,0.00016884775,0.7452694,0.000018158773,0.0032854308,0.000010374486,0.01862626,0.00004626506,0.22295488,0.006782921,0.00020200954,0.0010553952],"about_ca_topic_score_codex":0.006378195,"about_ca_topic_score_gemma":0.0036207496,"teacher_disagreement_score":0.20671113,"about_ca_system_score_codex":0.0020753832,"about_ca_system_score_gemma":0.00008434559,"threshold_uncertainty_score":0.99988806},"labels":[],"label_agreement":null},{"id":"W2112662632","doi":"10.1002/wics.198","title":"STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling","year":2012,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Principal component analysis; Multidimensional scaling; Linear discriminant analysis; Metric (unit); Similarity (geometry); Computer science; Mathematics; Set (abstract data type); Contingency table; Algorithm; Data mining; Statistics; Artificial intelligence; Image (mathematics)","score_opus":0.05564053156836534,"score_gpt":0.3654869761204985,"score_spread":0.30984644455213317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112662632","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000048844162,0.73585886,0.25907296,0.000011102048,0.00010003269,0.00045406827,0.00427055,0.00005324515,0.00013031183],"genre_scores_gemma":[0.00021727172,0.78997046,0.20208095,0.000018862129,0.00016591672,0.00017836942,0.0070218816,0.000090712325,0.00025558032],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946965,0.00022595799,0.0023465427,0.0012261682,0.0007514266,0.0007534018],"domain_scores_gemma":[0.994479,0.0026435298,0.0015607092,0.00052784407,0.00023237284,0.0005565382],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068694813,0.0011685281,0.0042837625,0.0011509403,0.0006213439,0.00019643537,0.00040519738,0.0003478951,0.0019108874],"category_scores_gemma":[0.00018751957,0.00096597243,0.0006545908,0.0018163731,0.00039871872,0.00024050484,0.0014029172,0.0008360066,0.00015074639],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028077804,0.00044813732,0.00097050914,0.027105592,0.00538931,0.000057357905,0.0002491647,0.0007498507,0.0000017870904,0.0014932458,0.001323459,0.96218354],"study_design_scores_gemma":[0.001695022,0.0002375522,0.0020686572,0.013092664,0.10163045,0.0005579855,0.0003020626,0.121084325,0.0000046700898,0.0037677733,0.75077575,0.004783091],"about_ca_topic_score_codex":0.00007053007,"about_ca_topic_score_gemma":0.00006205119,"teacher_disagreement_score":0.95740044,"about_ca_system_score_codex":0.00041090327,"about_ca_system_score_gemma":0.00012338434,"threshold_uncertainty_score":0.9992791},"labels":[],"label_agreement":null},{"id":"W2112671237","doi":"10.1016/j.jfoodeng.2010.03.001","title":"Categorization of pork quality using Gabor filter-based hyperspectral imaging technology","year":2010,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Principal component analysis; Hyperspectral imaging; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Gabor filter; Mathematics; Cluster analysis; Filter (signal processing); Texture (cosmology); Feature (linguistics); Computer science; Computer vision; Feature extraction; Image (mathematics)","score_opus":0.013542452395704662,"score_gpt":0.2709247598492669,"score_spread":0.2573823074535622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112671237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96240973,0.00038931996,0.036761034,0.00009589091,0.00014899817,0.000011315184,0.0000042279285,0.000031934956,0.00014751579],"genre_scores_gemma":[0.98115546,0.0000047323365,0.018626649,0.0000055426026,0.00018368904,3.9212009e-7,0.0000011774933,0.000016935928,0.000005407134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991246,0.0000031786126,0.00043299398,0.000088012246,0.00017745516,0.00017377989],"domain_scores_gemma":[0.99923086,0.00005020826,0.00034592013,0.00015698613,0.00016049817,0.000055500965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015081093,0.00011292748,0.0002763977,0.0004245998,0.000025773823,0.000015183189,0.00018559603,0.00009635436,0.00012022959],"category_scores_gemma":[0.0002734968,0.0001097143,0.000117405536,0.0005650301,0.000029533172,0.000108319495,0.000014162364,0.0004288968,2.7873637e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010190305,0.000043222833,0.005686521,0.00007130313,0.00006691883,0.000005436683,0.000024972773,0.006330323,0.9871941,0.000411248,0.0000038861217,0.0001518526],"study_design_scores_gemma":[0.00034095475,0.000033215132,0.00038479528,0.000028700044,0.000095175266,0.000071897295,0.00014509918,0.007899266,0.99073863,0.00010895927,0.000043619217,0.00010969174],"about_ca_topic_score_codex":0.000007598961,"about_ca_topic_score_gemma":0.00000102016,"teacher_disagreement_score":0.01874571,"about_ca_system_score_codex":0.00006513026,"about_ca_system_score_gemma":0.000070247086,"threshold_uncertainty_score":0.44740194},"labels":[],"label_agreement":null},{"id":"W2113566893","doi":"10.6090/jarq.43.157","title":"Relative Discrimination of Planophile and Erectophile Wheat Types using Multi-temporal Spectrum Measurements","year":2009,"lang":"en","type":"article","venue":"Japan Agricultural Research Quarterly JARQ","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Spectrum (functional analysis); Mathematics; Environmental science; Physics","score_opus":0.12407805692613716,"score_gpt":0.3698015814902149,"score_spread":0.24572352456407776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113566893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949327,0.0006765369,0.0000751568,0.00043865127,0.000025966487,0.00015272932,0.000024523992,0.000042672036,0.0036310446],"genre_scores_gemma":[0.99725115,0.000024318368,0.00083013525,0.000008168377,0.00013621934,0.0000072483804,0.00006662659,0.000010004357,0.0016661199],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978866,0.00007113092,0.00034651204,0.00040340645,0.00080408657,0.00048825378],"domain_scores_gemma":[0.99907,0.00012808228,0.00014030207,0.000210584,0.0002841375,0.00016689222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033968972,0.00021675687,0.0003393313,0.00023842337,0.00024633814,0.00009041479,0.00022358305,0.0001506571,0.00027648185],"category_scores_gemma":[0.00013499174,0.00014918738,0.000096840224,0.0006559855,0.00014788363,0.00042053097,0.00002710904,0.0004186411,0.000016090116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008146183,0.00028829725,0.009242145,0.000085272826,0.00011316097,0.000004085699,0.001952706,0.000002986005,0.9861896,0.00038130445,0.00019639745,0.0014625806],"study_design_scores_gemma":[0.0017899114,0.0014351051,0.24416918,0.00025971705,0.00020605062,0.00004254419,0.009793791,0.0004603587,0.73750794,0.0036809053,0.00006334735,0.0005911657],"about_ca_topic_score_codex":0.00044903439,"about_ca_topic_score_gemma":0.00010004296,"teacher_disagreement_score":0.24868168,"about_ca_system_score_codex":0.00023281651,"about_ca_system_score_gemma":0.000040808292,"threshold_uncertainty_score":0.60836846},"labels":[],"label_agreement":null},{"id":"W2113716593","doi":"10.1139/s08-037","title":"Soil type effects on petroleum contamination characterization using ultraviolet induced fluorescence excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC)","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Petroleum; Soil water; Fluorescence; Hydrocarbon; Matrix (chemical analysis); Environmental chemistry; Chemistry; Soil contamination; Contamination; Ultraviolet; Environmental science; Porosity; Analytical Chemistry (journal); Soil science; Materials science; Chromatography; Ecology; Organic chemistry","score_opus":0.0110010296037555,"score_gpt":0.22763929246265677,"score_spread":0.21663826285890125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113716593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983216,0.00015071503,0.0014398223,0.000010103614,0.00004052619,0.000016170874,0.0000036690979,0.0000067689975,0.000010608167],"genre_scores_gemma":[0.99887985,0.0005354534,0.000489202,0.000008132241,0.0000416673,4.2304998e-7,0.0000036289637,0.0000054438606,0.00003618235],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991845,0.0000062168633,0.00019640652,0.00015537282,0.00032728264,0.00013021372],"domain_scores_gemma":[0.9995559,0.000066332,0.00018503444,0.0000670895,0.00001551108,0.000110125126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010157193,0.000109868975,0.00018351537,0.00035582087,0.00014916767,0.000036787642,0.00009245509,0.0000465125,0.000024175559],"category_scores_gemma":[0.000075038406,0.00009424613,0.000037155856,0.00043678377,0.000071265524,0.00030498148,0.00001734152,0.00013015752,4.9829066e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014176355,0.000028850956,0.028924925,0.000017396655,0.000035117653,0.000006840878,0.00015451839,0.0017851065,0.9685173,0.0000017173516,2.8575622e-7,0.00051373546],"study_design_scores_gemma":[0.0002803006,0.000105701816,0.54123056,0.000039479324,0.00014896329,0.000056795,0.000071774055,0.06601081,0.39191884,0.0000014232508,0.000006312182,0.00012902399],"about_ca_topic_score_codex":0.0000038117512,"about_ca_topic_score_gemma":4.9868383e-8,"teacher_disagreement_score":0.57659847,"about_ca_system_score_codex":0.00010750351,"about_ca_system_score_gemma":0.000018712733,"threshold_uncertainty_score":0.38432455},"labels":[],"label_agreement":null},{"id":"W2114417659","doi":"10.6000/1929-5030.2013.02.01.4","title":"Rapid Detecting Total Acid Content and Classifying Different Types of Vinegar based on Near Infrared Spectroscopy and Ant Colony Optimization Partial Least-Squares Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Solution Chemistry and Modeling","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National High-tech Research and Development Program; Priority Academic Program Development of Jiangsu Higher Education Institutions; Program for New Century Excellent Talents in University; Government of Jiangsu Province","keywords":"Partial least squares regression; Ant colony optimization algorithms; Principal component analysis; Mathematics; Chemometrics; Near-infrared spectroscopy; Mean squared error; Coefficient of determination; Food science; Pattern recognition (psychology); Chemistry; Artificial intelligence; Statistics; Computer science; Algorithm; Chromatography; Biology","score_opus":0.027769188674453053,"score_gpt":0.24774611288779913,"score_spread":0.21997692421334608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114417659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89711165,0.00064633857,0.10162309,0.00005505405,0.000013099181,0.000045723693,0.000005482579,0.000012991729,0.00048653764],"genre_scores_gemma":[0.9938999,0.00022681589,0.0057069166,0.000020388083,0.00010040627,0.0000059423824,0.0000094272455,0.000011923212,0.000018298144],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878997,0.0000102649,0.000531448,0.00022842913,0.00023864408,0.00020126463],"domain_scores_gemma":[0.99905545,0.000081028316,0.00045964055,0.00011605988,0.00014699543,0.00014081488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014485311,0.00019525466,0.00047873877,0.000094569135,0.0001888048,0.000095338815,0.00006490161,0.00014739388,0.0001850793],"category_scores_gemma":[0.000078895515,0.00016820338,0.000108366985,0.00020392593,0.00009157228,0.000105121515,0.000037913112,0.0002773519,1.2690985e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003213617,0.00007312087,0.0005768755,0.00015258376,0.00030328386,0.0000010262611,0.00011833576,0.1996144,0.7983397,0.000004019785,0.0000044917533,0.00049073977],"study_design_scores_gemma":[0.00057081925,0.000040958384,0.000054236883,0.000034477136,0.000443317,0.00000609817,0.00047007442,0.5632529,0.43500942,0.000022188582,0.0000012315402,0.00009423613],"about_ca_topic_score_codex":0.000015348354,"about_ca_topic_score_gemma":5.9323554e-7,"teacher_disagreement_score":0.36363852,"about_ca_system_score_codex":0.00007100201,"about_ca_system_score_gemma":0.000039804167,"threshold_uncertainty_score":0.68591344},"labels":[],"label_agreement":null},{"id":"W2115842892","doi":"10.1111/ejss.12271","title":"Improved estimates of organic carbon using proximally sensed vis– <scp>NIR</scp> spectra corrected by piecewise direct standardization","year":2015,"lang":"en","type":"article","venue":"European Journal of Soil Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Institute of Soil Science, Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Spectral line; Partial least squares regression; Piecewise; Spectrometer; Soil test; Analytical Chemistry (journal); Field (mathematics); Chemistry; Soil water; Environmental science; Soil science; Statistics; Mathematics; Environmental chemistry; Optics; Physics","score_opus":0.016780124191110586,"score_gpt":0.252734184364465,"score_spread":0.2359540601733544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115842892","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97273743,0.0006633246,0.005579022,0.000017135064,0.00015197907,0.000037956273,0.000009411468,0.00004023332,0.020763535],"genre_scores_gemma":[0.996179,0.000037624985,0.0033916177,0.000016049715,0.00012654085,1.3924388e-7,0.0000020954967,0.000033375665,0.00021358937],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778247,0.000071952614,0.00062921364,0.00028228207,0.00085117156,0.0003828825],"domain_scores_gemma":[0.9973671,0.0001177923,0.0009593088,0.0002493244,0.0009846442,0.0003218713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016696935,0.0002042944,0.00039311365,0.00027888344,0.00012763105,0.00013563501,0.0005860637,0.000032457152,0.000032655644],"category_scores_gemma":[0.003222716,0.00017257292,0.00008633718,0.0018959208,0.000404718,0.0003368562,0.0000965564,0.00026199946,0.0000020534778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026306883,0.000084746716,0.003704771,0.000024484674,0.000041020707,0.000039098144,0.000491341,0.00040768774,0.9947671,0.0000011805491,0.000213332,0.00019891512],"study_design_scores_gemma":[0.0007392143,0.00022467358,0.00044575133,0.000082208106,0.00016026113,0.0001274896,0.00073103205,0.014382301,0.9829254,0.000016041606,0.000069106616,0.000096490636],"about_ca_topic_score_codex":0.000026385556,"about_ca_topic_score_gemma":0.0000027076246,"teacher_disagreement_score":0.023441564,"about_ca_system_score_codex":0.0003120992,"about_ca_system_score_gemma":0.0007041989,"threshold_uncertainty_score":0.70373195},"labels":[],"label_agreement":null},{"id":"W2115986327","doi":"10.1002/cjce.21861","title":"Use of a spectroscopic sensor to monitor droplet size distribution in emulsions using neural networks","year":2013,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade de São Paulo; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Emulsion; Process engineering; Turbidity; Artificial neural network; Materials science; Biological system; Computer science; Environmental science; Chemical engineering; Artificial intelligence; Engineering","score_opus":0.01492208123689797,"score_gpt":0.22874134701547574,"score_spread":0.21381926577857777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115986327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985289,0.00015453396,0.00087171566,0.00030701503,0.00006900704,0.000041616673,0.0000116341025,0.0000058124415,0.000009769749],"genre_scores_gemma":[0.99894047,0.0000019763374,0.00076831767,0.00003439969,0.00021148697,0.0000018567284,0.0000025918075,0.00001575351,0.000023142837],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905,0.0000072547978,0.00037235545,0.00008709764,0.00013463087,0.0003486371],"domain_scores_gemma":[0.99905,0.00021521647,0.00011196914,0.00015287315,0.000095475494,0.00037445474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006346652,0.00012866079,0.0002685073,0.00008850429,0.000035955698,0.0000567286,0.00021545577,0.000091943075,0.00026894413],"category_scores_gemma":[0.00064070063,0.00010445566,0.00009703588,0.00051551627,0.000039407485,0.00011359458,0.000018022665,0.00042896366,0.0000012608045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007881119,0.000011028399,0.0032738245,0.000020376789,0.000042476575,0.000018116656,0.00006878263,0.12167564,0.8746428,0.000016997821,0.00014932713,0.00007273835],"study_design_scores_gemma":[0.00026889192,0.00002108094,0.0021164296,0.00012796347,0.000098096214,0.00008112286,0.00006050117,0.2219947,0.7749426,0.000022543196,0.000062631894,0.00020347281],"about_ca_topic_score_codex":0.005315403,"about_ca_topic_score_gemma":0.00010587178,"teacher_disagreement_score":0.100319065,"about_ca_system_score_codex":0.0004134676,"about_ca_system_score_gemma":0.000070034985,"threshold_uncertainty_score":0.8035335},"labels":[],"label_agreement":null},{"id":"W2116944896","doi":"10.1142/s0217984909019065","title":"PRETREATMENT METHOD RESEARCH OF NEAR-INFRARED SPECTRA IN BLOOD COMPONENT NON-INVASIVE MEASUREMENT","year":2009,"lang":"en","type":"article","venue":"Modern Physics Letters B","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Wavelet transform; Near-infrared spectroscopy; Wavelet; Analyte; Calibration; Computer science; Biological system; Variable elimination; Absorption (acoustics); Materials science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Optics; Chemistry; Statistics; Physics; Chromatography","score_opus":0.06515254778471637,"score_gpt":0.3358804517036835,"score_spread":0.27072790391896717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116944896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96318066,0.00037957597,0.027535593,0.0009526729,0.000018741894,0.00020796835,0.00001638,0.000042004893,0.007666404],"genre_scores_gemma":[0.99170274,0.000038135615,0.0077231205,0.000247871,0.000115075156,0.000026694055,0.000017720995,0.000023316668,0.000105313266],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974467,0.00007070938,0.00037092116,0.0004834447,0.0010931893,0.0005350325],"domain_scores_gemma":[0.9988641,0.00014853614,0.00013400293,0.0006082415,0.00015158001,0.00009351071],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040590044,0.0002493424,0.00047693678,0.00014647853,0.00009970215,0.000046413632,0.00035463055,0.00008376451,0.00008152577],"category_scores_gemma":[0.000047438043,0.00024588787,0.00018213838,0.0007001415,0.000095885705,0.000090153546,0.00005675308,0.0004230431,0.000010625105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062219246,0.0011504602,0.0022170052,0.00008056196,0.00026357302,0.000041019983,0.0010677732,0.0012621447,0.9916183,0.00010164492,0.0004043501,0.001730947],"study_design_scores_gemma":[0.0011853778,0.000092165064,0.0023339335,0.00007844916,0.00015521552,0.0000023632708,0.00008655126,0.0021479372,0.9888614,0.0048266645,0.000013752269,0.000216183],"about_ca_topic_score_codex":0.00023630528,"about_ca_topic_score_gemma":0.000009307062,"teacher_disagreement_score":0.028522095,"about_ca_system_score_codex":0.00043683834,"about_ca_system_score_gemma":0.00010858194,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W2117691385","doi":"10.1007/978-3-642-32408-6_33","title":"Application of Multivariate Analysis Techniques for the Identification of Sulfates From Raman Spectra","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in earth system sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Raman spectroscopy; Identification (biology); Binary number; Principal component analysis; Multivariate statistics; Multivariate analysis; Pattern recognition (psychology); Chemistry; Analytical Chemistry (journal); Biological system; Computer science; Artificial intelligence; Chromatography; Mathematics; Machine learning; Physics; Optics; Biology","score_opus":0.015666442167708204,"score_gpt":0.2779057015241514,"score_spread":0.2622392593564432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117691385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013445983,0.010191354,0.93858176,0.00024732063,0.00019698929,0.0017231745,0.00057778583,0.00020390288,0.034831744],"genre_scores_gemma":[0.9947657,0.000083509185,0.0036519316,0.0000063680227,0.000121106255,0.000060675357,0.000088753884,0.000015035065,0.0012069094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980257,0.000015174453,0.0008705478,0.0005062024,0.0004099858,0.0001723869],"domain_scores_gemma":[0.9966555,0.0012220836,0.001322513,0.00055120856,0.00022436882,0.000024292274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050329,0.0002409359,0.00066926534,0.00048266558,0.000113396614,0.00005739844,0.00066272524,0.00031177918,0.00021590476],"category_scores_gemma":[0.00017061921,0.0001612705,0.00027468085,0.0007949073,0.00035518777,0.00006592741,0.000037146736,0.00016998456,0.000004093761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000763894,0.00010182163,0.005269325,0.0016239445,0.0029384687,7.635068e-7,0.0009276591,0.014090761,0.9067336,0.038449608,0.000018454002,0.02976926],"study_design_scores_gemma":[0.000099760735,0.000036436875,0.00039034043,0.00015557912,0.0015911928,6.923359e-7,0.00011449383,0.03863698,0.9519865,0.0063939663,0.0003473462,0.0002467175],"about_ca_topic_score_codex":0.0016934657,"about_ca_topic_score_gemma":0.00031065918,"teacher_disagreement_score":0.9813197,"about_ca_system_score_codex":0.000046165864,"about_ca_system_score_gemma":0.00005192061,"threshold_uncertainty_score":0.65764195},"labels":[],"label_agreement":null},{"id":"W2117857301","doi":"10.1080/10659360600562202","title":"QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions†","year":2006,"lang":"en","type":"article","venue":"SAR and QSAR in environmental research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Particle Physics","funders":"Engineering and Physical Sciences Research Council","keywords":"Quantitative structure–activity relationship; Similarity (geometry); Biological system; Molecular descriptor; Fuzzy logic; Mathematics; Function (biology); Pattern recognition (psychology); Computer science; Artificial intelligence; Machine learning; Biology","score_opus":0.055829570160172114,"score_gpt":0.3013853461117066,"score_spread":0.24555577595153447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117857301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9924855,0.0023520289,0.00084212754,0.000024961902,0.000022576378,0.00019804871,0.00012639022,0.000016476939,0.0039318656],"genre_scores_gemma":[0.9967438,0.00027383037,0.0010166771,0.000012727964,0.00014204218,0.00002623275,0.00016860699,0.000020752257,0.0015953397],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984545,0.000051964584,0.00020915833,0.0004389722,0.0004346692,0.0004107477],"domain_scores_gemma":[0.9995901,0.00010680817,0.00003626828,0.00015935933,0.00000918687,0.00009829954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053135975,0.00015182723,0.00017676562,0.0002148639,0.00028206973,0.0000562024,0.000090943075,0.00020514603,0.00025317844],"category_scores_gemma":[0.00003615451,0.00014411686,0.000051092287,0.0002249741,0.00020258974,0.00012207944,0.00010740343,0.00040334274,0.0000029307234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021051387,0.0006207836,0.6735548,0.00019260866,0.000066930734,0.000005431693,0.00018808087,0.00033458424,0.32330748,0.00010792624,0.00035984564,0.0010510274],"study_design_scores_gemma":[0.005494535,0.00051118655,0.2016304,0.00010344429,0.0003860111,0.000093956805,0.014366075,0.038442865,0.7265586,0.0064536044,0.004750308,0.0012090171],"about_ca_topic_score_codex":0.0003191612,"about_ca_topic_score_gemma":0.000024646057,"teacher_disagreement_score":0.4719244,"about_ca_system_score_codex":0.0003726238,"about_ca_system_score_gemma":0.000015885349,"threshold_uncertainty_score":0.5876915},"labels":[],"label_agreement":null},{"id":"W2119619499","doi":"10.24908/pceea.v0i0.3957","title":"DATA PRE-PROCESSING TECHNIQUES FOR MULTIVARIATE ANALYSIS TO TREAT INDUSTRIAL OPERATING DATA FOR RETROFIT DESIGN","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Newsprint; Outlier; EWMA chart; Multivariate statistics; Partial least squares regression; Computer science; Process (computing); Raw data; Data mining; Engineering; Process engineering; Statistics; Machine learning; Artificial intelligence; Control chart; Mathematics","score_opus":0.10326972800325394,"score_gpt":0.31839845588747956,"score_spread":0.2151287278842256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119619499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48803356,0.0016000086,0.4144187,0.018674055,0.0037332394,0.014265643,0.028888317,0.0024925512,0.02789391],"genre_scores_gemma":[0.87444246,0.000004043067,0.12131183,0.00012649049,0.0005244254,0.00026584326,0.0005692323,0.0000565956,0.00269906],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986425,0.0000036190104,0.00037283846,0.00044546675,0.00020472924,0.000330847],"domain_scores_gemma":[0.9982428,0.00012573555,0.00042739994,0.00044003938,0.0005871727,0.00017683646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008570259,0.00017435296,0.0002889592,0.0004599729,0.00024979786,0.00015304293,0.0012964591,0.00022440858,0.000052519175],"category_scores_gemma":[0.00600542,0.00016865402,0.000080459635,0.0012736672,0.0000097804605,0.00044235337,0.000116039555,0.00015571428,5.213131e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002780699,0.00089896156,0.23875502,0.0030634564,0.008470753,2.0805582e-7,0.010153377,0.001844819,0.33858907,0.003321208,0.35510886,0.03951619],"study_design_scores_gemma":[0.0011405587,0.000100333345,0.010757155,0.00034855446,0.0089516,0.0000020148648,0.0019614084,0.16122013,0.78660285,0.00028374506,0.027239816,0.0013918554],"about_ca_topic_score_codex":0.008290862,"about_ca_topic_score_gemma":0.001597698,"teacher_disagreement_score":0.44801375,"about_ca_system_score_codex":0.0011766439,"about_ca_system_score_gemma":0.00067333283,"threshold_uncertainty_score":0.998313},"labels":[],"label_agreement":null},{"id":"W2119758969","doi":"10.1109/jsen.2009.2038569","title":"Functional Kernel-Based Modeling of Wavelet Compressed Optical Emission Spectral Data: Prediction of Plasma Etch Process","year":2010,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Kernel (algebra); Wavelet; Process (computing); Materials science; Wavelet transform; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics","score_opus":0.04738812411264125,"score_gpt":0.29480925103430117,"score_spread":0.2474211269216599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119758969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861901,0.000034090077,0.011250578,0.00008621546,0.00031496974,0.000033840814,0.00012382114,0.00003327592,0.0019331462],"genre_scores_gemma":[0.99667305,0.000016474838,0.0022012896,0.000008783421,0.00079551927,9.980586e-7,0.000073906966,0.000024277035,0.00020570609],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980386,0.000017758495,0.0006673286,0.00030028637,0.00068995805,0.00028610605],"domain_scores_gemma":[0.99851453,0.00013552191,0.00037485216,0.00041588335,0.00036231964,0.00019686404],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028944842,0.00019548884,0.00037695345,0.00020224029,0.00013148993,0.00003532304,0.00040408815,0.0002367335,0.0015024823],"category_scores_gemma":[0.00024994425,0.00017004859,0.00013938716,0.00030570407,0.0001330536,0.00021510958,0.000028977369,0.0010379562,0.0000032012613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003351431,0.00030317067,0.0021614954,0.00013789874,0.00013374742,0.000010819206,0.000049524257,0.05125131,0.9446713,0.000011275697,0.0008232444,0.0001111142],"study_design_scores_gemma":[0.0007432911,0.000029316338,0.00016139683,0.000040696577,0.00014031165,0.00013114257,0.00012135163,0.42634448,0.57209694,0.0000827089,0.000023934103,0.000084444044],"about_ca_topic_score_codex":0.000009807804,"about_ca_topic_score_gemma":0.0000019592574,"teacher_disagreement_score":0.37509316,"about_ca_system_score_codex":0.000038954317,"about_ca_system_score_gemma":0.00023371674,"threshold_uncertainty_score":0.9994103},"labels":[],"label_agreement":null},{"id":"W2121197467","doi":"10.1109/rose.2008.4669192","title":"Feature selection for a real-time vision-based food inspection system","year":2008,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Consistency (knowledge bases); Automation; Machine vision; Feature extraction; Artificial intelligence; Feature (linguistics); Feature selection; Quality (philosophy); Visual inspection; Product (mathematics); Computer vision; Filter (signal processing); Data mining; Engineering; Mathematics","score_opus":0.011292606148614649,"score_gpt":0.2534196037415878,"score_spread":0.24212699759297313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121197467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82967806,0.00018171687,0.022805369,0.00055949,0.000118139134,0.0002864987,0.00004490463,0.0038739115,0.14245193],"genre_scores_gemma":[0.984006,0.00001092363,0.003217889,0.000034696943,0.0002575548,0.00005218934,0.00005286292,0.000025263822,0.01234261],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916846,0.0000071646314,0.00013862306,0.0002930579,0.00017702716,0.0002156612],"domain_scores_gemma":[0.99944293,0.000101382284,0.00008901894,0.00017455603,0.00012372126,0.0000683703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052663014,0.00015082261,0.00021426767,0.00014942254,0.00032189605,0.000023124996,0.000090262496,0.00019304751,0.00032206258],"category_scores_gemma":[0.000044995588,0.00013199677,0.00015157124,0.00060307095,0.000027652975,0.00007448115,0.00000812907,0.00011086912,0.00004277383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029930277,0.0003066907,0.0066341944,0.00051161135,0.00027918405,0.0000037192185,0.0000622989,0.00018894869,0.84838885,0.0009776632,0.142188,0.00015957077],"study_design_scores_gemma":[0.0009158687,0.00025209412,0.00032580292,0.000024683162,0.00013753689,0.00005537809,0.00017392547,0.031350076,0.96419287,0.000014459587,0.0023209318,0.00023640307],"about_ca_topic_score_codex":0.0000704289,"about_ca_topic_score_gemma":0.000017330081,"teacher_disagreement_score":0.15432797,"about_ca_system_score_codex":0.00029614,"about_ca_system_score_gemma":0.00006994866,"threshold_uncertainty_score":0.5382672},"labels":[],"label_agreement":null},{"id":"W2122562057","doi":"10.1002/cem.1038","title":"Standardization of line‐scan NIR imaging systems","year":2007,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ProSensus (Canada); McMaster University","funders":"","keywords":"Standardization; Pixel; Principal component analysis; Detector; Computer science; Sensitivity (control systems); Line (geometry); Homogeneous; Artificial intelligence; Pattern recognition (psychology); Mathematics; Electronic engineering; Engineering; Telecommunications","score_opus":0.012545227031285707,"score_gpt":0.29152458987140795,"score_spread":0.27897936284012226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122562057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48664933,0.018478617,0.46209177,0.00017808753,0.00058799976,0.00005528113,0.000028888007,0.00003891336,0.03189112],"genre_scores_gemma":[0.9965286,0.00033027076,0.0019354804,0.00002365732,0.0005080191,2.3972063e-7,0.0000035512362,0.0000229008,0.0006472607],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767935,0.000009017576,0.0010439078,0.00012219258,0.0008751924,0.00027031483],"domain_scores_gemma":[0.9969926,0.0003023789,0.0013295469,0.00020984557,0.0010069425,0.00015868928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012070743,0.00015205075,0.00051146507,0.0017785013,0.000045228895,0.000047217916,0.00031024357,0.000109765126,0.00020914862],"category_scores_gemma":[0.0014262283,0.00013327567,0.00021952837,0.0042844187,0.000056638044,0.00019752585,0.000034816097,0.00031497047,0.0000018257855],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050795823,0.00093107147,0.22896023,0.0014710936,0.0010555191,0.00023464333,0.0005347948,0.0032250218,0.7207713,0.00059068133,0.008164032,0.033553638],"study_design_scores_gemma":[0.00087673,0.00006804217,0.0007922438,0.00008132692,0.00029494535,0.00017847978,0.0012351747,0.0003717697,0.9864089,0.000091753456,0.00942032,0.000180343],"about_ca_topic_score_codex":0.000011140807,"about_ca_topic_score_gemma":5.559136e-7,"teacher_disagreement_score":0.5098793,"about_ca_system_score_codex":0.0003113158,"about_ca_system_score_gemma":0.00012130752,"threshold_uncertainty_score":0.54348236},"labels":[],"label_agreement":null},{"id":"W2122613964","doi":"10.1039/c4ay01155d","title":"Multivariate classification of pulp NIR spectra for end-product properties using discrete wavelet transform with orthogonal signal correction","year":2014,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canfor Pulp Products (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Uncorrelated; Wavelet; Multivariate statistics; Spectral line; Computer science; Pattern recognition (psychology); Pulp (tooth); Near-infrared spectroscopy; Biological system; SIGNAL (programming language); Calibration; Mathematics; Algorithm; Artificial intelligence; Statistics; Optics; Physics","score_opus":0.07998401184257155,"score_gpt":0.37796052305875777,"score_spread":0.29797651121618624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122613964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114140406,0.00006235558,0.88043034,0.00021725244,0.00006238249,0.00014949606,0.000015440912,0.000053885953,0.004868437],"genre_scores_gemma":[0.8345445,0.0000063004354,0.16436753,0.000017417107,0.00024362041,0.000022535953,0.00002524244,0.000030336452,0.0007425343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982773,0.00010139237,0.00048318395,0.0004914214,0.00028733726,0.00035938434],"domain_scores_gemma":[0.99868953,0.00047207018,0.00021000512,0.0003121573,0.00020279044,0.00011346945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008035216,0.0002476215,0.0005292658,0.00016206421,0.00013543044,0.00003652586,0.00017442915,0.00012775573,0.000451742],"category_scores_gemma":[0.00052845344,0.00017784393,0.00021052302,0.0005806299,0.00022727241,0.0001461629,0.000015733083,0.0002463155,0.0000014440283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000660629,0.00017649568,0.0011289908,0.0002598906,0.00042798943,5.907225e-7,0.00013903076,0.00030797737,0.9544118,0.0035698842,0.000020626634,0.038896106],"study_design_scores_gemma":[0.00040392185,0.00012749869,0.00066834985,0.000035466717,0.0006534569,0.000009810784,0.00018467206,0.33808866,0.65873647,0.0005897644,0.00030328243,0.00019866085],"about_ca_topic_score_codex":0.00010432504,"about_ca_topic_score_gemma":0.000012641504,"teacher_disagreement_score":0.7204041,"about_ca_system_score_codex":0.0001047892,"about_ca_system_score_gemma":0.000089435925,"threshold_uncertainty_score":0.72522646},"labels":[],"label_agreement":null},{"id":"W2125338773","doi":"10.5539/jas.v4n5p235","title":"Potato Sorting Based on Size and Color in Machine Vision System","year":2012,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Machine vision; Computer science; Damages; Artificial intelligence; Computer vision; Agricultural engineering; Solanum tuberosum; Engineering; Horticulture; Biology","score_opus":0.007810117419394206,"score_gpt":0.2659443481876493,"score_spread":0.2581342307682551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125338773","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960575,0.00031601838,0.00000893879,0.00022928618,0.00006059774,0.00001666561,7.94577e-7,0.000007452237,0.0033027295],"genre_scores_gemma":[0.9991582,0.0000075130524,0.0005836634,0.000042975826,0.000112517875,4.7162607e-7,1.7902236e-7,0.0000018662964,0.00009259931],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99892986,0.000010481497,0.00028117333,0.00009939329,0.0004301246,0.0002489716],"domain_scores_gemma":[0.9992316,0.00016292937,0.0002974005,0.00005457329,0.00008966186,0.00016386414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005694144,0.000086133026,0.00017590047,0.00009417945,0.00011608931,0.00006108102,0.00018787457,0.00003474499,0.000052433625],"category_scores_gemma":[0.00036248605,0.000043987733,0.000046514753,0.00081960065,0.000074230134,0.00040278843,0.000029669709,0.00018108932,0.0000024908366],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029623903,0.00010085968,0.06280543,0.000041467425,0.0000053961016,0.000007711797,0.0001604272,0.00016716102,0.93624336,0.00009312991,0.000055522636,0.00028991103],"study_design_scores_gemma":[0.00054781296,0.000129294,0.6941873,0.00019115648,0.00004324686,0.00021228554,0.0025399558,0.0010359455,0.3009167,0.000004620039,0.00004211832,0.0001495711],"about_ca_topic_score_codex":0.000017007233,"about_ca_topic_score_gemma":0.000002459887,"teacher_disagreement_score":0.6353267,"about_ca_system_score_codex":0.0002247326,"about_ca_system_score_gemma":0.00002933272,"threshold_uncertainty_score":0.17937677},"labels":[],"label_agreement":null},{"id":"W2126390793","doi":"10.1002/cem.641","title":"The PLS multivariate regression model: testing the significance of successive PLS components","year":2001,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multivariate statistics; Regression; Regression analysis; Partial least squares regression; Statistics; Mathematics; Computer science; Redundancy (engineering); Linear regression; Artificial intelligence","score_opus":0.07416967791318636,"score_gpt":0.3239736045217466,"score_spread":0.24980392660856027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126390793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9743999,0.004740673,0.012524482,0.0008015369,0.00018521036,0.000099278266,0.000013434174,0.000025321136,0.007210126],"genre_scores_gemma":[0.996386,0.0006463959,0.0015612572,0.000057744313,0.00025012784,0.0000031560369,0.0000015226643,0.0000257477,0.001068069],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974476,0.000040417803,0.00096922513,0.00017971752,0.0009973673,0.00036568134],"domain_scores_gemma":[0.993427,0.00294211,0.0021927485,0.00041977473,0.00088811346,0.00013026592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088909565,0.0002278865,0.0004997851,0.00042876072,0.0003357658,0.000090870046,0.0010779512,0.00014173886,0.000066848035],"category_scores_gemma":[0.0061287945,0.000117288786,0.0002533264,0.0042043696,0.00018739222,0.00019837552,0.00012382175,0.0006914441,0.00000255683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000415707,0.00049607776,0.028715825,0.0001298539,0.000509803,0.00006903464,0.0003781645,0.0060814046,0.9488373,0.00022942884,0.0017593509,0.012378051],"study_design_scores_gemma":[0.0019241206,0.00012869867,0.0034261348,0.00028584365,0.0005695635,0.00018123449,0.0011938969,0.037387688,0.9493236,0.00241837,0.0027681717,0.0003926573],"about_ca_topic_score_codex":0.000031935804,"about_ca_topic_score_gemma":7.624837e-7,"teacher_disagreement_score":0.031306285,"about_ca_system_score_codex":0.0001624447,"about_ca_system_score_gemma":0.00013568044,"threshold_uncertainty_score":0.7337182},"labels":[],"label_agreement":null},{"id":"W2127168989","doi":"10.1366/12-06879","title":"Novel Method for Quantifying Molecular Orientation by Polarized Raman Spectroscopy: A Comparative Simulations Study","year":2013,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Depolarization; Orientation (vector space); Raman spectroscopy; Isotropy; Depolarization ratio; Spectroscopy; Population; Chemistry; Constant (computer programming); Biological system; Analytical Chemistry (journal); Molecular physics; Computational physics; Optics; Physics; Mathematics; Geometry; Computer science; Chromatography","score_opus":0.04400078530621962,"score_gpt":0.37834436766111396,"score_spread":0.33434358235489436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127168989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38328803,0.00011580763,0.6100516,0.00012746773,0.000051243114,0.001298654,0.0001183874,0.00023574279,0.004713118],"genre_scores_gemma":[0.79642737,0.000003650091,0.20177077,0.00020150666,0.00012085447,0.00068901345,0.00037099363,0.00007890833,0.00033696502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967096,0.000037336613,0.0007446273,0.0010912318,0.0005316938,0.00088553526],"domain_scores_gemma":[0.9978982,0.000528631,0.0004173329,0.0007274614,0.00019039727,0.00023792982],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023755476,0.00059665105,0.0009211471,0.0002837644,0.00051300356,0.00033583966,0.00048466734,0.00020890645,0.0021491058],"category_scores_gemma":[0.000075029086,0.0006063189,0.00022387426,0.0010948956,0.00009548698,0.00025261016,0.00008720608,0.00045023498,0.00010761845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015255385,0.0011325779,0.0009639809,0.000041555268,0.00048337207,7.7316787e-7,0.0008487009,0.00013267432,0.99118805,0.0039270762,0.0011004873,0.000028194205],"study_design_scores_gemma":[0.0038897756,0.00020710292,0.00016852193,0.000007279985,0.00058466464,0.0000029586831,0.004507057,0.0062718093,0.9811797,0.0022880805,0.0002637351,0.0006293142],"about_ca_topic_score_codex":0.0006170187,"about_ca_topic_score_gemma":0.000047973645,"teacher_disagreement_score":0.41313934,"about_ca_system_score_codex":0.0003378504,"about_ca_system_score_gemma":0.00008405193,"threshold_uncertainty_score":0.9996388},"labels":[],"label_agreement":null},{"id":"W2127767112","doi":"10.1016/j.csda.2004.06.004","title":"An extended redundancy analysis and its applications to two practical examples","year":2004,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Computer science; Algorithm; Extension (predicate logic); Mathematics; Data mining; Mathematical optimization","score_opus":0.06267678262749984,"score_gpt":0.41874281960770926,"score_spread":0.35606603698020944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127767112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015724145,0.00022055596,0.97548753,0.0003063355,0.000006713995,0.00008395122,0.007885846,0.00007707996,0.00020786344],"genre_scores_gemma":[0.5904038,0.000053205997,0.389201,0.00012677562,0.00007312036,0.000029893554,0.020025682,0.000014870966,0.00007168142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749863,0.000038945967,0.00054099405,0.0010281033,0.00058274437,0.0003105856],"domain_scores_gemma":[0.9973456,0.0006289026,0.00023164503,0.0010298551,0.00036105627,0.00040290027],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002425629,0.00025347635,0.0005666465,0.0009300187,0.00034596722,0.00026229452,0.0005412681,0.00007052404,0.0013538193],"category_scores_gemma":[0.00042419773,0.0002693927,0.00011191809,0.005589128,0.000088518864,0.0003351978,0.00024019188,0.00018123693,0.000055711174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117446856,0.0023543322,0.03304746,0.00015843142,0.043981563,0.00009583212,0.0004661601,0.62798434,0.0042746626,0.27380025,0.0014095783,0.012309928],"study_design_scores_gemma":[0.0015081275,0.000101533435,0.10053056,0.000011241088,0.103000775,0.000029584802,0.0004083304,0.7045062,0.0021238117,0.08285894,0.0034093368,0.001511555],"about_ca_topic_score_codex":0.0007429587,"about_ca_topic_score_gemma":0.0009397938,"teacher_disagreement_score":0.5862865,"about_ca_system_score_codex":0.00011896231,"about_ca_system_score_gemma":0.00017299214,"threshold_uncertainty_score":0.9999758},"labels":[],"label_agreement":null},{"id":"W2128074388","doi":"10.1016/j.chemolab.2014.02.006","title":"Two novel methods to analyze the combined effect of multiple raw-materials and processing conditions on the product's final attributes: JRPLS and TPLS","year":2014,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University; Pfizer","keywords":"Troubleshooting; Computer science; Raw material; Product (mathematics); Process (computing); Process engineering; Quality (philosophy); Manufacturing process; Mixing (physics); Reliability engineering; Mathematics; Engineering; Materials science; Chemistry","score_opus":0.036872071801841565,"score_gpt":0.33467441612486143,"score_spread":0.2978023443230199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128074388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98850334,0.0050917743,0.005021611,0.00027189538,0.00014161556,0.00046791934,0.00020340372,0.000038857797,0.00025957974],"genre_scores_gemma":[0.99899536,0.00016559483,0.00024000434,0.00012736033,0.0001667904,0.0000949973,0.000026267153,0.000027603246,0.00015602245],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981394,0.00020326769,0.0005672869,0.00048412968,0.00030718461,0.00029874025],"domain_scores_gemma":[0.9956282,0.0030333651,0.0003731164,0.00046878264,0.00034178596,0.00015475608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025693476,0.0003207303,0.00070201594,0.0004212253,0.00034213575,0.00029237408,0.00026704452,0.00010466161,0.000044942826],"category_scores_gemma":[0.004411593,0.00018496622,0.000050832943,0.0026879653,0.000249091,0.000087072396,0.00012359837,0.0002095692,0.0000028519655],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012322931,0.00015240992,0.013579681,0.0013032127,0.00029674367,8.48513e-7,0.00033055063,0.00007594422,0.97963953,0.0016164102,0.00091702596,0.0019644436],"study_design_scores_gemma":[0.00053896225,0.00026567266,0.0008733274,0.00014167966,0.00022902637,0.000006479355,0.00051744987,0.0011126407,0.99294305,0.000023544064,0.0031132812,0.00023486112],"about_ca_topic_score_codex":0.00006988328,"about_ca_topic_score_gemma":0.0000018260267,"teacher_disagreement_score":0.013303575,"about_ca_system_score_codex":0.000050927134,"about_ca_system_score_gemma":0.0000374319,"threshold_uncertainty_score":0.7542703},"labels":[],"label_agreement":null},{"id":"W2129683196","doi":"10.5402/2012/197352","title":"Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification","year":2012,"lang":"en","type":"article","venue":"ISRN Computational Mathematics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"Mitacs; European Commission","keywords":"Kernel principal component analysis; Pattern recognition (psychology); Artificial intelligence; Wavelet; Computer science; Principal component analysis; Kernel (algebra); Kernel method; Feature extraction; Wavelet transform; Robustness (evolution); Mathematics; Support vector machine","score_opus":0.08498879523043323,"score_gpt":0.3449009446409023,"score_spread":0.2599121494104691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129683196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83620626,0.00021042053,0.15711837,0.0002164122,0.000053699176,0.00010564336,0.00021862007,0.00010283602,0.0057677464],"genre_scores_gemma":[0.9277359,0.000008273049,0.07014555,0.00004394696,0.0001184697,0.000012802553,0.0015660367,0.000018965502,0.0003500441],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982922,0.000020000161,0.00056471006,0.000313129,0.00047739962,0.000332549],"domain_scores_gemma":[0.99844563,0.00048284553,0.00025533276,0.0006223634,0.00007677181,0.000117053554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034078705,0.00018835711,0.000378833,0.00032203365,0.00007537796,0.00004630512,0.0004941996,0.00010353141,0.00070811505],"category_scores_gemma":[0.0001568073,0.00018314156,0.000099179495,0.0010627817,0.000061230035,0.00023546591,0.0001946579,0.00019361761,0.00013233085],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006397965,0.010613764,0.8599645,0.0012848447,0.0044805813,0.0000192793,0.005420219,0.047945086,0.011872216,0.053057376,0.0021697194,0.0031084556],"study_design_scores_gemma":[0.0004672521,0.000004872228,0.10852605,0.000018862058,0.00074479677,0.000010742088,0.00074946135,0.8851719,0.0012292014,0.0024357457,0.00033908384,0.00030203201],"about_ca_topic_score_codex":0.000034708773,"about_ca_topic_score_gemma":0.000035640394,"teacher_disagreement_score":0.8372268,"about_ca_system_score_codex":0.00015456103,"about_ca_system_score_gemma":0.000038835635,"threshold_uncertainty_score":0.7753363},"labels":[],"label_agreement":null},{"id":"W2129808345","doi":"10.5539/mas.v8n5p262","title":"Hardware-Software Complex for Laser Scanning in Color Sorting Machines","year":2014,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education and Science of the Russian Federation","keywords":"Sorting; Computer science; Software; Thresholding; Transmission (telecommunications); Pixel; Artificial intelligence; Basis (linear algebra); Computer vision; Sorting algorithm; Algorithm; Image (mathematics); Mathematics; Telecommunications","score_opus":0.029019931248691858,"score_gpt":0.296206602320437,"score_spread":0.26718667107174515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129808345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6688732,0.000038353937,0.30360225,0.00010375063,0.00003552249,0.00013405971,0.000012664189,0.00017218309,0.027028048],"genre_scores_gemma":[0.9824869,8.237329e-7,0.016559605,0.0002517382,0.000073991556,0.000074157106,0.000010587334,0.000017005912,0.00052518654],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837863,0.000003457786,0.00025577444,0.0005262399,0.00032698063,0.00050890085],"domain_scores_gemma":[0.99925804,0.00017307502,0.0001221762,0.0002879348,0.000058360656,0.00010041558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004696409,0.0001570071,0.00025294098,0.00018741743,0.0003551352,0.00010587312,0.0005564433,0.000058365684,0.00015576248],"category_scores_gemma":[0.00037315226,0.00014748068,0.000049873583,0.00076111563,0.00025609584,0.00010862683,0.0001225094,0.00013453787,0.000012074143],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003012323,0.000050041614,0.009721698,0.00007173048,0.00000636882,5.942033e-7,0.00029506182,0.0019086429,0.9778132,0.00079163583,0.00012521389,0.009185706],"study_design_scores_gemma":[0.00069196796,0.0000144076475,0.0018338804,0.000017491635,0.000026170808,0.0000019128065,0.00017563712,0.55683875,0.43296376,0.006415473,0.00069378177,0.00032675688],"about_ca_topic_score_codex":0.000034685723,"about_ca_topic_score_gemma":0.000030320629,"teacher_disagreement_score":0.5549301,"about_ca_system_score_codex":0.000116610536,"about_ca_system_score_gemma":0.00006609411,"threshold_uncertainty_score":0.6014087},"labels":[],"label_agreement":null},{"id":"W2129955232","doi":"10.1109/iembs.2009.5332499","title":"Model based clustering for tandem mass spectrum quality assessment","year":2009,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan; Université Laval","keywords":"Cluster analysis; Discriminative model; Computer science; Mixture model; Pattern recognition (psychology); Artificial intelligence; Probabilistic logic; Gaussian; Tandem; Expectation–maximization algorithm; Quality (philosophy); Posterior probability; Quality assessment; Mass spectrum; Cluster (spacecraft); Spectral line; Linear discriminant analysis; Mathematics; Statistics; Bayesian probability; Mass spectrometry; Chemistry; Maximum likelihood; Materials science; Physics; Engineering","score_opus":0.06682164370623579,"score_gpt":0.3746624719290271,"score_spread":0.3078408282227913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129955232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018687706,0.00003287047,0.8489716,0.0013285895,0.000017059701,0.000058819172,0.000013902008,0.00015917918,0.13073026],"genre_scores_gemma":[0.9282027,0.000004869534,0.06406208,0.0007294598,0.00008815814,0.000014136879,0.000024214141,0.000011547622,0.006862829],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989036,0.0000060053376,0.00027403247,0.00029431848,0.00020636528,0.00031568506],"domain_scores_gemma":[0.9993925,0.000094554845,0.00008579294,0.00030806538,0.000032056683,0.000087019944],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018254165,0.00015794621,0.00025975722,0.00006425524,0.00010105017,0.000051776235,0.00018113536,0.00009903051,0.001371834],"category_scores_gemma":[0.00003595094,0.00014263623,0.0001654705,0.00017676713,0.0000151776185,0.00006783448,0.000013356981,0.00011717768,0.000004080649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014393718,0.00064050016,0.008061904,0.00024364288,0.00019725051,0.0000045938264,0.000042583208,0.027655602,0.9377473,0.01907407,0.0035516745,0.0026369425],"study_design_scores_gemma":[0.00075684354,0.00003690338,0.00043103652,0.0000058222413,0.000072186514,4.786093e-7,0.0000904925,0.6385149,0.35076743,0.008885267,0.00020186647,0.00023680004],"about_ca_topic_score_codex":0.00001596761,"about_ca_topic_score_gemma":0.000015028311,"teacher_disagreement_score":0.909515,"about_ca_system_score_codex":0.00018887862,"about_ca_system_score_gemma":0.00007521824,"threshold_uncertainty_score":0.99954104},"labels":[],"label_agreement":null},{"id":"W2131222141","doi":"10.3168/jds.s0022-0302(00)75129-0","title":"Modeling of pH and Acidity for Industrial Cheese Production","year":2000,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Production (economics); Feedforward neural network; Process (computing); Sensitivity (control systems); Computer science; Mathematics; Biological system; Feed forward; Process engineering; Statistics; Food science; Chemistry; Artificial intelligence; Engineering; Biology; Control engineering","score_opus":0.05734479899954514,"score_gpt":0.312646845652676,"score_spread":0.25530204665313083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131222141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985021,0.00019752362,0.00025990244,0.00014182593,0.00008048448,0.000019495741,0.0000026151233,0.0000032650682,0.00079281477],"genre_scores_gemma":[0.9981161,0.00006679842,0.0011327481,0.000007445805,0.00034640403,4.2048222e-7,1.9912255e-7,0.0000024083474,0.00032749187],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921995,0.000003772967,0.00025868157,0.0001149623,0.000283057,0.00011957575],"domain_scores_gemma":[0.99949896,0.000020519214,0.00015655553,0.00009319656,0.00016482777,0.00006591386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005966519,0.00005200259,0.00014774082,0.00012020457,0.000091700436,0.000022947428,0.00019105565,0.00004128167,0.00016312845],"category_scores_gemma":[0.0005378664,0.000041325628,0.000049266022,0.00047033964,0.00018802284,0.00038935986,0.000013545845,0.00013361518,3.0223578e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017675242,0.000088621964,0.0017365267,0.000030668634,0.000022419792,9.331668e-7,0.00020565605,0.0038531488,0.9822161,0.0000129356395,0.00025887834,0.011397327],"study_design_scores_gemma":[0.00068330986,0.000112951675,0.00029411155,0.000043449854,0.0000937087,0.000073146104,0.00039087769,0.013457191,0.98388237,0.00057167205,0.00030750083,0.00008973002],"about_ca_topic_score_codex":0.000007604014,"about_ca_topic_score_gemma":5.1621305e-7,"teacher_disagreement_score":0.011307597,"about_ca_system_score_codex":0.000046899866,"about_ca_system_score_gemma":0.00018088166,"threshold_uncertainty_score":0.17861421},"labels":[],"label_agreement":null},{"id":"W2132229547","doi":"10.1366/10-06126","title":"Determination of Volume Fractions in Multicomponent Mixtures Using Ultrasound Frequency Analysis","year":2011,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Volume (thermodynamics); Analytical Chemistry (journal); Chemistry; Ultrasound; Distortion (music); Volume fraction; Waveform; Fraction (chemistry); Ultrasonic sensor; Chemical composition; Chromatography; Materials science; Acoustics; Thermodynamics","score_opus":0.026021184237978764,"score_gpt":0.2828310220749744,"score_spread":0.2568098378369956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132229547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9478383,0.0001611713,0.030275317,0.000005139083,0.000026242033,0.00007755625,0.00001988311,0.00008277449,0.021513615],"genre_scores_gemma":[0.9408278,0.000036948695,0.058920275,0.000016930655,0.00004526469,0.00002189282,0.000031725438,0.00002835396,0.00007083769],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984836,0.0000137616325,0.0005024981,0.00039923558,0.00025465712,0.00034622586],"domain_scores_gemma":[0.9990393,0.000098706754,0.0002991145,0.00043803695,0.000048987324,0.00007587926],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010651287,0.0002272467,0.0004940521,0.0007045561,0.00008521481,0.000019237259,0.00025329474,0.00016459459,0.0030699999],"category_scores_gemma":[0.000045893325,0.00023722237,0.00019375912,0.0017079465,0.000111076435,0.00010428361,0.000028406914,0.00026297188,0.000018597178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003120962,0.0003294951,0.10259868,0.00002762851,0.00023210184,0.0000038903418,0.00039516255,0.000033706205,0.89568114,0.0006215711,0.000005920627,0.000039494993],"study_design_scores_gemma":[0.0003360078,0.000015736958,0.02327921,0.0000065994054,0.0010684025,0.000003325612,0.00041360347,0.0011417588,0.97135454,0.002141063,0.000007321279,0.0002324248],"about_ca_topic_score_codex":0.0017638527,"about_ca_topic_score_gemma":0.00019410248,"teacher_disagreement_score":0.07931948,"about_ca_system_score_codex":0.00024383522,"about_ca_system_score_gemma":0.00004037848,"threshold_uncertainty_score":0.99784136},"labels":[],"label_agreement":null},{"id":"W2134020072","doi":"10.5539/cis.v4n3p106","title":"The Design of Pre-Processing Multidimensional Data Based on Component Analysis","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Malaysia Pahang","keywords":"Computer science; Outlier; Data mining; Component (thermodynamics); Independent component analysis; Cluster analysis; Data processing; Pattern recognition (psychology); Artificial intelligence; Database","score_opus":0.06240283923289637,"score_gpt":0.2963736477685805,"score_spread":0.23397080853568414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134020072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033936102,0.000043413667,0.96345997,0.00002596699,0.000024537147,0.00002879025,0.00000848272,0.000017594904,0.0024551263],"genre_scores_gemma":[0.97221214,0.00001217628,0.027619269,0.00012649677,0.000007668337,0.0000010093594,0.000014727478,8.262626e-7,0.000005708864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927354,0.0000063420302,0.00020251032,0.00011173908,0.0003051895,0.000100698475],"domain_scores_gemma":[0.9992494,0.00010767614,0.0001361149,0.00034457858,0.000119955606,0.000042269305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046099265,0.000055797165,0.000087579654,0.0002025815,0.00027112983,0.0000674312,0.00045024446,0.000016153966,0.000044287026],"category_scores_gemma":[0.000042293002,0.000034989647,0.000018511975,0.00096229394,0.00025843727,0.0011858196,0.0001413906,0.00004567293,0.0000024436617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013647514,0.001171477,0.06257828,0.0007426116,0.0010043861,0.0000033310357,0.017614745,0.21477349,0.040938683,0.01306988,0.0029107537,0.6438276],"study_design_scores_gemma":[0.00010086759,0.000015862577,0.013507964,0.0000061168485,0.0000473744,5.234551e-7,0.000050525257,0.96088445,0.02516074,0.000010390236,0.00016866524,0.00004651602],"about_ca_topic_score_codex":0.000011492563,"about_ca_topic_score_gemma":1.6360144e-7,"teacher_disagreement_score":0.938276,"about_ca_system_score_codex":0.0000124573435,"about_ca_system_score_gemma":0.00006249307,"threshold_uncertainty_score":0.20853388},"labels":[],"label_agreement":null},{"id":"W2134370449","doi":"10.5539/mas.v4n11p51","title":"Isolating Healthy Bananas from Unhealthy Ones Based on Feature Extraction and Clustering Method Using Neural Network","year":2010,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Cluster analysis; Sorting; Segmentation; Artificial neural network; k-means clustering; Feature (linguistics); Sample (material); Feature extraction; Enhanced Data Rates for GSM Evolution; Computer vision; Chromatography; Chemistry; Algorithm","score_opus":0.028010900545343765,"score_gpt":0.33905168507901107,"score_spread":0.3110407845336673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134370449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60747313,0.00010298088,0.38864124,0.00035194622,0.00017820389,0.00007797086,0.0000057120724,0.00009563302,0.0030731696],"genre_scores_gemma":[0.87736595,0.0000036039949,0.121576265,0.0006896203,0.0003083209,0.00000678622,0.000004531206,0.00001805439,0.000026891212],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981409,0.000013438148,0.00020849972,0.00069209846,0.0004133103,0.00053175463],"domain_scores_gemma":[0.9989046,0.00027043853,0.00019121087,0.00038475412,0.00003748496,0.00021153459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004943953,0.00020274143,0.00024938953,0.00014224557,0.00086429523,0.00016919071,0.00027718453,0.00015124855,0.000092161325],"category_scores_gemma":[0.000059722915,0.000191819,0.000037384943,0.0007935379,0.00016790707,0.00015818504,0.000082315615,0.000694654,0.0000018547205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092728,0.000025730691,0.0010723602,0.000025320127,0.0000045189545,0.0000014748916,0.00012004711,0.024677288,0.9642156,0.00005227244,0.00001240071,0.009700304],"study_design_scores_gemma":[0.00025155244,0.000015695396,0.00082093256,0.000012050802,0.00002991208,0.0000050427566,0.00009375688,0.93024343,0.067773454,0.00052961794,0.00004273146,0.00018184935],"about_ca_topic_score_codex":0.00034866628,"about_ca_topic_score_gemma":0.00015626861,"teacher_disagreement_score":0.9055661,"about_ca_system_score_codex":0.00011044656,"about_ca_system_score_gemma":0.00012836081,"threshold_uncertainty_score":0.7822152},"labels":[],"label_agreement":null},{"id":"W2135030452","doi":"10.1088/0031-9155/50/15/n02","title":"Prostate cancer multi-feature analysis using trans-rectal ultrasound images","year":2005,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Support vector machine; Computer science; Mutual information; Pixel; Feature selection; Segmentation; Mathematics","score_opus":0.10432530020413798,"score_gpt":0.4130979164742869,"score_spread":0.3087726162701489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135030452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9855427,0.009351653,0.0029031602,0.0010808007,0.00003378006,0.000044343957,0.000058859667,0.000029329743,0.0009553371],"genre_scores_gemma":[0.9951031,0.002306118,0.0013452366,0.00025565206,0.0005353094,0.000008140048,0.00006716849,0.0000089002615,0.00037037232],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921703,0.00001700187,0.00017114068,0.00029118126,0.00005882187,0.0002448363],"domain_scores_gemma":[0.9995922,0.00012426788,0.00007326199,0.00012277593,0.000035756617,0.000051769493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078974655,0.00015924725,0.00043312102,0.00014160447,0.000054318014,0.000006545203,0.0000912104,0.000109855675,0.00037347953],"category_scores_gemma":[0.000039169707,0.00011532783,0.00006756242,0.0008734267,0.00024469453,0.00005173883,0.0000143315965,0.0002489332,6.6872735e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027086311,0.000085137115,0.16206656,0.000040812425,0.0005238816,0.0000026141236,0.00075901946,0.0002616808,0.8302868,0.000047466063,0.00015620972,0.0057427515],"study_design_scores_gemma":[0.0055375355,0.00016134999,0.015082576,0.00013180177,0.0069409413,0.000029809273,0.0039141215,0.010253128,0.95075417,0.0015871968,0.0044989,0.0011085023],"about_ca_topic_score_codex":0.0005644868,"about_ca_topic_score_gemma":0.00021465016,"teacher_disagreement_score":0.146984,"about_ca_system_score_codex":0.00004803808,"about_ca_system_score_gemma":0.000020591406,"threshold_uncertainty_score":0.47029322},"labels":[],"label_agreement":null},{"id":"W2135351699","doi":"10.1109/icassp.2006.1660544","title":"Automated System for Image Analysis of Yeast Colonies: A Novel Application in Functional Genomics","year":2006,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Computer science; Genomics; Image processing; Segmentation; Artificial intelligence; Image segmentation; Data mining; Digital image processing; Functional genomics; Computer vision; Computational biology; Pattern recognition (psychology); Image (mathematics); Biology; Gene; Genome; Genetics","score_opus":0.01062352609841457,"score_gpt":0.25529950325679746,"score_spread":0.24467597715838288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135351699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84800655,0.00005315524,0.1407595,0.000023049233,0.000010231786,0.000092580354,0.00021849516,0.00020305954,0.010633375],"genre_scores_gemma":[0.99529654,9.3743495e-7,0.003505369,0.0000065476006,0.00003345182,0.00006942349,0.0003574772,0.000008363648,0.00072189653],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992968,0.0000019506313,0.00030268848,0.00018274509,0.00009723417,0.00011858624],"domain_scores_gemma":[0.999533,0.00006157885,0.00013892932,0.0001596977,0.00008952264,0.00001729147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007027948,0.000080784186,0.00025705234,0.00032399796,0.000026924694,0.000012212249,0.00008203564,0.00007527265,0.00014650804],"category_scores_gemma":[0.000013430772,0.00007888777,0.0001288489,0.0013038535,0.000026520504,0.00003374595,0.000012811459,0.000033019223,0.0000040268537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000467421,0.00021014643,0.012682614,0.00015734218,0.00049189635,2.3589418e-7,0.000018519138,0.0038964706,0.9798184,0.002268469,0.00039350506,0.000015652491],"study_design_scores_gemma":[0.0007070129,0.000008112608,0.016427215,0.000004786077,0.0012294658,0.0000015942991,0.00059448223,0.37949064,0.601146,0.000023178945,0.00023623562,0.00013130513],"about_ca_topic_score_codex":0.0008983281,"about_ca_topic_score_gemma":0.00031488558,"teacher_disagreement_score":0.37867245,"about_ca_system_score_codex":0.0002304935,"about_ca_system_score_gemma":0.000033783763,"threshold_uncertainty_score":0.321695},"labels":[],"label_agreement":null},{"id":"W2135829152","doi":"10.1002/cjce.20364","title":"Modelling and control of different types of polymerization processes using neural networks technique: A review","year":2010,"lang":"en","type":"review","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Nonlinear system; Polymerization; Process (computing); Computer science; Implementation; Process control; Process engineering; Work (physics); Control engineering; Biological system; Polymer; Artificial intelligence; Materials science; Engineering; Mechanical engineering; Physics","score_opus":0.019551349476901663,"score_gpt":0.25549299263421243,"score_spread":0.23594164315731078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135829152","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044729642,0.9850158,0.014376588,0.000011663262,0.000028414275,0.00009059527,0.000014145347,0.000004183547,0.000011333694],"genre_scores_gemma":[0.15114649,0.84840876,0.00023247745,0.0000075292896,0.00015590232,0.000004911875,0.0000058261003,0.000035218713,0.0000028805657],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988792,0.000009848993,0.00069447706,0.00009449447,0.00013517776,0.00018677977],"domain_scores_gemma":[0.99866724,0.00017588113,0.00069169654,0.00014913909,0.00014218813,0.00017383444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012724259,0.00022698329,0.0012695247,0.00016363304,0.000024061177,0.000015005127,0.0002913499,0.00022584284,0.00003428746],"category_scores_gemma":[0.00024162042,0.00015064185,0.00021291304,0.00039468668,0.00007617151,0.000038721355,0.000012787145,0.0006901373,1.8690363e-8],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041355528,0.00010326468,0.00006118919,0.61191463,0.0033099316,0.000055587054,0.0001564664,0.14382051,0.0887517,0.0002092457,0.000031804037,0.15154429],"study_design_scores_gemma":[0.00080999505,0.00009292722,1.6410992e-7,0.23363619,0.026912676,0.0022705258,0.000012065866,0.43220878,0.28385076,0.00007769255,0.018393923,0.0017343129],"about_ca_topic_score_codex":0.00013857397,"about_ca_topic_score_gemma":0.000005220307,"teacher_disagreement_score":0.37827846,"about_ca_system_score_codex":0.00007278343,"about_ca_system_score_gemma":0.0003398102,"threshold_uncertainty_score":0.61429965},"labels":[],"label_agreement":null},{"id":"W2139492976","doi":"10.1002/cem.1171","title":"The PLS model space revisited","year":2008,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space (punctuation); Latent variable; Mathematics; Applied mathematics; Variable (mathematics); Computer science; Algorithm; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.030984206612608596,"score_gpt":0.2759892706595332,"score_spread":0.24500506404692463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139492976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85390794,0.033474274,0.01688759,0.0027130367,0.00022703643,0.00006532405,0.000014563682,0.00007544481,0.0926348],"genre_scores_gemma":[0.97561306,0.007376364,0.0025428173,0.00015610554,0.0004205895,9.56829e-7,0.0000014132103,0.0000274354,0.0138612315],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811727,0.000011198115,0.0006239793,0.00013537139,0.0007904279,0.0003217427],"domain_scores_gemma":[0.9977266,0.0004872237,0.0007343468,0.00035072336,0.00051919126,0.00018190892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004417593,0.00017487873,0.0004088901,0.0006278195,0.00029270552,0.000058388978,0.00062627846,0.00013735423,0.00021969376],"category_scores_gemma":[0.002310415,0.0001141678,0.00032516287,0.0037287427,0.00012367444,0.00017076822,0.000062343825,0.0005789546,0.000016243896],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090741855,0.001683209,0.06534486,0.000506368,0.0029666638,0.0009747489,0.0014465227,0.0040336903,0.36963055,0.006042359,0.53310907,0.013354518],"study_design_scores_gemma":[0.0021454121,0.00014794299,0.00076088816,0.000059838563,0.00066153123,0.0019247489,0.0005959661,0.005128399,0.8956507,0.0028205235,0.08947182,0.0006322153],"about_ca_topic_score_codex":0.0000016298785,"about_ca_topic_score_gemma":1.4165694e-7,"teacher_disagreement_score":0.52602017,"about_ca_system_score_codex":0.0001792109,"about_ca_system_score_gemma":0.00016578972,"threshold_uncertainty_score":0.46556276},"labels":[],"label_agreement":null},{"id":"W2139631152","doi":"10.1109/ultsym.2007.627","title":"P6C-7 Ultrasound RF Time Series for Detection of Prostate Cancer: Feature Selection and Frame Rate Analysis","year":2007,"lang":"en","type":"article","venue":"Proceedings/Proceedings - IEEE Ultrasonics Symposium","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Radio frequency; Artificial intelligence; Prostate cancer; Pattern recognition (psychology); Feature (linguistics); Frame rate; Frame (networking); Ultrasound; Feature selection; Series (stratigraphy); Ultrasonic sensor; Echo (communications protocol); Cancer; Computer vision; Medicine; Radiology; Telecommunications; Biology","score_opus":0.005347006532032316,"score_gpt":0.24299667730854974,"score_spread":0.2376496707765174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139631152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993949,0.0007321092,0.0005981475,0.00027533344,0.00012671687,0.00044807701,0.00013193322,0.00035670216,0.0033819731],"genre_scores_gemma":[0.99221045,0.0012640614,0.0017659431,0.000066323955,0.00051855546,0.00016920878,0.000037262493,0.00011218113,0.003856001],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99648625,0.0000026680568,0.00081293477,0.0011326118,0.0005335901,0.0010319646],"domain_scores_gemma":[0.99676704,0.0002943699,0.0009808332,0.00012564544,0.0015222447,0.00030983365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009435839,0.000681064,0.0010381166,0.00088990823,0.00053608126,0.00034811054,0.00036299223,0.0006688892,0.00011001054],"category_scores_gemma":[0.0004631642,0.00068725826,0.0004095534,0.0038969582,0.00022351572,0.00095418404,0.000041117688,0.00065517053,0.0000034159227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006738897,0.00011780977,0.06679362,0.0009612482,0.0013859353,3.8102226e-7,0.0011662954,0.000030194362,0.9280333,0.00023342209,0.00039868866,0.0002052054],"study_design_scores_gemma":[0.0008364695,0.00028848034,0.0020046805,0.00007931422,0.003923457,0.00008216952,0.0019041933,0.0009314588,0.9874742,0.00034372843,0.0014064631,0.0007253846],"about_ca_topic_score_codex":0.000112449416,"about_ca_topic_score_gemma":0.000047476417,"teacher_disagreement_score":0.06478894,"about_ca_system_score_codex":0.00041986842,"about_ca_system_score_gemma":0.00008324461,"threshold_uncertainty_score":0.99955785},"labels":[],"label_agreement":null},{"id":"W2140586247","doi":"10.1366/000370210793335142","title":"A Matrix-Based Two-Dimensional Regularization Algorithm for Signal-to-Noise Ratio Enhancement of Multidimensional Spectral Data","year":2010,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of Victoria; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Smoothing; Algorithm; Regularization (linguistics); Image processing; Entropy (arrow of time); Signal processing; Principle of maximum entropy; Mathematics; Matrix (chemical analysis); Computer science; Pattern recognition (psychology); Digital signal processing; Artificial intelligence; Image (mathematics); Physics; Computer vision; Chemistry","score_opus":0.014751781618329348,"score_gpt":0.3003013324462745,"score_spread":0.28554955082794514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140586247","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2717292,0.000107693246,0.72269595,0.00041318848,0.00023343168,0.0009128353,0.00077885174,0.00018716161,0.0029416853],"genre_scores_gemma":[0.480014,0.0000022277811,0.51656735,0.00021999574,0.00045821012,0.00014458346,0.0017855697,0.0000626517,0.00074542034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99699986,0.000011505803,0.0006834085,0.001000734,0.0006893043,0.0006151839],"domain_scores_gemma":[0.9976768,0.00028220558,0.00034440894,0.0012707649,0.00018316878,0.00024266345],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036083357,0.00041762315,0.0005854929,0.0002815196,0.00021713797,0.000046269157,0.0006875636,0.00019714866,0.003734034],"category_scores_gemma":[0.00006948466,0.00041857464,0.00014886084,0.000656251,0.00014317514,0.00011655759,0.00018511647,0.00037871036,0.000058440673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002755291,0.00049839885,0.000083093255,0.000054476448,0.00013362675,0.0000019721533,0.0000247478,0.0005230122,0.9909858,0.005032569,0.001794668,0.0005920861],"study_design_scores_gemma":[0.0021969168,0.0001051869,0.00003526999,0.000015193311,0.00025622026,0.0000032433786,0.000028263823,0.056983426,0.9382558,0.0011319848,0.00058127206,0.0004072566],"about_ca_topic_score_codex":0.000043971206,"about_ca_topic_score_gemma":0.000020650228,"teacher_disagreement_score":0.20828478,"about_ca_system_score_codex":0.00011231348,"about_ca_system_score_gemma":0.00032547995,"threshold_uncertainty_score":0.9998266},"labels":[],"label_agreement":null},{"id":"W2142144738","doi":"10.1255/nirn.1086","title":"Detection of Hard Vitreous and Starchy Kernels in Amber Durum Wheat Samples Using Hyperspectral Imaging (GRL Number M306)","year":2008,"lang":"en","type":"article","venue":"NIR news","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Grading (engineering); Grain quality; Near-infrared spectroscopy; Environmental science; Remote sensing; Mathematics; Physics; Biology; Horticulture; Geology; Optics; Ecology","score_opus":0.033651336893303754,"score_gpt":0.2793279537746689,"score_spread":0.24567661688136516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142144738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946634,0.0007505133,0.00038049565,0.00006456603,0.00003524749,0.000040824187,0.000012137176,0.000042750296,0.0040100417],"genre_scores_gemma":[0.9968144,0.00025791742,0.00213878,0.000046786492,0.00011677766,0.000003508148,0.0000045500665,0.000025402356,0.0005918552],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885935,0.000018714232,0.0002862098,0.0003163846,0.00019796267,0.00032139372],"domain_scores_gemma":[0.99949217,0.00007364121,0.000101085534,0.00021790492,0.000039333285,0.000075856835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058118814,0.00017728894,0.00031653215,0.0001424231,0.00009268816,0.000020517198,0.00009155274,0.00008481189,0.0006436832],"category_scores_gemma":[0.00006500881,0.00017900886,0.000087380526,0.00040135995,0.00013240952,0.00015382585,0.00004435135,0.0002134299,0.0000080551545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004619594,0.00005337206,0.30698127,0.000038390503,0.000028109609,0.000027206825,0.00038824778,0.000018294711,0.69165945,0.000006563858,0.000050935374,0.00070200366],"study_design_scores_gemma":[0.00067735004,0.000012448568,0.014022782,0.000028769182,0.00007106348,0.00029037363,0.0010824598,0.0005944407,0.9823304,0.00019494173,0.0004630106,0.00023195904],"about_ca_topic_score_codex":0.0037032117,"about_ca_topic_score_gemma":0.00027907707,"teacher_disagreement_score":0.29295847,"about_ca_system_score_codex":0.00016275946,"about_ca_system_score_gemma":0.000041848776,"threshold_uncertainty_score":0.7299769},"labels":[],"label_agreement":null},{"id":"W2142336944","doi":"10.1109/icassp.2010.5495765","title":"Linear feature extraction using sufficient statistic","year":2010,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Heteroscedasticity; Discriminative model; Computer science; Feature (linguistics); Feature extraction; Statistic; Linear discriminant analysis; Dimensionality reduction; Mathematics; Sufficient statistic; Gaussian; Artificial intelligence; Algorithm; Feature vector; Feature selection; Statistics; Machine learning","score_opus":0.019118941843435225,"score_gpt":0.33310641945485453,"score_spread":0.3139874776114193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142336944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9458674,0.000055949025,0.020043992,0.00008343168,0.00014520528,0.000016745507,0.000011219234,0.00008055077,0.033695485],"genre_scores_gemma":[0.9703236,0.0000044849166,0.017162086,0.00004768259,0.0002217466,0.0000012284073,0.000016010325,0.00001124364,0.012211891],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994425,0.0000022375723,0.00009199682,0.00016552671,0.00013842048,0.00015928244],"domain_scores_gemma":[0.99960124,0.00005662073,0.00004972603,0.00018439423,0.0000454364,0.00006256061],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000041734824,0.00008864809,0.00009672859,0.000061398954,0.00008502248,0.000027204891,0.00008074171,0.00011180713,0.010910341],"category_scores_gemma":[0.000097995464,0.000075300326,0.000042069987,0.0002384384,0.000030390385,0.000052965544,0.000015732203,0.00033897927,0.000052289364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056758768,0.00006920317,0.0016484938,0.000016183407,0.000018561932,0.0000052087953,0.000020535004,0.00003728827,0.9963816,0.00056255935,0.001050431,0.0001842916],"study_design_scores_gemma":[0.000141597,0.0000071881827,0.00036697809,0.0000026235095,0.00009677308,0.00003566787,0.00024604873,0.011675404,0.98086035,0.00007718294,0.0063463124,0.00014386814],"about_ca_topic_score_codex":0.00009159468,"about_ca_topic_score_gemma":0.0000300157,"teacher_disagreement_score":0.024456207,"about_ca_system_score_codex":0.000031086274,"about_ca_system_score_gemma":0.000028756771,"threshold_uncertainty_score":0.9899938},"labels":[],"label_agreement":null},{"id":"W2142540406","doi":"10.5539/jas.v1n2p3","title":"Development of Physical and Optical Methods for In-shell Brazil Nuts Sorting and Aflatoxin Reduction","year":2009,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade Federal do Paraná; Universidade Federal de Santa Catarina","keywords":"Sorting; Nut; Shell (structure); Aflatoxin; Chromaticity; Brazil nut; Homogeneous; Mathematics; Food science; Materials science; Composite material; Chemistry; Physics; Optics; Combinatorics; Algorithm; Acoustics","score_opus":0.01927992649122574,"score_gpt":0.3569170549158312,"score_spread":0.33763712842460547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142540406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99859285,0.00021685135,0.0004984019,0.00019198419,0.000016292484,0.000021146163,1.2027857e-7,0.0000025239685,0.00045984337],"genre_scores_gemma":[0.89241904,0.00001557217,0.10746232,0.0000060843345,0.0000671764,3.7560758e-7,1.3548691e-7,8.940238e-7,0.000028401439],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924505,0.000005223832,0.00029556025,0.00011941864,0.00018626923,0.00014846318],"domain_scores_gemma":[0.9994595,0.00006954212,0.0002348726,0.000030225878,0.000120321456,0.000085523854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004452369,0.000069025504,0.00019643402,0.00007695966,0.00008432967,0.000033580243,0.00010773207,0.00002966121,0.0000031185293],"category_scores_gemma":[0.00021948855,0.0000398022,0.000039314065,0.00047224126,0.00012122773,0.00026614385,0.000021941045,0.000109414395,6.044181e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011233258,0.000045902485,0.00022010924,0.000012407694,0.000005655399,3.0232374e-7,0.00057975744,0.000007733301,0.9758395,0.00018421539,0.0000057806947,0.023087379],"study_design_scores_gemma":[0.00019655794,0.00006109097,0.06366368,0.000032553082,0.000024936877,0.00008081109,0.0009952523,0.00006915877,0.93443495,0.00036233134,0.00001699095,0.000061696606],"about_ca_topic_score_codex":7.8749656e-7,"about_ca_topic_score_gemma":4.424083e-7,"teacher_disagreement_score":0.10696392,"about_ca_system_score_codex":0.000065124215,"about_ca_system_score_gemma":0.000050619026,"threshold_uncertainty_score":0.16230866},"labels":[],"label_agreement":null},{"id":"W2143087872","doi":"10.7551/mitpress/7503.003.0118","title":"Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure","year":2007,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Series (stratigraphy); Bayesian probability; Computer science; Econometrics; Data mining; Artificial intelligence; Mathematics; Geology","score_opus":0.022645555613899023,"score_gpt":0.2383648771612751,"score_spread":0.21571932154737608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143087872","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044563383,0.0014947784,0.00011187107,0.000006031486,0.00002623588,0.00020767817,0.00033553547,0.000047966245,0.95320654],"genre_scores_gemma":[0.7649021,0.000032542368,0.00009524305,0.0000068745717,0.00004891224,0.000006524989,0.000024345049,0.000037975115,0.23484549],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988507,0.000009492586,0.00039678047,0.00024121594,0.00032455983,0.0001772314],"domain_scores_gemma":[0.9988774,0.00008086574,0.00047814907,0.0004501194,0.0000774728,0.000035982364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057454356,0.00029945243,0.0005590236,0.00015632133,0.00003557092,0.000015833462,0.00035748663,0.00033764308,0.00074193213],"category_scores_gemma":[0.000011609271,0.00019503527,0.000098314165,0.000024196024,0.00028505168,0.000032097258,0.000069075046,0.00040230955,7.912805e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033365735,0.00013170703,0.0017031438,0.0045571076,0.005130497,0.00009172446,0.0061757816,0.00006855614,0.94535697,0.012375794,0.00015301714,0.020919114],"study_design_scores_gemma":[0.00030603452,0.00009145029,0.00012560877,0.00033818145,0.0004670831,0.000015312777,0.000049415543,0.00001970632,0.9937765,0.0037635735,0.0007743782,0.00027272897],"about_ca_topic_score_codex":0.0002925799,"about_ca_topic_score_gemma":0.00034174824,"teacher_disagreement_score":0.7203387,"about_ca_system_score_codex":0.000050977043,"about_ca_system_score_gemma":0.000038061884,"threshold_uncertainty_score":0.8123637},"labels":[],"label_agreement":null},{"id":"W2144374153","doi":"10.1109/isbi.2006.1624837","title":"A Hybrid Data Analysis and Mesh Refinement Paradigm for Conformal Voxel Spectroscopy","year":2006,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Cuboid; Voxel; SIGNAL (programming language); Conformal map; Computer science; Spectroscopy; Algorithm; Artificial intelligence; Physics; Mathematics; Geometry","score_opus":0.022085518979373314,"score_gpt":0.29272447395151324,"score_spread":0.2706389549721399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144374153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55965877,0.00215969,0.23984163,0.0014853289,0.00006410476,0.00029593875,0.0015603602,0.00039194126,0.19454224],"genre_scores_gemma":[0.9715283,0.00010560903,0.014607957,0.0001855483,0.0002278956,0.000028566952,0.0020689233,0.000019110323,0.011228128],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854517,0.0000037394439,0.00035418908,0.00051419577,0.00020179077,0.0003809259],"domain_scores_gemma":[0.99882936,0.00009877711,0.00012087656,0.0008337603,0.00003087541,0.00008632481],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014650662,0.00020509506,0.0004037078,0.00021815572,0.000131081,0.00010494926,0.00038397603,0.000054529304,0.0029803403],"category_scores_gemma":[0.0000338482,0.00017663192,0.00012619352,0.0004749085,0.00006767909,0.00016669145,0.00014797131,0.000088852816,0.000008978502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086139946,0.0018308131,0.1946874,0.0009015995,0.016147908,0.00006117631,0.00010191349,0.00012994149,0.3582315,0.11145161,0.31227693,0.0033178127],"study_design_scores_gemma":[0.001407852,0.000059125432,0.0018460153,0.0000046100586,0.00492234,0.0000129439095,0.0001257339,0.013949205,0.941931,0.0025192655,0.032721873,0.0005000527],"about_ca_topic_score_codex":0.0008006503,"about_ca_topic_score_gemma":0.00023234126,"teacher_disagreement_score":0.58369946,"about_ca_system_score_codex":0.00004839284,"about_ca_system_score_gemma":0.00003431623,"threshold_uncertainty_score":0.99793106},"labels":[],"label_agreement":null},{"id":"W2144700031","doi":"10.1016/j.ijpharm.2006.12.014","title":"Cohesive, multicomponent, dense powder flow characterization by NIR","year":2006,"lang":"en","type":"article","venue":"International Journal of Pharmaceutics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Food and Drug Administration","keywords":"Process analytical technology; Materials science; Dosage form; Analytical Chemistry (journal); Tableting; Chromatography; Chemistry; Chemical engineering; Composite material","score_opus":0.017289501798626424,"score_gpt":0.3188339968396803,"score_spread":0.30154449504105385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144700031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989059,0.0006715273,0.006196521,0.0010791202,0.0006738668,0.000027187849,0.00015741806,0.000028835297,0.0021065795],"genre_scores_gemma":[0.9953859,0.00033400688,0.00076999754,0.00039972196,0.0009594226,0.000001202013,0.00016679877,0.000020904181,0.0019620839],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985997,0.000016440757,0.0005195333,0.00011895853,0.0005766171,0.0001687585],"domain_scores_gemma":[0.9987001,0.000082978564,0.00051652844,0.00008009116,0.00053300784,0.00008728383],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010246082,0.00014692315,0.00019454716,0.00019381853,0.000050375485,0.00008284053,0.00040671826,0.00007609005,0.00186063],"category_scores_gemma":[0.000055409557,0.00013828657,0.0001453946,0.00013375407,0.00004567992,0.00022709285,0.00004023173,0.00027218126,0.000025625255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010635115,0.00031583474,0.009783619,0.0000104616665,0.00030784274,0.00006715819,0.00003706118,0.00015442468,0.98526376,0.00003774807,0.0023215963,0.0015941327],"study_design_scores_gemma":[0.0012579907,0.000009669377,0.0007228554,0.000019436973,0.00018003237,0.00016200182,0.000037127495,0.0032193507,0.9321391,0.0000819206,0.06202965,0.00014084482],"about_ca_topic_score_codex":0.000012809278,"about_ca_topic_score_gemma":7.7629e-7,"teacher_disagreement_score":0.05970805,"about_ca_system_score_codex":0.00019522634,"about_ca_system_score_gemma":0.000044674714,"threshold_uncertainty_score":0.9990518},"labels":[],"label_agreement":null},{"id":"W2145892124","doi":"10.1366/12-06798","title":"Principles, Performance, and Applications of Spectral Reconstitution (SR) in Quantitative Analysis of Oils by Fourier Transform Infrared Spectroscopy (FT-IR)","year":2013,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Diluent; Dilution; Analytical Chemistry (journal); Chemistry; Fourier transform infrared spectroscopy; Infrared spectroscopy; Spectroscopy; Infrared; Fourier transform; Absorption spectroscopy; Matrix (chemical analysis); Optics; Chromatography; Thermodynamics; Nuclear chemistry","score_opus":0.011900228443099762,"score_gpt":0.25746745184118924,"score_spread":0.24556722339808948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145892124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8179732,0.00067780114,0.012454605,0.00005737604,0.000014938422,0.00050877844,0.00015038338,0.00006309838,0.1680998],"genre_scores_gemma":[0.9781063,0.0014767803,0.018828357,0.000033085133,0.000041654723,0.00036606457,0.00022834551,0.000034439447,0.00088495156],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997183,0.000017411814,0.0011098085,0.0006666313,0.00041863055,0.00060448056],"domain_scores_gemma":[0.9984002,0.00017513064,0.0005357904,0.0006043971,0.00011726014,0.00016722141],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023834602,0.0004151605,0.0011246797,0.0010154891,0.00011983704,0.000045585894,0.0003740548,0.00024616937,0.0021488038],"category_scores_gemma":[0.00003100427,0.00041650276,0.00021541904,0.0032835177,0.00053113763,0.00028302133,0.00005086757,0.0004161814,0.000019380785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017972982,0.0004477806,0.026784059,0.0002680853,0.0010463401,6.686048e-7,0.00044594496,0.00009961591,0.9470124,0.022727646,0.00039601582,0.0005917096],"study_design_scores_gemma":[0.00100762,0.00012942805,0.005681919,0.000026695365,0.001098995,0.0000020040693,0.00070185674,0.0019218262,0.9860634,0.0019744402,0.0009788186,0.00041300338],"about_ca_topic_score_codex":0.0003213215,"about_ca_topic_score_gemma":0.00011482171,"teacher_disagreement_score":0.16721486,"about_ca_system_score_codex":0.0002403655,"about_ca_system_score_gemma":0.0001271171,"threshold_uncertainty_score":0.9998287},"labels":[],"label_agreement":null},{"id":"W2146353198","doi":"10.5539/jas.v7n2p152","title":"Application of Bio-Image Analysis for Classification of Different Ripening Stages of Banana","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hue; Ripening; Histogram; Color histogram; Pixel; Artificial intelligence; Computer science; Image processing; Pattern recognition (psychology); Computer vision; Banana peel; Mathematics; Color image; Image (mathematics); Food science; Chemistry","score_opus":0.03060623915160494,"score_gpt":0.306167146007431,"score_spread":0.27556090685582607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146353198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99089634,0.00015720978,0.008367559,0.00010867681,0.000017081762,0.000035301953,0.000010641546,0.0000027135459,0.00040444697],"genre_scores_gemma":[0.9974667,0.000023133513,0.0023970315,0.0000015608986,0.000036237896,0.0000016212251,0.0000049951423,0.00000155611,0.00006718348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867165,0.000007897546,0.0005434664,0.00011840565,0.0005438611,0.00011471265],"domain_scores_gemma":[0.9971228,0.0000783857,0.0014221703,0.00012644149,0.0011598308,0.000090324196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037777948,0.000077039025,0.00034874366,0.0002566997,0.00003891648,0.00001552793,0.00039605206,0.000037426154,0.000014671683],"category_scores_gemma":[0.00028036672,0.0000426815,0.00020077644,0.0020232317,0.00020144449,0.00025404274,0.000026741514,0.000063575644,1.6462246e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016505457,0.00007907303,0.05228236,0.000035946887,0.00010139775,5.0731092e-8,0.00013681996,0.00008514357,0.94676405,0.00015003301,0.000053486823,0.0002951498],"study_design_scores_gemma":[0.00015054272,0.000060929528,0.3283003,0.0000107928245,0.00040773497,0.0000016687591,0.0014948025,0.00039747878,0.66907406,0.00005210256,0.000008011293,0.000041554176],"about_ca_topic_score_codex":0.000035273624,"about_ca_topic_score_gemma":0.000007056059,"teacher_disagreement_score":0.27768996,"about_ca_system_score_codex":0.00010191998,"about_ca_system_score_gemma":0.000057486388,"threshold_uncertainty_score":0.17405011},"labels":[],"label_agreement":null},{"id":"W2147178948","doi":"10.1109/igarss.1990.688562","title":"Spectral Reflectance Characteristics Of Wheat Residues","year":2005,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Reflectivity; Remote sensing; Environmental science; Materials science; Optics; Geology; Physics","score_opus":0.016469777892626904,"score_gpt":0.28935415260124436,"score_spread":0.2728843747086175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147178948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7300624,0.00029954046,0.0001261486,0.00026902705,0.000012512677,0.000010169957,0.0000079080355,0.00006353687,0.26914874],"genre_scores_gemma":[0.97270656,0.0001535924,0.0028079597,0.00009541146,0.000282147,0.0000018564119,0.00000796056,0.000011027012,0.023933485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992443,0.0000033546528,0.00024569116,0.00016350998,0.00014790971,0.0001952529],"domain_scores_gemma":[0.9995273,0.000041608684,0.00008464331,0.000252619,0.0000434198,0.000050457485],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003879135,0.00010470659,0.0002239806,0.00006222737,0.000031673546,0.000012115969,0.00016197469,0.00006894871,0.009363134],"category_scores_gemma":[0.000051126204,0.000092225506,0.000071541566,0.0002386374,0.000050879335,0.00006912305,0.000021131253,0.0001071831,0.00005814234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036930305,0.00010483742,0.018071169,0.00005287697,0.000056939796,0.0000026964838,0.00006792362,0.0000015481347,0.9757963,0.0020002744,0.002665091,0.0011434193],"study_design_scores_gemma":[0.00013811493,0.00001490854,0.0024606553,0.000008584026,0.000049037477,0.0000044913163,0.000060918803,0.000051440034,0.99354357,0.00016221678,0.0033951192,0.00011096145],"about_ca_topic_score_codex":0.000018860743,"about_ca_topic_score_gemma":0.000028592443,"teacher_disagreement_score":0.24521525,"about_ca_system_score_codex":0.0000531068,"about_ca_system_score_gemma":0.000022075172,"threshold_uncertainty_score":0.99154246},"labels":[],"label_agreement":null},{"id":"W2147333290","doi":"10.1002/cjs.11166","title":"Fisher information matrix: A tool for dimension reduction, projection pursuit, independent component analysis, and more","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Fisher information; Dimension (graph theory); Dimensionality reduction; Principal component analysis; Projection pursuit; Matrix (chemical analysis); Mathematics; Sufficient dimension reduction; Projection (relational algebra); Statistics; Data Matrix; Markov chain; Multivariate statistics; Computer science; Algorithm; Pattern recognition (psychology); Artificial intelligence; Combinatorics","score_opus":0.012177933873115835,"score_gpt":0.26129162654638305,"score_spread":0.24911369267326722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147333290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80927676,0.0010471905,0.18772762,0.00034919055,0.00042607813,0.00015412453,0.0007296274,0.000011556227,0.00027782618],"genre_scores_gemma":[0.98746,0.000048546244,0.011861513,0.0000439522,0.0001997468,0.000004292698,0.00013928593,0.000008131757,0.00023452475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991052,0.000008804511,0.0003946287,0.00006106643,0.00019555373,0.0002347606],"domain_scores_gemma":[0.9988855,0.000047657846,0.00034950918,0.00008653926,0.00032236238,0.00030840348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002230649,0.00010153965,0.00021354573,0.0005550826,0.00014669946,0.00009040246,0.00007363679,0.0000809252,0.000231233],"category_scores_gemma":[0.0001749914,0.00009511085,0.00006540662,0.00037291506,0.00004718884,0.00040579558,0.0000065376917,0.0001492328,0.0000017794198],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005171271,0.00032898344,0.7344409,0.0013090156,0.0076003284,0.00004369063,0.012499085,0.0011737577,0.020630497,0.008589595,0.1690823,0.04378472],"study_design_scores_gemma":[0.008729004,0.00094088365,0.6264223,0.00024991343,0.031523935,0.0031076795,0.028612021,0.014119448,0.11730793,0.0034340785,0.16261394,0.002938885],"about_ca_topic_score_codex":0.0012776059,"about_ca_topic_score_gemma":0.0005152101,"teacher_disagreement_score":0.17818321,"about_ca_system_score_codex":0.00027724687,"about_ca_system_score_gemma":0.0002302876,"threshold_uncertainty_score":0.38785082},"labels":[],"label_agreement":null},{"id":"W2148574115","doi":"10.1139/w10-062","title":"FTIR spectroscopic discrimination of<i>Saccharomyces cerevisiae</i>and<i>Saccharomyces bayanus</i>strains","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Microbiology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Saccharomyces cerevisiae; Saccharomyces; Biology; Yeast; Strain (injury); Genetics; Microbiology; Anatomy","score_opus":0.009685399199464494,"score_gpt":0.23571711716384725,"score_spread":0.22603171796438276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148574115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925669,0.0029687434,0.000027994556,0.0008460978,0.00034015422,0.00003560255,0.000104412175,0.000007561321,0.0031025598],"genre_scores_gemma":[0.99760807,0.00023543407,0.0012535839,0.00011820321,0.0003006081,0.0000012134345,0.000023306386,0.000018303359,0.0004412844],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872863,0.000026245216,0.0005637904,0.00021390428,0.000061998835,0.00040540332],"domain_scores_gemma":[0.9986159,0.00009559519,0.00051134184,0.00021636083,0.00018480534,0.0003759727],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021835313,0.0002103574,0.0005244189,0.00045294635,0.000115348725,0.00003972069,0.00042963558,0.00025515872,0.0036582632],"category_scores_gemma":[0.00021529515,0.00018369843,0.0001594454,0.00025135186,0.0004224348,0.00013458986,0.00002213099,0.0005969942,0.000010214388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007699959,0.00002022918,0.013515738,0.00008920472,0.00011504017,0.00002853851,0.00025846946,3.5772106e-7,0.98363006,0.0007340153,0.0010673202,0.00053332036],"study_design_scores_gemma":[0.0006075606,0.00012820317,0.0035337296,0.0000735586,0.00026782748,0.00095571025,0.0006793101,0.0000017418877,0.97900486,0.00062237587,0.013882185,0.00024292995],"about_ca_topic_score_codex":0.0011137115,"about_ca_topic_score_gemma":0.009959835,"teacher_disagreement_score":0.012814865,"about_ca_system_score_codex":0.000069818554,"about_ca_system_score_gemma":0.00058937736,"threshold_uncertainty_score":0.9972525},"labels":[],"label_agreement":null},{"id":"W2149706364","doi":"10.1002/mrc.1271","title":"The advantages of forward linear prediction over multiple aliasing for obtaining high‐resolution HSQC spectra in systems with extreme spectral crowding","year":2003,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heteronuclear single quantum coherence spectroscopy; Aliasing; Chemistry; Spectral line; Resolution (logic); Algorithm; Biological system; Statistical physics; Two-dimensional nuclear magnetic resonance spectroscopy; Artificial intelligence; Computer science; Physics; Quantum mechanics","score_opus":0.016415046002463747,"score_gpt":0.2516703493750728,"score_spread":0.23525530337260908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149706364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98260295,0.012787124,0.00087370805,0.00002740932,0.000065130094,0.000206918,0.000045232417,0.000043513264,0.0033480073],"genre_scores_gemma":[0.9956092,0.00047544824,0.0022375803,0.000004486639,0.00014963244,0.00011121435,0.000020290565,0.000036608508,0.0013555391],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99800116,0.000023361239,0.0006156477,0.0004633561,0.00033223187,0.0005642667],"domain_scores_gemma":[0.9987446,0.0005022408,0.00024262154,0.0003844773,0.00006715037,0.000058923397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003722156,0.00026154314,0.00039094148,0.000074391646,0.00012962644,0.000053035557,0.00023510888,0.00018414778,0.00010927696],"category_scores_gemma":[0.00053887285,0.00021953118,0.00008560607,0.00057257694,0.00015287987,0.00010775355,0.00002315077,0.00032397974,5.273226e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006854729,0.00025455162,0.40630063,0.00095574185,0.000045537174,0.000031313717,0.00038257753,0.0060920543,0.58359367,0.00029622306,0.00013250264,0.0012297578],"study_design_scores_gemma":[0.0036177563,0.0001232746,0.015621524,0.0006427672,0.000093555085,0.000045591478,0.0024750598,0.03566368,0.937049,0.00026321,0.0039576935,0.00044689237],"about_ca_topic_score_codex":0.0002150886,"about_ca_topic_score_gemma":0.00006712944,"teacher_disagreement_score":0.3906791,"about_ca_system_score_codex":0.00033001794,"about_ca_system_score_gemma":0.00009043737,"threshold_uncertainty_score":0.8952222},"labels":[],"label_agreement":null},{"id":"W2149722246","doi":"10.1109/hpcsa.2002.1019145","title":"A fast algorithm for the computation of robust Q-mode principal component analysis in L","year":2005,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Principal component analysis; Computation; Computer science; Algorithm; Mode (computer interface); Component (thermodynamics); Algorithm design; Artificial intelligence; Physics","score_opus":0.023391902714239476,"score_gpt":0.307674444884368,"score_spread":0.28428254217012855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149722246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17485557,0.00024328615,0.8210686,0.00028518648,0.0000088678025,0.00007319252,0.000026350643,0.000026553347,0.0034124195],"genre_scores_gemma":[0.9639441,0.000016507016,0.03513821,0.000039346465,0.000057088466,0.000017616298,0.000039204002,0.0000059468434,0.0007419779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992824,0.0000058601217,0.00026876925,0.00015326824,0.00014735397,0.00014234289],"domain_scores_gemma":[0.99949825,0.00017848997,0.000097462136,0.00014512225,0.000054774617,0.000025900983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009799829,0.000086010885,0.00022986383,0.00018007113,0.00004074688,0.000013018321,0.00013964872,0.000048604885,0.00041861556],"category_scores_gemma":[0.000016755539,0.000060460956,0.00016451789,0.0008296,0.000035342397,0.00003812217,0.00002564002,0.00007075154,0.0000024941398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059822636,0.0009072234,0.021172239,0.00009029062,0.002629073,0.0000012619082,0.000709086,0.8575792,0.016782751,0.0005906364,0.00037200487,0.09910643],"study_design_scores_gemma":[0.00041379614,0.000008152204,0.0022138632,0.0000027859408,0.0005703108,5.5694727e-7,0.0004973792,0.9217711,0.074263155,0.000023998127,0.00015869914,0.00007616575],"about_ca_topic_score_codex":0.00034670837,"about_ca_topic_score_gemma":0.00041297724,"teacher_disagreement_score":0.78908855,"about_ca_system_score_codex":0.000074282696,"about_ca_system_score_gemma":0.000016029588,"threshold_uncertainty_score":0.45835468},"labels":[],"label_agreement":null},{"id":"W2151592614","doi":"10.1002/aic.11494","title":"Optimal purchasing of raw materials: A data‐driven approach","year":2008,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Raw material; Purchasing; Sequential quadratic programming; Process (computing); Quality (philosophy); Product (mathematics); Refining (metallurgy); Process engineering; Purchasing process; Coke; Computer science; Mathematical optimization; Nonlinear programming; Quadratic programming; Engineering; Mathematics; Nonlinear system; Waste management; Materials science; Operations management; Chemistry","score_opus":0.06897971071900155,"score_gpt":0.3050988430664795,"score_spread":0.23611913234747794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151592614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98550475,0.00084117503,0.005678944,0.00005687563,0.000060444454,0.00001833334,0.00003470606,0.000025471129,0.007779275],"genre_scores_gemma":[0.98168373,0.0003773475,0.016500842,0.000029882834,0.0005164563,9.42225e-7,0.00005585355,0.000020574009,0.000814356],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879396,0.000024524,0.00040103638,0.0002021543,0.0003132663,0.00026505286],"domain_scores_gemma":[0.9990108,0.00004858008,0.0003040099,0.00045467034,0.00007459722,0.00010732186],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024253066,0.00013915985,0.00033527034,0.00012061935,0.00018028413,0.000039923914,0.0006448387,0.0001054882,0.002431684],"category_scores_gemma":[0.00013774063,0.00011829943,0.000077487006,0.00026631568,0.0001226377,0.00024137006,0.00017073746,0.00030642754,0.000012396307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094282506,0.00027675676,0.0021822632,0.00007310264,0.00032240897,0.000080866834,0.0005036618,0.000512831,0.98863083,0.000031403353,0.0070776134,0.00021397074],"study_design_scores_gemma":[0.0007182171,0.00003284636,0.00031428022,0.000026401778,0.0002435523,0.0026367477,0.0006772778,0.0010483351,0.9931844,0.000034698332,0.0008792629,0.0002039756],"about_ca_topic_score_codex":0.000022457074,"about_ca_topic_score_gemma":3.231517e-7,"teacher_disagreement_score":0.010821898,"about_ca_system_score_codex":0.000046345438,"about_ca_system_score_gemma":0.000101674785,"threshold_uncertainty_score":0.9984802},"labels":[],"label_agreement":null},{"id":"W2152585669","doi":"10.1002/prep.200600034","title":"Study of the Daughter Products of Akardite‐II","year":2006,"lang":"en","type":"article","venue":"Propellants Explosives Pyrotechnics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Daughter; Diphenylamine; Chemistry; Medicinal chemistry; Organic chemistry; Law; Political science","score_opus":0.020290609979491846,"score_gpt":0.24236588975296142,"score_spread":0.22207527977346958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152585669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983057,0.00058638153,0.000029043442,0.000093330294,0.00004502921,0.0003708831,0.000021481961,0.00006315769,0.015733691],"genre_scores_gemma":[0.99537075,0.00003886675,0.00017922313,0.000008107628,0.0001019954,0.000036130477,0.0000051834522,0.000029986973,0.004229768],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984076,0.000025957323,0.0004944462,0.00034182152,0.00046430537,0.0002658563],"domain_scores_gemma":[0.9984573,0.00003415819,0.00040234884,0.00091152056,0.00017046223,0.000024210642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012223246,0.00020645025,0.0003936408,0.000108284024,0.00012732457,0.0000105151485,0.0006590226,0.00010555996,0.00018090606],"category_scores_gemma":[0.00011558499,0.00013370866,0.000108808235,0.0008227033,0.0001437653,0.000063532076,0.00038680236,0.00022950099,0.0000056315976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050918792,0.0021708363,0.017933153,0.00025632992,0.00015134182,0.0000066332327,0.0005485385,0.000068978945,0.9766727,0.00020176459,0.0016642509,0.00027457162],"study_design_scores_gemma":[0.0004268867,0.0001278595,0.0010105235,0.000044416505,0.00018101708,0.000008400601,0.00090002746,0.0000144777405,0.996056,0.0003486635,0.00071975216,0.00016194886],"about_ca_topic_score_codex":0.000095813644,"about_ca_topic_score_gemma":0.000008534624,"teacher_disagreement_score":0.019383343,"about_ca_system_score_codex":0.00003055638,"about_ca_system_score_gemma":0.000045226414,"threshold_uncertainty_score":0.54524803},"labels":[],"label_agreement":null},{"id":"W2153078532","doi":"10.1243/09544070jauto562","title":"An optical method for automated roadside detection and counting of vehicle occupants","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Occupancy; Enforcement; Officer; Transport engineering; Law enforcement; Population; Statistic; Traffic flow (computer networking); Traffic congestion; Engineering; Business; Computer science; Computer security; Geography; Civil engineering; Statistics; Environmental health","score_opus":0.012429455723481808,"score_gpt":0.26437758014239426,"score_spread":0.25194812441891246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153078532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9243633,0.00017766021,0.075113036,0.000018502376,0.00012992077,0.00008165589,0.0000057149364,0.00006696231,0.00004321838],"genre_scores_gemma":[0.9696276,0.000047228492,0.030201372,0.0000037031673,0.00009198713,0.0000057404777,4.7839967e-7,0.000016517806,0.0000053704134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985599,0.0000030871197,0.0007697148,0.00013755118,0.00033926097,0.00019047935],"domain_scores_gemma":[0.998647,0.0001405261,0.0005724201,0.00008556002,0.0004552913,0.00009921108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005690131,0.00015847562,0.0004945142,0.00020079468,0.00006283439,0.000008957829,0.0002639608,0.00015468047,0.0000106057205],"category_scores_gemma":[0.0009427758,0.00012909675,0.00019422118,0.00041533803,0.00007256972,0.00029782322,0.000035890655,0.00025818511,6.994175e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076059674,0.00008667014,0.00015082047,0.00032870236,0.00013814576,6.4620576e-7,0.00007583807,0.056341466,0.94151115,0.0010261829,0.00001587078,0.000248436],"study_design_scores_gemma":[0.00045452992,0.00012634601,0.00013284347,0.0001334632,0.00014438205,0.000096012795,0.00010585969,0.35159332,0.64706564,0.00002611355,0.000045176443,0.000076344724],"about_ca_topic_score_codex":0.000005691368,"about_ca_topic_score_gemma":1.8026675e-7,"teacher_disagreement_score":0.29525185,"about_ca_system_score_codex":0.00006362968,"about_ca_system_score_gemma":0.00005823104,"threshold_uncertainty_score":0.5264413},"labels":[],"label_agreement":null},{"id":"W2153979575","doi":"10.1016/j.anifeedsci.2003.08.006","title":"Near-infrared evaluation of wet mink diets","year":2003,"lang":"en","type":"article","venue":"Animal Feed Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture","funders":"University of Pittsburgh","keywords":"Calibration; Mink; Population; Partial least squares regression; Standard error; Mathematics; Animal science; Analytical Chemistry (journal); Biology; Chemistry; Statistics; Chromatography; Ecology","score_opus":0.023813965251949093,"score_gpt":0.3044802505367151,"score_spread":0.280666285284766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153979575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95200145,0.0007175504,0.000010346384,0.00020598114,0.000013905885,0.00003149273,0.0000016440886,0.000046633464,0.046971],"genre_scores_gemma":[0.99928516,0.000025170855,0.00044818828,0.000016795233,0.000005244699,0.0000073679957,6.4316293e-7,0.0000036110407,0.00020781747],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989508,0.00000637532,0.00014057297,0.00026683125,0.00041016942,0.0002252715],"domain_scores_gemma":[0.99925506,0.000024588388,0.0000764951,0.00021990528,0.00038415758,0.000039767816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053788046,0.0000785734,0.00014478239,0.00026361906,0.00017738887,0.000024318151,0.00022700948,0.000121753335,0.0003755809],"category_scores_gemma":[0.0011847854,0.000071021874,0.00001772222,0.0021764562,0.0010611844,0.00011630461,0.000051210805,0.00010407204,0.000006706983],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061640344,0.000039511106,0.021174937,0.000010332103,0.000011999374,0.0000010215966,0.000041971183,6.051733e-7,0.9676621,0.008392488,0.00008008247,0.0025788043],"study_design_scores_gemma":[0.00027693892,0.00008602192,0.0028691497,0.0000051975076,0.00007416847,0.000015843225,0.00060345064,0.0007476799,0.9891444,0.0053225122,0.0007665355,0.00008812375],"about_ca_topic_score_codex":0.000010619953,"about_ca_topic_score_gemma":0.000002639766,"teacher_disagreement_score":0.047283716,"about_ca_system_score_codex":0.000049488932,"about_ca_system_score_gemma":0.0002518091,"threshold_uncertainty_score":0.41123474},"labels":[],"label_agreement":null},{"id":"W2154771795","doi":"10.5194/npg-16-57-2009","title":"Information theoretic measures of dependence, compactness, and non-gaussianity for multivariate probability distributions","year":2009,"lang":"en","type":"article","venue":"Nonlinear processes in geophysics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Canadian Institute for Advanced Research","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Aeronautics and Space Administration; National Science Foundation","keywords":"Compact space; Mathematics; Bivariate analysis; Measure (data warehouse); Entropy (arrow of time); Gaussian; Invariant (physics); Probability measure; Probability density function; Differential entropy; Kullback–Leibler divergence; Mutual information; Multivariate normal distribution; Statistical physics; Multivariate statistics; Statistics; Applied mathematics; Principle of maximum entropy; Mathematical analysis; Computer science; Transfer entropy; Data mining; Physics","score_opus":0.018334524433323916,"score_gpt":0.28344871579914416,"score_spread":0.26511419136582026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154771795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96246135,0.00019131691,0.034114797,0.00010919171,0.00001943347,0.00026474224,0.00042066054,0.000049051203,0.0023694562],"genre_scores_gemma":[0.9955723,0.000049567938,0.004102804,0.000021140437,0.00004821182,0.00002228845,0.00016712531,0.000004942973,0.000011581584],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991497,0.000007500735,0.00032827747,0.00014339059,0.00017493451,0.00019619556],"domain_scores_gemma":[0.99906677,0.0001568961,0.00019031744,0.00019090988,0.00034906887,0.000046021527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013898814,0.00014674575,0.0002736761,0.000060835868,0.00008327875,0.000034705718,0.0001623903,0.00009890286,0.000016974696],"category_scores_gemma":[0.00072341174,0.00012957003,0.000046126584,0.0006070197,0.00012717326,0.00037545594,0.000024553066,0.00015091783,0.0000013478146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054764603,0.02285082,0.30107597,0.06034237,0.0013050867,0.000010065845,0.022972971,0.0039873812,0.20376207,0.17550015,0.00041861524,0.20229803],"study_design_scores_gemma":[0.0024597063,0.00024696908,0.041242342,0.00024591063,0.0003158937,0.0000050741587,0.00089156936,0.009784488,0.7707888,0.17301364,0.00035425602,0.00065138936],"about_ca_topic_score_codex":0.00005823388,"about_ca_topic_score_gemma":0.000016283726,"teacher_disagreement_score":0.5670267,"about_ca_system_score_codex":0.000044368117,"about_ca_system_score_gemma":0.00016507093,"threshold_uncertainty_score":0.5283713},"labels":[],"label_agreement":null},{"id":"W2155781200","doi":"10.1109/icme.2003.1221051","title":"Optimized local discriminant basis algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminant; Linear discriminant analysis; Pattern recognition (psychology); Weighting; Artificial intelligence; Basis (linear algebra); Principal component analysis; Disjoint sets; Feature extraction; Computer science; Algorithm; Projection (relational algebra); Feature (linguistics); Multiple discriminant analysis; Optimal discriminant analysis; Mathematics","score_opus":0.014235941097350796,"score_gpt":0.26257453888590065,"score_spread":0.24833859778854986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155781200","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038142202,0.0004386678,0.4834691,0.00010702721,0.00004888916,0.000022202941,0.000006885463,0.00013382285,0.5119592],"genre_scores_gemma":[0.7642097,0.00015590292,0.15098794,0.00032560722,0.00008695581,0.000024168216,0.000020460011,0.000039020317,0.08415025],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991224,0.000009291207,0.00017543478,0.0002458368,0.00016648628,0.00028057976],"domain_scores_gemma":[0.9994948,0.00005934365,0.00003791218,0.00026926066,0.000030763844,0.000107932734],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006628154,0.00014451487,0.00022054854,0.00006257023,0.00007566271,0.000028110653,0.00013244485,0.00008864113,0.030426426],"category_scores_gemma":[0.00006660864,0.00011144522,0.00012212912,0.00031051278,0.00007094331,0.000057130783,0.000021736958,0.00011703379,0.00013938041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034190022,0.0060590124,0.008708769,0.00073853973,0.003907289,0.00067801244,0.001213387,0.0014285264,0.20780198,0.09648793,0.1398275,0.5328072],"study_design_scores_gemma":[0.0006694802,0.000012227289,0.000017355755,0.000003973106,0.00017091441,0.000022208895,0.0013740711,0.0021408868,0.98271036,0.0004312971,0.012212529,0.0002346902],"about_ca_topic_score_codex":0.000078389756,"about_ca_topic_score_gemma":0.0000019737115,"teacher_disagreement_score":0.77490836,"about_ca_system_score_codex":0.000063018,"about_ca_system_score_gemma":0.000032655807,"threshold_uncertainty_score":0.9704599},"labels":[],"label_agreement":null},{"id":"W2156483301","doi":"10.1109/pacrim.1993.407265","title":"Recent developments in the application of computer vision for automated herring roe assessment","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Grading (engineering); Herring; Computer science; Artificial intelligence; Computer vision; Engineering drawing; Engineering; Fish <Actinopterygii>","score_opus":0.02757477665611731,"score_gpt":0.34277976101324853,"score_spread":0.31520498435713123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156483301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7379243,0.00048244852,0.15091035,0.0012907932,0.00007038039,0.00061870966,0.000008968515,0.00042057707,0.10827348],"genre_scores_gemma":[0.977947,0.000068306996,0.021580748,0.00010719718,0.000019202591,0.000046927496,0.000017701788,0.0000053553817,0.00020757355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999418,0.0000063667744,0.00020310529,0.00013051572,0.00013438493,0.00010758711],"domain_scores_gemma":[0.9996644,0.0000737124,0.00007045734,0.00014106836,0.00003655121,0.000013762534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010583413,0.00006497616,0.00010469172,0.000053246848,0.000034309116,0.000013912515,0.00014722542,0.000041611078,0.00027943737],"category_scores_gemma":[0.000007370505,0.00004513899,0.000026901533,0.00030743633,0.000009779924,0.000034117536,0.000022381912,0.00004928433,0.00000508692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000635615,0.0033646054,0.076175466,0.0006126944,0.00037833586,0.0000042630577,0.0014689385,0.0013600435,0.31667686,0.004021593,0.026443046,0.5694306],"study_design_scores_gemma":[0.0012806884,0.00006150521,0.026813759,0.00003307118,0.00005676059,0.0000042519805,0.00043856312,0.75897455,0.18500122,0.000119261735,0.026975764,0.0002405757],"about_ca_topic_score_codex":0.000014595704,"about_ca_topic_score_gemma":0.0000070341916,"teacher_disagreement_score":0.75761455,"about_ca_system_score_codex":0.00007712342,"about_ca_system_score_gemma":0.0000074536347,"threshold_uncertainty_score":0.30596432},"labels":[],"label_agreement":null},{"id":"W2156951240","doi":"10.1002/cem.2598","title":"Search prefilters for mid‐infrared absorbance spectra of clear coat automotive paint smears using stacked and linear classifiers","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"National Institute of Justice; Office of Justice Programs; U.S. Department of Justice","keywords":"Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Wavelet; Computer science; Partial least squares regression; Spectral line; Stacking; Biological system; Mathematics; Physics; Machine learning; Biology; Nuclear magnetic resonance","score_opus":0.045584940799409106,"score_gpt":0.31065437913857,"score_spread":0.2650694383391609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156951240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97486216,0.0010558628,0.022208745,0.00018193253,0.00011244679,0.00008865874,0.000030816132,0.000020001879,0.0014393908],"genre_scores_gemma":[0.9722523,0.0004015664,0.026350494,0.00008279983,0.00033936917,0.0000013091201,0.0000046999153,0.000048625374,0.00051883294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775296,0.00003954665,0.0008162153,0.0002668865,0.0006981404,0.00042625886],"domain_scores_gemma":[0.9968658,0.0008572852,0.0008975382,0.0002757221,0.0008532049,0.00025046951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010394341,0.00024446912,0.00076367304,0.0011586107,0.00009557495,0.00005409788,0.0003710571,0.0002273226,0.0001300083],"category_scores_gemma":[0.002629798,0.00022307035,0.0003113328,0.0021497083,0.0002312852,0.0002012015,0.00006713606,0.0005213161,0.0000010526993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015327899,0.00072034565,0.018352726,0.0020312467,0.0013934305,0.000024719764,0.0014371197,0.0014576718,0.9638648,0.00020512773,0.0025640177,0.0064159883],"study_design_scores_gemma":[0.002136742,0.00051060977,0.00087157113,0.0001156359,0.00038217555,0.00005032363,0.0014167068,0.009212683,0.9828316,0.00041042158,0.001783162,0.0002783702],"about_ca_topic_score_codex":0.000009619439,"about_ca_topic_score_gemma":3.0051743e-7,"teacher_disagreement_score":0.018966783,"about_ca_system_score_codex":0.0002944186,"about_ca_system_score_gemma":0.00015918066,"threshold_uncertainty_score":0.9096545},"labels":[],"label_agreement":null},{"id":"W2157352949","doi":"10.1109/imtc.1999.776814","title":"Signal processing support for the evaluation of blood toxic oxidation activity","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; SIGNAL (programming language); Signal processing; Digital signal processing; Interpretation (philosophy); Quality (philosophy); Artificial intelligence; Computer hardware","score_opus":0.060143050265115856,"score_gpt":0.35178074046185037,"score_spread":0.2916376901967345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157352949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9269483,0.00027970076,0.033146415,0.00005025572,0.000011345484,0.00012998526,0.0000034598365,0.000026335589,0.03940422],"genre_scores_gemma":[0.99776936,0.00000518874,0.0006332163,0.00001560208,0.000028916604,0.0000392557,0.0000066053262,0.00000611666,0.0014957229],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993518,0.0000132808245,0.00012087569,0.00012128038,0.00028979237,0.00010299411],"domain_scores_gemma":[0.99942476,0.00012917866,0.00011470784,0.00011709927,0.00019721036,0.000017049086],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00053029804,0.00006456302,0.00009196695,0.000034313874,0.00008362443,0.000016972488,0.000071356124,0.00004934777,0.0049468265],"category_scores_gemma":[0.00018924403,0.00004445308,0.000056191657,0.00022747616,0.000025213254,0.00009045166,0.000005220593,0.00004582923,0.0000017349684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016056196,0.00014350968,0.00067959,0.00005941637,0.00008225424,3.536008e-8,0.00004034225,0.0000579387,0.98049015,0.0002081351,0.000100692174,0.018121906],"study_design_scores_gemma":[0.0004235046,0.000019982801,0.0002541842,0.0000029290434,0.0009064831,0.0000017830287,0.00021581622,0.0050230525,0.99262,0.0003256521,0.00015014996,0.000056452333],"about_ca_topic_score_codex":0.000007867078,"about_ca_topic_score_gemma":0.0000046008677,"teacher_disagreement_score":0.07082109,"about_ca_system_score_codex":0.000027253185,"about_ca_system_score_gemma":0.00014269387,"threshold_uncertainty_score":0.9959628},"labels":[],"label_agreement":null},{"id":"W2160179547","doi":"10.1016/j.talanta.2012.10.040","title":"Three-dimensional cluster resolution for guiding automatic chemometric model optimization","year":2012,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Metric (unit); Principal component analysis; Projection (relational algebra); Variable (mathematics); Cluster (spacecraft); Chemistry; Chemometrics; Ranking (information retrieval); Pattern recognition (psychology); Statistics; Artificial intelligence; Algorithm; Mathematics; Computer science; Chromatography","score_opus":0.04392346872577145,"score_gpt":0.28842950836958886,"score_spread":0.2445060396438174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160179547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55569917,0.0015428689,0.43292952,0.0002255919,0.00014208784,0.0002059281,0.00005523478,0.00033241243,0.008867174],"genre_scores_gemma":[0.9674758,0.000012483579,0.030182192,0.00012615125,0.00029740954,0.00004531318,0.00018664198,0.000034452216,0.0016396056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989304,0.0000040183913,0.00025051352,0.00019728096,0.00022678997,0.0003909804],"domain_scores_gemma":[0.9993507,0.00013105014,0.000125325,0.00023007875,0.00006208755,0.00010074493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019643415,0.00015353922,0.00020065522,0.00024206766,0.00014616956,0.000026407874,0.00013624424,0.00013633948,0.0007218499],"category_scores_gemma":[0.00019743656,0.00014342039,0.00010272857,0.0005696355,0.000025749232,0.00022339144,0.00006058603,0.00006717859,0.000029217756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035008581,0.0015776572,0.020547837,0.0014726169,0.00086604815,0.0000026869736,0.0006043936,0.6190883,0.24411036,0.002649696,0.1044506,0.0042797336],"study_design_scores_gemma":[0.00047153304,0.000009409924,0.00006684883,0.000016362528,0.00017630421,0.000012985651,0.000020952193,0.9348629,0.06389612,0.00016565998,0.00011853423,0.00018236328],"about_ca_topic_score_codex":0.000013276756,"about_ca_topic_score_gemma":0.000001749172,"teacher_disagreement_score":0.41177657,"about_ca_system_score_codex":0.00015042441,"about_ca_system_score_gemma":0.000028121665,"threshold_uncertainty_score":0.790375},"labels":[],"label_agreement":null},{"id":"W2161028092","doi":"10.1366/13-07446","title":"Determination of Spectroscopic Band Shapes Using Second Derivatives, Part I: Theory","year":2015,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Materials science; Chemistry; Analytical Chemistry (journal); Optics; Physics; Organic chemistry","score_opus":0.032982398097750915,"score_gpt":0.29261401366748535,"score_spread":0.25963161556973446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161028092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8008742,0.0007943723,0.019944778,0.000024028577,0.000073471616,0.00013804974,0.000028134542,0.0001318193,0.17799115],"genre_scores_gemma":[0.9818867,0.000026711745,0.015990783,0.000092218,0.0003296321,0.000026479807,0.000032394502,0.000056768684,0.0015583318],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99791664,0.00003059556,0.0005443479,0.00053662923,0.00043813337,0.00053365045],"domain_scores_gemma":[0.9986956,0.00016884868,0.0003925394,0.00043233356,0.00010410481,0.00020653571],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031912478,0.0003669446,0.0006082174,0.00023737077,0.000118040305,0.000056926216,0.0003850606,0.00019517278,0.0063091908],"category_scores_gemma":[0.000099915014,0.00036097263,0.00012246953,0.00065319624,0.00025297303,0.00016760858,0.000074585005,0.00029677333,0.000047960762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014672066,0.00014511045,0.0013815658,0.00009660031,0.00009341418,0.000004576165,0.00036949152,0.000008173609,0.98919827,0.008002711,0.0003742247,0.00017915021],"study_design_scores_gemma":[0.0009343458,0.00007124195,0.00015114709,0.000022695853,0.00019850444,0.000008147216,0.00090852537,0.00014738108,0.9756472,0.020577272,0.0009935192,0.00034001443],"about_ca_topic_score_codex":0.000017378017,"about_ca_topic_score_gemma":0.000010262463,"teacher_disagreement_score":0.18101248,"about_ca_system_score_codex":0.00029185915,"about_ca_system_score_gemma":0.00018402677,"threshold_uncertainty_score":0.99988425},"labels":[],"label_agreement":null},{"id":"W2161260069","doi":"10.1117/1.3589100","title":"Image processing and classification algorithm for yeast cell morphology in a microfluidic chip","year":2011,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":355,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Computer science; Artificial intelligence; Support vector machine; Segmentation; Mathematical morphology; Image processing; Noise (video); Image segmentation; Pattern recognition (psychology); Microfluidic chip; Algorithm; Microfluidics; Computer vision; Image (mathematics); Chip; Materials science","score_opus":0.029702742216359244,"score_gpt":0.28672979735788817,"score_spread":0.2570270551415289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161260069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8330579,0.0059469244,0.15761293,0.00043493952,0.00012818447,0.00007212184,0.000028536182,0.000016102378,0.0027023535],"genre_scores_gemma":[0.79035026,0.0010529478,0.2079664,0.00009302297,0.0003211129,0.000004165915,0.000009364832,0.000021151423,0.0001815597],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999111,0.000008047789,0.00043153527,0.00011500024,0.00015302222,0.0001814034],"domain_scores_gemma":[0.9993478,0.0000468753,0.0002977579,0.00006996957,0.00010423248,0.0001333481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022925425,0.000092167196,0.00024274633,0.00023076292,0.000031261672,0.000017860004,0.00014418177,0.00015514773,0.000121487814],"category_scores_gemma":[0.00009877846,0.000075360535,0.000061300714,0.0002697165,0.00019079486,0.00008320593,0.000021881917,0.00023652121,0.0000020621246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004602002,0.0004287925,0.0007062451,0.0001400567,0.000028577519,0.000048189497,0.00027345828,7.773475e-9,0.967018,0.000011689181,0.00067772204,0.03062121],"study_design_scores_gemma":[0.0041882666,0.00055041426,0.0036736375,0.00014351119,0.00048575806,0.00070246664,0.0028636467,0.010038277,0.9733044,0.0011769143,0.002505904,0.00036680273],"about_ca_topic_score_codex":0.0000028350023,"about_ca_topic_score_gemma":1.347638e-7,"teacher_disagreement_score":0.05035347,"about_ca_system_score_codex":0.000048725262,"about_ca_system_score_gemma":0.000092966795,"threshold_uncertainty_score":0.30731133},"labels":[],"label_agreement":null},{"id":"W2161936690","doi":"10.1089/cmb.2008.0221","title":"Parallel GPU Implementation of Iterative PCA Algorithms","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Principal component analysis; Orthogonalization; Graphics processing unit; Computer science; Algorithm; Orthogonality; CUDA; Graphics; Parallel computing; Artificial intelligence; Mathematics; Computer graphics (images)","score_opus":0.019470933568587052,"score_gpt":0.35851686782559833,"score_spread":0.3390459342570113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161936690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87942487,0.0010829606,0.11545,0.0016244105,0.00009978726,0.00003538394,0.000041402636,0.000010877632,0.0022302906],"genre_scores_gemma":[0.9793962,0.000033888133,0.02012875,0.00019074907,0.00016696776,4.1357413e-7,0.000038025595,0.0000027486515,0.000042252443],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918365,0.000021523598,0.00048206048,0.000077717166,0.0001336115,0.00010142307],"domain_scores_gemma":[0.99889517,0.00015771436,0.00055009505,0.000042304644,0.00031421662,0.000040523817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010351154,0.0000793576,0.00024695712,0.00016172315,0.000031131734,0.000008005964,0.00011829969,0.00005783846,0.0008164624],"category_scores_gemma":[0.00003041857,0.00006460363,0.000111456204,0.0001790085,0.00004333176,0.000076847165,0.000008686764,0.00011509654,0.0000027737972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011648847,0.001887127,0.1011241,0.0001332466,0.0034897123,0.00009710388,0.0038581414,0.046453863,0.53996897,0.07457342,0.011668171,0.21558125],"study_design_scores_gemma":[0.007872748,0.0033753396,0.11480156,0.000060986156,0.00058617233,0.0006392327,0.003120392,0.0036423535,0.5018932,0.35919043,0.0041613337,0.0006562129],"about_ca_topic_score_codex":0.000005244944,"about_ca_topic_score_gemma":6.2198257e-7,"teacher_disagreement_score":0.28461704,"about_ca_system_score_codex":0.000040962797,"about_ca_system_score_gemma":0.00008908499,"threshold_uncertainty_score":0.89396906},"labels":[],"label_agreement":null},{"id":"W2163265669","doi":"10.1109/acc.2002.1024873","title":"Multi-grade modelling - paperboard quality modeling","year":2003,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tembec","funders":"","keywords":"Paperboard; Computer science; Product (mathematics); Process (computing); Constraint (computer-aided design); Quality (philosophy); Multivariate statistics; Engineering drawing; Engineering; Mathematics; Machine learning; Programming language; Mechanical engineering","score_opus":0.12246200280605651,"score_gpt":0.346284530831103,"score_spread":0.22382252802504649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163265669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33759588,0.0007176667,0.56541413,0.00004605569,0.000029363286,0.000023437076,0.0000030951737,0.00021221202,0.09595814],"genre_scores_gemma":[0.93672276,0.0000915025,0.05575516,0.00012588926,0.000037874015,0.000006825562,0.0000056793447,0.000020744497,0.0072335745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987922,0.00001806423,0.0003189449,0.0003360573,0.00021008245,0.00032467287],"domain_scores_gemma":[0.99938077,0.000060902967,0.00005357994,0.00034824727,0.000045907276,0.00011056722],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017365778,0.00017199367,0.00025080855,0.00005775039,0.000118751355,0.00003981631,0.0001550157,0.00012753023,0.00334858],"category_scores_gemma":[0.000093102506,0.0001542567,0.00014423883,0.00027989177,0.000024333225,0.000093035946,0.000019597883,0.00020325792,0.000058039583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002378704,0.00072807586,0.005514623,0.00019633061,0.00033409835,0.000008812395,0.00036694226,0.5317047,0.44174933,0.018972296,0.00018106872,0.00021990469],"study_design_scores_gemma":[0.00043114822,0.0000031898255,0.0000019557815,0.0000050933413,0.00006545409,0.0000027174035,0.0008346506,0.6856589,0.31124285,0.00087492284,0.00059922994,0.00027993397],"about_ca_topic_score_codex":0.0002625512,"about_ca_topic_score_gemma":0.000011285145,"teacher_disagreement_score":0.5991269,"about_ca_system_score_codex":0.000062985295,"about_ca_system_score_gemma":0.000030803167,"threshold_uncertainty_score":0.99756247},"labels":[],"label_agreement":null},{"id":"W2164966224","doi":"10.1002/0470027320.s6601","title":"Near‐Infrared Spectroscopy of Cereals","year":2001,"lang":"en","type":"other","venue":"Handbook of Vibrational Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spectroscopy; Near-infrared spectroscopy; Infrared spectroscopy; Calibration; Biochemical engineering; Materials science; Computer science; Biological system; Chemistry; Mathematics; Biology; Optics; Physics; Statistics; Engineering","score_opus":0.012339335250152454,"score_gpt":0.2723299929449893,"score_spread":0.2599906576948368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164966224","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030685848,0.011111584,0.040187504,0.000058241083,0.00014994472,0.0002274574,0.00074674474,0.00024937087,0.9469623],"genre_scores_gemma":[0.001157072,0.009452806,0.42065737,0.00018239005,0.0022516137,0.00010300343,0.0016433082,0.0013710024,0.56318146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99665344,0.00003054473,0.0010059713,0.00074377004,0.0009700613,0.00059623376],"domain_scores_gemma":[0.9972058,0.00015466793,0.0013800896,0.0009304039,0.0001361049,0.00019296313],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010439921,0.0007050706,0.0014928427,0.00055998174,0.000098043856,0.000060054717,0.00070471654,0.00070593867,0.1747884],"category_scores_gemma":[0.00009453781,0.00070957217,0.0004881346,0.0007407696,0.00054550014,0.000101865044,0.00011091069,0.0004570618,0.00015136959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009722859,0.00031032567,0.002110144,0.0004234575,0.0005715503,0.000008766869,0.000037929778,0.000010077569,0.7228023,0.0010839731,0.2725188,0.000025428593],"study_design_scores_gemma":[0.00088937837,0.00015742167,0.000044026685,0.00059303915,0.00043117083,0.000012487613,0.000050279425,0.000043050233,0.96360934,0.0016587201,0.031962827,0.00054824026],"about_ca_topic_score_codex":0.000568925,"about_ca_topic_score_gemma":0.00008836042,"teacher_disagreement_score":0.38378084,"about_ca_system_score_codex":0.00017502734,"about_ca_system_score_gemma":0.0004892945,"threshold_uncertainty_score":0.99953556},"labels":[],"label_agreement":null},{"id":"W2165615771","doi":"10.1002/1438-9312(200112)103:12<815::aid-ejlt1111815>3.0.co;2-p","title":"Lipid analysis by vibrational spectroscopy","year":2001,"lang":"en","type":"article","venue":"European Journal of Lipid Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of British Columbia","keywords":"Library science; Chemistry; Computer science","score_opus":0.009180134161476432,"score_gpt":0.2518849416914954,"score_spread":0.24270480753001897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165615771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94682926,0.00085423555,0.012454912,0.0034238265,0.000090311274,0.000014593237,0.000004628194,0.000058016492,0.036270212],"genre_scores_gemma":[0.99604625,0.00049024046,0.0027888175,0.0001152647,0.00026061677,3.084887e-7,0.0000013424888,0.000009074712,0.00028809288],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856657,0.000020556321,0.00039907568,0.0002565716,0.00045479782,0.0003024473],"domain_scores_gemma":[0.99891686,0.00003762029,0.00034390634,0.00022903344,0.00033469623,0.00013790332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008196827,0.00012844922,0.00029101892,0.0013811808,0.00023987787,0.00009743998,0.00070355076,0.00004328133,0.0007484937],"category_scores_gemma":[0.00036447126,0.000105148014,0.00008199167,0.005574831,0.0008425843,0.00028597665,0.00010849225,0.00032559785,0.00001668745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017495526,0.00007408534,0.042776637,0.0000031763539,0.00016560713,0.000053175096,0.00003288185,0.0000069916337,0.9524141,0.0004764882,0.0018758882,0.002103467],"study_design_scores_gemma":[0.00056721363,0.0003053559,0.0032619522,0.000011945455,0.0005150408,0.0005845156,0.00056481874,0.0002876299,0.9679231,0.00026424308,0.025454778,0.00025940832],"about_ca_topic_score_codex":0.0000014429565,"about_ca_topic_score_gemma":8.3885953e-7,"teacher_disagreement_score":0.04921698,"about_ca_system_score_codex":0.00006333461,"about_ca_system_score_gemma":0.000108803135,"threshold_uncertainty_score":0.81954813},"labels":[],"label_agreement":null},{"id":"W2166099897","doi":"10.1109/igarss.2000.861714","title":"Multi-vs. hyperspectral characteristics of common coral reef features","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperspectral imaging; Coral reef; Coral; Reef; Remote sensing; Seagrass; Environmental science; Benthic zone; Oceanography; Geology; Ecosystem; Ecology; Biology","score_opus":0.027494397596154182,"score_gpt":0.2740509343591431,"score_spread":0.2465565367629889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166099897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9267793,0.00066651066,0.000068098954,0.0002605595,0.000054595504,0.000030759453,0.000051470415,0.00011982,0.07196885],"genre_scores_gemma":[0.9688442,0.00014484987,0.0034811324,0.00012677096,0.00009959653,0.0000024867786,0.000017770972,0.000016589958,0.027266655],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911785,0.00000658873,0.00025299602,0.00020079587,0.00017735179,0.00024440786],"domain_scores_gemma":[0.9993577,0.00006743011,0.00012178804,0.00032203385,0.00005100969,0.000080036494],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000030359211,0.00015675332,0.00035115218,0.00008170004,0.000053348336,0.000021246899,0.00023344667,0.00012978307,0.011629425],"category_scores_gemma":[0.0000813986,0.00013602413,0.0001290634,0.00027443998,0.000096493175,0.000052378833,0.000039650688,0.00021211228,0.000066980276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012341546,0.0022447964,0.15918523,0.00027988042,0.00044981472,0.00006152611,0.0006105582,0.0000016634715,0.7986124,0.001522914,0.03250694,0.004400861],"study_design_scores_gemma":[0.0008371595,0.000072740535,0.0479813,0.000016618147,0.0002106979,0.000030256964,0.0004527533,0.00082037866,0.94788826,0.000046357938,0.0013022304,0.00034126107],"about_ca_topic_score_codex":0.000089881665,"about_ca_topic_score_gemma":0.000012215924,"teacher_disagreement_score":0.14927584,"about_ca_system_score_codex":0.000044945267,"about_ca_system_score_gemma":0.0000062626937,"threshold_uncertainty_score":0.9892741},"labels":[],"label_agreement":null},{"id":"W2166232961","doi":"10.1002/cjoc.201180425","title":"Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods","year":2011,"lang":"en","type":"article","venue":"Chinese Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chemometrics; Chemistry; Partial least squares regression; Biological system; Context (archaeology); Multivariate statistics; Analytical Chemistry (journal); Calibration; Chromatography; Statistics; Mathematics","score_opus":0.0230355060173107,"score_gpt":0.3172831884119162,"score_spread":0.2942476823946055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166232961","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766107,0.015341856,0.0031357917,0.000036428333,0.000033477856,0.00003358224,0.000032720625,0.000009382016,0.004766017],"genre_scores_gemma":[0.9584915,0.0018726959,0.03940708,0.000007687179,0.000054162927,0.0000027285964,0.000014679365,0.00002145179,0.00012801062],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981695,0.000035743386,0.0010491066,0.00020612666,0.00031854297,0.00022097523],"domain_scores_gemma":[0.99771947,0.00042853813,0.001127811,0.00027249896,0.000323358,0.0001283229],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006206536,0.0002633026,0.0008811895,0.00051031186,0.00003438611,0.00001684437,0.0003990636,0.00019005749,0.00093382824],"category_scores_gemma":[0.0013248385,0.00021490108,0.00022710461,0.0015880866,0.00021960748,0.0002210558,0.00007027908,0.00033115532,2.351543e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007029034,0.00065540086,0.023361484,0.00037278503,0.0001251743,0.0000034881382,0.0002773276,5.063188e-7,0.97309333,0.000014954451,0.0000540007,0.0019712483],"study_design_scores_gemma":[0.0014658665,0.000056249275,0.0036550567,0.000058274967,0.000241539,0.000060116025,0.00032014068,0.00008884208,0.99219334,0.0016310716,0.00005702712,0.00017248925],"about_ca_topic_score_codex":0.00002416501,"about_ca_topic_score_gemma":4.348295e-7,"teacher_disagreement_score":0.036271285,"about_ca_system_score_codex":0.00006750791,"about_ca_system_score_gemma":0.00007738238,"threshold_uncertainty_score":0.99997944},"labels":[],"label_agreement":null},{"id":"W2166581432","doi":"10.1039/c1an15719a","title":"Adaptive multiscale regression for reliable Raman quantitative analysis","year":2011,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Higher Education Discipline Innovation Project","keywords":"Raman spectroscopy; Robustness (evolution); Calibration; Preprocessor; Computer science; Wavelet; Biological system; Artificial intelligence; Algorithm; Mathematics; Chemistry; Physics; Optics; Statistics","score_opus":0.08548781346411628,"score_gpt":0.3227171726969616,"score_spread":0.23722935923284533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166581432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7273962,0.004369225,0.09118495,0.0004351337,0.00010411573,0.0003762118,0.0002153546,0.00034251192,0.17557628],"genre_scores_gemma":[0.97772974,0.00006211287,0.008287758,0.00006618363,0.00006041764,0.00005028642,0.00005394687,0.00002040931,0.013669127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879974,0.000024487885,0.00029674123,0.00036139163,0.000205674,0.00031198768],"domain_scores_gemma":[0.9986585,0.0002446279,0.00024980897,0.0006115493,0.00015588716,0.00007966109],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025428252,0.0001996478,0.00045423512,0.00026195115,0.00031327843,0.000027768752,0.00043799522,0.000100558886,0.0023946324],"category_scores_gemma":[0.00008329217,0.00012238571,0.00050702,0.0018157782,0.00013637099,0.00009909377,0.00006376471,0.00016327268,0.000053712367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009781348,0.0050209756,0.28461775,0.0006847409,0.13197878,0.00012536632,0.04626936,0.0036506096,0.3512058,0.091633454,0.068352394,0.0066794134],"study_design_scores_gemma":[0.0018696041,0.0003552478,0.0070012333,0.00006597376,0.036985192,0.000005324568,0.022866512,0.07099805,0.84569186,0.006928675,0.0060939975,0.0011383578],"about_ca_topic_score_codex":0.000995533,"about_ca_topic_score_gemma":0.00022281196,"teacher_disagreement_score":0.49448603,"about_ca_system_score_codex":0.00005761443,"about_ca_system_score_gemma":0.00002159651,"threshold_uncertainty_score":0.99851733},"labels":[],"label_agreement":null},{"id":"W2166850364","doi":"","title":"Modeling profiles in chemical processes","year":2010,"lang":"en","type":"article","venue":"International Conference on Modelling, Identification and Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Partial least squares regression; Basis (linear algebra); Discretization; Biological system; Multivariate statistics; Applied mathematics; Computer science; Basis function; Estimation theory; Regression analysis; Algorithm; Mathematical optimization; Mathematics; Statistics","score_opus":0.03441483804739775,"score_gpt":0.29739757104329345,"score_spread":0.2629827329958957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166850364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89698815,0.00007469762,0.09016204,0.0010126433,0.00010986624,0.00008004946,0.000030034547,0.00006453309,0.011478013],"genre_scores_gemma":[0.9982168,0.0001274537,0.00026048342,0.00007872195,0.00011624885,0.000071331466,0.000049239363,0.00001101621,0.0010686842],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989073,0.0000057628313,0.00032555877,0.00035816425,0.00025646976,0.00014671756],"domain_scores_gemma":[0.99932766,0.000043250508,0.000096419586,0.00016452614,0.0003067833,0.00006135279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011565682,0.00013533286,0.00015336997,0.00017774822,0.00004876145,0.00017638275,0.00027467203,0.00011552309,0.000877536],"category_scores_gemma":[0.00013207787,0.00013246201,0.00003492822,0.0001273414,0.000053782784,0.00015898317,0.000016643882,0.00032758922,0.000022575114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001614776,0.000307708,0.0037303017,0.000075316755,0.00007596265,0.0000032987712,0.00025007993,0.007864984,0.9277083,0.0579745,0.000019633755,0.0018284351],"study_design_scores_gemma":[0.00053178717,0.000003987365,0.000022966173,0.000020089481,0.000022323204,0.000003052474,0.00013476555,0.9166878,0.07588482,0.006474349,0.00007157935,0.00014250442],"about_ca_topic_score_codex":0.00008512092,"about_ca_topic_score_gemma":0.000042153126,"teacher_disagreement_score":0.9088228,"about_ca_system_score_codex":0.000029024593,"about_ca_system_score_gemma":0.00007873401,"threshold_uncertainty_score":0.9608404},"labels":[],"label_agreement":null},{"id":"W2167968600","doi":"10.1109/bibm.2008.46","title":"Feature Selection for Tandem Mass Spectrum Quality Assessment","year":2008,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Computer science; Support vector machine; Feature selection; Pattern recognition (psychology); Artificial intelligence; Set (abstract data type); Feature (linguistics); Quality (philosophy); Selection (genetic algorithm); Tandem; Relevance (law); Quality assessment; Feature vector; Machine learning; Data mining; Evaluation methods; Engineering","score_opus":0.04490895665356944,"score_gpt":0.3503914680301046,"score_spread":0.3054825113765352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167968600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4530663,0.00032551883,0.19543557,0.004581977,0.00015022047,0.00020566721,0.000034156306,0.00064031867,0.34556028],"genre_scores_gemma":[0.91058326,0.0000501195,0.011462957,0.0001540075,0.00028734654,0.000028951765,0.000036091144,0.000015316737,0.077381946],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911195,0.000008491961,0.00014624854,0.00027171095,0.0002014855,0.00026010786],"domain_scores_gemma":[0.9995294,0.00009873168,0.00008344013,0.00016901255,0.000051488943,0.00006792786],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010269902,0.0001370655,0.00022285536,0.000058625643,0.00021986522,0.00002135783,0.00011168624,0.00014597991,0.0026926154],"category_scores_gemma":[0.000046530902,0.000118553166,0.0001455801,0.00034613523,0.000030207191,0.00006936653,0.000012478296,0.00018001717,0.000010997495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069729285,0.0003120385,0.40735176,0.00014239256,0.00042479747,0.000004170516,0.00005812819,0.000031153013,0.4978578,0.009850181,0.08362435,0.00027349152],"study_design_scores_gemma":[0.00074195414,0.000050968436,0.010858572,0.0000032129756,0.000100737365,0.00002311281,0.00021668643,0.00069817976,0.9657811,0.0019133853,0.01932002,0.00029205077],"about_ca_topic_score_codex":0.000055159147,"about_ca_topic_score_gemma":0.00003414674,"teacher_disagreement_score":0.4679233,"about_ca_system_score_codex":0.00021056269,"about_ca_system_score_gemma":0.0000754683,"threshold_uncertainty_score":0.9982191},"labels":[],"label_agreement":null},{"id":"W2168140434","doi":"10.1109/tfsa.1994.467256","title":"Criteria to select kernel functions for positive time-frequency distributions of theoretical time-varying signals","year":2002,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Kernel (algebra); Time–frequency analysis; Mathematics; SIGNAL (programming language); Oscillation (cell signaling); Simple (philosophy); Function (biology); Entropy (arrow of time); Representation (politics); Computer science; Applied mathematics; Algorithm; Discrete mathematics; Physics","score_opus":0.01492134379033809,"score_gpt":0.28118385080838426,"score_spread":0.26626250701804616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168140434","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11355559,0.00052482483,0.2660043,0.003124256,0.000065576845,0.00048313217,0.003222358,0.00041752867,0.6126024],"genre_scores_gemma":[0.9774317,0.0000053758895,0.0036982815,0.00012896126,0.00010665536,0.00005702273,0.0001745114,0.000020763695,0.01837672],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988833,0.000015638345,0.0003079988,0.0003000913,0.00015609221,0.00033688243],"domain_scores_gemma":[0.99893504,0.00038522627,0.00006459223,0.00024968598,0.0002209888,0.0001444349],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008344519,0.00017216112,0.0003007342,0.00010943463,0.00016849705,0.0000351681,0.00018146749,0.00011365148,0.09854629],"category_scores_gemma":[0.00039558008,0.00015594062,0.00019310127,0.0006490018,0.00015207451,0.00007778449,0.000039627805,0.000103890256,0.000478879],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035367066,0.00026264274,0.00013696941,0.000029919545,0.00021915504,0.0000013627372,0.00006235354,0.00000987903,0.96111155,0.01240084,0.02554368,0.00018628206],"study_design_scores_gemma":[0.00033483616,0.00014848157,0.000044964683,0.000024942603,0.0003636534,0.0000103941275,0.000053561085,0.004383436,0.9893766,0.0047778115,0.00022645325,0.0002548644],"about_ca_topic_score_codex":0.000016087199,"about_ca_topic_score_gemma":3.4734828e-7,"teacher_disagreement_score":0.8638761,"about_ca_system_score_codex":0.00009200556,"about_ca_system_score_gemma":0.00002092341,"threshold_uncertainty_score":0.90227777},"labels":[],"label_agreement":null},{"id":"W2169523809","doi":"10.1021/ma034199m","title":"Deformation and Relaxation of Polymers Studied by Ultrarapid Scanning FT-IR Spectrometry","year":2003,"lang":"en","type":"article","venue":"Macromolecules","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Fourier transform infrared spectroscopy; Polystyrene; Amorphous solid; Polymer; Materials science; Glass transition; Relaxation (psychology); Analytical Chemistry (journal); Infrared; Infrared spectroscopy; Polymer chemistry; Chemistry; Crystallography; Optics; Composite material; Organic chemistry","score_opus":0.007658863736711555,"score_gpt":0.23901941263175797,"score_spread":0.23136054889504643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169523809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95705414,0.003997697,0.001608986,0.000037473324,0.000018451901,0.000034000193,0.000017643966,0.000054469976,0.03717716],"genre_scores_gemma":[0.99835294,0.00021720356,0.0009655026,0.000036007546,0.000010987671,0.000005434907,0.00003506281,0.000014358568,0.00036249467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991351,0.00001982688,0.00026288113,0.00019180917,0.0001914243,0.0001989817],"domain_scores_gemma":[0.999492,0.000057818885,0.00020797878,0.00015062816,0.000036444686,0.000055169105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010478998,0.00013988111,0.00021171683,0.00014516611,0.000093419214,0.000024189203,0.00007966025,0.000078888836,0.00040023652],"category_scores_gemma":[0.00016414825,0.00013551107,0.00005236122,0.0004902789,0.00008036912,0.000101685575,0.00001533139,0.00010603797,0.000004457166],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001436801,0.000067675464,0.018146476,0.00009898329,0.00013603154,0.0000019001765,0.00029078338,0.0000023786592,0.9792126,0.0008102691,0.00039735602,0.0008212113],"study_design_scores_gemma":[0.00038327192,0.000033418335,0.0013279646,0.000015914018,0.000091492795,0.0000135201635,0.0014356188,0.000036872367,0.99613684,0.0002035733,0.00017365742,0.0001478579],"about_ca_topic_score_codex":0.000052296193,"about_ca_topic_score_gemma":0.0000019725246,"teacher_disagreement_score":0.04129883,"about_ca_system_score_codex":0.000053238808,"about_ca_system_score_gemma":0.000017532631,"threshold_uncertainty_score":0.5525981},"labels":[],"label_agreement":null},{"id":"W2171705504","doi":"10.1590/s0103-90162009000300001","title":"Grapevine leaf water potential based upon near infrared spectroscopy","year":2009,"lang":"en","type":"article","venue":"Scientia Agricola","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Constellation Brands (Canada)","funders":"","keywords":"Environmental science; Multivariate statistics; Water stress; Horticulture; Agronomy; Mathematics; Biology; Statistics","score_opus":0.006674349005165519,"score_gpt":0.2356358437260004,"score_spread":0.22896149472083488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171705504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.941896,0.0003253496,0.0024943994,0.0011778192,0.0003329814,0.00012543386,0.000023533717,0.00035153993,0.05327289],"genre_scores_gemma":[0.9863298,0.000010366322,0.0026580875,0.00059967046,0.00027480183,0.00000822486,0.00013788004,0.000020195517,0.009961009],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975211,0.000021661632,0.00035324035,0.00065156975,0.0006300177,0.00082243327],"domain_scores_gemma":[0.9989322,0.000023612049,0.00010106968,0.0005977997,0.00010845287,0.00023689777],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021567597,0.00030866312,0.0003406335,0.00026928162,0.00045738576,0.00039803496,0.0005725823,0.00017274934,0.011661524],"category_scores_gemma":[0.000044502587,0.00022548676,0.0002758412,0.0011984584,0.00013001027,0.00023910134,0.000066492714,0.00030474502,0.00043305982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057901845,0.00028939432,0.00059987605,0.000021673593,0.000040105617,0.000025590462,0.000044381228,0.00018779167,0.9876384,0.00007612244,0.010820898,0.00019787559],"study_design_scores_gemma":[0.00082420424,0.00010402606,0.0020034395,0.000012515578,0.00019219384,0.000010313543,0.000045386256,0.0021109376,0.97971386,0.0007582363,0.013859634,0.00036523436],"about_ca_topic_score_codex":0.000019296209,"about_ca_topic_score_gemma":0.0000041411113,"teacher_disagreement_score":0.044433717,"about_ca_system_score_codex":0.00011364612,"about_ca_system_score_gemma":0.00008729107,"threshold_uncertainty_score":0.98924196},"labels":[],"label_agreement":null},{"id":"W2180691840","doi":"10.1111/j.0014-3820.2004.tb00466.x","title":"A LIKELIHOOD-BASED APPROACH TO ESTIMATING AND TESTING FOR ISOLATION BY DISTANCE","year":2004,"lang":"en","type":"article","venue":"Evolution","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture Food and Rural Development","funders":"","keywords":"Biology; Isolation (microbiology); Isolation by distance; Maximum likelihood; Evolutionary biology; Computational biology; Statistics; Genetics; Bioinformatics; Mathematics","score_opus":0.015540601581577121,"score_gpt":0.26105360467542593,"score_spread":0.24551300309384883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180691840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122717015,0.00027965065,0.87490934,0.00008668764,0.000014107946,0.0000731892,0.00001619751,0.00007665252,0.001827146],"genre_scores_gemma":[0.76673645,1.6226758e-7,0.23300669,0.000033082924,0.00007152878,0.000039943498,0.00003635376,0.000008855475,0.00006690506],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939245,0.000002518236,0.00012624105,0.0002169854,0.00008895189,0.00017285191],"domain_scores_gemma":[0.99966687,0.00006790104,0.00006516117,0.000093315844,0.00005024651,0.000056495228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007293673,0.00008638422,0.000096109005,0.00004620117,0.00014814232,0.000029206483,0.000048892696,0.000052401792,0.000005142375],"category_scores_gemma":[0.00040623406,0.00009042963,0.000023259678,0.00032636023,0.000016998702,0.00007513326,0.00000976393,0.00005604101,0.0000020372725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101868914,0.00041379238,0.031341232,0.00068760803,0.000049327573,3.4318865e-7,0.00032209273,0.03381414,0.92461073,0.0025753202,0.0016462363,0.004437328],"study_design_scores_gemma":[0.002097243,0.00015817284,0.002191271,0.00014690017,0.00018492433,0.000005959263,0.00038978783,0.7932278,0.19253111,0.008144243,0.00037409877,0.00054848386],"about_ca_topic_score_codex":0.00007093831,"about_ca_topic_score_gemma":0.0000029004739,"teacher_disagreement_score":0.75941366,"about_ca_system_score_codex":0.00021716658,"about_ca_system_score_gemma":0.000033303077,"threshold_uncertainty_score":0.36876133},"labels":[],"label_agreement":null},{"id":"W2182295589","doi":"10.1016/j.aca.2015.11.028","title":"Generalized error-dependent prediction uncertainty in multivariate calibration","year":2015,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Investigaciones Científicas y Técnicas; Agencia Nacional de Promoción Científica y Tecnológica; Universidad Nacional de Rosario","keywords":"Heteroscedasticity; Multivariate statistics; Calibration; Context (archaeology); Independent and identically distributed random variables; Propagation of uncertainty; Statistics; Observational error; Noise (video); Chemistry; Applied mathematics; Mathematics; Computer science; Artificial intelligence; Random variable","score_opus":0.040767673529411304,"score_gpt":0.3016439525572872,"score_spread":0.2608762790278759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182295589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977706,0.000028130871,0.00012045936,0.0009996286,0.00007632503,0.00008407097,0.000051841813,0.00016352443,0.020769993],"genre_scores_gemma":[0.99676985,0.00003227538,0.00026800367,0.0001667446,0.00018781517,0.000017326698,0.00018531192,0.000023596007,0.0023490584],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984045,0.000039806688,0.0004264492,0.00042023478,0.00036713533,0.0003418465],"domain_scores_gemma":[0.99911755,0.00006498031,0.00013708508,0.00040003602,0.00007415833,0.0002061741],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020495796,0.00021208159,0.00033187543,0.00023945686,0.000055318927,0.000062865976,0.00025069434,0.00018817313,0.0010025309],"category_scores_gemma":[0.00029982894,0.000194591,0.000103047845,0.000634181,0.000051231757,0.00021990723,0.00006949333,0.0002680404,0.000026582407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068758684,0.0011302555,0.05327048,0.000095375755,0.00058600697,0.000041936994,0.0012549598,0.002208782,0.9257162,0.002182024,0.0126321,0.00019427518],"study_design_scores_gemma":[0.0069038253,0.00012553358,0.004237406,0.000058843958,0.00087535667,0.000031391977,0.0016517931,0.6801493,0.2969835,0.0042511774,0.0037308568,0.0010010281],"about_ca_topic_score_codex":0.00048245944,"about_ca_topic_score_gemma":0.00014246926,"teacher_disagreement_score":0.6779405,"about_ca_system_score_codex":0.0003411028,"about_ca_system_score_gemma":0.00012566466,"threshold_uncertainty_score":0.9999107},"labels":[],"label_agreement":null},{"id":"W2184185441","doi":"","title":"Nonlinear Chemometrics Model for Prediction Retention Behavior Petroleum Hydrocarbons","year":2013,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Chemometrics; Partial least squares regression; Kernel (algebra); Set (abstract data type); Oil spill; Biological system; Computer science; Mathematics; Chemistry; Chromatography; Machine learning; Petroleum engineering; Engineering","score_opus":0.028440603897267405,"score_gpt":0.2684537265366616,"score_spread":0.2400131226393942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184185441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.819981,0.0001755096,0.14236046,0.00023437697,0.000096734184,0.0002931146,0.0001692754,0.00047638649,0.03621316],"genre_scores_gemma":[0.9438939,0.000039360537,0.012703655,0.000070415954,0.00019492919,0.00042411804,0.00028813974,0.000035839166,0.04234962],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987423,0.000002706874,0.00031540744,0.00035139394,0.00025679616,0.00033141853],"domain_scores_gemma":[0.9992917,0.000055720793,0.000106391075,0.00031803106,0.00010243132,0.00012569675],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007533668,0.00018136212,0.00021855303,0.0002988592,0.00011830533,0.00006885097,0.00018257392,0.00020603441,0.00146352],"category_scores_gemma":[0.000120417964,0.0001685465,0.00020481502,0.0005817029,0.00003477583,0.00022727028,0.00003577429,0.00015667653,0.00006287299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051103736,0.0014372971,0.03466456,0.0002236819,0.00029706163,0.0000016534852,0.000081972146,0.00055923604,0.9402287,0.0005625039,0.018200886,0.0036913624],"study_design_scores_gemma":[0.0005882658,0.00004316736,0.00040625068,0.0000042274146,0.0003795514,0.0000042577817,0.00011350821,0.7379186,0.2595933,0.00046165576,0.00028703178,0.00020015995],"about_ca_topic_score_codex":0.000057158028,"about_ca_topic_score_gemma":0.0000034582768,"teacher_disagreement_score":0.73735934,"about_ca_system_score_codex":0.00012804591,"about_ca_system_score_gemma":0.000033310895,"threshold_uncertainty_score":0.99944925},"labels":[],"label_agreement":null},{"id":"W2185626047","doi":"10.82308/16878","title":"Non-destructive measurement of tomato quality using visible and near-infrared reflectance spectroscopy","year":2009,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Reflectivity; Near infrared reflectance spectroscopy; Spectroscopy; Remote sensing; Infrared; Materials science; Quality (philosophy); Infrared spectroscopy; Near-infrared spectroscopy; Optics; Environmental science; Analytical Chemistry (journal); Chemistry; Geology; Physics; Environmental chemistry; Astronomy","score_opus":0.03671857887442893,"score_gpt":0.30445092167742194,"score_spread":0.267732342802993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185626047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92730427,0.00050019607,0.000008342912,0.000017901077,0.00006307834,0.00014799178,0.00014414745,0.00014045049,0.071673594],"genre_scores_gemma":[0.9913571,0.00007955107,0.008101175,0.00013331333,0.000034728666,0.000009201787,0.00000856124,0.000051814593,0.00022454625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964428,0.00008413578,0.00088767306,0.00088007544,0.0010045771,0.0007007457],"domain_scores_gemma":[0.9977723,0.000101529076,0.000579612,0.00080576335,0.0004358585,0.0003049766],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075547234,0.0005130042,0.0008721494,0.00020704078,0.0007043246,0.000087609806,0.00044224432,0.00031331065,0.00036761185],"category_scores_gemma":[0.00055569655,0.0005409679,0.00023508896,0.0011456539,0.00018079701,0.00058349746,0.00012059206,0.00064524636,0.000014308991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019398486,0.00020546878,0.0007852331,0.00015180599,0.00018142405,0.000012313041,0.0000086705295,0.000019224151,0.98990816,0.0067506414,0.0000021481674,0.0017809508],"study_design_scores_gemma":[0.0010253267,0.0001473919,0.0028890797,0.00013402861,0.00022755934,0.00003111004,0.000163843,0.00012284126,0.97004145,0.024391845,0.0002652479,0.000560281],"about_ca_topic_score_codex":0.00021783583,"about_ca_topic_score_gemma":0.000038297578,"teacher_disagreement_score":0.07144905,"about_ca_system_score_codex":0.00097376795,"about_ca_system_score_gemma":0.00008105825,"threshold_uncertainty_score":0.9997042},"labels":[],"label_agreement":null},{"id":"W2187306276","doi":"10.82308/46382","title":"Analysis of acidity in oil-based matrices by infrared spectroscopy","year":2010,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Infrared spectroscopy; Infrared; Chemistry; Spectroscopy; Analytical Chemistry (journal); Environmental chemistry; Organic chemistry; Physics; Optics","score_opus":0.010247616258931148,"score_gpt":0.2513073431753028,"score_spread":0.24105972691637165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187306276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9218122,0.00026383845,0.0000015922097,0.000026340504,0.0001018844,0.00005250658,0.0015574814,0.0001646397,0.07601956],"genre_scores_gemma":[0.9958595,0.00016342169,0.0016048307,0.00014538786,0.000026735068,0.000037544938,0.00026426488,0.0000650287,0.0018333112],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99643534,0.000085435,0.0010079151,0.00092958484,0.00076585775,0.00077585987],"domain_scores_gemma":[0.9972333,0.00048751853,0.0006023395,0.0011588853,0.00020200858,0.0003159796],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064553384,0.00053609884,0.0011342752,0.0014075281,0.00031466893,0.0000625332,0.0009722753,0.000578819,0.006017997],"category_scores_gemma":[0.0009374016,0.0005589538,0.0005950401,0.0057652756,0.00018634139,0.0004703449,0.00015491646,0.0014821751,0.000056709083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107878885,0.0005606493,0.012591707,0.00013268339,0.0007409206,0.00001878914,0.0000019847453,0.00006805851,0.9819114,0.0012568532,0.000021469272,0.0025875713],"study_design_scores_gemma":[0.0009817211,0.000042869113,0.001738847,0.000023815068,0.0015252937,0.0000024723818,0.00005197637,0.00038575727,0.9846456,0.0014118297,0.008609215,0.0005806006],"about_ca_topic_score_codex":0.000766397,"about_ca_topic_score_gemma":0.0009263345,"teacher_disagreement_score":0.07418625,"about_ca_system_score_codex":0.00035711116,"about_ca_system_score_gemma":0.000053068467,"threshold_uncertainty_score":0.9996862},"labels":[],"label_agreement":null},{"id":"W2188714923","doi":"","title":"Feature Space Dimensionality Reduction for Real-Time Vision-Based Food Inspection","year":2009,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Visual inspection; Curse of dimensionality; Dimensionality reduction; Feature (linguistics); Machine vision; Process (computing); Product (mathematics); Feature extraction; Quality (philosophy); Pattern recognition (psychology); Filter (signal processing); Computer vision; Data mining; Mathematics","score_opus":0.1285540503188417,"score_gpt":0.528084756183565,"score_spread":0.3995307058647233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188714923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97395194,0.007075174,0.0008968611,0.0025404086,0.00034509393,0.0004435019,0.00013770434,0.00024962373,0.014359696],"genre_scores_gemma":[0.99391574,0.0016264262,0.0014486446,0.0001433152,0.0004972436,0.000030719257,0.000104713436,0.000048859343,0.002184317],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99753183,0.0000759748,0.00059545855,0.0006451991,0.0007269495,0.00042458944],"domain_scores_gemma":[0.9973552,0.00032877395,0.00094298244,0.0006070031,0.000505032,0.00026096028],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068053225,0.0003903946,0.0008104746,0.0007107472,0.00052315235,0.000649948,0.0012015558,0.00028973666,0.0067986636],"category_scores_gemma":[0.00042722604,0.00036134679,0.0004024132,0.0016354522,0.00010788137,0.0012432971,0.00014252086,0.0004582163,0.000011079868],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058765686,0.00053194404,0.006714194,0.00008778502,0.00021192414,0.00000420142,0.000023568604,0.00024517337,0.8726205,0.00013763322,0.1170915,0.0017439057],"study_design_scores_gemma":[0.0010213419,0.00006530874,0.0561831,0.00022369868,0.0003660291,0.00001788627,0.00008019169,0.00039846532,0.92926484,0.007518895,0.0043431325,0.00051709596],"about_ca_topic_score_codex":0.00017943817,"about_ca_topic_score_gemma":0.0000070889805,"teacher_disagreement_score":0.11274836,"about_ca_system_score_codex":0.0003352213,"about_ca_system_score_gemma":0.00017866449,"threshold_uncertainty_score":0.99988383},"labels":[],"label_agreement":null},{"id":"W2188981074","doi":"10.20982/tqmp.04.1.p035","title":"How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times","year":2008,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":273,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MATLAB; Gaussian; Probability distribution; Mathematics; Distribution (mathematics); Statistics; Applied mathematics; Statistical physics; Computer science; Physics; Mathematical analysis; Programming language","score_opus":0.21176402230706046,"score_gpt":0.4906537519717247,"score_spread":0.2788897296646643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188981074","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59332556,0.00009350556,0.40160707,0.0036009788,0.00031777256,0.00048651666,0.00034701233,0.000021654028,0.00019992478],"genre_scores_gemma":[0.36955228,0.00001261519,0.62431294,0.00074958225,0.00021919173,0.00063794584,0.000021509713,0.00004139704,0.0044525643],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.997934,0.00073078333,0.00041032955,0.0005093865,0.00011412808,0.0003014071],"domain_scores_gemma":[0.9943013,0.0047691185,0.00014383347,0.0004964969,0.00017523588,0.00011397024],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0023777601,0.00018942039,0.0005017909,0.00020215168,0.00012328089,0.00002914793,0.00021775378,0.00014704539,0.00013265766],"category_scores_gemma":[0.015098779,0.00014292404,0.00009396047,0.001052754,0.00021393258,0.00007409015,0.000060717553,0.00013339918,0.000011537063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015824968,0.0006223422,0.012603196,0.000103911974,0.00031240185,0.000003469918,0.0071683777,0.000057716265,0.91555816,0.027168877,0.017532773,0.003043831],"study_design_scores_gemma":[0.002192729,0.002402799,0.055829283,0.00006431104,0.00025250082,0.000032193817,0.002543094,0.000038306636,0.3847034,0.018342702,0.53288704,0.00071164663],"about_ca_topic_score_codex":0.00010993879,"about_ca_topic_score_gemma":0.000031213,"teacher_disagreement_score":0.5308547,"about_ca_system_score_codex":0.000096429605,"about_ca_system_score_gemma":0.00004136848,"threshold_uncertainty_score":0.99319744},"labels":[],"label_agreement":null},{"id":"W2189072119","doi":"","title":"INTELLIGENT COMPUTER VISION SYSTEM (SAIF) FOR AUTOMATED INSPECTION OF GINSENG ROOTS QUALITY","year":2005,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Thresholding; Process (computing); Software; Image (mathematics)","score_opus":0.02624415418336703,"score_gpt":0.3482568052121535,"score_spread":0.3220126510287865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189072119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83042896,0.00021106255,0.15112051,0.0001448487,0.00012307585,0.00013754118,0.000021288319,0.0023657607,0.015446934],"genre_scores_gemma":[0.9896716,0.000008743144,0.009242642,0.000032959873,0.00023626526,0.000009617282,0.000025965697,0.000013860676,0.00075833825],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890983,0.000013877304,0.0004749986,0.0002415203,0.00018377737,0.00017601161],"domain_scores_gemma":[0.99925643,0.00012778684,0.0001826956,0.00024679472,0.00012901456,0.0000572753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017560503,0.00013241348,0.00029484794,0.00011858537,0.00007386978,0.000022576925,0.00012743077,0.00011814587,0.0002351712],"category_scores_gemma":[0.0000285083,0.000113325455,0.00014726647,0.00029379915,0.000030244135,0.00008319522,0.00003500664,0.000073123876,0.00002538577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005069032,0.0016524182,0.006040263,0.0039886287,0.0009019518,0.0000034074192,0.0006471986,0.0060134144,0.90035933,0.01574077,0.032254722,0.031891007],"study_design_scores_gemma":[0.00034873222,0.000053661075,0.0008322533,0.000039661645,0.00006353783,0.0000053526555,0.0004476235,0.23879145,0.7579683,0.00000920391,0.0013025346,0.000137667],"about_ca_topic_score_codex":0.00013717472,"about_ca_topic_score_gemma":0.000039693707,"teacher_disagreement_score":0.23277804,"about_ca_system_score_codex":0.00023980516,"about_ca_system_score_gemma":0.000019170775,"threshold_uncertainty_score":0.46212777},"labels":[],"label_agreement":null},{"id":"W2189326083","doi":"10.1007/978-3-319-40643-5_2","title":"On the PLS Algorithm for Multiple Regression (PLS1)","year":2016,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Conjugate gradient method; Curse of dimensionality; Partial least squares regression; Algorithm; Estimator; Lanczos resampling; Krylov subspace; Applied mathematics; Mathematical optimization; Statistics; Iterative method; Eigenvalues and eigenvectors","score_opus":0.028263644092639106,"score_gpt":0.2796860828668493,"score_spread":0.2514224387742102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189326083","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021099353,0.0005110308,0.16317694,0.00021870618,0.00024130002,0.0009815734,0.0022146516,0.00022483392,0.83221996],"genre_scores_gemma":[0.00109496,0.0006885902,0.26688448,0.00014577502,0.000662477,0.00028351395,0.000064517044,0.00039210156,0.7297836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973462,0.0000018233186,0.00083465956,0.0006295525,0.00064828276,0.0005394661],"domain_scores_gemma":[0.9960817,0.0021838655,0.0008680064,0.00042611524,0.0003312479,0.00010903964],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038911912,0.0006869974,0.0008277844,0.00028076957,0.00019677062,0.00012561401,0.00067431794,0.00052533054,0.0016020447],"category_scores_gemma":[0.0013862,0.000436093,0.0001945012,0.000075485375,0.00020013051,0.000058652284,0.00017439405,0.00070850796,0.00012251534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027561058,0.00012935992,0.000015436157,0.0014976303,0.00022161359,0.000012600322,0.00038080054,3.1583704e-7,0.0013519648,0.97076666,0.0134321945,0.012163839],"study_design_scores_gemma":[0.0007929742,0.000081831495,0.0000014155781,0.0023556228,0.0004323451,0.0000089961895,0.00021337521,0.0030774754,0.019894855,0.9397372,0.03252844,0.0008754409],"about_ca_topic_score_codex":0.0000018107542,"about_ca_topic_score_gemma":0.000002382736,"teacher_disagreement_score":0.10370754,"about_ca_system_score_codex":0.00032656404,"about_ca_system_score_gemma":0.00006934456,"threshold_uncertainty_score":0.9998091},"labels":[],"label_agreement":null},{"id":"W2193010029","doi":"10.5539/apr.v7n6p61","title":"Identification of Bacterial Diseases in Rice Plants Leaves by the Use of Spectroscopic Imaging","year":2015,"lang":"en","type":"article","venue":"Applied Physics Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multispectral image; Streak; Bacterial wilt; Xanthomonas; Biology; Horticulture; Environmental science; Remote sensing; Optics; Computer science; Physics; Bacteria; Artificial intelligence","score_opus":0.10313748915253576,"score_gpt":0.37283526421120783,"score_spread":0.2696977750586721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193010029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968583,0.0002539615,0.00016493323,0.00003571731,0.000016620075,0.000092105984,0.00009607005,0.000010360415,0.0024719546],"genre_scores_gemma":[0.99940354,0.00005213779,0.000023412158,0.000005506985,0.00009914911,0.000027684204,0.00007138595,0.000012843259,0.00030436864],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864286,0.00003830194,0.00027308788,0.00020938927,0.00058145286,0.00025492202],"domain_scores_gemma":[0.99891484,0.00039453642,0.00012518765,0.00039231445,0.00011472684,0.00005836946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024810954,0.00008912831,0.00019494075,0.00009184512,0.00004805712,0.000051397485,0.0003407873,0.000033140426,0.00007274369],"category_scores_gemma":[0.00011711574,0.000072265015,0.000035495195,0.0007023445,0.0002494643,0.000107691296,0.000109230656,0.00021895225,0.000013716759],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010759306,0.00023096555,0.0056999885,0.0000664892,0.000029200146,5.262253e-7,0.00028572927,0.000030504272,0.9888778,0.00073910796,0.0034516302,0.0004804808],"study_design_scores_gemma":[0.0003623569,0.000006949353,0.0010986631,0.0000092867285,0.000019048279,1.7728202e-7,0.0008131217,0.00025032696,0.9948528,0.002338854,0.00018334352,0.000065069704],"about_ca_topic_score_codex":0.00026815242,"about_ca_topic_score_gemma":0.000003240373,"teacher_disagreement_score":0.005975018,"about_ca_system_score_codex":0.000080671816,"about_ca_system_score_gemma":0.000095768446,"threshold_uncertainty_score":0.2946882},"labels":[],"label_agreement":null},{"id":"W2194928743","doi":"10.1016/j.fuel.2015.08.029","title":"Correlations among thermophysical properties, ignition quality, volatility, chemical composition, and kinematic viscosity of petroleum distillates","year":2015,"lang":"en","type":"article","venue":"Fuel","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; National Research Council Canada","funders":"","keywords":"Distillation; Cetane number; Analytical Chemistry (journal); Chemistry; Thermodynamics; Thermal diffusivity; Volatility (finance); Asphaltene; Raman spectroscopy; Hydrocarbon; Chemical composition; Materials science; Organic chemistry; Optics","score_opus":0.04248932690853738,"score_gpt":0.2801334705000223,"score_spread":0.23764414359148492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2194928743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923646,0.00031281004,0.0007355476,0.000090328926,0.000018377616,0.0000502961,0.000035382127,0.0000564263,0.006336269],"genre_scores_gemma":[0.9993082,0.000005628548,0.00028122903,0.000013269739,0.00007479256,0.000010235896,0.00009680911,0.000011217156,0.00019861321],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905866,0.00002776314,0.00031823353,0.00020191148,0.00025378444,0.0001396319],"domain_scores_gemma":[0.99929714,0.00009504167,0.00015305643,0.00020801048,0.00013061478,0.0001161152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009969062,0.00013459596,0.00029807983,0.000045943107,0.000050091505,0.000028621482,0.00010387669,0.00009308524,0.00014033169],"category_scores_gemma":[0.00014189609,0.00011388218,0.00006721824,0.00018071287,0.00025250472,0.00011996896,0.00007828626,0.00014766629,0.000007799128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001471841,0.0008495554,0.25011092,0.0009908042,0.00020340695,0.0000021775654,0.0009484818,0.00006806832,0.74493736,0.0012202697,0.0004401743,0.00008161359],"study_design_scores_gemma":[0.002950756,0.00012390446,0.12070408,0.00035451583,0.001090698,0.000028697039,0.0020296245,0.042728495,0.8146635,0.014282105,0.000052358435,0.0009912417],"about_ca_topic_score_codex":0.00013913742,"about_ca_topic_score_gemma":0.000006445259,"teacher_disagreement_score":0.12940684,"about_ca_system_score_codex":0.000057031117,"about_ca_system_score_gemma":0.000029444864,"threshold_uncertainty_score":0.46439806},"labels":[],"label_agreement":null},{"id":"W2219380250","doi":"10.52151/jae2012491.1464","title":"Detection of Fungal Infection in Canola using Near-Infrared Hyperspectral Imaging","year":2012,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering (India)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Canola; Veterinary medicine; Biology; Remote sensing; Medicine; Botany; Geography","score_opus":0.008128209970654499,"score_gpt":0.22504769523181972,"score_spread":0.21691948526116522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219380250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978274,0.0012191919,0.0003672206,0.000009876205,0.0002676425,0.000022045291,0.0000017980584,0.000021573192,0.0002632432],"genre_scores_gemma":[0.9982799,0.000051430965,0.0011208103,0.0000034659122,0.00050296116,8.28425e-7,0.0000013214554,0.000012091157,0.000027178716],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989443,0.000009008338,0.000422906,0.000081299186,0.00023520461,0.0003073218],"domain_scores_gemma":[0.999359,0.00004287855,0.0003135623,0.00006927975,0.00010655751,0.00010873195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014980738,0.00015538666,0.00029579864,0.00023689451,0.000043572018,0.000034084474,0.000092370814,0.00009063972,0.000045413602],"category_scores_gemma":[0.00011651019,0.00012064198,0.00017726596,0.00067468756,0.000016646485,0.00053567527,0.000017938028,0.00041519405,0.0000010577669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012619513,0.00005441015,0.112713024,0.000055858647,0.000072694864,0.000005996947,0.0002206362,0.007997867,0.87871116,0.000005322366,0.000009414378,0.00014097728],"study_design_scores_gemma":[0.000289314,0.000018789788,0.4363544,0.000059125538,0.000104312254,0.00046100575,0.00021078554,0.0008661438,0.56148034,0.0000015588093,0.000028096025,0.00012612248],"about_ca_topic_score_codex":0.000119508295,"about_ca_topic_score_gemma":0.0000029793255,"teacher_disagreement_score":0.32364136,"about_ca_system_score_codex":0.00046120514,"about_ca_system_score_gemma":0.000029409812,"threshold_uncertainty_score":0.4919637},"labels":[],"label_agreement":null},{"id":"W2222280278","doi":"10.1088/0957-0233/27/2/025204","title":"Goniometrical measurements of fluorescence quantum efficiency","year":2016,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; European Commission; European Association of National Metrology Institutes","keywords":"Fluorescence; Quantum; Materials science; Optics; Optoelectronics; Physics; Quantum mechanics","score_opus":0.04909988464803501,"score_gpt":0.28211607973231223,"score_spread":0.23301619508427723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222280278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98086375,0.0019804807,0.00404848,0.0021426503,0.00011646364,0.0001097486,0.0000047437775,0.00023252601,0.010501133],"genre_scores_gemma":[0.9995524,0.00006267915,0.00030105727,0.000010021907,0.000012529724,0.000010531253,7.434202e-8,0.0000060441444,0.000044631317],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967712,0.0000065727663,0.00029031612,0.00048097837,0.001991074,0.00045985985],"domain_scores_gemma":[0.9980423,0.000032657794,0.00014077408,0.0004200633,0.0012667039,0.00009749461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361303,0.00014289895,0.00024828006,0.0013586299,0.00018612426,0.00001780124,0.0006942033,0.00011579668,0.00015154674],"category_scores_gemma":[0.003767605,0.00009222782,0.00003152514,0.0056271367,0.0017993086,0.00014539743,0.00016334101,0.00009745645,0.00001373727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061480437,0.00010681549,0.060130253,0.000017836725,0.0000140638585,8.648221e-7,0.000007512019,5.260212e-8,0.92341185,0.0020835632,0.000048950533,0.014172093],"study_design_scores_gemma":[0.00038877412,0.00008514349,0.0017534895,0.000041169027,0.000036572837,0.0000067968954,0.00008907694,0.000024695379,0.99634,0.0008912094,0.00021058541,0.00013249578],"about_ca_topic_score_codex":0.000010287758,"about_ca_topic_score_gemma":0.0000019324807,"teacher_disagreement_score":0.072928146,"about_ca_system_score_codex":0.00033685184,"about_ca_system_score_gemma":0.00028388514,"threshold_uncertainty_score":0.66296273},"labels":[],"label_agreement":null},{"id":"W2227473712","doi":"10.1007/978-3-540-79224-6_5","title":"Measures of Nonlinearity – A Review","year":2008,"lang":"en","type":"review","venue":"Advances in industrial control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonlinear system; Computer science; Physics","score_opus":0.12691760387578102,"score_gpt":0.3905115012371376,"score_spread":0.26359389736135663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2227473712","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.8379477e-7,0.99029267,0.000028288385,0.000017427466,0.00016018651,0.00041815816,0.0002340153,0.0000342978,0.008814602],"genre_scores_gemma":[0.000025127805,0.99867004,0.0000467145,0.00004686876,0.00067762134,0.00015386747,0.00007808675,0.00003979303,0.00026184972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99695814,0.00013232783,0.0015573453,0.00050648977,0.0004621281,0.00038359797],"domain_scores_gemma":[0.9971583,0.00087445084,0.001162867,0.00060821464,0.00010496462,0.00009122658],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028244697,0.0005193297,0.0043861275,0.00027553205,0.000043027478,0.000008697942,0.00067763345,0.0007311899,0.0007772743],"category_scores_gemma":[0.0023033514,0.00042067684,0.00086613634,0.0013314218,0.00017127865,0.00015307259,0.000051573752,0.0013103412,0.000017086624],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023963274,0.000106340565,0.000059318805,0.020042174,0.00022985782,0.000021015601,0.0000021839446,0.0000016592652,0.000001118632,0.000026815123,0.00066680595,0.9788188],"study_design_scores_gemma":[0.0010540467,0.000024976793,5.8208524e-8,0.030799946,0.0016403225,0.00002845705,0.0000038631333,0.0000011883135,0.000033938828,0.000019006005,0.96605664,0.00033755673],"about_ca_topic_score_codex":0.000049626316,"about_ca_topic_score_gemma":0.00001741166,"teacher_disagreement_score":0.9784812,"about_ca_system_score_codex":0.00022288809,"about_ca_system_score_gemma":0.00062992476,"threshold_uncertainty_score":0.9998245},"labels":[],"label_agreement":null},{"id":"W2235017372","doi":"10.1016/j.marpolbul.2016.01.001","title":"Oil species identification technique developed by Gabor wavelet analysis and support vector machine based on concentration-synchronous-matrix-fluorescence spectroscopy","year":2016,"lang":"en","type":"article","venue":"Marine Pollution Bulletin","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Gabor wavelet; Support vector machine; Pattern recognition (psychology); Matrix (chemical analysis); Artificial intelligence; Oil spill; Wavelet; Fluorescence spectroscopy; Petroleum; Environmental science; Principal component analysis; Biological system; Wavelet transform; Chemistry; Computer science; Chromatography; Fluorescence; Discrete wavelet transform; Environmental engineering; Biology","score_opus":0.006401154099791561,"score_gpt":0.24065508624034962,"score_spread":0.23425393214055806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2235017372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21087524,0.00084981706,0.5705136,0.09588287,0.00043885576,0.00089247426,0.004346053,0.0021820182,0.11401903],"genre_scores_gemma":[0.9691565,0.00023827789,0.0051292046,0.00026404363,0.00010442208,0.00009284965,0.0004733439,0.00003129151,0.02451008],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978207,0.000052549218,0.000567345,0.00066095206,0.0004168778,0.00048161874],"domain_scores_gemma":[0.99872416,0.00014673274,0.00032299093,0.00050474046,0.00012316533,0.00017819817],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000282673,0.00034022357,0.0004302588,0.00030359524,0.00021644047,0.00010413989,0.00026002087,0.00016705312,0.02440622],"category_scores_gemma":[0.00028638943,0.00027427584,0.00014426766,0.0010222864,0.00019728267,0.00007006471,0.00007758506,0.00017862579,0.00014235514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018303396,0.00022969778,0.02376694,0.000068787274,0.00024008175,0.000007849481,0.000013589571,0.0000043586233,0.96303916,0.0009004719,0.007813739,0.0037323118],"study_design_scores_gemma":[0.00081595243,0.0000610394,0.014310985,0.000021513488,0.00043072295,0.0000056797867,0.000018962288,0.00044348344,0.9579344,0.000030039773,0.025528418,0.00039879757],"about_ca_topic_score_codex":0.00019675452,"about_ca_topic_score_gemma":0.00003393314,"teacher_disagreement_score":0.75828123,"about_ca_system_score_codex":0.00046235241,"about_ca_system_score_gemma":0.000105080806,"threshold_uncertainty_score":0.9999709},"labels":[],"label_agreement":null},{"id":"W2245674118","doi":"10.1117/12.516519","title":"Evolution of FTIR technology as applied to chemical detection and quantification","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ABB (Canada)","funders":"","keywords":"Fourier transform infrared spectroscopy; Infrared; Materials science; Infrared spectroscopy; Environmental science; Remote sensing; Analytical Chemistry (journal); Chemistry; Optics; Environmental chemistry; Geology; Physics","score_opus":0.00790315715836051,"score_gpt":0.23226205970505193,"score_spread":0.2243589025466914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245674118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962961,0.00009984869,0.00022841437,0.0010556031,0.000040793773,0.00019747812,0.000014340756,0.00008065304,0.0019867744],"genre_scores_gemma":[0.9663344,0.000033975408,0.03331445,0.000018812882,0.00012347939,0.00009401562,0.0000033731149,0.00002729778,0.000050204908],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985138,2.8735148e-9,0.0005014524,0.00034442244,0.0003989109,0.00024140526],"domain_scores_gemma":[0.9986744,0.00005020903,0.0003037549,0.000056719266,0.00083298265,0.000081962375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020344574,0.0002060503,0.00033203594,0.00019894064,0.000052982734,0.00003260745,0.0004680447,0.00026523764,0.000012437762],"category_scores_gemma":[0.00054712023,0.00018884645,0.00022997746,0.00071541977,0.00021770626,0.00016691485,0.00011196724,0.00023303622,0.0000015076722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007115889,0.00007755464,0.00012891408,0.00026114297,0.00015196981,1.1780896e-8,0.00006153681,0.000042982458,0.6993646,0.29962388,0.00003400759,0.0001822677],"study_design_scores_gemma":[0.00059302285,0.00010537344,0.00019701605,0.000085728374,0.00016065448,0.000011041602,0.001471057,0.00075047486,0.9909105,0.0053658425,0.00017860712,0.00017069928],"about_ca_topic_score_codex":0.000018863937,"about_ca_topic_score_gemma":1.4892885e-7,"teacher_disagreement_score":0.29425803,"about_ca_system_score_codex":0.00028848203,"about_ca_system_score_gemma":0.000029026707,"threshold_uncertainty_score":0.77009344},"labels":[],"label_agreement":null},{"id":"W2252638482","doi":"10.13031/trans.57.10125","title":"Comparing Two Statistical Discriminant Models with a Back-Propagation Neural Network Model for Pairwise Classification of Location and Crop Year Specific Wheat Classes at Three Selected Moisture Contents Using NIR Hyperspectral Images","year":2014,"lang":"en","type":"article","venue":"Transactions of the ASABE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Principal component analysis; Hyperspectral imaging; Moisture; Mathematics; Linear discriminant analysis; Backpropagation; Pairwise comparison; Artificial neural network; Pattern recognition (psychology); Artificial intelligence; Statistics; Remote sensing; Geography; Computer science; Meteorology","score_opus":0.06520256830060987,"score_gpt":0.28002201559524365,"score_spread":0.21481944729463376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252638482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42154008,0.00007867614,0.5780538,0.00006032283,0.000013871636,0.00011680245,0.0000291031,0.000014484118,0.000092870076],"genre_scores_gemma":[0.9820208,0.000021575244,0.017696297,0.0000051258153,0.00003548481,0.000015703628,0.00003157329,0.0000235208,0.00014993375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904406,0.00002586391,0.00029032546,0.0002498283,0.00019085704,0.0001990737],"domain_scores_gemma":[0.999098,0.0001293216,0.00019795094,0.000279982,0.00024880763,0.000045916553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074631425,0.00015848801,0.0002911978,0.000049622737,0.0002057168,0.000024678588,0.00014146215,0.00006426343,0.000020905096],"category_scores_gemma":[0.000019217925,0.00011654098,0.000062843625,0.00032533563,0.0002107487,0.0001597423,0.000009453552,0.00013519192,3.1199696e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007155877,0.0001624979,0.007120607,0.00026480595,0.00013265433,7.1242496e-8,0.00021381544,0.5724,0.41712308,0.0015101753,0.00008389953,0.00027284567],"study_design_scores_gemma":[0.00087959727,0.000048734768,0.007209545,0.000059624657,0.00043041728,0.0000052181563,0.00019345958,0.91370285,0.07647018,0.0008743168,0.000001074264,0.00012499998],"about_ca_topic_score_codex":0.00011350906,"about_ca_topic_score_gemma":0.00024572044,"teacher_disagreement_score":0.5604807,"about_ca_system_score_codex":0.00011705138,"about_ca_system_score_gemma":0.0000323172,"threshold_uncertainty_score":0.47524032},"labels":[],"label_agreement":null},{"id":"W2266257451","doi":"10.1007/978-3-319-02741-8_33","title":"Pomelo’s Quality Classification Based on Combination of Color Information and Gabor Filter","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gabor filter; Rambutan; Artificial intelligence; Texture (cosmology); Filter (signal processing); Computer science; Pattern recognition (psychology); Quality (philosophy); Mathematics; Image (mathematics); Computer vision","score_opus":0.029253790684420473,"score_gpt":0.30185965557394734,"score_spread":0.27260586488952687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266257451","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06624021,0.019816432,0.039521407,0.00012975879,0.0007166521,0.001048355,0.00012828765,0.00014248725,0.8722564],"genre_scores_gemma":[0.993188,0.0011059056,0.00019140172,0.000037799062,0.000054972756,0.000013458414,0.00011146532,0.000014278057,0.0052827015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985259,0.000016943157,0.00083847094,0.00023873332,0.0002487694,0.00013113834],"domain_scores_gemma":[0.9983744,0.00036903558,0.00086658075,0.00019682731,0.00015123314,0.000041945346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002462195,0.00022054183,0.0004512004,0.00027231002,0.000059523572,0.000057822177,0.0001006442,0.00021283545,0.0001121426],"category_scores_gemma":[0.00006990469,0.00020562463,0.000048129932,0.000073063515,0.000078556586,0.00021630179,0.000034820572,0.00021693567,0.00000967222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002451565,0.00026911809,0.013326042,0.012707138,0.00023566156,0.000002696262,0.0011078363,0.006967764,0.0012454242,0.7744555,0.00029029464,0.18914737],"study_design_scores_gemma":[0.0033234584,0.00082018686,0.004820517,0.010120412,0.00044382756,0.000020926296,0.006477165,0.7625463,0.023119392,0.0123384595,0.17294537,0.0030239902],"about_ca_topic_score_codex":0.00008071229,"about_ca_topic_score_gemma":0.0000049056944,"teacher_disagreement_score":0.92694783,"about_ca_system_score_codex":0.0001148142,"about_ca_system_score_gemma":0.000021881862,"threshold_uncertainty_score":0.8385129},"labels":[],"label_agreement":null},{"id":"W2270148074","doi":"10.5740/jaoacint.15-044","title":"Method Modification of the Thermo Scientific SureTect Listeria monocytogenes Assay for Raw Meat, Dairy, Produce, and Seafood","year":2015,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Food science; Pasteurization; Listeria monocytogenes; Listeria; Raw material; Raw meat; Food safety; Chemistry; Biotechnology; Biology; Bacteria","score_opus":0.09622453940037144,"score_gpt":0.37191123516725133,"score_spread":0.2756866957668799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270148074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9743093,0.0007858364,0.018824214,0.0017225102,0.00076676515,0.00010526903,0.000055658213,0.0000085823895,0.0034218275],"genre_scores_gemma":[0.99102545,0.000008995119,0.0058571957,0.000019319954,0.00032106254,0.0000047386725,0.0000070917195,0.00001039066,0.0027457671],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988551,0.00004101875,0.00039010035,0.00015337071,0.0004637697,0.00009662513],"domain_scores_gemma":[0.9981177,0.00013949093,0.0006066088,0.00018396023,0.0008944089,0.00005781827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010582389,0.00009487261,0.00019502768,0.0001512357,0.000072507944,0.00008986015,0.0004137687,0.00005982194,0.00008744329],"category_scores_gemma":[0.0006231356,0.00006292824,0.00014789989,0.00020656372,0.00008721709,0.00015828405,0.000056715966,0.000111445435,5.6507355e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042187324,0.00018351058,0.012063839,0.00006182384,0.0006324148,0.0000011417395,0.0005548846,0.0003197239,0.9772262,0.001036999,0.003788722,0.0037088862],"study_design_scores_gemma":[0.00077845296,0.000048315593,0.0017384746,0.000022016065,0.00016458929,0.00007216068,0.00027441772,0.0018802218,0.9875405,0.001743988,0.005660445,0.0000764382],"about_ca_topic_score_codex":0.000007698852,"about_ca_topic_score_gemma":0.0000043649325,"teacher_disagreement_score":0.016716102,"about_ca_system_score_codex":0.0000867146,"about_ca_system_score_gemma":0.0001531575,"threshold_uncertainty_score":0.25661388},"labels":[],"label_agreement":null},{"id":"W2273282029","doi":"10.5740/jaoacint.13-317","title":"Evaluation of the Thermo Scientific™ SureTect™ Listeria species Assay","year":2014,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Listeria; Food science; Spinach; Listeria monocytogenes; Significant difference; Chemistry; Biology; Bacteria; Mathematics","score_opus":0.04122715170590473,"score_gpt":0.31155684115540505,"score_spread":0.2703296894495003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273282029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91333663,0.00013505876,0.0005817242,0.0007094684,0.0011747717,0.000019205485,0.000011960877,0.0000040871887,0.0840271],"genre_scores_gemma":[0.99442595,0.000004364197,0.00014039173,0.000027158152,0.000494071,7.4846935e-7,0.000002557476,0.0000061809524,0.004898558],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778694,0.00007848029,0.000385072,0.0000901351,0.0015759383,0.000083435625],"domain_scores_gemma":[0.99755126,0.00012774458,0.00065942045,0.00017609126,0.0014565027,0.000028999602],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025283513,0.0000748711,0.00014646367,0.00014458828,0.00007209742,0.00007967567,0.0005349894,0.000049792783,0.006324647],"category_scores_gemma":[0.0015293997,0.00004812929,0.00019569472,0.00022255832,0.00011858572,0.00012369329,0.00005156764,0.00015941543,0.0000056094786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053681106,0.00015411562,0.0150996605,0.000016564865,0.0005279919,7.0021207e-7,0.00023227338,0.00035971095,0.9728299,0.0018353609,0.0049063764,0.0039837034],"study_design_scores_gemma":[0.0007065325,0.000025839647,0.02005851,0.000044390665,0.0003988575,0.00004051047,0.00017562938,0.0030240002,0.9583914,0.0028657124,0.014185237,0.0000833495],"about_ca_topic_score_codex":0.0000036213567,"about_ca_topic_score_gemma":0.0000048427164,"teacher_disagreement_score":0.08108934,"about_ca_system_score_codex":0.00016349532,"about_ca_system_score_gemma":0.00013179188,"threshold_uncertainty_score":0.9945837},"labels":[],"label_agreement":null},{"id":"W2274473591","doi":"10.1006/jaer.2000.0559","title":"Review Paper (AE—Automation and Emerging Technologies)","year":2000,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":131,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Automation; Artificial intelligence; Computer science; Machine learning; Function (biology); Contextual image classification; Data mining; Image (mathematics); Pattern recognition (psychology); Engineering; Biology","score_opus":0.018063913511550508,"score_gpt":0.31435008873972736,"score_spread":0.29628617522817685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274473591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8701298,0.12336571,0.000019250812,0.0030899171,0.000025955938,0.00005294235,0.0000014179499,0.00011310293,0.003201875],"genre_scores_gemma":[0.8991715,0.09846129,0.0008417461,0.000033290944,0.000185987,0.0000055193336,0.0000025992535,0.000011146011,0.0012869753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896896,0.000009763676,0.00029332357,0.000101678575,0.00038360897,0.00024264428],"domain_scores_gemma":[0.9994528,0.00010872364,0.00006477277,0.00009907246,0.00021080479,0.00006382533],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039126346,0.00009821432,0.00022452859,0.00015266183,0.00006956751,0.000044484175,0.00021056535,0.0000783419,0.0011313624],"category_scores_gemma":[0.00040489182,0.000059510174,0.000074881755,0.0008257996,0.00002882154,0.00024969105,0.00003327478,0.0006033436,0.00001246467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019341715,0.00009484032,0.00037090006,0.0030216312,0.0003077372,0.00004568586,0.0001382348,0.0012036444,0.8971551,0.00016588745,0.02414648,0.073330514],"study_design_scores_gemma":[0.002033602,0.00051355513,0.034410384,0.012755185,0.0006814411,0.0037623844,0.0037962105,0.0012754432,0.6333367,0.0002832019,0.3056906,0.0014612888],"about_ca_topic_score_codex":0.000005267313,"about_ca_topic_score_gemma":2.3601235e-7,"teacher_disagreement_score":0.2815441,"about_ca_system_score_codex":0.00009210527,"about_ca_system_score_gemma":0.000013561954,"threshold_uncertainty_score":0.9997817},"labels":[],"label_agreement":null},{"id":"W2276623803","doi":"","title":"Geographic classification of wines using Vis-NIR spectroscopy","year":2006,"lang":"en","type":"dissertation","venue":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Water Research Institute","keywords":"Spectroscopy; Geography; Cartography; Pattern recognition (psychology); Information retrieval; Artificial intelligence; Computer science; Physics; Astronomy","score_opus":0.07910474404380732,"score_gpt":0.36535968261925383,"score_spread":0.2862549385754465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276623803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97747916,0.004350761,0.00029684126,0.0001674446,0.00010423642,0.00032708628,0.00019871199,0.00011941391,0.016956357],"genre_scores_gemma":[0.9796108,0.0015684067,0.00352507,0.0000065438085,0.0002148098,0.0000031758702,0.0016588211,0.0001282735,0.01328407],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9936055,0.0003425712,0.00091322255,0.001326784,0.0026024356,0.0012094767],"domain_scores_gemma":[0.99429417,0.00064758846,0.0012634073,0.0013874454,0.0020248874,0.0003824827],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0015451742,0.00067787874,0.0013480977,0.0032846418,0.00086142245,0.00011800096,0.0020593286,0.0014178215,0.0022156395],"category_scores_gemma":[0.0006375103,0.0008610389,0.0008632838,0.004257994,0.00083052047,0.0011373806,0.00023832676,0.0033797852,0.000060866387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078099617,0.00054602715,0.025010044,0.0020456647,0.0007594435,0.00007026728,0.00042457407,0.00004755537,0.9676389,0.0012983958,0.00089192705,0.00048620623],"study_design_scores_gemma":[0.0026439813,0.00042905263,0.11530664,0.002170793,0.0024982425,0.000031051502,0.02478132,0.0006874959,0.8410372,0.0070300377,0.0015130723,0.0018710777],"about_ca_topic_score_codex":0.005177498,"about_ca_topic_score_gemma":0.0010773209,"teacher_disagreement_score":0.12660167,"about_ca_system_score_codex":0.0006576839,"about_ca_system_score_gemma":0.0009106735,"threshold_uncertainty_score":0.9998785},"labels":[],"label_agreement":null},{"id":"W2280337647","doi":"10.13031/trans.58.11053","title":"Three-Dimensional Transient Heat, Mass, and Momentum Transfer Model to Predict Conditions of Canola Stored inside Silo Bags under Canadian Prairie Conditions: Part II. Model of Canola Bulk Temperature and Moisture Content","year":2015,"lang":"en","type":"article","venue":"Transactions of the ASABE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canola Council of Canada","keywords":"Canola; Silo; Transient (computer programming); Environmental science; Water content; Mass transfer; Momentum (technical analysis); Moisture; Heat transfer; Mechanics; Agronomy; Engineering; Meteorology; Physics; Geotechnical engineering; Mechanical engineering; Biology","score_opus":0.037232593233512,"score_gpt":0.249506279396386,"score_spread":0.212273686162874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280337647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98038507,0.00074292294,0.0075643444,0.003843868,0.00006506971,0.0003548712,0.006549923,0.000028221868,0.00046573568],"genre_scores_gemma":[0.9982213,0.000034108794,0.00037752622,0.00021860287,0.0000145734275,0.000065576845,0.00008603023,0.000027701986,0.00095461216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99847275,0.000019335308,0.00047978215,0.0003256623,0.00040740735,0.00029508985],"domain_scores_gemma":[0.99873763,0.000057181467,0.000068026435,0.00041680026,0.0003190279,0.00040133685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010303202,0.00026700608,0.00047706245,0.00022580747,0.00026437765,0.000013667584,0.00020622008,0.00022449272,0.00011707272],"category_scores_gemma":[0.000020485712,0.00022319966,0.00015967175,0.0003978086,0.00036453837,0.00014406843,0.000009946352,0.0003217655,2.5487984e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017431584,0.00037137722,0.0007287756,0.0001671505,0.0007820649,0.0000014363629,0.0017552824,0.4236187,0.56836456,0.0008860263,0.0031391096,0.000011218373],"study_design_scores_gemma":[0.0058777286,0.0005211323,0.0029259026,0.00049989804,0.00328143,0.000059135487,0.0044746595,0.12240739,0.85351217,0.00511288,0.00035518213,0.00097249105],"about_ca_topic_score_codex":0.020230051,"about_ca_topic_score_gemma":0.05976246,"teacher_disagreement_score":0.30121133,"about_ca_system_score_codex":0.00020711213,"about_ca_system_score_gemma":0.00082887855,"threshold_uncertainty_score":0.9862943},"labels":[],"label_agreement":null},{"id":"W2283489879","doi":"10.1366/0003702054615115","title":"Federation of Analytical Chemistry and Spectroscopy Societies (FACSS) and International Conference on Analytical Sciences and Spectroscopy (ICASS), October 9–13, 2005: Quebec City Convention Center, Quebec City, Canada","year":2005,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spectroscopy; Convention; Center (category theory); Library science; Chemistry; Analytical Chemistry (journal); Engineering physics; Regional science; Political science; Engineering; Geography; Environmental chemistry; Physics; Law; Crystallography; Computer science","score_opus":0.02775835711141444,"score_gpt":0.29718295205295076,"score_spread":0.2694245949415363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283489879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.933056,0.00042241774,0.0008480856,0.003936008,0.00007439793,0.00019556982,0.00014190012,0.000084375104,0.061241243],"genre_scores_gemma":[0.9902639,0.000831594,0.0022759107,0.00037238022,0.00038955975,0.00002295375,0.00012528333,0.000035431964,0.0056830063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99626034,0.000029890647,0.0007907829,0.0011822444,0.00090496073,0.0008317647],"domain_scores_gemma":[0.9984963,0.00019950623,0.0004038008,0.00038593425,0.00012678915,0.00038766072],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003433068,0.0006168946,0.00084930804,0.00015732237,0.00047068627,0.0004861109,0.00040793553,0.00030664142,0.003225899],"category_scores_gemma":[0.000083991545,0.00060859177,0.00012351871,0.00039190377,0.0013630912,0.0002911829,0.00022866044,0.00065652217,0.0000047820513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068732165,0.0008566384,0.053408913,0.00033927753,0.0006817578,0.000014595829,0.00026905167,0.000023299252,0.8790278,0.04925105,0.015037481,0.00040284786],"study_design_scores_gemma":[0.0021888455,0.00010662176,0.0052072257,0.00008786125,0.00032325165,0.000040864048,0.0013766435,0.008201479,0.97761405,0.002246425,0.0017891509,0.000817592],"about_ca_topic_score_codex":0.07336652,"about_ca_topic_score_gemma":0.29710326,"teacher_disagreement_score":0.22373672,"about_ca_system_score_codex":0.0008944674,"about_ca_system_score_gemma":0.0007088105,"threshold_uncertainty_score":0.99963653},"labels":[],"label_agreement":null},{"id":"W2284571010","doi":"10.1002/cem.2778","title":"Using the L<sub>1</sub>norm to select basis set vectors for multivariate calibration and calibration updating","year":2016,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Credit Valley Hospital","funders":"National Science Foundation","keywords":"Singular value decomposition; Basis (linear algebra); Mathematics; Calibration; Principal component analysis; Rank (graph theory); Projection (relational algebra); Norm (philosophy); Algorithm; Computer science; Pattern recognition (psychology); Artificial intelligence; Statistics; Combinatorics; Geometry","score_opus":0.04532770201890339,"score_gpt":0.3146445278603715,"score_spread":0.2693168258414681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284571010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8255055,0.00023426532,0.17302974,0.0009402215,0.00009333209,0.00008175329,0.000029649742,0.000016983351,0.00006853929],"genre_scores_gemma":[0.9935578,0.000094064526,0.0055973344,0.00017295194,0.00048356302,0.000004483658,0.0000043348728,0.000031276366,0.000054176777],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984984,0.000027056101,0.00059116713,0.00019819834,0.00040478248,0.00028039634],"domain_scores_gemma":[0.9977965,0.00089911546,0.0006484455,0.00016886323,0.00032808553,0.00015899244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000598076,0.00018677402,0.0003353811,0.00058875495,0.00019865109,0.00013116912,0.00021575663,0.00014103488,0.000048426005],"category_scores_gemma":[0.0029868651,0.000112504444,0.0001476275,0.0024031815,0.000036458874,0.0004513517,0.000053513042,0.00017752798,5.656016e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007981926,0.000036324567,0.00607498,0.000050330906,0.00013483061,0.00000188458,0.00015749889,0.00009345086,0.98741347,0.000047743717,0.0007477736,0.0051618707],"study_design_scores_gemma":[0.00072761264,0.00008314365,0.00034906855,0.000048216596,0.00025193393,0.000046412555,0.00026910045,0.0043291445,0.9931872,0.00020046317,0.00033468913,0.00017300247],"about_ca_topic_score_codex":0.000009587485,"about_ca_topic_score_gemma":0.0000028435975,"teacher_disagreement_score":0.1680523,"about_ca_system_score_codex":0.00024844505,"about_ca_system_score_gemma":0.0001117426,"threshold_uncertainty_score":0.45877978},"labels":[],"label_agreement":null},{"id":"W2288453928","doi":"10.1016/j.meatsci.2016.02.042","title":"Prediction of pork fat attributes using NIR Images of frozen and thawed pork","year":2016,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Intramuscular fat; Marbled meat; Texture (cosmology); Loin; Mathematics; Food science; Chemistry; Artificial intelligence; Animal science; Computer science; Image (mathematics); Biology","score_opus":0.04149513078110474,"score_gpt":0.2787617382976764,"score_spread":0.2372666075165717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288453928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942603,0.00046002283,0.00243101,0.000066627224,0.000033568434,0.000020300526,0.000061890634,0.000019904148,0.0026463817],"genre_scores_gemma":[0.99821043,0.000059170037,0.0012071898,0.0000055850896,0.000036205958,7.0718e-7,9.813466e-7,0.0000039210413,0.0004757905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991032,0.0000049167566,0.00017951652,0.00022627944,0.00030954467,0.00017658282],"domain_scores_gemma":[0.9994036,0.00008655269,0.00012712003,0.00021231997,0.00011351523,0.00005689215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017350333,0.00007676247,0.00016833858,0.00012430313,0.00008657622,0.000014096399,0.00021083676,0.00004091518,0.00023259189],"category_scores_gemma":[0.00030521612,0.00005096587,0.000033184166,0.0006941876,0.0006903242,0.00022403385,0.00008056992,0.000035006684,0.0000012086937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055205032,0.00001559279,0.1670572,0.000019591138,0.000007633559,3.991145e-7,0.000033497883,0.0000014325315,0.8324379,0.000044722332,0.00005783948,0.00031868715],"study_design_scores_gemma":[0.0001712621,0.000018273875,0.015778566,0.000053312302,0.00004239627,0.000004656276,0.00014396584,0.00008368881,0.9833589,0.00026839707,0.000023614697,0.000052936204],"about_ca_topic_score_codex":0.000059935806,"about_ca_topic_score_gemma":7.9426593e-7,"teacher_disagreement_score":0.15127864,"about_ca_system_score_codex":0.00003886154,"about_ca_system_score_gemma":0.000045035667,"threshold_uncertainty_score":0.2546718},"labels":[],"label_agreement":null},{"id":"W2294807484","doi":"","title":"Principal component analysis based feature extraction methods applied to biomedical and communication network data","year":2010,"lang":"en","type":"article","venue":"The Atrium (University of Guelph)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Feature extraction; Pattern recognition (psychology); Principal component analysis; Kernel principal component analysis; Artificial intelligence; Computer science; Wavelet; Univariate; Feature (linguistics); Data mining; Kernel method; Multivariate statistics; Support vector machine; Machine learning","score_opus":0.02834179710920388,"score_gpt":0.3106490925863747,"score_spread":0.2823072954771708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294807484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82545716,0.00037330308,0.16269615,0.006982233,0.000063589316,0.00014465196,0.0000940109,0.00009708753,0.004091823],"genre_scores_gemma":[0.90811116,0.000035607372,0.09115353,0.00005183834,0.000057243895,2.218151e-7,0.00031603832,0.000005307378,0.00026904698],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992519,0.000053878866,0.00009728158,0.0002514561,0.00019310224,0.00015240764],"domain_scores_gemma":[0.9980976,0.00035625853,0.00015435816,0.0012346192,0.00004799001,0.00010915935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065649924,0.00010011033,0.00025492208,0.0001583666,0.00029691518,0.00001751893,0.0009534471,0.00014235082,0.0006497624],"category_scores_gemma":[0.000051098483,0.00009221828,0.00007756404,0.0012413795,0.0002155308,0.00007458302,0.00048636217,0.0003867927,0.0000052633245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018080308,0.000110897476,0.0020282948,0.000032101856,0.00069038285,0.0000017033927,0.00025799978,0.00024805428,0.98983365,0.00038232876,0.003088565,0.0031452414],"study_design_scores_gemma":[0.0032006302,0.00010060892,0.57253367,0.000034384044,0.020243771,0.000021119691,0.010707751,0.1398229,0.04337848,0.00075967034,0.20801468,0.0011823123],"about_ca_topic_score_codex":0.00039854503,"about_ca_topic_score_gemma":0.00029357112,"teacher_disagreement_score":0.9464551,"about_ca_system_score_codex":0.000026157093,"about_ca_system_score_gemma":0.000028730334,"threshold_uncertainty_score":0.71144426},"labels":[],"label_agreement":null},{"id":"W2295675245","doi":"10.1255/jnirs.1174","title":"Determination of Optical Parameters and Moisture Content of Wood with Visible–Near Infrared Spectroscopy","year":2015,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; University of New Brunswick","funders":"","keywords":"Analytical Chemistry (journal); Coefficient of determination; Partial least squares regression; Attenuation coefficient; Spectral line; Water content; Absorption (acoustics); Integrating sphere; Near-infrared spectroscopy; Spectroscopy; Moisture; Materials science; Scattering; Correlation coefficient; Chemistry; Optics; Mathematics; Chromatography; Physics; Statistics","score_opus":0.029518115339941732,"score_gpt":0.2798288788303923,"score_spread":0.25031076349045056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295675245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98353195,0.0012727819,0.0043707644,0.00016269789,0.00010130434,0.00012750506,0.000031477754,0.000027105063,0.010374426],"genre_scores_gemma":[0.8019878,0.00022468499,0.19694416,0.00007482318,0.00014002933,0.0000047546077,0.000010343695,0.00005554534,0.0005578121],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99687105,0.00005285995,0.0012222998,0.00033633143,0.0010480258,0.0004694083],"domain_scores_gemma":[0.9965887,0.00023138204,0.0015012854,0.00048164534,0.00076946797,0.0004275286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042410294,0.00040542477,0.0011872132,0.0003555326,0.000092243536,0.00014375428,0.00046068447,0.00027794784,0.00021110306],"category_scores_gemma":[0.000565255,0.00032311,0.00025894202,0.0007724016,0.00062879146,0.00044419811,0.00008754146,0.0007101189,0.000003486872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031974805,0.00076389994,0.031281102,0.00036807387,0.0007559102,0.00013945288,0.0016227973,0.00013685734,0.95917064,0.0003017025,0.0019998515,0.00026223232],"study_design_scores_gemma":[0.0036199135,0.0025457891,0.0026754308,0.00023661525,0.0006410071,0.00030978094,0.0017584043,0.0004615851,0.9854455,0.0015251822,0.00043776247,0.00034303596],"about_ca_topic_score_codex":0.000035695648,"about_ca_topic_score_gemma":0.0000055653063,"teacher_disagreement_score":0.1925734,"about_ca_system_score_codex":0.00027909141,"about_ca_system_score_gemma":0.00053742557,"threshold_uncertainty_score":0.9999221},"labels":[],"label_agreement":null},{"id":"W2296766337","doi":"","title":"Accretion regimes and variability in young stars : imprints on UV photometry","year":2015,"lang":"fr","type":"preprint","venue":"theses.fr (ABES)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics; Astrophysics; Astronomy; Accretion (finance); Stars; Debris disk; Thick disk; Planet; Photometry (optics); Planetary system; Galaxy","score_opus":0.030093159139136655,"score_gpt":0.30490887457343535,"score_spread":0.2748157154342987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296766337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74837923,0.0014078277,0.00016556111,0.00033713225,0.00045597972,0.0003889615,0.0001867659,0.00013014633,0.2485484],"genre_scores_gemma":[0.99322563,0.0021365727,0.0006533301,0.00007946999,0.00045033614,0.000105613304,0.00016380737,0.00010672279,0.0030785133],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99449855,0.00049074367,0.0011126293,0.0018984143,0.0009514544,0.0010482054],"domain_scores_gemma":[0.99534994,0.0011385004,0.0006618359,0.0020530503,0.00030058017,0.00049606676],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0031413783,0.0010424055,0.001562922,0.0006887055,0.00015042462,0.0002323326,0.0010174899,0.0015451586,0.0040361728],"category_scores_gemma":[0.0030713102,0.0010657671,0.0003329727,0.0014154345,0.00067371794,0.00023818931,0.0011871062,0.0024991115,0.00027662606],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022956957,0.00578963,0.7918874,0.0062072473,0.0031461783,0.000282412,0.015480987,0.0007193828,0.110170506,0.04688382,0.0015206729,0.0156160705],"study_design_scores_gemma":[0.0053860797,0.0004426786,0.11743334,0.0024408218,0.002646349,0.00017266131,0.01817044,0.0036473365,0.7721858,0.062935695,0.009719435,0.004819378],"about_ca_topic_score_codex":0.010585021,"about_ca_topic_score_gemma":0.00024593316,"teacher_disagreement_score":0.67445403,"about_ca_system_score_codex":0.0019772388,"about_ca_system_score_gemma":0.00038882846,"threshold_uncertainty_score":0.9998022},"labels":[],"label_agreement":null},{"id":"W2298009130","doi":"10.1111/biom.12457","title":"Interpretable Functional Principal Component Analysis","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Component (thermodynamics); Functional principal component analysis; Computer science; Statistics; Mathematics; Artificial intelligence","score_opus":0.06869988469016369,"score_gpt":0.29509746393790487,"score_spread":0.22639757924774118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298009130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8280144,0.0030834405,0.051537026,0.000143907,0.00034730107,0.000048216174,0.00008334416,0.0002926956,0.116449684],"genre_scores_gemma":[0.98999023,0.000024389428,0.0011903183,0.00007189362,0.00015163592,0.000007391074,0.00014513746,0.000012220068,0.008406779],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.998568,0.000009372105,0.00027114988,0.00031053548,0.00055661134,0.00028432687],"domain_scores_gemma":[0.9989669,0.000112559464,0.00011729296,0.00035109906,0.0001876601,0.0002645439],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022390454,0.00015645658,0.0003170654,0.0025742815,0.0000610672,0.00006994506,0.00024581407,0.000119777644,0.0035332667],"category_scores_gemma":[0.00041413345,0.00014298137,0.00022389325,0.015344295,0.00005510227,0.000090948226,0.00011904413,0.00014032354,0.00015852909],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030659494,0.0020422863,0.8635204,0.00013147682,0.010492692,0.0000606543,0.00036922542,0.0019619756,0.0679059,0.002045826,0.0459723,0.0051906593],"study_design_scores_gemma":[0.00498908,0.00031971824,0.07116234,0.000019781217,0.015301391,0.0000634139,0.0025317366,0.03848758,0.38547653,0.000984785,0.47798723,0.0026763978],"about_ca_topic_score_codex":0.00010165887,"about_ca_topic_score_gemma":0.0000042411207,"teacher_disagreement_score":0.79235804,"about_ca_system_score_codex":0.0002957187,"about_ca_system_score_gemma":0.00007048045,"threshold_uncertainty_score":0.99737763},"labels":[],"label_agreement":null},{"id":"W2303265427","doi":"10.1016/j.aca.2016.03.016","title":"Stoichiometric determination of moisture in edible oils by Mid-FTIR spectroscopy","year":2016,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; McGill University","funders":"","keywords":"Chemistry; Moisture; Water content; Fourier transform infrared spectroscopy; Analytical Chemistry (journal); Stoichiometry; Chromatography; Organic chemistry; Chemical engineering","score_opus":0.010092863534619051,"score_gpt":0.26729452209697285,"score_spread":0.2572016585623538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303265427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96515673,0.00014204456,0.00024155165,0.001159262,0.000043618296,0.00007288413,0.000084208834,0.00006800251,0.033031717],"genre_scores_gemma":[0.9952314,0.00043882945,0.0012422603,0.000070223556,0.000097737684,0.0000108103095,0.000028081251,0.00003122454,0.0028494254],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980541,0.000021885016,0.00053963385,0.0004887979,0.00041689095,0.00047871296],"domain_scores_gemma":[0.9985383,0.00039619833,0.00028352367,0.00055597775,0.00008715409,0.00013883915],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015904468,0.0002728075,0.00053224084,0.0009680554,0.00004971291,0.000028487382,0.00049845537,0.00022159015,0.0024570436],"category_scores_gemma":[0.000591127,0.00020282684,0.00018128994,0.0027308438,0.00014128411,0.00021154777,0.00007930426,0.00022055507,0.000032040072],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045942244,0.00027281605,0.009962027,0.000060197217,0.00006741652,0.000003228096,0.000030481417,2.1635614e-8,0.98543733,0.000058301328,0.0034794651,0.0005827569],"study_design_scores_gemma":[0.0010730246,0.000055577308,0.001518566,0.00005273444,0.00018330682,0.0000045217544,0.0000561635,0.00006280316,0.99523276,0.00055850355,0.0009377078,0.00026434573],"about_ca_topic_score_codex":0.000048528753,"about_ca_topic_score_gemma":0.000030001305,"teacher_disagreement_score":0.03018229,"about_ca_system_score_codex":0.0002502087,"about_ca_system_score_gemma":0.00007116106,"threshold_uncertainty_score":0.99845487},"labels":[],"label_agreement":null},{"id":"W2308181338","doi":"10.1002/jsfa.7733","title":"Novel technologies for monitoring the in‐line quality of virgin olive oil during manufacturing and storage","year":2016,"lang":"en","type":"review","venue":"Journal of the Science of Food and Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Organoleptic; Olive oil; Ripening; Quality (philosophy); Process engineering; Peroxide value; Production (economics); Agricultural engineering; Process (computing); Food science; Environmental science; Pulp and paper industry; Biotechnology; Biochemical engineering; Computer science; Engineering; Chemistry; Biology","score_opus":0.03859554996857869,"score_gpt":0.3262581172639485,"score_spread":0.2876625672953698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308181338","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25050968,0.7491843,0.0000052734085,0.00009556294,0.00006941442,0.000043234635,0.000033955628,0.000003863753,0.00005473766],"genre_scores_gemma":[0.16283736,0.8367286,0.00014512229,6.0556897e-7,0.00012706126,0.0000030421254,1.2560331e-7,0.000004208852,0.00015390801],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987493,0.00001275897,0.0005633124,0.0001653742,0.00033289724,0.00017635836],"domain_scores_gemma":[0.9980515,0.00021030346,0.0014163412,0.00017730102,0.00011445869,0.00003008189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312122,0.0001791873,0.00076979597,0.0001439497,0.00015573142,0.00003532398,0.0007744909,0.00014907373,0.0000014908518],"category_scores_gemma":[0.0003698863,0.000061671126,0.00025752495,0.00045965752,0.00038461442,0.00014425951,0.00020863372,0.00038529656,2.264491e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026974036,0.00011362813,0.00005043468,0.020114407,0.00030468268,0.0000012918953,0.00024211452,0.000012056696,0.8260749,0.00010290018,0.000014006298,0.15294258],"study_design_scores_gemma":[0.00031446214,0.000051916883,0.00017851009,0.011262514,0.00029581622,0.00011166287,0.0015912454,3.212584e-7,0.9847652,0.00014216841,0.0011358921,0.0001503481],"about_ca_topic_score_codex":0.00000416487,"about_ca_topic_score_gemma":0.0000017122751,"teacher_disagreement_score":0.15869023,"about_ca_system_score_codex":0.000076147844,"about_ca_system_score_gemma":0.00006946867,"threshold_uncertainty_score":0.25148755},"labels":[],"label_agreement":null},{"id":"W2309028480","doi":"10.1007/978-1-62703-059-5_23","title":"Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression","year":2012,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":440,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Partial least squares regression; Latent variable; Mathematics; Statistics; Partial correlation; Covariance; Multivariate statistics; Data set; Set (abstract data type); Correlation; Computer science","score_opus":0.03617235603248909,"score_gpt":0.42665000973953515,"score_spread":0.39047765370704607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2309028480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100331254,0.008944801,0.8878505,0.0002808981,0.0006210495,0.00019930144,0.00003363135,0.00014283032,0.001595715],"genre_scores_gemma":[0.65624243,0.00015963435,0.34209386,0.00018173842,0.0007184283,0.00013231965,0.00014807694,0.00007834474,0.00024513894],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99441504,0.0022491538,0.0009194019,0.0009653029,0.00026962088,0.0011814688],"domain_scores_gemma":[0.99755484,0.00082979235,0.0004427785,0.0007215092,0.00010130907,0.00034977027],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021411888,0.0006099642,0.0009818992,0.0004038614,0.00025863954,0.000068576024,0.00039393493,0.0008928785,0.0013030245],"category_scores_gemma":[0.0020417508,0.0005371472,0.00028929044,0.0007961654,0.00050944625,0.00020563346,0.00032274408,0.00084067794,0.000030309679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037892832,0.00032955644,0.084838204,0.00012637899,0.00018414804,0.000023404233,0.0005854786,0.00015292568,0.8547718,0.0035014418,0.000111748115,0.05499598],"study_design_scores_gemma":[0.0012693203,0.0001749472,0.0014279907,0.000079460726,0.00037238203,0.00007064843,0.0010179724,0.0043642125,0.978386,0.0012865107,0.010813468,0.00073712727],"about_ca_topic_score_codex":0.0001874637,"about_ca_topic_score_gemma":0.000010781628,"teacher_disagreement_score":0.5559112,"about_ca_system_score_codex":0.00014105272,"about_ca_system_score_gemma":0.000072633346,"threshold_uncertainty_score":0.999708},"labels":[],"label_agreement":null},{"id":"W2313709582","doi":"10.3390/s16040501","title":"A Low Cost Compact Measurement System Constructed Using a Smart Electrochemical Sensor for the Real-Time Discrimination of Fruit Ripening","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"State Key Laboratory of Wheat and Maize Crop Science; Education Department of Henan Province; National Natural Science Foundation of China","keywords":"Ripening; Electrochemical gas sensor; Embedded system; Real-time computing; System of measurement; Automotive engineering; Computer science; Engineering; Process engineering; Electronic engineering; Electrochemistry; Chemistry; Food science; Physics; Electrode","score_opus":0.036059239913836465,"score_gpt":0.2782616449245679,"score_spread":0.24220240501073143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313709582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99268526,0.000083169594,0.0046742302,0.00010534055,0.000044367807,0.00023970028,0.000055896006,0.000086249216,0.002025809],"genre_scores_gemma":[0.99885494,0.000014394972,0.0006846671,0.000006417128,0.000117266696,0.000010723155,0.00000939344,0.000026699736,0.00027552957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998561,0.00003249841,0.00036649645,0.00023464557,0.00042130894,0.000384004],"domain_scores_gemma":[0.99854404,0.0005299821,0.00028397422,0.00029001085,0.00026959137,0.00008240844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027945894,0.00019212656,0.00035973283,0.00008661808,0.00013384646,0.000020871867,0.0001525852,0.00010608985,0.0001445695],"category_scores_gemma":[0.00043653377,0.00011649457,0.00017303988,0.0002410881,0.0001342814,0.000051218783,0.000018167357,0.00008481435,0.0000063925486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011864409,0.000038803635,0.0015425268,0.00019093018,0.00022566243,7.779539e-7,0.00009255087,0.000011696952,0.99736446,0.0001248047,0.0001032678,0.00018587471],"study_design_scores_gemma":[0.0007312232,0.000017145458,0.000190498,0.0002689822,0.00044884122,0.00002884106,0.0012857281,0.0053445636,0.9914512,0.000009509881,0.000057848978,0.00016562827],"about_ca_topic_score_codex":0.000087265806,"about_ca_topic_score_gemma":0.0000033951815,"teacher_disagreement_score":0.0061696707,"about_ca_system_score_codex":0.00052519847,"about_ca_system_score_gemma":0.000066895765,"threshold_uncertainty_score":0.47505108},"labels":[],"label_agreement":null},{"id":"W2314608762","doi":"10.2524/jtappij.55.1241","title":"Optimized PM Control with QCM","year":2001,"lang":"en","type":"article","venue":"JAPAN TAPPI JOURNAL","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Ethernet; Control system; Embedded system; Engineering; Control (management); Computer science; Automotive engineering; Electrical engineering; Computer hardware; Artificial intelligence","score_opus":0.009655156493075924,"score_gpt":0.243118194940544,"score_spread":0.2334630384474681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314608762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82073325,0.0013629282,0.015562735,0.0013639102,0.00010366952,0.000057576166,0.000009104216,0.00013771324,0.16066913],"genre_scores_gemma":[0.98185277,0.00025119513,0.0027899537,0.00037654827,0.0008359467,0.000005868341,0.0000036617541,0.00002986876,0.013854204],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871784,0.00001758442,0.00029705462,0.00019351231,0.0003556496,0.00041837638],"domain_scores_gemma":[0.9990574,0.00008740646,0.0002459753,0.0002396839,0.00011706347,0.00025247075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015979541,0.00019662433,0.0003594723,0.00014632313,0.00022796413,0.000144656,0.0002948348,0.00010577555,0.015035212],"category_scores_gemma":[0.00008212103,0.00014129444,0.00015557303,0.00040063198,0.00007344471,0.0001502361,0.000016704738,0.00054958765,0.00007660415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010249016,0.002990048,0.41948134,0.0002276548,0.009708,0.003337796,0.0022491487,0.008840111,0.41029227,0.0008613647,0.105233766,0.02652947],"study_design_scores_gemma":[0.10942554,0.0018416996,0.015029096,0.000584716,0.0082441745,0.052906506,0.019750435,0.011091231,0.42934296,0.0036043948,0.34232408,0.005855171],"about_ca_topic_score_codex":0.000014082741,"about_ca_topic_score_gemma":0.0000021488172,"teacher_disagreement_score":0.40445223,"about_ca_system_score_codex":0.0001024556,"about_ca_system_score_gemma":0.00008026915,"threshold_uncertainty_score":0.9858652},"labels":[],"label_agreement":null},{"id":"W2322744709","doi":"10.1021/ie302408b","title":"Development of a Model Selection Criterion for Accurate Model Predictions at Desired Operating Conditions","year":2012,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Honeywell (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Variance (accounting); Model selection; Monte Carlo method; Computer science; Nonlinear system; Computation; Selection (genetic algorithm); Mean squared error; Estimation theory; Linear model; Mathematical optimization; Algorithm; Mathematics; Statistics; Machine learning","score_opus":0.27312596297434255,"score_gpt":0.4157107935098835,"score_spread":0.14258483053554094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322744709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98342127,0.00009068046,0.014655127,0.000030083878,0.00004060382,0.00017496373,0.00014725693,0.00012882982,0.0013111625],"genre_scores_gemma":[0.99181825,0.000006821917,0.004561118,0.0000020832313,0.00045639536,0.00033424632,0.00019671903,0.00004125582,0.0025831184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99814564,0.000008220666,0.00046445653,0.00027616424,0.00042661323,0.000678894],"domain_scores_gemma":[0.9989869,0.00020054732,0.00008414281,0.00021175371,0.00029522344,0.00022142306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000557554,0.00019525734,0.0002574244,0.00014291906,0.00042312642,0.000044781347,0.00022280816,0.00036058383,0.00028946451],"category_scores_gemma":[0.00076494814,0.00021613755,0.00009345769,0.00060029887,0.000053473457,0.00020053514,0.000118128206,0.0005518959,0.0000030749572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032884065,0.00009676569,0.00012917272,0.00016811166,0.00009023282,1.2677776e-7,0.0002402471,0.12862808,0.86997545,0.00002798174,0.00056497054,0.000046004192],"study_design_scores_gemma":[0.00037246628,0.0000047705807,0.000003762113,0.000037255606,0.000033320674,0.0000037966936,0.00009379965,0.40825817,0.5908945,0.000005980509,0.00017840297,0.00011379211],"about_ca_topic_score_codex":0.0000063608372,"about_ca_topic_score_gemma":6.6294933e-7,"teacher_disagreement_score":0.2796301,"about_ca_system_score_codex":0.0007287134,"about_ca_system_score_gemma":0.0003773155,"threshold_uncertainty_score":0.8813833},"labels":[],"label_agreement":null},{"id":"W2322875576","doi":"10.1142/9789814704199_0004","title":"Towards Machine Vision Based Grain Classification: Challenges and Future Prospects","year":2015,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Machine vision; Artificial intelligence; Machine learning","score_opus":0.04269205355219152,"score_gpt":0.30337693617405065,"score_spread":0.2606848826218591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322875576","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040890265,0.04837668,0.00079725956,0.03446312,0.00015882352,0.00012278475,0.000016859387,0.00048360493,0.8746906],"genre_scores_gemma":[0.99245054,0.0011430653,0.002170196,0.00019235048,0.00043219596,0.000013511472,0.00003116055,0.00001622943,0.0035507435],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917734,0.000009527893,0.00013322047,0.00028891847,0.00023462847,0.00015634827],"domain_scores_gemma":[0.9994152,0.000020209853,0.00005379194,0.00027278953,0.00007357278,0.00016439302],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001265786,0.0001409484,0.00017218266,0.000075651966,0.000047323338,0.00003432998,0.000118807955,0.00012316374,0.00095450983],"category_scores_gemma":[0.00004608906,0.00010702102,0.000039022307,0.00019288014,0.00004638023,0.00007934136,0.00003215202,0.00012625834,0.000018951383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069995737,0.0024886932,0.015276938,0.0016084166,0.00057375745,0.00009346292,0.0034176186,0.000006747947,0.15768966,0.1965125,0.05734309,0.56428915],"study_design_scores_gemma":[0.0057443916,0.00048266634,0.026034128,0.000052902928,0.0003920176,0.000059173708,0.01127112,0.012734503,0.538358,0.010214409,0.39321637,0.0014403595],"about_ca_topic_score_codex":0.000015958134,"about_ca_topic_score_gemma":0.00001778353,"teacher_disagreement_score":0.95156026,"about_ca_system_score_codex":0.000055122735,"about_ca_system_score_gemma":0.000049797556,"threshold_uncertainty_score":0.99995875},"labels":[],"label_agreement":null},{"id":"W2324286875","doi":"10.1021/ie400104a","title":"Multivariate Image Regression for Quality Control of Natural Fiber Composites","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Multivariate statistics; Materials science; Chemometrics; Composite material; Fiber; Partial least squares regression; Natural fiber; Filler (materials); Hyperspectral imaging; Computer science; Mathematics; Artificial intelligence; Statistics; Machine learning","score_opus":0.08362866905457592,"score_gpt":0.39442498809540266,"score_spread":0.3107963190408267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324286875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950222,0.00045299236,0.00013955431,0.00026482862,0.0000847465,0.0003383351,0.00013687288,0.00013006189,0.003430452],"genre_scores_gemma":[0.9921321,0.0000052737228,0.0004099603,0.000003759327,0.0006296949,0.0001464582,0.000077868266,0.00003908646,0.006555754],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978244,0.00002988288,0.0005240686,0.00039672782,0.00057986606,0.0006450853],"domain_scores_gemma":[0.9968753,0.0017634146,0.00014436457,0.00046125436,0.0005713113,0.0001843394],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00063088385,0.0002479184,0.00050062645,0.00010624678,0.00011490751,0.00009032296,0.00048925873,0.00040542305,0.0033568768],"category_scores_gemma":[0.0025614004,0.00021830111,0.00019969075,0.00051909516,0.00014105595,0.00014484476,0.000110476016,0.0010397151,0.00003278867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001360127,0.000089456975,0.00034568578,0.0003531337,0.0001522915,0.0000016175164,0.00002344218,0.00010860327,0.99674934,0.000010574526,0.0015833322,0.00044649612],"study_design_scores_gemma":[0.0022865047,0.000018472243,0.00010914201,0.00011403566,0.000040373416,0.0000027244964,0.00008321885,0.004999726,0.9907512,0.000023071334,0.001347065,0.00022447572],"about_ca_topic_score_codex":0.0003225849,"about_ca_topic_score_gemma":1.6639989e-7,"teacher_disagreement_score":0.005998163,"about_ca_system_score_codex":0.00017481681,"about_ca_system_score_gemma":0.00009413943,"threshold_uncertainty_score":0.9975542},"labels":[],"label_agreement":null},{"id":"W2324818308","doi":"10.1364/aio.2013.am4b.3","title":"Assessment of Lubricant Quality by Fiber-Coupled Fluorescence Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Imaging and Applied Optics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gastops (Canada); Queen's University","funders":"","keywords":"Lubricant; Fluorescence; Fluorescence spectroscopy; Materials science; Spectroscopy; Fiber; Matrix (chemical analysis); Analytical Chemistry (journal); Chemistry; Optics; Composite material; Chromatography","score_opus":0.009432803710589232,"score_gpt":0.29285317215857515,"score_spread":0.2834203684479859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324818308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9192851,0.00045965647,0.0033998638,0.00031900255,0.00002167055,0.00010010921,0.000026500227,0.00009728871,0.07629081],"genre_scores_gemma":[0.97398704,0.0001276319,0.025012279,0.00007158261,0.00004730336,0.000022952909,0.000021214237,0.000018102499,0.00069189264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877846,0.0000061351243,0.00034385046,0.0003132953,0.00025369084,0.00030455078],"domain_scores_gemma":[0.9992071,0.00012290035,0.00018476,0.00031222808,0.000060836097,0.00011219535],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000115497816,0.00018583593,0.00034709138,0.000051978073,0.00008517816,0.00007618704,0.00018171729,0.000049896666,0.000955729],"category_scores_gemma":[0.000026200347,0.00017077473,0.000051941806,0.00022747954,0.00018417177,0.0000614657,0.00007471598,0.00020495082,0.000015103876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003946581,0.00010956338,0.008237897,0.00009747566,0.000043299115,5.04461e-7,0.000029926143,0.000007350573,0.9879171,0.0012711913,0.0011759544,0.0011057603],"study_design_scores_gemma":[0.00058519724,0.000012893444,0.005668771,0.000018234176,0.0001244154,0.000003535362,0.0007570084,0.0077189426,0.98364013,0.0008860058,0.00024109153,0.00034377188],"about_ca_topic_score_codex":0.00021350558,"about_ca_topic_score_gemma":4.030977e-7,"teacher_disagreement_score":0.07559891,"about_ca_system_score_codex":0.000051303483,"about_ca_system_score_gemma":0.000034804238,"threshold_uncertainty_score":0.99995756},"labels":[],"label_agreement":null},{"id":"W2328937735","doi":"10.1021/jf402888e","title":"Chemometric Tools to Highlight Possible Migration of Compounds from Packaging to Sunflower Oils","year":2013,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université Libanaise; Agence Universitaire de la Francophonie","keywords":"Ethylbenzene; Hexanal; Low-density polyethylene; Chemistry; Chromatography; Heptanal; Sunflower oil; Tenax; Dibutyl phthalate; Polyethylene; Gas chromatography–mass spectrometry; Solid-phase microextraction; Nonanal; Diethyl phthalate; Gas chromatography; Polystyrene; Volatile organic compound; Mass spectrometry; Phthalate; Benzene; Organic chemistry; Polymer; Food science","score_opus":0.011629927479479184,"score_gpt":0.2203297549581655,"score_spread":0.20869982747868632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328937735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99411404,0.0009079978,0.000040560273,0.0016486079,0.000047181384,0.000045454282,0.00003582642,0.000016148964,0.0031441918],"genre_scores_gemma":[0.9968558,0.00007787221,0.001336131,0.000107575645,0.00046204004,0.0000054264765,0.000024912422,0.000009841217,0.0011204073],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984974,0.0000056200665,0.00062161236,0.00022305784,0.00039234664,0.00025996575],"domain_scores_gemma":[0.9985025,0.00012329126,0.00040245042,0.00016017453,0.00045166202,0.0003599014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006620297,0.0002355213,0.0005035998,0.00012142298,0.00006904187,0.00015969068,0.0002967561,0.00014716292,0.0008436952],"category_scores_gemma":[0.00015212654,0.00015090567,0.00018416396,0.0009966048,0.000028652184,0.00033092697,0.00008069836,0.00024988418,0.000011652582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019799894,0.00010049122,0.0036998289,0.00008193207,0.00019956303,0.0000014536746,0.00018991374,0.00001005734,0.98787934,0.0000024584092,0.007316993,0.00049817783],"study_design_scores_gemma":[0.00038600326,0.000108842054,0.03995371,0.00009411283,0.00014496925,0.000052245086,0.0011653017,0.0000017222646,0.95732415,0.00004544837,0.0004999464,0.00022353673],"about_ca_topic_score_codex":0.00007052365,"about_ca_topic_score_gemma":0.0000042783586,"teacher_disagreement_score":0.03625388,"about_ca_system_score_codex":0.00009753259,"about_ca_system_score_gemma":0.000025698326,"threshold_uncertainty_score":0.92378706},"labels":[],"label_agreement":null},{"id":"W2329383950","doi":"10.1115/wtc2005-64282","title":"A Comparative Study of Air-Coupled Ultrasound Sensor and Accelerometer in Detecting Bearing Defects","year":2005,"lang":"en","type":"article","venue":"World Tribology Congress III, Volume 2","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Accelerometer; Acoustics; Bearing (navigation); Ultrasound; Vibration; Condition monitoring; Ultrasonic sensor; Computer science; Materials science; Electronic engineering; Engineering; Electrical engineering; Artificial intelligence; Physics","score_opus":0.02544962276844685,"score_gpt":0.3008416242704814,"score_spread":0.2753920015020345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329383950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970452,0.0005493151,0.000022654087,0.000033994227,0.00005998495,0.0001941944,0.000006666226,0.000060599337,0.0020273868],"genre_scores_gemma":[0.9966707,0.000015828118,0.0002875764,0.00003882837,0.00008445291,0.00004295962,0.000006755318,0.000021322883,0.0028315745],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998175,0.00006910016,0.00057534344,0.00052957196,0.00018749641,0.0004634882],"domain_scores_gemma":[0.99842334,0.00075835723,0.0003058666,0.0003353383,0.00008548244,0.00009162272],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021697297,0.00028396148,0.0008975731,0.0006214427,0.00012845644,0.000028677892,0.00024003269,0.0001374887,0.0011511433],"category_scores_gemma":[0.00019447297,0.00028879364,0.00007963848,0.0010282622,0.00021308385,0.00014346193,0.000121787954,0.00050367933,0.000012404213],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017579658,0.0005457324,0.85113084,0.000039112638,0.0003266916,0.000021557533,0.0017259207,0.00016423453,0.1455478,0.0000061429196,0.000060482103,0.00025567858],"study_design_scores_gemma":[0.0124580255,0.00039978288,0.35467303,0.00009161195,0.0008131301,0.00007456151,0.014691558,0.003417078,0.61177665,0.000043747197,0.0005885259,0.0009723166],"about_ca_topic_score_codex":0.0005254642,"about_ca_topic_score_gemma":0.0048670787,"teacher_disagreement_score":0.49645782,"about_ca_system_score_codex":0.00010621304,"about_ca_system_score_gemma":0.0000287612,"threshold_uncertainty_score":0.9999564},"labels":[],"label_agreement":null},{"id":"W2329390681","doi":"10.1021/ac502203d","title":"Template-Oriented Genetic Algorithm Feature Selection of Analyte Wavelets in the Raman Spectrum of a Complex Mixture","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; FPInnovations","keywords":"Analyte; Chemistry; Genetic algorithm; Feature selection; Feature (linguistics); Biological system; Algorithm; Variance (accounting); Calibration; Pattern recognition (psychology); Selection (genetic algorithm); Artificial intelligence; Computer science; Chromatography; Statistics; Machine learning; Mathematics","score_opus":0.01046807809116761,"score_gpt":0.2598020938088423,"score_spread":0.24933401571767466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329390681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96313244,0.00020777494,0.0042582997,0.0007628546,0.00001636934,0.00007899836,0.00004997382,0.0000471798,0.03144613],"genre_scores_gemma":[0.99672824,0.000023128925,0.0016184124,0.00008007305,0.00017541183,0.000005295897,0.00008355683,0.000019228703,0.0012666367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821985,0.000032335447,0.00050237693,0.00039743382,0.00047006554,0.00037791827],"domain_scores_gemma":[0.9988506,0.00021654562,0.00025137834,0.00048238563,0.000102998885,0.00009610493],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020257686,0.000261449,0.00056928035,0.00010181304,0.00005627136,0.000018778455,0.00043390205,0.0003078754,0.001832658],"category_scores_gemma":[0.00020480307,0.00020247,0.00024971465,0.0014972673,0.0002066877,0.000035058474,0.000053062242,0.00054338033,0.0000039446772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020561654,0.0014322033,0.08401342,0.0015370026,0.0010154331,0.00004859035,0.0003244963,0.00024817025,0.8989815,0.00050042843,0.009005624,0.0026874866],"study_design_scores_gemma":[0.000941556,0.000061709594,0.015196653,0.000056292403,0.000562452,0.000074892334,0.0003707267,0.05108695,0.92799383,0.0005081861,0.0027994202,0.0003473111],"about_ca_topic_score_codex":0.000092882365,"about_ca_topic_score_gemma":0.0000115876055,"teacher_disagreement_score":0.068816766,"about_ca_system_score_codex":0.00007150391,"about_ca_system_score_gemma":0.000044896005,"threshold_uncertainty_score":0.9990798},"labels":[],"label_agreement":null},{"id":"W2330632797","doi":"10.1016/j.meatsci.2014.07.010","title":"Rapid authentication of enhanced quality pork by visible and near infrared spectroscopy","year":2014,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Livestock and Meat Agency; Agriculture and Agri-Food Canada; University of Alberta","funders":"","keywords":"Quality (philosophy); Authentication (law); Spectroscopy; Near infrared reflectance spectroscopy; Infrared spectroscopy; Infrared; Chemistry; Food science; Near-infrared spectroscopy; Computer science; Optics; Physics; Computer security","score_opus":0.013544545184635355,"score_gpt":0.2989154596900097,"score_spread":0.28537091450537433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330632797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95484793,0.00027424918,0.0054679373,0.00007991698,0.000027033735,0.000028219612,0.0000056993927,0.000036090238,0.039232936],"genre_scores_gemma":[0.9956027,0.0000441446,0.002755579,0.00004604388,0.000020568687,0.000003039107,0.0000042249726,0.000005014035,0.0015186783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881023,0.000014476508,0.00022643933,0.00033817062,0.00037608002,0.00023458611],"domain_scores_gemma":[0.9992088,0.000088054854,0.0001447917,0.0003805544,0.000081883525,0.000095926065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047344173,0.00009582148,0.00019248667,0.00007358327,0.0001839585,0.000071898736,0.00031622776,0.000045524193,0.0005702003],"category_scores_gemma":[0.00049219996,0.00008656133,0.000031621566,0.0008765702,0.0006728269,0.00018084895,0.000061265666,0.00007616723,0.000011213435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000087194685,0.000036716872,0.0022283471,0.000030256551,0.000004928294,3.0457272e-8,0.0002048496,6.6985e-7,0.9961025,0.0006712403,0.0001814331,0.00053026853],"study_design_scores_gemma":[0.00018284244,0.0000306304,0.0025716964,0.000008571802,0.00002016055,5.171417e-7,0.00012335794,0.0004517885,0.99494165,0.000986555,0.00058282434,0.00009939574],"about_ca_topic_score_codex":0.00005206994,"about_ca_topic_score_gemma":0.0000013640124,"teacher_disagreement_score":0.04075479,"about_ca_system_score_codex":0.000037185455,"about_ca_system_score_gemma":0.000058585145,"threshold_uncertainty_score":0.6243294},"labels":[],"label_agreement":null},{"id":"W2332259264","doi":"10.1021/jf1036678","title":"Rapid Determination of Lignin Content of Straw Using Fourier Transform Mid-Infrared Spectroscopy","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Lignin; Fourier transform infrared spectroscopy; Straw; Infrared spectroscopy; Infrared; Fourier transform spectroscopy; Content (measure theory); Spectroscopy; Fourier transform; Analytical Chemistry (journal); Chemistry; Materials science; Environmental chemistry; Mathematics; Organic chemistry; Chemical engineering; Inorganic chemistry; Optics; Physics","score_opus":0.021461399519978174,"score_gpt":0.2441920640030712,"score_spread":0.22273066448309303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332259264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944707,0.00087244244,0.00004690254,0.000070929615,0.000054062617,0.000028205535,0.000026636902,0.0000066004786,0.0044234903],"genre_scores_gemma":[0.9960059,0.00017953594,0.0031862226,0.0000048072347,0.00025356488,7.1811496e-7,0.000007304126,0.000007710411,0.0003542664],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986809,0.0000045423276,0.00067740446,0.00013292689,0.000320684,0.00018354462],"domain_scores_gemma":[0.9985778,0.000053339372,0.00078308786,0.00011173736,0.0003449983,0.00012906056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010174312,0.00019048672,0.00047522382,0.000047592574,0.000049415477,0.000022368846,0.00019533555,0.00020133507,0.0002679254],"category_scores_gemma":[0.000058322395,0.00012371014,0.0002655296,0.00020820933,0.000121136574,0.00017607966,0.000019604442,0.00040280443,1.144032e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045493704,0.00012468558,0.00063060585,0.00030251965,0.00015641504,0.000001994331,0.00015147773,7.377223e-7,0.9979494,0.0000040934046,0.00006888502,0.0005636858],"study_design_scores_gemma":[0.0008322777,0.00016006803,0.0015300054,0.00008398147,0.00034691344,0.00029836124,0.0016900855,0.000013204797,0.9947376,0.000109695306,0.00004384094,0.00015393454],"about_ca_topic_score_codex":0.000006030821,"about_ca_topic_score_gemma":0.0000015528715,"teacher_disagreement_score":0.004069224,"about_ca_system_score_codex":0.000036463494,"about_ca_system_score_gemma":0.0000493458,"threshold_uncertainty_score":0.5044753},"labels":[],"label_agreement":null},{"id":"W2332384380","doi":"10.1021/jf104980d","title":"Feasibility Study on Chemometric Discrimination of Roasted Arabica Coffees by Solvent Extraction and Fourier Transform Infrared Spectroscopy","year":2011,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Ethyl acetate; Fourier transform infrared spectroscopy; Chemistry; Acetic acid; Roasting; Acetone; Solvent; Chemometrics; Partial least squares regression; Principal component analysis; Chromatography; Torrefaction; Extraction (chemistry); Analytical Chemistry (journal); Mathematics; Organic chemistry; Statistics","score_opus":0.02862164526339469,"score_gpt":0.2636899556491016,"score_spread":0.23506831038570689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332384380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99258864,0.0010796984,0.000037418526,0.000071515926,0.000027216112,0.00008371877,0.000026727916,0.000012769957,0.0060723107],"genre_scores_gemma":[0.9988572,0.00025390746,0.0002079519,0.000005619985,0.00008268233,0.000003922665,0.000013202819,0.00000823596,0.0005672742],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858356,0.000012028288,0.0005857996,0.000237064,0.00039183424,0.00018972156],"domain_scores_gemma":[0.9988724,0.00006324433,0.0005831152,0.00012949394,0.00019344193,0.00015829901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001495683,0.0002397685,0.0004533517,0.00008248215,0.000083627165,0.000033307988,0.00014569535,0.00014520464,0.00020750744],"category_scores_gemma":[0.00008628266,0.00015249118,0.00014448943,0.00042121002,0.00008534428,0.00023559778,0.000024060135,0.00036558614,2.526544e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029626905,0.0018238048,0.018471766,0.00027106897,0.00035442432,0.0000026412088,0.0011060986,3.937694e-7,0.9768109,0.0000025944546,0.00037228852,0.00048779865],"study_design_scores_gemma":[0.0011728501,0.000764761,0.14719795,0.000047102996,0.0003788183,0.00008133866,0.0064884075,0.0000020576138,0.8435756,0.00010647472,0.000018574825,0.0001660342],"about_ca_topic_score_codex":0.000010660936,"about_ca_topic_score_gemma":0.0000018078622,"teacher_disagreement_score":0.13323523,"about_ca_system_score_codex":0.000100018995,"about_ca_system_score_gemma":0.000018929539,"threshold_uncertainty_score":0.621841},"labels":[],"label_agreement":null},{"id":"W2333654335","doi":"10.1190/1.3513825","title":"Application of Crossplot and Offset‐Coordinate Amplitude Polynomial Method","year":2010,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Offset (computer science); Computer science; Amplitude; Algorithm; Optics; Physics; Programming language","score_opus":0.007737377786048406,"score_gpt":0.30794830057257244,"score_spread":0.30021092278652406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333654335","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91176766,0.000096797565,0.051114816,0.00018919155,0.000029218596,0.000041052655,0.000013653684,0.00007874091,0.036668863],"genre_scores_gemma":[0.97982454,0.0000072780294,0.0178333,0.000037502687,0.00007796821,0.000009852435,0.000007999928,0.00000956883,0.0021919743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994065,0.0000036914955,0.0001730435,0.00020081142,0.00009093253,0.000125016],"domain_scores_gemma":[0.9994737,0.00009129527,0.0000853669,0.00024653794,0.00004438806,0.00005874421],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010676356,0.0000888684,0.00017227369,0.00006120096,0.00004860216,0.000021444077,0.0001226322,0.00010547498,0.0009419543],"category_scores_gemma":[0.000053651383,0.00007721309,0.000045315715,0.00019023709,0.00007950277,0.00004933512,0.000047154343,0.00015127273,0.000008750255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016172424,0.0000462285,0.014637929,0.000031168922,0.00003288485,1.5259215e-7,0.000027694672,5.8593463e-7,0.976593,0.0023196733,0.00025355356,0.006040965],"study_design_scores_gemma":[0.00027032508,0.000007976285,0.0032664551,9.771842e-7,0.000063989544,0.0000063203993,0.00007402942,0.0005906528,0.9908728,0.0004903887,0.0042602513,0.00009583904],"about_ca_topic_score_codex":0.0003887706,"about_ca_topic_score_gemma":0.000029720015,"teacher_disagreement_score":0.06805689,"about_ca_system_score_codex":0.000008320323,"about_ca_system_score_gemma":0.000014570028,"threshold_uncertainty_score":0.99997133},"labels":[],"label_agreement":null},{"id":"W2334684482","doi":"10.2166/wqrjc.2014.002","title":"Application of a solid phase extraction-liquid chromatography method to quantify phenolic compounds in woodwaste leachate","year":2014,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Recherche Industrielle du Québec; Université Laval","funders":"","keywords":"Leachate; Chromatography; Extraction (chemistry); Chemistry; Solid phase extraction; Detection limit; Phenol; Sample preparation; Phenols; High-performance liquid chromatography; Contamination; Environmental chemistry; Organic chemistry","score_opus":0.1217517882165964,"score_gpt":0.5200273524035177,"score_spread":0.39827556418692134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334684482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88390505,0.00014098997,0.11281693,0.0010192923,0.00002391116,0.000109785295,0.000016414959,0.000028306733,0.0019393247],"genre_scores_gemma":[0.99524796,0.000042377185,0.0040586395,0.000054538978,0.00025683452,0.0000387083,0.000020788677,0.000025165673,0.0002549735],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960919,0.0007746704,0.00093768956,0.00038516617,0.0010495142,0.0007610628],"domain_scores_gemma":[0.9981252,0.00043346809,0.0002088318,0.0004754455,0.00042250805,0.00033454504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00797073,0.00018637051,0.0005310647,0.0011541072,0.00026553147,0.00010673981,0.0005664798,0.00020006225,0.0005442273],"category_scores_gemma":[0.00020908826,0.0001468513,0.00023824244,0.0012416535,0.00014775044,0.00021537977,0.00012727591,0.0014327408,0.000060917642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055237056,0.0006754505,0.0020515942,0.00016195724,0.00012855309,0.0000039559636,0.0010443984,0.000063160864,0.99112433,0.0003729589,0.00020997504,0.0036112873],"study_design_scores_gemma":[0.0013948572,0.0002669447,0.00036914385,0.000045660654,0.00003128833,0.000058014506,0.0011404054,0.000782416,0.98786604,0.0019904575,0.005863813,0.00019094124],"about_ca_topic_score_codex":0.0005451339,"about_ca_topic_score_gemma":0.000037867063,"teacher_disagreement_score":0.11134293,"about_ca_system_score_codex":0.00016382275,"about_ca_system_score_gemma":0.00006570738,"threshold_uncertainty_score":0.6224625},"labels":[],"label_agreement":null},{"id":"W2334959938","doi":"10.1021/ie4008248","title":"Recursive Wavelength-Selection Strategy to Update Near-Infrared Spectroscopy Model with an Industrial Application","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Suncor Energy (Canada); University of Alberta","funders":"","keywords":"Robustness (evolution); Near-infrared spectroscopy; Computer science; Selection (genetic algorithm); Process (computing); Wavelength; Set (abstract data type); Property (philosophy); Algorithm; Biological system; Artificial intelligence; Materials science; Chemistry; Optics","score_opus":0.06863009539537455,"score_gpt":0.33226673282447106,"score_spread":0.2636366374290965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334959938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905761,0.000034745823,0.0017382222,0.0003091122,0.000042827363,0.00059598713,0.00005057285,0.00038263615,0.0062697865],"genre_scores_gemma":[0.99288917,0.000015499703,0.0014808204,0.000019338331,0.0018159673,0.0006036364,0.00022401952,0.000121990895,0.002829556],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961899,0.00003293134,0.0005084644,0.0009761904,0.001049765,0.0012427627],"domain_scores_gemma":[0.9975046,0.00015524984,0.00012120239,0.00078242976,0.0005406816,0.00089583657],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004780777,0.00046803395,0.00048106004,0.00023205602,0.0003286416,0.0005436994,0.0007887718,0.000856967,0.0022966382],"category_scores_gemma":[0.00063257746,0.00046962424,0.000088574234,0.0022808597,0.00012875775,0.00044549632,0.00013207839,0.0024623435,0.00012987989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003102269,0.00016703266,0.00027644157,0.00005592981,0.00013517763,0.00000519276,0.000055844568,0.040469285,0.9528984,0.00009658968,0.0034781294,0.0020517549],"study_design_scores_gemma":[0.0012375694,0.0002247107,0.000011711844,0.000053310618,0.00005432445,0.000015291631,0.00022561479,0.09853325,0.89744526,0.00023779903,0.0014562932,0.0005048831],"about_ca_topic_score_codex":0.00041579135,"about_ca_topic_score_gemma":0.000005103293,"teacher_disagreement_score":0.058063965,"about_ca_system_score_codex":0.0008650106,"about_ca_system_score_gemma":0.00076453655,"threshold_uncertainty_score":0.999839},"labels":[],"label_agreement":null},{"id":"W2341066319","doi":"10.32614/rj-2013-004","title":"Hypothesis Tests for Multivariate Linear Models Using the car Package","year":2013,"lang":"en","type":"article","venue":"The R Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":259,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; McMaster University","keywords":"Multivariate statistics; R package; Multivariate analysis; Econometrics; Computer science; Generalized linear model; Statistics; Mathematics","score_opus":0.08815247068049628,"score_gpt":0.3203991098254604,"score_spread":0.2322466391449641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341066319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94198537,0.000983177,0.05328503,0.0011626866,0.0000758547,0.00012858136,0.000015864323,0.000035537796,0.0023279146],"genre_scores_gemma":[0.9927639,0.00009755068,0.0046393746,0.00019348772,0.0006612406,0.000008319549,6.560444e-7,0.000027804746,0.0016076689],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991614,0.000035434543,0.00021977618,0.00009696068,0.00018354981,0.0003028705],"domain_scores_gemma":[0.9986661,0.00065704965,0.00017820192,0.0003051471,0.00011840021,0.000075085176],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032452377,0.00013449354,0.00016432037,0.000042117987,0.00057044125,0.000101262136,0.00044495735,0.000066538465,0.0009959303],"category_scores_gemma":[0.00022531819,0.00006517603,0.00015229943,0.0001774637,0.000067505265,0.00015198684,0.000043135835,0.000333208,0.000021209244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005270351,0.000093169285,0.00033914755,0.000032581163,0.000512326,0.0000033923325,0.0013285544,0.0062379884,0.9852119,0.00014306753,0.0037813908,0.0022637476],"study_design_scores_gemma":[0.0010771743,0.000039283215,0.00025261755,0.000048812584,0.0010915332,0.00039943293,0.0034533872,0.27646393,0.68624717,0.02984944,0.0006893919,0.0003878079],"about_ca_topic_score_codex":0.00014664819,"about_ca_topic_score_gemma":0.0000021667618,"teacher_disagreement_score":0.29896474,"about_ca_system_score_codex":0.00006774131,"about_ca_system_score_gemma":0.00004526867,"threshold_uncertainty_score":0.99991727},"labels":[],"label_agreement":null},{"id":"W2341601851","doi":"10.5539/apr.v8n3p32","title":"Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis","year":2016,"lang":"en","type":"article","venue":"Applied Physics Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Uppsala Universitet","keywords":"Principal component analysis; Arabica coffee; Multivariate statistics; Artificial intelligence; Pattern recognition (psychology); Laser; Computer science; Materials science; Mathematics; Optics; Statistics; Physics; Horticulture; Biology","score_opus":0.0853433091163011,"score_gpt":0.377824821185089,"score_spread":0.29248151206878786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341601851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9137575,0.000059236627,0.07805538,0.00008633217,0.000016333446,0.00014371022,0.00009588633,0.000054639164,0.007730959],"genre_scores_gemma":[0.9980535,0.000007296415,0.0014249912,0.000011001871,0.00012148084,0.000026367588,0.00012498512,0.000030914347,0.00019946412],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99766666,0.000050194252,0.00034535956,0.0005017333,0.000986575,0.00044947275],"domain_scores_gemma":[0.9977213,0.0007708362,0.00024147694,0.0006059926,0.0005600194,0.00010033165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005051674,0.0001863785,0.00037642982,0.0011529984,0.00019119866,0.000052986303,0.0003772583,0.00011027078,0.0006018926],"category_scores_gemma":[0.00018808752,0.00014913734,0.00014037758,0.008942439,0.00026696362,0.00013464768,0.0000614386,0.00025095823,0.000016920001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006944922,0.00013398047,0.008692307,0.00006758185,0.00039470993,9.734337e-7,0.00006087052,0.00020871802,0.9824197,0.0018655788,0.00003677433,0.0060493546],"study_design_scores_gemma":[0.0008228429,0.000006650351,0.014602246,0.000012552422,0.0004438898,2.9292758e-7,0.00028596734,0.028182723,0.9534511,0.0019514587,0.000040020885,0.00020028498],"about_ca_topic_score_codex":0.00027398512,"about_ca_topic_score_gemma":0.0000062183417,"teacher_disagreement_score":0.08429598,"about_ca_system_score_codex":0.00038130203,"about_ca_system_score_gemma":0.00012379284,"threshold_uncertainty_score":0.6590302},"labels":[],"label_agreement":null},{"id":"W2346590743","doi":"10.1117/12.2223758","title":"On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Domain (mathematical analysis); Content (measure theory); Computer science; Water content; Frequency domain; Remote sensing; Environmental science; Biological system; Mathematics; Engineering; Computer vision; Biology; Geology","score_opus":0.02649686463482863,"score_gpt":0.2586432406484914,"score_spread":0.23214637601366278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346590743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977555,0.000075672346,0.000007725384,0.0013885703,0.000050652838,0.00017032782,0.000013628859,0.000014465253,0.0005234276],"genre_scores_gemma":[0.99730134,0.00008031552,0.0023373775,0.000021210028,0.00013131782,0.000043194075,4.4721025e-7,0.000019530542,0.00006529246],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859446,4.6852044e-8,0.00046289482,0.00023475602,0.00042372465,0.0002841442],"domain_scores_gemma":[0.99909043,0.00040759114,0.00018503188,0.00005239344,0.00023016172,0.000034370572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070121826,0.00018671625,0.00026733926,0.0001256996,0.00005573467,0.000046260502,0.00048555984,0.000116896306,0.0000150306405],"category_scores_gemma":[0.00051145966,0.00009198437,0.00020517453,0.00022296382,0.00017124484,0.00017552602,0.000071335664,0.00023580506,2.6506908e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029283232,0.00007602905,0.0029602984,0.000121221376,0.00012573891,1.496582e-7,0.00040874982,0.000011073658,0.970318,0.025588924,0.00002098295,0.00007596317],"study_design_scores_gemma":[0.00070844026,0.0001027562,0.0030594287,0.00032975277,0.000045984147,0.000011416239,0.004195463,0.0006153675,0.9890992,0.0016782616,0.000032518667,0.000121431694],"about_ca_topic_score_codex":0.000034960856,"about_ca_topic_score_gemma":3.879638e-7,"teacher_disagreement_score":0.023910662,"about_ca_system_score_codex":0.00017402423,"about_ca_system_score_gemma":0.000012189271,"threshold_uncertainty_score":0.3751014},"labels":[],"label_agreement":null},{"id":"W234923548","doi":"","title":"Quantitative analyses aspect of vibrational spectroscopy and its applications in agriculture","year":2010,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Calibration; Raman spectroscopy; Spectroscopy; Principal component analysis; Infrared spectroscopy; Partial least squares regression; Infrared; Computer science; Artificial intelligence; Biological system; Pattern recognition (psychology); Analytical Chemistry (journal); Chemistry; Optics; Machine learning; Mathematics; Physics; Statistics","score_opus":0.02761523205500828,"score_gpt":0.34751141508560024,"score_spread":0.319896183030592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W234923548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94176364,0.0006454564,0.004036066,0.00016678673,0.000010654614,0.00007033052,0.000035304627,0.0000376997,0.05323407],"genre_scores_gemma":[0.9847179,0.00006416401,0.014751435,0.000017402523,0.00004291346,0.000022446546,0.000041274194,0.000006084289,0.00033638833],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929243,0.0000052086684,0.00021734541,0.00021920499,0.00013908712,0.00012670239],"domain_scores_gemma":[0.99945456,0.00019311838,0.00009555631,0.00012863109,0.00007751632,0.00005061619],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000044685607,0.00011644422,0.00021176814,0.00014086503,0.00004732854,0.000018485047,0.00011879992,0.000094585215,0.0022678527],"category_scores_gemma":[0.00010277129,0.00008659107,0.000051536812,0.00070711115,0.000059592603,0.00010190053,0.000026194391,0.00021471051,0.0000076859515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073730844,0.000094824216,0.012235841,0.00002342251,0.00003467344,3.908917e-7,0.00005001536,0.000006355417,0.9404571,0.047004,0.00007907518,0.0000069513353],"study_design_scores_gemma":[0.00015343464,0.000016653332,0.008050409,0.0000030193596,0.000055466622,0.0000025128275,0.0006545512,0.0002043277,0.9893217,0.0013686877,0.0000656465,0.0001035889],"about_ca_topic_score_codex":0.00007372059,"about_ca_topic_score_gemma":0.00030671325,"teacher_disagreement_score":0.05289768,"about_ca_system_score_codex":0.000014514964,"about_ca_system_score_gemma":0.000030365904,"threshold_uncertainty_score":0.99864423},"labels":[],"label_agreement":null},{"id":"W2356615642","doi":"","title":"Preliminary Study on the Antifungal Activity of Cocculus orbiculatus(L.) DC","year":2009,"lang":"en","type":"article","venue":"Southern Horticulture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Petroleum ether; Chemistry; Antifungal; EC50; Mycelium; Methanol; Traditional medicine; Extraction (chemistry); Chromatography; Stereochemistry; Botany; Biology; Microbiology; Biochemistry; Organic chemistry; In vitro; Medicine","score_opus":0.017488802364491317,"score_gpt":0.2727964326042181,"score_spread":0.2553076302397268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356615642","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892861,0.00024672417,0.000009376972,0.0003043196,0.000022468043,0.00014737225,0.00004225649,0.00007016501,0.009871197],"genre_scores_gemma":[0.99766743,0.000003344669,0.000011723301,0.000079034864,0.00011187002,0.0000095180685,0.0000049750874,0.000013156009,0.0020989375],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878615,0.00003894604,0.0002116682,0.00031373109,0.00039393237,0.00025557587],"domain_scores_gemma":[0.99905485,0.00008210631,0.0001747939,0.0005477435,0.00007720307,0.00006332672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114345465,0.00022713162,0.00030137916,0.000038411268,0.00014593205,0.000038590675,0.0003163578,0.0001445109,0.0008891803],"category_scores_gemma":[0.00012077846,0.00012537932,0.00017530359,0.0004495526,0.00006515453,0.000041165596,0.000036873247,0.00038127552,0.000056395802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007054088,0.004730278,0.10366411,0.000049138966,0.0007690827,0.00005975718,0.013659791,0.00008406404,0.87113184,0.000308405,0.0033638044,0.001474319],"study_design_scores_gemma":[0.00077637186,0.0008985211,0.1263509,0.00004846715,0.0007519843,0.0000106886455,0.038547806,0.000047156347,0.83169293,0.00024187718,0.00020794006,0.00042537285],"about_ca_topic_score_codex":0.000023408986,"about_ca_topic_score_gemma":0.0000044260228,"teacher_disagreement_score":0.039438933,"about_ca_system_score_codex":0.000037507743,"about_ca_system_score_gemma":0.00001999106,"threshold_uncertainty_score":0.9735901},"labels":[],"label_agreement":null},{"id":"W2358979711","doi":"10.1006/jaer.2000.0690","title":"AE—Automation and Emerging Technologies","year":2001,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Kernel (algebra); Mathematics; Segmentation; Curvature; Binary image; Artificial intelligence; Ellipse; Object (grammar); Image segmentation; Boundary (topology); Binary number; Pattern recognition (psychology); Computer vision; Algorithm; Image (mathematics); Image processing; Computer science; Geometry; Combinatorics; Mathematical analysis","score_opus":0.02281828575685045,"score_gpt":0.3146698963523744,"score_spread":0.29185161059552395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358979711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99542874,0.0024279908,0.00015341843,0.000802804,0.000026939997,0.000015003349,4.1236353e-7,0.000072590905,0.0010720731],"genre_scores_gemma":[0.99743795,0.0011371479,0.0007925167,0.0000014934589,0.00013838314,0.0000015860371,7.944458e-7,0.0000054649518,0.0004846693],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990796,0.000005568994,0.00021233242,0.000083770006,0.00038346005,0.00023529856],"domain_scores_gemma":[0.99941057,0.00013038973,0.00007047525,0.00007025845,0.00026429023,0.000054028053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027391306,0.000081661936,0.000154514,0.00031245363,0.000074659045,0.00006732838,0.00018036306,0.00008188193,0.00008241508],"category_scores_gemma":[0.0005298935,0.000050515166,0.00005150351,0.0008265866,0.00003162947,0.0002106173,0.000056561217,0.0005212414,0.0000033354056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011857963,0.000035700647,0.0036458287,0.00009686831,0.00009633038,0.00004422483,0.00013883862,0.0014193035,0.9892857,0.00020460166,0.0010412239,0.0039795004],"study_design_scores_gemma":[0.00082920986,0.00020530702,0.07579578,0.00028634092,0.000107495434,0.0022025744,0.010547352,0.0018782322,0.897644,0.00024863138,0.009827684,0.00042741312],"about_ca_topic_score_codex":0.000005965625,"about_ca_topic_score_gemma":4.3856755e-7,"teacher_disagreement_score":0.09164174,"about_ca_system_score_codex":0.000099856916,"about_ca_system_score_gemma":0.000011517859,"threshold_uncertainty_score":0.22645633},"labels":[],"label_agreement":null},{"id":"W2359124805","doi":"","title":"Study on Detecting Oil Content in Jatropha Curca Seed by Nuclear Magnetic Resonance Technique","year":2009,"lang":"en","type":"article","venue":"Seed","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Jatropha; Nuclear magnetic resonance; Chemistry; Physics; Biodiesel","score_opus":0.018749078178068205,"score_gpt":0.26336944993767664,"score_spread":0.24462037175960843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359124805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762038,0.0013740371,0.0000067620276,0.00015636932,0.00001742825,0.00009369329,0.00000557366,0.00016423422,0.021978123],"genre_scores_gemma":[0.99535924,0.00004210959,0.00017052477,0.0002651194,0.000048134007,0.000024475636,0.0000026737148,0.000025241194,0.004062506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987013,0.000026828677,0.00029476854,0.00039305037,0.00024198259,0.00034207254],"domain_scores_gemma":[0.99940014,0.000058822385,0.00008629354,0.0003534241,0.000033258813,0.00006803376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013333876,0.00020556626,0.00029660904,0.0001348762,0.00008700634,0.00003688363,0.0002562376,0.00012166339,0.00018623704],"category_scores_gemma":[0.00014197097,0.00020016794,0.00007089147,0.00057015684,0.000029336274,0.000038887025,0.000031814867,0.0003972753,0.000039366427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015407518,0.0007138029,0.01955685,0.00001435702,0.000013950983,0.000060386352,0.00033539932,0.0000019097397,0.97511214,0.000019512054,0.0001202291,0.003897415],"study_design_scores_gemma":[0.0026152194,0.001208008,0.114916556,0.00010655754,0.000089188354,0.000015465763,0.0054801204,0.000078362136,0.87299716,0.000096986216,0.0018378064,0.0005585691],"about_ca_topic_score_codex":0.00019769392,"about_ca_topic_score_gemma":0.000013281804,"teacher_disagreement_score":0.102114946,"about_ca_system_score_codex":0.00016893576,"about_ca_system_score_gemma":0.000014430471,"threshold_uncertainty_score":0.8162612},"labels":[],"label_agreement":null},{"id":"W2371905106","doi":"10.1117/12.2235593","title":"Recent advances in rapid and non-destructive assessment of meat quality using hyperspectral imaging","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Computer science; Marbled meat; Quality (philosophy); Food quality; Food processing; Traceability; Process engineering; Biochemical engineering; Environmental science; Agricultural engineering; Artificial intelligence; Food science","score_opus":0.015402665904735534,"score_gpt":0.294106633929141,"score_spread":0.27870396802440545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371905106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943012,0.0009473873,0.00006364667,0.00078022823,0.00005663822,0.00014209008,0.00003829818,0.00002471574,0.0036457796],"genre_scores_gemma":[0.9388024,0.002194666,0.058740754,0.000019426721,0.00014359536,0.00002763266,0.0000018174849,0.000031591368,0.000038074082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99812883,1.6778989e-8,0.0006688449,0.00036921163,0.0005121931,0.00032092375],"domain_scores_gemma":[0.9983288,0.000192994,0.0004590816,0.000051647203,0.0008856769,0.00008180127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004222438,0.00025044856,0.000487899,0.00012858164,0.00004489263,0.000036739588,0.00045144907,0.00011044955,0.00004423219],"category_scores_gemma":[0.0004699448,0.00018414791,0.0003054477,0.00036508657,0.00030976717,0.0006126481,0.00012461524,0.00020479722,9.459267e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006621701,0.00009710953,0.019036444,0.0004261298,0.00022074442,6.666336e-8,0.00010378857,0.000015648648,0.890829,0.08788856,0.000025855568,0.0012904274],"study_design_scores_gemma":[0.0016317845,0.00009184833,0.006904617,0.000449865,0.00020934528,0.000012419417,0.0038286047,0.0054119793,0.97850895,0.0023033838,0.00030044172,0.00034675657],"about_ca_topic_score_codex":0.00001352241,"about_ca_topic_score_gemma":2.3895998e-7,"teacher_disagreement_score":0.08767995,"about_ca_system_score_codex":0.00030418296,"about_ca_system_score_gemma":0.000043127562,"threshold_uncertainty_score":0.75093335},"labels":[],"label_agreement":null},{"id":"W2375922510","doi":"","title":"Comparative Study on Pollen Viability Detection Methods for Prunus pseudocerasu L.","year":2011,"lang":"en","type":"article","venue":"Seed","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pollen; Prunus; Biology; Botany","score_opus":0.09827152540523852,"score_gpt":0.4057708107278118,"score_spread":0.30749928532257326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375922510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95214236,0.00005017684,0.0131844515,0.000017596009,0.000084377534,0.00032996238,0.000009461102,0.00012625399,0.034055345],"genre_scores_gemma":[0.99335945,8.523626e-7,0.005378058,0.000026084957,0.000076274904,0.00011242903,0.0000026876216,0.000013208473,0.0010309618],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909234,0.000058525336,0.00020825872,0.0003353027,0.00010248926,0.00020305622],"domain_scores_gemma":[0.9991859,0.00024545423,0.00009847526,0.00032136103,0.00008637096,0.000062410276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002777075,0.0001567966,0.00031995474,0.0000885767,0.00012975096,0.000014860015,0.00015284138,0.000082213126,0.00037513382],"category_scores_gemma":[0.00015488965,0.00013587782,0.00011269907,0.00032382642,0.000041585146,0.00005020023,0.000025387293,0.00014672718,0.000021631571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019981475,0.004495434,0.046001446,0.00012908033,0.0012813355,0.000002810549,0.013547703,0.0000062822332,0.92878765,0.00014916513,0.000103476355,0.0034974837],"study_design_scores_gemma":[0.0009614726,0.00040400948,0.05488992,0.0000023588273,0.00020529419,0.0000010580999,0.0048751947,0.00021757265,0.9370624,0.001090265,0.0001291442,0.00016130142],"about_ca_topic_score_codex":0.00013174197,"about_ca_topic_score_gemma":0.000022173079,"teacher_disagreement_score":0.041217066,"about_ca_system_score_codex":0.00011058848,"about_ca_system_score_gemma":0.000019767574,"threshold_uncertainty_score":0.5540937},"labels":[],"label_agreement":null},{"id":"W2379100577","doi":"","title":"The effect of physiological indices in two Oilseed rape leaves under salt stress","year":2014,"lang":"en","type":"article","venue":"Journal of Northwest Normal University","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"APX; Proline; Salt (chemistry); Chlorophyll; Stress (linguistics); Chemistry; Horticulture; Antioxidant; Food science; Agronomy; Biology; Catalase; Biochemistry","score_opus":0.006380551432809132,"score_gpt":0.24051166233107252,"score_spread":0.23413111089826338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2379100577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99773026,0.0001204745,0.000055787386,0.00006284824,0.000044604276,0.000015938833,0.000007096979,0.0000064175592,0.0019565679],"genre_scores_gemma":[0.99954605,0.0001249941,0.000027127844,0.000009684888,0.00011674257,5.0957627e-8,0.0000027010103,0.0000042661222,0.00016837071],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991506,0.00009629247,0.0002497044,0.00010169685,0.00020906284,0.00019268849],"domain_scores_gemma":[0.99863476,0.00059804483,0.0004737919,0.0001396885,0.00007414391,0.000079556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029249745,0.00012113884,0.0003453965,0.00015285806,0.00012711252,0.00001425842,0.00044259234,0.00006995025,0.000108714456],"category_scores_gemma":[0.00009053363,0.00008009235,0.00018750584,0.00037457535,0.00017144684,0.00017584997,0.00007569333,0.00038523317,0.0000016369698],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072667963,0.00012013438,0.9778997,0.000067731824,0.00018761342,0.00004736784,0.0000910779,0.0016453278,0.018246915,0.00013301213,0.000034728466,0.00079975853],"study_design_scores_gemma":[0.004051486,0.0008344167,0.74550706,0.00009041409,0.00040774254,0.000042731193,0.0015767744,0.00018006167,0.24635705,0.0000956677,0.00061357475,0.0002430042],"about_ca_topic_score_codex":0.00031458563,"about_ca_topic_score_gemma":0.0006629084,"teacher_disagreement_score":0.23239258,"about_ca_system_score_codex":0.00008209963,"about_ca_system_score_gemma":0.000034961602,"threshold_uncertainty_score":0.32660714},"labels":[],"label_agreement":null},{"id":"W2379461708","doi":"","title":"Current Status and Prospects on DUS Testing Technique System for New Plant Variety Protection","year":2007,"lang":"en","type":"article","venue":"Seed","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Current (fluid); Variety (cybernetics); Risk analysis (engineering); Computer science; Medicine; Engineering; Electrical engineering; Artificial intelligence","score_opus":0.030362790045195296,"score_gpt":0.2802363099562886,"score_spread":0.24987351991109333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2379461708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7749511,0.0021306807,0.20237656,0.000103710474,0.00037971864,0.0025345427,0.00011109986,0.0015145084,0.01589807],"genre_scores_gemma":[0.9966645,0.000006961801,0.0027038308,0.0000068873483,0.00026164003,0.00005909213,0.00001556451,0.000018114994,0.0002633948],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999129,0.0000034131758,0.00018168408,0.00025618385,0.00012982072,0.0002999063],"domain_scores_gemma":[0.99949783,0.000092445705,0.00011202567,0.00013767695,0.00004780091,0.000112217895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016571279,0.00013365701,0.00016060841,0.000090352936,0.000119926524,0.000034211153,0.00005490762,0.00009808623,0.0000057876705],"category_scores_gemma":[0.00020563958,0.00011796609,0.00003569989,0.00033058182,0.0000142909275,0.000034700024,0.000018632056,0.00018821537,0.0000024175536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036283836,0.0001555106,0.024756402,0.0016688443,0.00008113717,0.00000823807,0.00017263083,0.000009282681,0.9634445,0.0014804405,0.00013615037,0.007724078],"study_design_scores_gemma":[0.00052409,0.0001240419,0.0047595445,0.00017350313,0.000065942964,0.000023253831,0.00021529692,0.0002743573,0.9923929,0.00026355064,0.0010135949,0.00016990256],"about_ca_topic_score_codex":0.00015230304,"about_ca_topic_score_gemma":0.0000087610615,"teacher_disagreement_score":0.2217134,"about_ca_system_score_codex":0.00021748088,"about_ca_system_score_gemma":0.000059134647,"threshold_uncertainty_score":0.48105174},"labels":[],"label_agreement":null},{"id":"W2394220557","doi":"","title":"Varietal Identification of Oat(Avena spp.)by Ultrathin-layer Isoelectric Focusing of Seed Protein","year":2007,"lang":"en","type":"article","venue":"Acta Agrestia Sinica","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Avena; Isoelectric point; Isoelectric focusing; Biology; Botany; Horticulture; Agronomy","score_opus":0.012716087554689788,"score_gpt":0.27502759587653625,"score_spread":0.26231150832184646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394220557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895157,0.0008642184,0.0013311079,0.00010327765,0.000034953042,0.0001230379,0.00002621875,0.000068652385,0.007932828],"genre_scores_gemma":[0.9979786,0.00003543179,0.00044720745,0.000018515697,0.00008544047,0.0000053396566,0.00007125363,0.000024476301,0.001333683],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979105,0.000023323282,0.00089361385,0.00036975348,0.00043156126,0.00037125536],"domain_scores_gemma":[0.9981443,0.0002910326,0.0007470276,0.0005417948,0.00017687626,0.000098953606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051600725,0.00021067896,0.00040638572,0.00026258576,0.000080577665,0.000026226793,0.00039145086,0.0002443467,0.0003911738],"category_scores_gemma":[0.00056493824,0.00020527106,0.00016833296,0.0016529746,0.00015140101,0.00015700361,0.00004909474,0.0002759059,0.000019024354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094473384,0.0002629282,0.002236231,0.00012716533,0.00012327754,0.000004911056,0.000093036884,0.000001970462,0.9953088,0.000056645455,0.0009557748,0.0007347649],"study_design_scores_gemma":[0.0004054882,0.000063128464,0.003928043,0.000035920733,0.0001687213,0.000008080441,0.00010311072,0.00009975556,0.9942523,0.00019183125,0.00054322265,0.00020036056],"about_ca_topic_score_codex":0.00013057326,"about_ca_topic_score_gemma":0.0000034384943,"teacher_disagreement_score":0.008462946,"about_ca_system_score_codex":0.00006494477,"about_ca_system_score_gemma":0.00015778249,"threshold_uncertainty_score":0.83707106},"labels":[],"label_agreement":null},{"id":"W2394280547","doi":"","title":"Optimized Conditions of the Electrical Conductivity Method for Determination of Seed Vigor in Maize","year":2008,"lang":"en","type":"article","venue":"Seed","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Electrical resistivity and conductivity; Conductivity; Materials science; Hybrid; Mathematics; Agronomy; Chemistry; Biology; Physics","score_opus":0.027329237503272635,"score_gpt":0.32892533445760563,"score_spread":0.301596096954333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394280547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96394277,0.000089794405,0.03175323,0.00012274405,0.000028630626,0.00018220066,0.00007038208,0.000019958128,0.003790278],"genre_scores_gemma":[0.97772115,0.000006800307,0.021468874,0.000020811998,0.000018739569,0.00003171268,0.000014856292,0.0000082199385,0.00070882385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999306,0.000035789348,0.00025663828,0.00014067019,0.00013185639,0.00012904285],"domain_scores_gemma":[0.9990308,0.00048364408,0.00018433228,0.0001860332,0.00009293865,0.000022202354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001378616,0.00008230342,0.00028767018,0.000103026905,0.00005285467,0.0000022644122,0.00014230439,0.000096141506,0.00009387461],"category_scores_gemma":[0.0004929232,0.00006511671,0.00014602911,0.0005934987,0.000093051916,0.00004710048,0.00001980406,0.000109132015,5.242338e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009891782,0.00018532103,0.008295058,0.000043987984,0.00003777478,6.8605976e-7,0.00012756675,0.00004666555,0.99081206,0.00026931963,0.000037303187,0.000045332275],"study_design_scores_gemma":[0.0013689336,0.000023735805,0.023233218,0.000007601747,0.00007670876,0.000007692407,0.00009325429,0.006278422,0.968046,0.0007797697,0.000014497044,0.00007018518],"about_ca_topic_score_codex":0.00008001755,"about_ca_topic_score_gemma":0.000004647178,"teacher_disagreement_score":0.022766085,"about_ca_system_score_codex":0.00005608943,"about_ca_system_score_gemma":0.000086670945,"threshold_uncertainty_score":0.26553822},"labels":[],"label_agreement":null},{"id":"W2396266990","doi":"10.1021/acs.chemrestox.6b00096","title":"Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields","year":2016,"lang":"en","type":"article","venue":"Chemical Research in Toxicology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; National Institutes of Health","keywords":"Multivariate statistics; Multivariate analysis; Feature (linguistics); Statistics; Mathematics; Econometrics; Computer science","score_opus":0.09281943599278422,"score_gpt":0.4221436146430772,"score_spread":0.32932417865029295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396266990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98977953,0.00011471493,0.00381144,0.0022567452,0.000017556875,0.00009978437,0.00009743716,0.000033260225,0.0037895138],"genre_scores_gemma":[0.9976079,0.00006494391,0.0013312578,0.00011778746,0.00004722844,0.000056586465,0.000014683943,0.00001664953,0.0007430028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99732083,0.0001860395,0.0004000569,0.00062517216,0.0006616585,0.00080626336],"domain_scores_gemma":[0.99100876,0.007900321,0.00009228135,0.00054598064,0.0002140793,0.00023858256],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008830487,0.00018959996,0.00062701566,0.0008676972,0.000047426962,0.000015913307,0.0006166019,0.0006135215,0.0026574817],"category_scores_gemma":[0.0072130444,0.0001339237,0.00012400752,0.002701192,0.00074867875,0.000057278507,0.00019791786,0.0009425813,0.00003221151],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004784845,0.00034393542,0.0061708977,0.000038124123,0.00048424298,0.000062071114,0.000043608063,0.00005425375,0.98629993,0.0018169314,0.0016965254,0.0025109658],"study_design_scores_gemma":[0.0006744435,0.00014780492,0.0055706785,0.00004561406,0.00018550672,0.0000018380068,0.00004048887,0.0003888794,0.9905931,0.0018617939,0.0003230533,0.00016680766],"about_ca_topic_score_codex":0.00005846603,"about_ca_topic_score_gemma":0.000010160429,"teacher_disagreement_score":0.007828312,"about_ca_system_score_codex":0.0004057921,"about_ca_system_score_gemma":0.0001581572,"threshold_uncertainty_score":0.99825424},"labels":[],"label_agreement":null},{"id":"W2403069542","doi":"10.1016/j.biosystemseng.2016.04.001","title":"Performance evaluation of a model for the classification of contaminants from wheat using near-infrared hyperspectral imaging","year":2016,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Hyperspectral imaging; VNIR; Contamination; Canola; Environmental science; Identification (biology); Classifier (UML); Spectral signature; Artificial intelligence; Agronomy; Remote sensing; Computer science; Biology; Botany; Ecology","score_opus":0.05573403160731253,"score_gpt":0.2873335790014156,"score_spread":0.23159954739410307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403069542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679022,0.0010130742,0.030722091,0.000015504951,0.00006394204,0.000117626565,0.00005485696,0.000023822988,0.000086893306],"genre_scores_gemma":[0.9982745,0.000020193467,0.0015388215,8.429604e-7,0.00007218985,0.000025807321,0.0000046281143,0.000016288688,0.00004672578],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991944,0.000005568967,0.0002818294,0.00014220316,0.00023682919,0.0001391785],"domain_scores_gemma":[0.9992485,0.0001461417,0.00016194278,0.00024404723,0.00017790761,0.000021433525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002533169,0.000100199075,0.0001878272,0.000055658515,0.00004436196,0.000010861977,0.0001336182,0.00004882351,0.000023039383],"category_scores_gemma":[0.000120791796,0.00006614886,0.000071791874,0.00013618854,0.000024711766,0.00010510234,0.000012518156,0.000032497243,4.455603e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000112647185,0.000009817528,0.0045410586,0.00009803317,0.00006297734,2.6701432e-8,0.00014769234,0.0071922056,0.9868544,0.00002304971,0.000005955518,0.0010535615],"study_design_scores_gemma":[0.00031384345,0.0000024088465,0.0016747412,0.00011156863,0.00015060016,6.8021507e-7,0.00012986333,0.58825487,0.40930274,0.0000024018605,0.000005082098,0.000051163664],"about_ca_topic_score_codex":0.00005372147,"about_ca_topic_score_gemma":0.0000012679955,"teacher_disagreement_score":0.5810627,"about_ca_system_score_codex":0.00016904277,"about_ca_system_score_gemma":0.000060320814,"threshold_uncertainty_score":0.26974723},"labels":[],"label_agreement":null},{"id":"W2415542276","doi":"10.25560/29842","title":"Frequency domain analysis and simulation of multi-channel complex-valued time series","year":2013,"lang":"en","type":"dissertation","venue":"Spiral (Imperial College London)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Imperial College London; Eli Lilly and Company","keywords":"Series (stratigraphy); Time domain; Computer science; Time series; Frequency domain; Geology; Machine learning","score_opus":0.01579079501836354,"score_gpt":0.2815092134706454,"score_spread":0.26571841845228183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415542276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98951155,0.0009435161,0.00013022056,0.000016480362,0.00018766065,0.00033426858,0.0009832177,0.00011004846,0.007783017],"genre_scores_gemma":[0.9718574,0.00007182316,0.0030628676,0.00002437458,0.00034749066,0.000069860085,0.0045181005,0.0000838969,0.019964142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99732816,0.000058154987,0.0009575118,0.0007158248,0.0004954705,0.00044490534],"domain_scores_gemma":[0.9980581,0.00010808393,0.00077830476,0.0005145684,0.00037253622,0.00016844063],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013647278,0.00062481186,0.0015371011,0.00074193295,0.0002374853,0.00008743656,0.00034477847,0.00071918085,0.0082447175],"category_scores_gemma":[0.00018171282,0.00061950675,0.00052899605,0.0016879478,0.00015903031,0.00027832235,0.000058032438,0.00032313057,0.000056311586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012058577,0.0004507479,0.0014803548,0.0013542917,0.005701003,0.00003016263,0.0014475202,0.000939131,0.98557055,0.0012890327,0.00040947783,0.000121874786],"study_design_scores_gemma":[0.012286515,0.0009145521,0.011037953,0.00033848532,0.023210663,0.000016719567,0.010389349,0.03553434,0.88906556,0.011446538,0.00060819014,0.005151112],"about_ca_topic_score_codex":0.0007820715,"about_ca_topic_score_gemma":0.0005201528,"teacher_disagreement_score":0.096504964,"about_ca_system_score_codex":0.00013223205,"about_ca_system_score_gemma":0.00012824078,"threshold_uncertainty_score":0.9996256},"labels":[],"label_agreement":null},{"id":"W2418047027","doi":"10.1016/j.foodchem.2016.05.168","title":"Chemical morphology of Areca nut characterized directly by Fourier transform near-infrared and mid-infrared microspectroscopic imaging in reflection modes","year":2016,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; Tsinghua University; National Natural Science Foundation of China; Public Health Agency of Canada","keywords":"Chemical imaging; Attenuated total reflection; Infrared; Reflection (computer programming); Fourier transform infrared spectroscopy; Fourier transform; Optics; Near-infrared spectroscopy; Materials science; Chemistry; Analytical Chemistry (journal); Remote sensing; Hyperspectral imaging; Physics; Chromatography; Computer science; Geology","score_opus":0.008380126503925755,"score_gpt":0.24381660562670912,"score_spread":0.23543647912278337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418047027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860409,0.0013929615,0.0002203865,0.00031835522,0.000028169601,0.0000733964,0.00019047315,0.000107957865,0.011627424],"genre_scores_gemma":[0.9954697,0.00026091508,0.0007097813,0.000042882748,0.00008668518,0.00005273373,0.00009109916,0.00004879327,0.003237438],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980008,0.000011927625,0.00057425944,0.00063209585,0.00022452549,0.00055639015],"domain_scores_gemma":[0.99901104,0.00011087652,0.00023292715,0.00042667458,0.000066057604,0.0001524004],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009271775,0.00036649028,0.0006199845,0.000070313086,0.00007129412,0.000043041484,0.00028151082,0.0003355513,0.0012313498],"category_scores_gemma":[0.00012108029,0.0003241995,0.00014554292,0.00035503352,0.00031738894,0.0001734399,0.000050879542,0.00029169495,0.0000035262628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025439338,0.00014829988,0.0036387872,0.00023762521,0.000085513355,0.0000069858183,0.00011548137,8.280618e-8,0.9938986,0.0000017926438,0.0004385124,0.0011739767],"study_design_scores_gemma":[0.0024515328,0.000028132341,0.00010315368,0.00011668253,0.000086465705,0.000049736776,0.00011231976,0.000036244743,0.99557763,0.00049924507,0.0005787869,0.00036004753],"about_ca_topic_score_codex":0.000025558946,"about_ca_topic_score_gemma":0.0000015238592,"teacher_disagreement_score":0.009428795,"about_ca_system_score_codex":0.000244892,"about_ca_system_score_gemma":0.000081709826,"threshold_uncertainty_score":0.999921},"labels":[],"label_agreement":null},{"id":"W2418805578","doi":"10.1093/jaoac/90.2.446","title":"Improved Determination of Isolated trans Isomers in Edible Oils by Fourier Transform Infrared Spectroscopy Using Spectral Reconstitution","year":2007,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Infrared; Fourier transform; Spectroscopy; Fourier transform spectroscopy; Infrared spectroscopy; Analytical Chemistry (journal); Chemistry; Infrared spectroscopy correlation table; Materials science; Chromatography; Organic chemistry; Mathematics; Physics; Optics","score_opus":0.012359402872201743,"score_gpt":0.2950413975801756,"score_spread":0.2826819947079739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418805578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76760334,0.00020294842,0.21832432,0.00017945118,0.00036346872,0.000048241345,0.00003758922,0.000010028406,0.013230639],"genre_scores_gemma":[0.98237634,0.0001376659,0.01675141,0.0000315366,0.00027582675,7.269966e-7,0.00001926238,0.000014757672,0.0003924729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981482,0.000010718902,0.0010003466,0.00015177751,0.0004248706,0.00026406886],"domain_scores_gemma":[0.99884325,0.00008342631,0.0006316838,0.00009071208,0.00026209143,0.00008881734],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046234607,0.00016471853,0.00033226775,0.0007054417,0.000039550152,0.00003076977,0.00027087936,0.00016462123,0.0011107937],"category_scores_gemma":[0.00009603534,0.00016234437,0.00022478333,0.00045378943,0.00009342537,0.0005390236,0.0000084009225,0.00039501654,6.817577e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006068964,0.00025511018,0.0030744562,0.000031531647,0.0001514799,0.000024213869,0.00027262676,0.000042023363,0.99331456,0.000049995633,0.00013030904,0.002046822],"study_design_scores_gemma":[0.0017971484,0.00013975546,0.00024004078,0.00008168126,0.0001168879,0.00015277916,0.00035727362,0.008249521,0.98781866,0.0005386643,0.0003513779,0.00015620304],"about_ca_topic_score_codex":0.00005847895,"about_ca_topic_score_gemma":0.000037477417,"teacher_disagreement_score":0.21477301,"about_ca_system_score_codex":0.0005904711,"about_ca_system_score_gemma":0.00014146615,"threshold_uncertainty_score":0.99980235},"labels":[],"label_agreement":null},{"id":"W2419330900","doi":"10.5539/ijc.v8n3p25","title":"Effect of Mobile Phase Variation and Purification on Chromatogram View by Using Fruits and Rose Extracts and HPLC Method","year":2016,"lang":"en","type":"article","venue":"International Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Yildiz Teknik Üniversitesi","keywords":"Chemistry; Chromatography; Gradient elution; Anthocyanin; High-performance liquid chromatography; Pigment; Elution; Chromatographic separation; Phase (matter); Food science","score_opus":0.011038904611015393,"score_gpt":0.3581894132384448,"score_spread":0.34715050862742936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2419330900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99457365,0.0020172966,0.0028871421,0.00009167367,0.00003226479,0.000030107281,0.000035503243,0.0000065636873,0.00032581104],"genre_scores_gemma":[0.9980195,0.00079843425,0.0008866541,0.00001505441,0.0001329233,0.000003059759,0.000005482933,0.00001030504,0.00012855296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906534,0.000023438031,0.0003702001,0.00015581016,0.00029922285,0.000086017666],"domain_scores_gemma":[0.9986834,0.00042311742,0.0005564112,0.00008446197,0.00016587373,0.00008673281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035010377,0.00012691432,0.00025358153,0.000065508044,0.000025911317,0.000041230753,0.00012292646,0.00010032533,0.00019419799],"category_scores_gemma":[0.00029091985,0.0000910121,0.000051473606,0.00006628978,0.000060581657,0.00016997066,0.000027836371,0.000111567635,2.759407e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000169808,0.0001134048,0.0005778371,0.000118737094,0.00016852221,0.000005756456,0.0000449834,0.0000020377836,0.95030725,0.0000058186533,0.000037639347,0.04844819],"study_design_scores_gemma":[0.0018858587,0.00011983906,0.00012900625,0.00021065093,0.00015728021,0.00027155608,0.00001751948,0.00018704556,0.99645406,0.000083016945,0.00039615616,0.00008799408],"about_ca_topic_score_codex":0.000007133807,"about_ca_topic_score_gemma":3.903958e-8,"teacher_disagreement_score":0.048360195,"about_ca_system_score_codex":0.00006806389,"about_ca_system_score_gemma":0.00002678711,"threshold_uncertainty_score":0.3711366},"labels":[],"label_agreement":null},{"id":"W2460597508","doi":"10.1016/j.foodchem.2016.07.016","title":"Durbin-Watson partial least-squares regression applied to MIR data on adulteration with edible oils of different origins","year":2016,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Partial least squares regression; Mathematics; Sunflower oil; Statistics; Regression analysis; Regression; Food science; Chemistry","score_opus":0.026512722193665638,"score_gpt":0.27713986606960717,"score_spread":0.25062714387594154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460597508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990206,0.00007264166,0.000860224,0.0003243822,0.00003791988,0.00007725482,0.00030616674,0.0000960041,0.008019394],"genre_scores_gemma":[0.9976796,0.00002538033,0.00028020144,0.000035871293,0.0004354569,0.00004457792,0.00025965288,0.000034329394,0.0012049398],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99814254,0.000006984714,0.0003380875,0.0006731556,0.00048606945,0.00035315947],"domain_scores_gemma":[0.99816185,0.000103553386,0.00021986658,0.0012493043,0.000071976545,0.0001934395],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000054905573,0.0003157555,0.000393941,0.000053197873,0.00008216935,0.000032837495,0.0005749349,0.00017413066,0.0014357157],"category_scores_gemma":[0.00009907726,0.00018862123,0.00006041988,0.00025432283,0.00006985107,0.000088409535,0.00016562747,0.00016177245,0.000026977654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000592696,0.0003007235,0.00071335334,0.00027832735,0.00011518265,0.0000028058173,0.000051971954,0.00000506864,0.9943059,0.00006502961,0.0021087816,0.0014601666],"study_design_scores_gemma":[0.0011122099,0.00016002616,0.000040775245,0.00032918804,0.00010990101,0.000004094104,0.0001267116,0.00001685839,0.99565697,0.00003276841,0.002125977,0.00028449786],"about_ca_topic_score_codex":0.000010296697,"about_ca_topic_score_gemma":0.000007884261,"teacher_disagreement_score":0.0074735773,"about_ca_system_score_codex":0.00014619376,"about_ca_system_score_gemma":0.00006047831,"threshold_uncertainty_score":0.9994771},"labels":[],"label_agreement":null},{"id":"W2468200964","doi":"10.1016/j.ifacol.2015.09.629","title":"An approximate probability graphical modelling method for complex industrial fermentation processes","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Process (computing); Fermentation; Conditional independence; Computer science; Batch processing; Process engineering; Independence (probability theory); Statistics; Mathematics; Data mining; Engineering; Chemistry; Food science","score_opus":0.2054402393061006,"score_gpt":0.3865017634024204,"score_spread":0.18106152409631976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468200964","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5509641,0.00015850578,0.44603673,0.00068020856,0.00007206819,0.00043848247,0.0002758214,0.00031147606,0.0010625685],"genre_scores_gemma":[0.25001952,0.000011661347,0.7480703,0.000119178294,0.00055617536,0.000118256474,0.00085581036,0.00003555789,0.00021353258],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982395,0.00004109737,0.00040570344,0.0005707904,0.0003562194,0.00038667818],"domain_scores_gemma":[0.9987586,0.00015067396,0.00017633523,0.00032225013,0.0003289447,0.00026321152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004507322,0.0002657862,0.00041028226,0.000108331486,0.00014785785,0.00008516568,0.0002871398,0.00025746503,0.00021580329],"category_scores_gemma":[0.0003753418,0.00023796999,0.00011489309,0.0006595779,0.00008450044,0.00025123838,0.000029873765,0.00025256985,0.0000032572652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055345935,0.0076221507,0.021430003,0.003431491,0.0012808407,0.000016432614,0.006011985,0.06837264,0.8627147,0.0030942117,0.0002638279,0.02022711],"study_design_scores_gemma":[0.0055360897,0.00041484076,0.000038440838,0.000038716127,0.0006568264,0.000017725317,0.0032440838,0.76273507,0.21347456,0.012381438,0.0006413985,0.00082081475],"about_ca_topic_score_codex":0.00010679698,"about_ca_topic_score_gemma":0.000025380421,"teacher_disagreement_score":0.6943624,"about_ca_system_score_codex":0.00013497862,"about_ca_system_score_gemma":0.00021332705,"threshold_uncertainty_score":0.97041345},"labels":[],"label_agreement":null},{"id":"W2470335766","doi":"10.1002/mats.201600004","title":"Applying Multidimensional Method of Moments for Modeling and Estimating Parameters for Arborescent Polyisobutylene Production in Batch Reactor","year":2016,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Monte Carlo method; Weight estimation; Biological system; Monomer; Isobutylene; Moment (physics); Estimation theory; Method of moments (probability theory); Batch reactor; Applied mathematics; Mathematics; Algorithm; Statistical physics; Materials science; Computer science; Chemistry; Statistics; Physics; Organic chemistry","score_opus":0.023346678575558114,"score_gpt":0.3193998934530244,"score_spread":0.2960532148774663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470335766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50945485,0.000095449606,0.4901394,0.0000443837,0.000014541334,0.00019796223,0.000039051527,0.0000088489105,0.000005535654],"genre_scores_gemma":[0.84994745,0.000007040477,0.14982437,0.000013223224,0.000020148973,0.00011642823,0.00001984691,0.00001391421,0.00003759143],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919534,0.00003420277,0.0002537773,0.00027591304,0.00008142691,0.00015935022],"domain_scores_gemma":[0.9989205,0.00075131404,0.0000925614,0.0001275365,0.00006731312,0.00004077577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003792749,0.00011245504,0.0001915807,0.00011360351,0.00009510793,0.00000961936,0.000039622882,0.000061542334,0.000012916057],"category_scores_gemma":[0.0008022292,0.00009102576,0.00005187963,0.000107580876,0.000038996288,0.000069386915,0.000026385984,0.00004293605,6.7708626e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000273496,0.000078576006,0.00086448516,0.00014682069,0.00008899593,4.1580387e-7,0.00016179716,0.018902117,0.9648193,0.0024643636,8.948332e-7,0.012198726],"study_design_scores_gemma":[0.0009585008,0.00002486572,0.000020477537,0.00008804479,0.000120493736,0.0000026766693,0.00014362934,0.47900546,0.5003513,0.019148767,0.000009367521,0.00012639254],"about_ca_topic_score_codex":0.000008533004,"about_ca_topic_score_gemma":0.0000015117378,"teacher_disagreement_score":0.46446797,"about_ca_system_score_codex":0.00003014436,"about_ca_system_score_gemma":0.0000126943905,"threshold_uncertainty_score":0.37119228},"labels":[],"label_agreement":null},{"id":"W2481636219","doi":"10.1109/i2mtc.2016.7520575","title":"A multisensor fusion and integration system design and its application","year":2016,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Recherche Industrielle du Québec","funders":"Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Sensor fusion; Computer science; Preprocessor; Artificial neural network; Data mining; Data pre-processing; Artificial intelligence; Projection (relational algebra); Fusion; Data integration; Control engineering; Pattern recognition (psychology); Engineering; Algorithm","score_opus":0.018883368955607337,"score_gpt":0.2608232306804722,"score_spread":0.24193986172486487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481636219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6300437,0.00041547196,0.36469233,0.00015520777,0.000008134601,0.000104607796,0.0000030688047,0.00013536564,0.0044421027],"genre_scores_gemma":[0.9952364,0.00012284535,0.0017118283,0.000011355232,0.000023643044,0.000024117822,0.0000016849325,0.00000592908,0.0028621976],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999588,0.000007488695,0.00009567691,0.00017120433,0.00006251691,0.000075073614],"domain_scores_gemma":[0.999704,0.00009252484,0.000041159186,0.00008464096,0.00003388003,0.000043802014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005610131,0.00007001757,0.00008557292,0.000043425305,0.00005041689,0.000014520703,0.000030796848,0.000049948314,0.000114372124],"category_scores_gemma":[0.000037793394,0.00004135931,0.000009598562,0.00008309489,0.000015181822,0.00006680179,0.00001668012,0.000024626923,0.000018580735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001882068,0.00000842837,0.00026676082,0.0000346277,0.000008419478,3.2935935e-7,0.00001700347,3.5994194e-7,0.9839188,0.0014664682,0.000037558213,0.014222418],"study_design_scores_gemma":[0.0003209029,0.000018715264,0.00018088342,0.000027020722,0.000037128855,0.000010908176,0.00016828161,0.009527819,0.98949695,0.000042633277,0.000093719056,0.0000750437],"about_ca_topic_score_codex":0.00002544284,"about_ca_topic_score_gemma":0.0000032652474,"teacher_disagreement_score":0.36519268,"about_ca_system_score_codex":0.000052121988,"about_ca_system_score_gemma":0.0000058068063,"threshold_uncertainty_score":0.16865839},"labels":[],"label_agreement":null},{"id":"W2488611653","doi":"10.1016/b978-0-12-802232-0.00016-5","title":"Wheat Quality Evaluation","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); Agriculture Food and Rural Development; Government of Manitoba; University of Manitoba","funders":"","keywords":"Postharvest; Wheat grain; Quality (philosophy); Agricultural engineering; Agronomy; Biotechnology; Grain quality; Environmental science; Food science; Mathematics; Engineering; Horticulture; Biology","score_opus":0.0471521293014722,"score_gpt":0.33204084372485226,"score_spread":0.28488871442338004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488611653","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015808821,0.003089161,0.000005568728,0.00006563601,0.000129893,0.0001397059,0.00008417063,0.0001182335,0.99620956],"genre_scores_gemma":[0.0044503673,0.00013870718,0.000059125003,0.00014648862,0.0007808728,0.000043701344,0.000059242262,0.00008491252,0.9942366],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976398,0.000020464517,0.0005757184,0.0006110282,0.0008408581,0.00031215497],"domain_scores_gemma":[0.99817926,0.0001428006,0.0003993488,0.0008973156,0.00024539925,0.00013588925],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004692283,0.00045625362,0.0006545918,0.00016389428,0.00010911986,0.000042508098,0.00034333763,0.00058925047,0.030805025],"category_scores_gemma":[0.000096800766,0.00036717235,0.0003923612,0.000016804517,0.0001379695,0.000040532017,0.00009612215,0.0004033823,0.00087061926],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017529504,0.000008658151,0.00002401203,0.00014054133,0.00027058594,0.0000039835786,0.0000355704,5.4079052e-8,0.0038764123,0.0069469674,0.00032310528,0.9883526],"study_design_scores_gemma":[0.0005004704,0.00001380821,0.000008863943,0.00024089591,0.0010079439,0.0000063665093,0.000009844979,0.0000020581058,0.017096832,0.040883012,0.93960655,0.0006233319],"about_ca_topic_score_codex":6.906345e-7,"about_ca_topic_score_gemma":0.000009642392,"teacher_disagreement_score":0.98772925,"about_ca_system_score_codex":0.00042331874,"about_ca_system_score_gemma":0.00019272296,"threshold_uncertainty_score":0.9999073},"labels":[],"label_agreement":null},{"id":"W2500107511","doi":"10.1016/b978-0-12-374753-2.10004-8","title":"Hyperspectral Image Processing Techniques","year":2010,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Image processing; Computer science; Sample (material); Artificial intelligence; Software; Pixel; Chemical imaging; Computer vision; Full spectral imaging; Imaging spectroscopy; MATLAB; Remote sensing; Pattern recognition (psychology); Image (mathematics); Geography; Physics","score_opus":0.01250797096075655,"score_gpt":0.2670614008837466,"score_spread":0.25455342992299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500107511","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000081326674,0.002100361,0.000012409695,0.000056717683,0.000054884855,0.00011292372,0.000036162546,0.00051145436,0.9970338],"genre_scores_gemma":[0.0008056281,0.00012893334,0.009256606,0.00015604278,0.0010764922,0.000034836274,0.000040187962,0.00018402361,0.98831725],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801093,0.000003303151,0.00045593886,0.00067893096,0.00039783583,0.0004530585],"domain_scores_gemma":[0.9985725,0.000035534358,0.00036161664,0.0007100393,0.00015546355,0.0001648348],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000103752674,0.000626463,0.0007310949,0.00026587828,0.00020022831,0.00014408054,0.0005517593,0.000967617,0.005361954],"category_scores_gemma":[0.000031219883,0.0005919434,0.00039883066,0.00003180591,0.0003594243,0.0000712618,0.00012924426,0.001732987,0.00017810309],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000119139895,0.000016678696,0.0000039766,0.00031138604,0.00013042879,0.00005964134,0.00007149957,2.3970081e-9,0.19464013,0.0010336201,0.00011279131,0.80360794],"study_design_scores_gemma":[0.00008149898,0.000013136182,3.9286098e-7,0.00014766307,0.00038024146,0.000042510117,0.000014569398,5.7140903e-7,0.39623803,0.0066625588,0.5959222,0.000496635],"about_ca_topic_score_codex":8.81509e-7,"about_ca_topic_score_gemma":0.000009658352,"teacher_disagreement_score":0.8031113,"about_ca_system_score_codex":0.00015773418,"about_ca_system_score_gemma":0.00018057122,"threshold_uncertainty_score":0.9996532},"labels":[],"label_agreement":null},{"id":"W2505409898","doi":"10.2202/1544-6115.1295","title":"Multiple Testing Issues in Discriminating Compound-Related Peaks and Chromatograms from High Frequency Noise, Spikes and Solvent-Based Noise in LC - MS Data Sets","year":2007,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"False positive paradox; Noise (video); Chromatography; Sample (material); Chemistry; Pattern recognition (psychology); Computer science; Artificial intelligence","score_opus":0.022958027026440155,"score_gpt":0.3317837427468998,"score_spread":0.30882571572045964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505409898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8823707,0.0034024715,0.113347165,0.00007769722,0.0000067680694,0.00014958486,0.00044366953,0.000015180041,0.00018672731],"genre_scores_gemma":[0.8794085,0.00014962538,0.11892222,0.00003256215,0.000010126664,0.000030493413,0.0014317278,0.000013177832,0.0000016122423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998665,0.000028276143,0.00041580116,0.0005450315,0.00006648702,0.00027944692],"domain_scores_gemma":[0.9988299,0.0006666301,0.00008241516,0.00030966772,0.000027037111,0.00008433127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016195496,0.00016849785,0.0002719142,0.00014029877,0.00006220077,0.000031596097,0.00017189956,0.00016342172,0.00002404699],"category_scores_gemma":[0.00029011167,0.00016734048,0.000008829047,0.0003453238,0.00032114398,0.000017232895,0.00016610247,0.00020480625,5.6324296e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008718252,0.00017577004,0.56992805,0.00006191195,0.000016366728,0.000025406785,0.00006850585,0.000016598227,0.41794965,0.003798056,0.000002340684,0.007948632],"study_design_scores_gemma":[0.0038539474,0.00016045097,0.70456207,0.00015612993,0.00024612638,0.000019165072,0.0010805563,0.07098879,0.12903354,0.08864147,0.0002133883,0.0010443523],"about_ca_topic_score_codex":0.003651056,"about_ca_topic_score_gemma":0.0015316185,"teacher_disagreement_score":0.2889161,"about_ca_system_score_codex":0.000027414035,"about_ca_system_score_gemma":0.000023219374,"threshold_uncertainty_score":0.6823947},"labels":[],"label_agreement":null},{"id":"W2505718162","doi":"10.1016/b978-0-12-385881-8.00018-5","title":"Artificial Neural Network (ANN) Based Process Modeling","year":2013,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Process (computing); Computer science; Artificial intelligence; Machine learning","score_opus":0.028617149949938147,"score_gpt":0.2659450000388791,"score_spread":0.23732785008894097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505718162","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007817808,0.0017452256,0.00010767871,0.000051873307,0.0001745414,0.0001811735,0.000026800511,0.00023333478,0.9966976],"genre_scores_gemma":[0.05790858,0.000020028074,0.00028664616,0.00041142423,0.002301722,0.00007421319,0.00010870028,0.00018678445,0.9387019],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973282,0.000008264749,0.000722784,0.0007604343,0.00052793615,0.0006523592],"domain_scores_gemma":[0.9984374,0.00007354948,0.00036002634,0.00071211316,0.00019852842,0.00021835338],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011286406,0.00070149405,0.00085430633,0.0001658449,0.00022893083,0.00013448729,0.0005374056,0.00070544553,0.0097787045],"category_scores_gemma":[0.000027956565,0.00067210326,0.00045712772,0.00005146288,0.000116400864,0.000062316794,0.00008446806,0.0010054121,0.00042523848],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010599218,0.000052750296,0.000026962254,0.0009991339,0.00067787175,0.000075985234,0.00011701655,0.013056479,0.0009395174,0.0018273136,0.000935992,0.98118496],"study_design_scores_gemma":[0.00091124716,0.00013333625,9.1488283e-7,0.0014811069,0.004125766,0.00004355874,0.00009983798,0.3481349,0.019396238,0.10841407,0.51209486,0.0051641827],"about_ca_topic_score_codex":0.0000017446602,"about_ca_topic_score_gemma":0.000007823306,"teacher_disagreement_score":0.9760208,"about_ca_system_score_codex":0.000120353616,"about_ca_system_score_gemma":0.00017783069,"threshold_uncertainty_score":0.999573},"labels":[],"label_agreement":null},{"id":"W2509345453","doi":"10.1002/bit.26064","title":"Monitoring of adherent live cells morphology using the undecimated wavelet transform multivariate image analysis (UWT‐MIA)","year":2016,"lang":"en","type":"article","venue":"Biotechnology and Bioengineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canada Foundation for Innovation","keywords":"Artificial intelligence; Pattern recognition (psychology); Segmentation; Multivariate statistics; Computer science; Wavelet transform; Biological system; Wavelet; Mathematical morphology; Principal component analysis; Computer vision; Partial least squares regression; Image processing; Image (mathematics); Biology","score_opus":0.017716738045350282,"score_gpt":0.26281396409127555,"score_spread":0.24509722604592526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509345453","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95565385,0.0008514348,0.04286147,0.0003110563,0.000056529352,0.000047775262,0.000033016244,0.00014019478,0.000044680888],"genre_scores_gemma":[0.99486434,0.0006565861,0.0043459516,0.000003347045,0.000036052563,0.000005739294,0.000001913744,0.00001562294,0.00007043906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902296,0.0000075968396,0.00027962105,0.00028344442,0.00008877547,0.0003175883],"domain_scores_gemma":[0.99939895,0.000109178545,0.00010847347,0.00030569077,0.000036849742,0.00004085429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008986761,0.00019831891,0.00036941166,0.00035912637,0.000082848106,0.000010686266,0.00023436926,0.00040347857,0.00021241151],"category_scores_gemma":[0.000029709709,0.00012118846,0.0001302724,0.00081738085,0.0002534938,0.00005784945,0.00007248866,0.00020241758,0.000004018287],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016104155,0.000027506183,0.0016150476,0.000038191323,0.0008726995,0.000009542184,0.000047452737,0.000055584595,0.99557537,0.00006053154,0.0000013936698,0.0016805802],"study_design_scores_gemma":[0.0003306013,0.000026013857,0.00059413817,0.000033371784,0.00085082086,0.00001670571,0.00037676492,0.0025722024,0.99496835,0.00003423313,0.00003567371,0.0001611279],"about_ca_topic_score_codex":0.000095614334,"about_ca_topic_score_gemma":0.0000027113706,"teacher_disagreement_score":0.039210506,"about_ca_system_score_codex":0.000063129635,"about_ca_system_score_gemma":0.000011447922,"threshold_uncertainty_score":0.49419218},"labels":[],"label_agreement":null},{"id":"W2510529468","doi":"10.1007/s11947-016-1788-9","title":"Assessment of Fusarium and Deoxynivalenol Using Optical Methods","year":2016,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Mitacs","keywords":"Fusarium; Mycotoxin; Zearalenone; Biotechnology; Biology; Environmental science; Livestock; Toxicology; Botany; Ecology","score_opus":0.024646501834350784,"score_gpt":0.3667828715867481,"score_spread":0.3421363697523973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510529468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97378176,0.0015338333,0.022314845,0.00045794074,0.000012441836,0.000023761824,0.00001056143,0.0000832766,0.0017815711],"genre_scores_gemma":[0.9729965,0.00018394277,0.026696267,0.000008392664,0.000021182314,0.0000050951635,8.6508e-7,0.000009018216,0.00007878643],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992662,0.000004293042,0.00017845331,0.00026447326,0.00007334071,0.00021322811],"domain_scores_gemma":[0.99961805,0.000053249365,0.00006568711,0.00016209294,0.000058148773,0.000042744083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008809037,0.00011891614,0.00027150084,0.00022302015,0.000053493615,0.000008513064,0.00012850612,0.00027050666,0.00008611713],"category_scores_gemma":[0.00006720579,0.00007004616,0.000024601064,0.00029128254,0.00038556324,0.000046580863,0.00014929358,0.00010581999,3.241181e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051200186,0.0000414345,0.01232707,0.00013157056,0.000072091665,0.0000012388401,0.0000065436593,4.5556348e-8,0.96614605,0.00536125,7.0017376e-7,0.015906863],"study_design_scores_gemma":[0.0003114671,0.00013490586,0.00039460303,0.000021939339,0.000101055106,0.000052322775,0.00020080675,0.00006214921,0.99366534,0.0047972132,0.00014200475,0.00011619552],"about_ca_topic_score_codex":0.0000026760235,"about_ca_topic_score_gemma":6.5297866e-7,"teacher_disagreement_score":0.027519265,"about_ca_system_score_codex":0.000020428555,"about_ca_system_score_gemma":0.000023884462,"threshold_uncertainty_score":0.28563997},"labels":[],"label_agreement":null},{"id":"W2511835183","doi":"10.1007/s40003-016-0227-5","title":"Detection of Broken Kernels Content in Bulk Wheat Samples Using Near-Infrared Hyperspectral Imaging","year":2016,"lang":"en","type":"article","venue":"Agricultural Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Linear discriminant analysis; Partial least squares regression; Hyperspectral imaging; Mathematics; Principal component analysis; Principal component regression; Quadratic classifier; Correlation coefficient; Linear regression; Statistics; Regression analysis; Pattern recognition (psychology); Coefficient of determination; Artificial intelligence; Support vector machine; Computer science","score_opus":0.11940804163060857,"score_gpt":0.3548904607393352,"score_spread":0.23548241910872664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511835183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940835,0.00082873047,0.000049594462,0.000315317,0.000024836076,0.00008352075,0.000014304479,0.000034083998,0.0045660827],"genre_scores_gemma":[0.99672693,0.00012970273,0.00027466167,0.0000076047054,0.00011534456,0.000012877375,0.0000047499893,0.000011246309,0.00271687],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813205,0.000056610632,0.00030667635,0.00034028702,0.00057949725,0.0005848703],"domain_scores_gemma":[0.99886125,0.00039318885,0.00007189145,0.00021268961,0.00035327632,0.000107722975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031048554,0.00014795491,0.00026333952,0.0001758892,0.00014806088,0.000070094,0.0002750237,0.00009248106,0.00061099656],"category_scores_gemma":[0.0005891275,0.00008235265,0.000115621086,0.0008785286,0.00021621729,0.00020179794,0.00010991066,0.0002867889,0.000022034606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052121883,0.000052133815,0.019292353,0.000030522893,0.00003326172,0.000007237965,0.00013546276,0.0000040999466,0.9780846,0.00004027833,0.000098048105,0.0021698303],"study_design_scores_gemma":[0.00056948845,0.00002676626,0.03923154,0.000087067296,0.000018945615,0.000021156577,0.0022913527,0.00008778575,0.9572075,0.00015783568,0.00015979474,0.00014078646],"about_ca_topic_score_codex":0.0039018323,"about_ca_topic_score_gemma":0.00013301178,"teacher_disagreement_score":0.02087717,"about_ca_system_score_codex":0.00056580536,"about_ca_system_score_gemma":0.000056653676,"threshold_uncertainty_score":0.66899836},"labels":[],"label_agreement":null},{"id":"W2511877250","doi":"10.1006/jaer.2001.0724","title":"AE—Automation and Emerging Technologies","year":2001,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Backpropagation; Roundness (object); Computer science; Kernel (algebra); Automation; Identification (biology); Set (abstract data type); Mathematics; Engineering; Biology","score_opus":0.02281828575685045,"score_gpt":0.3146698963523744,"score_spread":0.29185161059552395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511877250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99542874,0.0024279908,0.00015341843,0.000802804,0.000026939997,0.000015003349,4.1236353e-7,0.000072590905,0.0010720731],"genre_scores_gemma":[0.99743795,0.0011371479,0.0007925167,0.0000014934589,0.00013838314,0.0000015860371,7.944458e-7,0.0000054649518,0.0004846693],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990796,0.000005568994,0.00021233242,0.000083770006,0.00038346005,0.00023529856],"domain_scores_gemma":[0.99941057,0.00013038973,0.00007047525,0.00007025845,0.00026429023,0.000054028053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027391306,0.000081661936,0.000154514,0.00031245363,0.000074659045,0.00006732838,0.00018036306,0.00008188193,0.00008241508],"category_scores_gemma":[0.0005298935,0.000050515166,0.00005150351,0.0008265866,0.00003162947,0.0002106173,0.000056561217,0.0005212414,0.0000033354056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011857963,0.000035700647,0.0036458287,0.00009686831,0.00009633038,0.00004422483,0.00013883862,0.0014193035,0.9892857,0.00020460166,0.0010412239,0.0039795004],"study_design_scores_gemma":[0.00082920986,0.00020530702,0.07579578,0.00028634092,0.000107495434,0.0022025744,0.010547352,0.0018782322,0.897644,0.00024863138,0.009827684,0.00042741312],"about_ca_topic_score_codex":0.000005965625,"about_ca_topic_score_gemma":4.3856755e-7,"teacher_disagreement_score":0.09164174,"about_ca_system_score_codex":0.000099856916,"about_ca_system_score_gemma":0.000011517859,"threshold_uncertainty_score":0.22645633},"labels":[],"label_agreement":null},{"id":"W2514433050","doi":"","title":"Rapid Detection of Listeria monocytogenes in Milk Using Confocal Micro-raman Spectroscopy and Chemometric Analysis","year":2015,"lang":"en","type":"article","venue":"2015 Annual Meeting (July 25 - 28, 2015)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Listeria monocytogenes; Raman spectroscopy; Confocal; Food science; Chromatography; Analytical Chemistry (journal); Chemistry; Materials science; Biology; Bacteria; Optics; Physics","score_opus":0.0385819167365959,"score_gpt":0.3179044786590318,"score_spread":0.27932256192243593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514433050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889435,0.006851057,0.0006648524,0.000058484326,0.00013848113,0.00010864546,0.0001118513,0.000080852216,0.0030422686],"genre_scores_gemma":[0.99519813,0.00024023862,0.0036932093,0.000037092806,0.00026466927,0.000009577488,0.000045695488,0.00005013986,0.00046122848],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99713033,0.00009523178,0.0008861176,0.000712671,0.0005231205,0.0006525247],"domain_scores_gemma":[0.99797344,0.00017854529,0.0005809272,0.0005266275,0.0004012958,0.00033916198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008792484,0.00045071228,0.0010515363,0.0017806484,0.00011131235,0.000101123274,0.0003588739,0.00037607312,0.00015511047],"category_scores_gemma":[0.0006850085,0.0004537003,0.00023964023,0.004098961,0.00021933769,0.00028511905,0.00019947957,0.0003607886,0.000018631548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027999183,0.00022212492,0.04132959,0.00017120903,0.00069628755,0.000016825965,0.0012008413,0.00016099577,0.95458597,0.0000045418133,0.0006630834,0.0006685434],"study_design_scores_gemma":[0.0013947148,0.0001564919,0.0025868658,0.000055504323,0.0012315768,0.000028690132,0.003111896,0.002379641,0.98744947,0.00006063059,0.0010217354,0.000522779],"about_ca_topic_score_codex":0.0019191342,"about_ca_topic_score_gemma":0.00013751052,"teacher_disagreement_score":0.03874272,"about_ca_system_score_codex":0.0003505218,"about_ca_system_score_gemma":0.00012793427,"threshold_uncertainty_score":0.9997915},"labels":[],"label_agreement":null},{"id":"W2515023934","doi":"10.1021/acs.oprd.5b00045","title":"In Situ Monitoring of Emulsion Polymerization by Raman Spectroscopy: A Robust and Versatile Chemometric Analysis Method","year":2015,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Monomer; Emulsion polymerization; Raman spectroscopy; Polymer; Polymerization; Emulsion; Calibration; In situ; Materials science; Chemistry; Analytical Chemistry (journal); Organic chemistry; Optics; Mathematics","score_opus":0.047620209162216896,"score_gpt":0.37823425727634963,"score_spread":0.33061404811413275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515023934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862159,0.0023984648,0.009048041,0.0001421651,0.000019198413,0.00010697909,0.000004257881,0.000036813308,0.0020281798],"genre_scores_gemma":[0.98976094,0.00030447866,0.009009852,0.0000062218255,0.000037876263,0.000033611817,0.00003890596,0.00002696372,0.00078114285],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99724865,0.000063740634,0.00046660966,0.0005326631,0.0011490142,0.00053934625],"domain_scores_gemma":[0.99881315,0.00017274766,0.00015189234,0.00025862784,0.00035073626,0.00025284174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009964112,0.00020100505,0.0004736308,0.00182039,0.00011718072,0.000071859475,0.0003600313,0.00014833326,0.0005023471],"category_scores_gemma":[0.00084358815,0.00019725853,0.00003835639,0.0116142845,0.00007471591,0.00023499808,0.00019921732,0.0003733922,0.000009683867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060194427,0.00020991605,0.08181329,0.00019114595,0.00029470766,0.0000049352707,0.0019366448,0.000027310036,0.9142081,0.0000047934723,0.00013619999,0.0011127293],"study_design_scores_gemma":[0.00059708225,0.000032898748,0.003275493,0.00002939884,0.00010257223,0.0000019637448,0.0032516245,0.00020360886,0.9921562,0.00008416262,0.00007308996,0.00019191415],"about_ca_topic_score_codex":0.00012506697,"about_ca_topic_score_gemma":0.000016003594,"teacher_disagreement_score":0.0785378,"about_ca_system_score_codex":0.0006307379,"about_ca_system_score_gemma":0.00039699752,"threshold_uncertainty_score":0.8043969},"labels":[],"label_agreement":null},{"id":"W2517223984","doi":"","title":"Dependence of Isoprene SOA Yield on the Phase of Ammonium Sulfate Seed Particles","year":2014,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Isoprene; Yield (engineering); Ammonium sulfate; Sulfate; Phase (matter); Chemistry; Ammonium; Organic chemistry; Materials science; Copolymer; Composite material; Polymer","score_opus":0.03463672731054925,"score_gpt":0.2851702966388681,"score_spread":0.2505335693283189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517223984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96905637,0.00014638885,0.00002826984,0.00027281215,0.00003065379,0.00003584609,0.000008326724,0.000042291947,0.03037902],"genre_scores_gemma":[0.99935484,0.000027973241,0.00021519761,0.00007282938,0.00007800101,0.000005597057,0.0000037311797,0.000016477414,0.00022534131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861544,0.000025042373,0.000483962,0.00024204205,0.00034225342,0.00029128586],"domain_scores_gemma":[0.997553,0.0013566024,0.00046919918,0.00045081694,0.00009347655,0.00007692467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004871649,0.00016573184,0.00029346344,0.00005951975,0.00008086849,0.000020463838,0.00035904534,0.000108473985,0.000054676035],"category_scores_gemma":[0.0018567517,0.00012210422,0.00011435673,0.0002423548,0.00011785055,0.000051776777,0.000048029466,0.0002297557,0.000021663272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018711924,0.0004257195,0.026155528,0.00013899816,0.00011553072,0.0000044743547,0.00020966606,0.0010896672,0.9702864,0.00028536672,0.00016177379,0.0009397199],"study_design_scores_gemma":[0.00037952518,0.00008277126,0.0044148937,0.0001387097,0.000111212124,0.000002175803,0.0003208526,0.0003831292,0.9936763,0.00029639152,0.00006895041,0.00012510133],"about_ca_topic_score_codex":0.0024245523,"about_ca_topic_score_gemma":0.00031851404,"teacher_disagreement_score":0.030298462,"about_ca_system_score_codex":0.00001723607,"about_ca_system_score_gemma":0.000026929818,"threshold_uncertainty_score":0.49792653},"labels":[],"label_agreement":null},{"id":"W2517732947","doi":"10.1002/jsfa.8014","title":"Non‐destructive detection of dicyandiamide in infant formula powder using multi‐spectral imaging coupled with chemometrics","year":2016,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Canadian Food Inspection Agency","keywords":"Chemometrics; Partial least squares regression; Infant formula; Support vector machine; Least squares support vector machine; Residual; Pattern recognition (psychology); Mathematics; Artificial intelligence; Artificial neural network; Chemistry; Biological system; Analytical Chemistry (journal); Chromatography; Computer science; Statistics; Food science; Algorithm; Biology","score_opus":0.0076020563058302034,"score_gpt":0.23303664946139815,"score_spread":0.22543459315556796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517732947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991546,0.0003325206,0.0002886419,0.00007878293,0.00003827783,0.00003325254,0.0000037295645,0.0000019630688,0.00006823341],"genre_scores_gemma":[0.99942976,0.000045407447,0.00045126714,0.000004364536,0.000043111653,3.065504e-7,4.632745e-8,0.0000031887055,0.000022535489],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989795,0.0000053332164,0.00029609914,0.00012308743,0.00041631606,0.00017967579],"domain_scores_gemma":[0.99889046,0.000044048193,0.00062678446,0.00009483232,0.00028336854,0.000060533464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023554482,0.00010582776,0.00025101594,0.0002797233,0.00008811514,0.000022312954,0.00027151295,0.000043590837,0.0000065173044],"category_scores_gemma":[0.00018746195,0.000041211857,0.00008541697,0.0019664592,0.00031823845,0.0003862854,0.000047584042,0.00016525766,2.9082123e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003935458,0.00004572869,0.018356822,0.000021536976,0.000026919055,0.0000013657333,0.00015331437,0.000049211623,0.9810599,0.000007898068,0.0000010055795,0.00023693334],"study_design_scores_gemma":[0.0007943798,0.000089794485,0.05304565,0.0001783938,0.000073083276,0.00017477869,0.000845006,0.0001988038,0.9444236,0.000100349105,6.181935e-7,0.00007554738],"about_ca_topic_score_codex":0.000033129225,"about_ca_topic_score_gemma":0.000013547292,"teacher_disagreement_score":0.03663631,"about_ca_system_score_codex":0.00011786017,"about_ca_system_score_gemma":0.00008572987,"threshold_uncertainty_score":0.16805708},"labels":[],"label_agreement":null},{"id":"W2517834837","doi":"10.1094/cfw-61-4-0140","title":"Machine Vision Combined with Near-Infrared Spectroscopy to Guarantee Food Safety","year":2016,"lang":"en","type":"article","venue":"Cereal Foods World","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Food safety; Infrared; Spectroscopy; Infrared spectroscopy; Business; Food science; Chemistry; Optics; Physics","score_opus":0.009363259334868338,"score_gpt":0.2587688176565742,"score_spread":0.24940555832170588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517834837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52017486,0.00072195916,0.0062698014,0.005219752,0.00022084414,0.0004156699,0.00042189308,0.0008009057,0.4657543],"genre_scores_gemma":[0.96041316,0.000052908337,0.0033147973,0.0003482683,0.00018824096,0.000028315084,0.00004139399,0.00007728331,0.03553565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977777,0.000022743834,0.0004146608,0.00066076143,0.00044164257,0.0006824804],"domain_scores_gemma":[0.99847555,0.00020051039,0.00014426647,0.0008239396,0.00008553371,0.00027020703],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000111630085,0.000418481,0.000555407,0.00024078703,0.0002520802,0.00010129283,0.0004930186,0.00010745678,0.006103804],"category_scores_gemma":[0.00006619498,0.0002666085,0.00016113093,0.0013483479,0.00014163357,0.00015897135,0.0001416187,0.00024530862,0.00024890638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.023255633,0.0022760683,0.090347014,0.0004702299,0.0027469078,0.00016217692,0.00073197414,0.00005467873,0.7660136,0.023126498,0.065334894,0.02548032],"study_design_scores_gemma":[0.0053398954,0.0024629282,0.0047625606,0.00024375615,0.00029664015,0.000022343818,0.00006984096,0.000069370486,0.9187478,0.0028955915,0.06415766,0.00093165087],"about_ca_topic_score_codex":0.000054499378,"about_ca_topic_score_gemma":0.00048502072,"teacher_disagreement_score":0.44023827,"about_ca_system_score_codex":0.00026542562,"about_ca_system_score_gemma":0.00008488352,"threshold_uncertainty_score":0.9999786},"labels":[],"label_agreement":null},{"id":"W2517986199","doi":"10.1016/j.clinbiochem.2015.07.093","title":"Online sample preparation integrated with LC/MS–MS for routine use in a clinical laboratory","year":2015,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Constant false alarm rate; Benchmark (surveying); Artificial intelligence; Bayesian network; Pattern recognition (psychology); Probabilistic logic; Classifier (UML); False discovery rate; Data mining; False alarm; Fault detection and isolation; ALARM; Naive Bayes classifier; Process (computing); Machine learning; Support vector machine; Engineering","score_opus":0.10683481533813655,"score_gpt":0.4259493172835303,"score_spread":0.31911450194539376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517986199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99661815,0.0002805755,0.001221827,0.00022420441,0.00010229006,0.00014370003,0.00069743744,0.0001447892,0.000567006],"genre_scores_gemma":[0.9833668,0.000071702896,0.011565574,0.00045329926,0.00089604594,0.000053429732,0.0017957548,0.000047954596,0.0017494712],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996804,0.000064589134,0.0015220689,0.0009008245,0.00028471253,0.0004238002],"domain_scores_gemma":[0.9957201,0.0020569088,0.00045365145,0.00079646514,0.0005259277,0.0004469471],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010837836,0.000330371,0.00076885056,0.000060785922,0.000047599064,0.00007500536,0.00038357484,0.0007260034,0.0002631422],"category_scores_gemma":[0.014124854,0.0002740886,0.00024738238,0.0007473674,0.0003551784,0.0001421937,0.00008851255,0.000784704,0.000014551058],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043141753,0.0051919557,0.9507431,0.00023090825,0.00043802193,0.000030668052,0.00005929518,0.000035411478,0.014221538,0.000037111782,0.023645392,0.0010524213],"study_design_scores_gemma":[0.0240982,0.0020383499,0.01745285,0.00046347996,0.0011992203,0.0000244362,0.0019175824,0.005710315,0.8795592,0.00058621174,0.06462111,0.0023290066],"about_ca_topic_score_codex":0.00015170498,"about_ca_topic_score_gemma":0.00016453057,"teacher_disagreement_score":0.93329024,"about_ca_system_score_codex":0.00017216125,"about_ca_system_score_gemma":0.000732406,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W2519045169","doi":"10.1016/j.foodchem.2016.09.051","title":"Determination of trace thiophanate-methyl and its metabolite carbendazim with teratogenic risk in red bell pepper (Capsicumannuum L.) by surface-enhanced Raman imaging technique","year":2016,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":157,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Guangdong Province","keywords":"Metabolite; Carbendazim; Chemistry; Partial least squares regression; Support vector machine; Multivariate statistics; Pepper; Chromatography; Biological system; Horticulture; Artificial intelligence; Mathematics; Food science; Biology; Fungicide; Biochemistry; Computer science; Statistics","score_opus":0.004834230312909985,"score_gpt":0.22759423057335681,"score_spread":0.22276000026044682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519045169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99048054,0.005266159,0.00063983764,0.00007673713,0.0000100910265,0.00011538353,0.0002344284,0.000078117126,0.00309871],"genre_scores_gemma":[0.9971898,0.00045944232,0.00072583486,0.000008318191,0.00003513226,0.000047878828,0.00002774261,0.000046950467,0.0014588793],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982566,0.000026017462,0.00043946042,0.00058884715,0.00028520118,0.00040383806],"domain_scores_gemma":[0.9988578,0.00011768561,0.00037125027,0.00040020308,0.00013685903,0.00011619234],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019143875,0.00034317543,0.00048278598,0.000079298,0.00007107057,0.000028742455,0.00027179992,0.00021820278,0.00031656554],"category_scores_gemma":[0.00012283181,0.00026760754,0.00009338232,0.00046035068,0.000113854694,0.0002178018,0.0000728877,0.0002977606,0.0000015864047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007672967,0.000088305875,0.0031178452,0.00024034045,0.00008002764,0.000004745759,0.00012093392,0.0000012848889,0.99536914,0.0000020818663,0.00003624442,0.0008623532],"study_design_scores_gemma":[0.0013071183,0.00003495118,0.00015170568,0.00012345125,0.00019212974,0.000018659453,0.00021798568,0.000057685,0.9972834,0.00009829947,0.0001515534,0.0003630499],"about_ca_topic_score_codex":0.000029179155,"about_ca_topic_score_gemma":0.000009660889,"teacher_disagreement_score":0.0067092846,"about_ca_system_score_codex":0.00011167659,"about_ca_system_score_gemma":0.000053927954,"threshold_uncertainty_score":0.9999776},"labels":[],"label_agreement":null},{"id":"W2539779491","doi":"10.1109/rose.2005.1588334","title":"Unsupervised texture segmentation for 2D probabilistic occupancy maps","year":2006,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Probabilistic logic; Pattern recognition (psychology); Segmentation; Computer science; Metric (unit); Occupancy; Statistic; Image segmentation; Image texture; Local binary patterns; Similarity (geometry); Logarithm; Measure (data warehouse); Computer vision; Scale-space segmentation; Histogram; Mathematics; Data mining; Statistics; Image (mathematics)","score_opus":0.013738797644146972,"score_gpt":0.2697706568648644,"score_spread":0.2560318592207174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2539779491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56871,0.0014624427,0.04820816,0.0008382168,0.00015143103,0.00069672597,0.00030787932,0.0007884013,0.37883672],"genre_scores_gemma":[0.96126306,0.0000062345302,0.005544197,0.00013247933,0.00026714156,0.0001018167,0.00045947943,0.000020666772,0.03220493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920565,0.0000034703974,0.0002005519,0.00024394786,0.00012886686,0.00021751672],"domain_scores_gemma":[0.9995701,0.00008722347,0.000053890177,0.00018660683,0.000065053486,0.000037138332],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000038377355,0.00013446803,0.0001545937,0.000058569654,0.000085839165,0.00004017072,0.00011546482,0.00009655463,0.0030422],"category_scores_gemma":[0.000045976005,0.00011313051,0.00009694977,0.0002635291,0.000026681888,0.000068753936,0.000013223939,0.000064491,0.00003282595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017111929,0.00079242914,0.03714007,0.0010608516,0.00020746059,0.000005757147,0.00011573583,0.0001681337,0.8240954,0.019686827,0.11227324,0.0042829644],"study_design_scores_gemma":[0.0016023426,0.000044291788,0.001036631,0.00001355926,0.00034421007,0.0000056954013,0.0005205754,0.0009814593,0.9706079,0.015899839,0.0085376445,0.00040584715],"about_ca_topic_score_codex":0.000091426686,"about_ca_topic_score_gemma":0.000032306998,"teacher_disagreement_score":0.39255303,"about_ca_system_score_codex":0.00007817028,"about_ca_system_score_gemma":0.00002702253,"threshold_uncertainty_score":0.99786913},"labels":[],"label_agreement":null},{"id":"W2540865653","doi":"10.1177/0003702816675362","title":"Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis","year":2016,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Residual; Absorbance; Estimator; Heteroscedasticity; Mathematics; Statistics; Mean squared error; Least absolute deviations; Chemistry; Algorithm; Chromatography","score_opus":0.01446301927039981,"score_gpt":0.3137682997473625,"score_spread":0.29930528047696264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540865653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015309414,0.00024677734,0.88061905,0.000708888,0.000036574584,0.00037858254,0.00033549735,0.0003127495,0.10205248],"genre_scores_gemma":[0.6650485,0.000117407646,0.3244507,0.00022589848,0.00027480823,0.00072318304,0.00014930233,0.000116320254,0.00889391],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971309,0.000026285012,0.00057802466,0.00095917913,0.00043460907,0.0008710606],"domain_scores_gemma":[0.997712,0.0009879614,0.0002846383,0.00060547725,0.00019303759,0.00021684736],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028442193,0.00051479094,0.0009969302,0.0006494158,0.00030642588,0.0000754989,0.00045336093,0.00028863293,0.0033204372],"category_scores_gemma":[0.000104400955,0.00037759825,0.0006246837,0.0025445751,0.00016342859,0.00017629833,0.000035952424,0.00024782616,0.000072996925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010952755,0.00022548913,0.0007774112,0.00007162215,0.004847085,0.0000022125198,0.00047379854,0.000008639404,0.94746965,0.039308425,0.0033455177,0.002374844],"study_design_scores_gemma":[0.0019268077,0.0001524904,0.00018959626,0.000012163852,0.0029104669,0.00000155392,0.0005381197,0.00082338066,0.9546347,0.033588868,0.0046409657,0.0005808361],"about_ca_topic_score_codex":0.000059139882,"about_ca_topic_score_gemma":0.000085150925,"teacher_disagreement_score":0.649739,"about_ca_system_score_codex":0.00038857694,"about_ca_system_score_gemma":0.00012377462,"threshold_uncertainty_score":0.9998676},"labels":[],"label_agreement":null},{"id":"W2541546981","doi":"10.1016/j.microc.2016.10.024","title":"Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents","year":2016,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Principal component analysis; Hyperspectral imaging; Pattern recognition (psychology); Artificial intelligence; Projection (relational algebra); Projection pursuit; Computer science; White (mutation); Chemistry","score_opus":0.015885361137404522,"score_gpt":0.24374942561844717,"score_spread":0.22786406448104266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541546981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99834377,0.00066610536,0.00009226803,0.00040137745,0.000014042568,0.000048092286,0.000027721971,0.000022692564,0.00038395883],"genre_scores_gemma":[0.99619865,0.00015669815,0.0025596218,0.00009332748,0.00007034358,0.00000445343,0.000004540563,0.00001876575,0.0008935721],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893516,0.00001417071,0.00027016288,0.00024195733,0.00023844998,0.0003000834],"domain_scores_gemma":[0.9992073,0.00011254145,0.00018738776,0.00010596943,0.00013576461,0.00025103232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009665587,0.00018003372,0.00028565415,0.0001017881,0.00011571583,0.0001011945,0.00011869872,0.0001081427,0.00019656325],"category_scores_gemma":[0.0003704577,0.0001175154,0.00005177872,0.00024558068,0.0002766268,0.00015034966,0.0000691232,0.00020336895,0.0000018179032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013384574,0.00006492577,0.019412335,0.000030792948,0.00018418924,0.000024417843,0.00018600679,2.1584607e-7,0.9781584,0.0000031242691,0.0012260013,0.0005757455],"study_design_scores_gemma":[0.0011680817,0.00016482006,0.0018095976,0.000285308,0.00017448494,0.0005409029,0.00030730624,0.0000032910623,0.99489456,0.0003320096,0.00013872578,0.0001808888],"about_ca_topic_score_codex":0.00002688064,"about_ca_topic_score_gemma":0.0000045590937,"teacher_disagreement_score":0.017602736,"about_ca_system_score_codex":0.00014262919,"about_ca_system_score_gemma":0.000064769694,"threshold_uncertainty_score":0.4792139},"labels":[],"label_agreement":null},{"id":"W2542764092","doi":"10.1109/nssmic.1991.259310","title":"A framework for the evaluation of texture features with application to diagnostic ultrasound images","year":2002,"lang":"en","type":"article","venue":"Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Texture (cosmology); Artificial intelligence; Set (abstract data type); Computer science; Outlier; Image texture; Pattern recognition (psychology); Feature (linguistics); Computer vision; Image (mathematics); Image processing","score_opus":0.021237504211181365,"score_gpt":0.305654920823618,"score_spread":0.28441741661243664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542764092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83836246,0.0011469718,0.116953,0.029182302,0.00066386384,0.0016921522,0.00008408093,0.00012214546,0.011793042],"genre_scores_gemma":[0.9967021,0.00048658054,0.0022607907,0.0002785139,0.00007799932,0.00006012203,0.0000010287206,0.000012760875,0.000120107434],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770993,0.000034849792,0.00027786978,0.00044113045,0.0012150949,0.0003211507],"domain_scores_gemma":[0.99679345,0.0013931034,0.0002652068,0.0005856898,0.000770274,0.00019227427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008265354,0.0001832109,0.00026960875,0.00009237362,0.0003513036,0.00012581672,0.0011908733,0.0000904511,0.00052369223],"category_scores_gemma":[0.003882459,0.00010269686,0.000066848974,0.00072649313,0.0018200352,0.0001655736,0.00010698363,0.0003087024,0.0000030311232],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078110665,0.00023403388,0.009172809,0.0002706883,0.00009054796,7.5708914e-7,0.00291561,0.00007684127,0.7405603,0.008696173,0.0013133833,0.23659077],"study_design_scores_gemma":[0.0028627156,0.0004808951,0.033253834,0.0031992912,0.0030906198,0.00021953005,0.010279349,0.47954348,0.4434818,0.01871772,0.003278032,0.0015927416],"about_ca_topic_score_codex":0.00017672057,"about_ca_topic_score_gemma":0.000030045345,"teacher_disagreement_score":0.47946662,"about_ca_system_score_codex":0.000050896986,"about_ca_system_score_gemma":0.00022507284,"threshold_uncertainty_score":0.6705995},"labels":[],"label_agreement":null},{"id":"W2547438229","doi":"10.1109/nssmic.1991.259311","title":"A multiresolution texture segmentation approach with application to diagnostic ultrasound images","year":2002,"lang":"en","type":"article","venue":"Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Multiresolution analysis; Computer vision; Image segmentation; Image texture; Simulated annealing; Image resolution; Pattern recognition (psychology); Ultrasound; Wavelet; Algorithm; Wavelet transform; Medicine; Radiology","score_opus":0.010440664552396143,"score_gpt":0.2475499846272278,"score_spread":0.23710932007483165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547438229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9059101,0.00015599852,0.061724108,0.0051033935,0.0002497756,0.00055844407,0.000030055886,0.00018432277,0.026083816],"genre_scores_gemma":[0.9954036,0.0003919299,0.0034807597,0.00028120118,0.00006251919,0.0000325342,0.0000032598768,0.000015508598,0.00032869168],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977469,0.00003109582,0.00029910286,0.00061356626,0.00089409295,0.0004152426],"domain_scores_gemma":[0.9984461,0.00023516016,0.00021005854,0.00047061208,0.0002866299,0.00035144706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030094158,0.00022632195,0.0002741882,0.0001465405,0.00038740487,0.00018669583,0.0009213998,0.00008463777,0.00047177717],"category_scores_gemma":[0.00068029173,0.00015468424,0.00005008254,0.0008728205,0.001579149,0.00034439343,0.00013760285,0.00031702998,0.000016011929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021654163,0.00017835891,0.01069607,0.00011842199,0.000019416713,0.0000018159018,0.0011298116,0.000021876403,0.942716,0.00046636933,0.00036144652,0.044268757],"study_design_scores_gemma":[0.0024745292,0.00028285314,0.015567992,0.001189296,0.0006697226,0.00043030758,0.0072951056,0.6689165,0.29875362,0.0005583233,0.002151023,0.0017107225],"about_ca_topic_score_codex":0.00032446143,"about_ca_topic_score_gemma":0.000019539984,"teacher_disagreement_score":0.66889465,"about_ca_system_score_codex":0.00008858127,"about_ca_system_score_gemma":0.0001238944,"threshold_uncertainty_score":0.63078403},"labels":[],"label_agreement":null},{"id":"W2548878763","doi":"10.1007/s11947-016-1817-8","title":"Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products","year":2016,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":276,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Sample (material); Imaging spectroscopy; Artificial intelligence; Principal component analysis; Computer science; Data processing; Chemical imaging; Remote sensing; Chemometrics; Full spectral imaging; Biological system; Machine learning; Chemistry; Geography; Chromatography","score_opus":0.011043709303643161,"score_gpt":0.25336289648304144,"score_spread":0.2423191871793983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548878763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99276924,0.0026666028,0.0023146009,0.0014788136,0.0000137828365,0.00011910093,0.00045920108,0.00007023157,0.000108451495],"genre_scores_gemma":[0.99655044,0.0003005508,0.002962664,0.0000049507507,0.000062488965,0.000017816457,0.00008649522,0.00000609642,0.000008518473],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991908,0.0000019887332,0.0002466481,0.00031015562,0.000082331804,0.0001680749],"domain_scores_gemma":[0.99933434,0.00004351613,0.0002352496,0.00023986396,0.00011775934,0.000029261595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049062215,0.00013253978,0.00023115962,0.00017263228,0.00005661655,0.000016211043,0.00017677156,0.0001372069,0.0000049845867],"category_scores_gemma":[0.00012300476,0.00009100368,0.000017085584,0.00030065601,0.00024660843,0.0004627675,0.00008101296,0.00007737164,2.1880449e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053524283,0.000042815496,0.007531839,0.00019866209,0.000075993295,6.877096e-8,0.00008756494,3.9658236e-8,0.9686218,0.002273376,0.0000159293,0.021098403],"study_design_scores_gemma":[0.00068754336,0.00019386424,0.0032273012,0.000024927467,0.00013855059,0.000037002716,0.0022481375,0.000091571856,0.9886763,0.004354246,0.00018303312,0.00013752803],"about_ca_topic_score_codex":0.000018845965,"about_ca_topic_score_gemma":0.000013519284,"teacher_disagreement_score":0.020960875,"about_ca_system_score_codex":0.000021390446,"about_ca_system_score_gemma":0.000019116756,"threshold_uncertainty_score":0.37110224},"labels":[],"label_agreement":null},{"id":"W2551378236","doi":"10.1002/imic.200990090","title":"FTIR Microspectroscopy in Agriculture","year":2009,"lang":"en","type":"article","venue":"Imaging & Microscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"University of Kansas","keywords":"Agriculture; Fourier transform infrared spectroscopy; Materials science; Nanotechnology; Chemical engineering; Engineering; Geography; Archaeology","score_opus":0.004309798723154462,"score_gpt":0.2721030922388867,"score_spread":0.2677932935157322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551378236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94907725,0.006455364,0.00021985875,0.0020296383,0.00013437435,0.00009928669,0.00003063087,0.00037397176,0.0415796],"genre_scores_gemma":[0.98965913,0.00017960653,0.00487308,0.0015144673,0.00022978047,0.00000984269,0.000049372913,0.000032281838,0.003452464],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99786246,0.000013622077,0.00043346433,0.00065264304,0.00022853832,0.0008093014],"domain_scores_gemma":[0.99913263,0.000034003828,0.00014107658,0.0004894565,0.00006266076,0.00014018329],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010968899,0.00039537763,0.0004594509,0.00025418302,0.00014612106,0.0001922333,0.00050788437,0.0001406127,0.0011689321],"category_scores_gemma":[0.000054469736,0.00036440758,0.00017213907,0.0010359213,0.000103107384,0.0002495768,0.00004809958,0.00056012603,0.0001619401],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028575278,0.00019752598,0.03114075,0.000020893429,0.000016074328,0.000045476998,0.00015062383,0.0000030626168,0.9590431,0.00012538837,0.008689111,0.0005394606],"study_design_scores_gemma":[0.0007805403,0.000019696487,0.007183964,0.00005539311,0.00005666482,0.000049447655,0.00021072091,0.000021275064,0.9805682,0.00079760375,0.009819933,0.00043656546],"about_ca_topic_score_codex":0.00014655173,"about_ca_topic_score_gemma":0.000016863758,"teacher_disagreement_score":0.04058183,"about_ca_system_score_codex":0.0003070475,"about_ca_system_score_gemma":0.000058700192,"threshold_uncertainty_score":0.9998808},"labels":[],"label_agreement":null},{"id":"W2554252322","doi":"10.1016/j.talanta.2016.11.013","title":"Using multiple Process Analytical Technology probes to monitor multivitamin blends in a tableting feed frame","year":2016,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Chemistry; Process analytical technology; Tableting; Process engineering; Critical quality attributes; Ascorbic acid; Analytical Chemistry (journal); Chemical engineering; Chromatography","score_opus":0.02423910568801309,"score_gpt":0.31878384915771146,"score_spread":0.2945447434696984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554252322","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99739134,0.00008251207,0.00067646604,0.00037913842,0.000022399807,0.00008019812,0.000021439608,0.00014348078,0.0012030066],"genre_scores_gemma":[0.9978726,0.000006575912,0.00080511475,0.000024615705,0.00009896839,0.00003736524,0.00000295437,0.000028676022,0.0011231614],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856657,0.000008938278,0.000287269,0.00043527034,0.00018812976,0.0005138436],"domain_scores_gemma":[0.999307,0.00016376891,0.000082632985,0.0002888881,0.000058760394,0.000099001154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009484613,0.00020020269,0.0003426884,0.0004856262,0.00006403934,0.000026379066,0.0002939517,0.00022600633,0.0003148059],"category_scores_gemma":[0.0007397749,0.00015019695,0.000049994003,0.0012845529,0.000085098174,0.00009852688,0.00011807693,0.00020193338,0.00004776256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041044703,0.00012285964,0.25687012,0.00006914688,0.000037456233,0.00002634676,0.00011398997,0.000022776852,0.7405887,0.000030928466,0.0000208677,0.0020557172],"study_design_scores_gemma":[0.00077477895,0.000023967526,0.0009896938,0.00020502966,0.000056411605,0.00002309352,0.0010093722,0.0033223275,0.9930658,0.00012642365,0.000115804694,0.00028732483],"about_ca_topic_score_codex":0.0001100531,"about_ca_topic_score_gemma":0.000036775353,"teacher_disagreement_score":0.25588045,"about_ca_system_score_codex":0.00017596984,"about_ca_system_score_gemma":0.0000468126,"threshold_uncertainty_score":0.6124854},"labels":[],"label_agreement":null},{"id":"W2560387924","doi":"10.1016/j.foostr.2016.12.003","title":"Oil spots and moisture pocket re-distributions between crust and core regions of potato strips during post-frying holding","year":2016,"lang":"en","type":"article","venue":"Food Structure","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Moisture; Spots; Canola; Fractal dimension; STRIPS; Materials science; Core (optical fiber); Surface roughness; Composite material; Fractal; Food science; Chemistry; Mathematics","score_opus":0.022314629476542374,"score_gpt":0.257490885445141,"score_spread":0.23517625596859862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560387924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99447644,0.0016355596,0.0000358322,0.00043161903,0.000029854415,0.000024554503,0.0027046474,0.00006483848,0.000596679],"genre_scores_gemma":[0.99869275,0.00013877216,0.00029098138,0.000019730687,0.0001721183,0.0000029870312,0.00010318312,0.000024153234,0.00055533],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987701,0.000013796627,0.00028888966,0.00039431138,0.00020145942,0.0003314123],"domain_scores_gemma":[0.9990273,0.00014061839,0.0002040644,0.00035224386,0.00010771619,0.00016805367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045125653,0.00025250186,0.0003871966,0.0001372267,0.00032830596,0.000036866273,0.0001821683,0.00023604746,0.00031993858],"category_scores_gemma":[0.00030354285,0.0001893159,0.000079197795,0.0003702887,0.00016806096,0.00012772057,0.00013337957,0.0003214865,4.9577756e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006140312,0.000019207846,0.13547042,0.00042906127,0.00041148308,0.000015470503,0.0003381639,9.928763e-7,0.859573,0.0010070654,0.00041194764,0.0022618065],"study_design_scores_gemma":[0.0016214761,0.00009734655,0.22871202,0.00030562215,0.0005898231,0.00013888879,0.0018767023,0.0000020847979,0.76337576,0.002028396,0.0007087598,0.00054313504],"about_ca_topic_score_codex":0.000036389276,"about_ca_topic_score_gemma":0.0000328982,"teacher_disagreement_score":0.09619725,"about_ca_system_score_codex":0.000071900125,"about_ca_system_score_gemma":0.000037305297,"threshold_uncertainty_score":0.7720079},"labels":[],"label_agreement":null},{"id":"W2562965105","doi":"10.3390/app7010003","title":"Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities","year":2016,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Centers for Disease Control and Prevention; University of Maryland, Baltimore County; U.S. Department of Agriculture","keywords":"Hyperspectral imaging; Optics; Laser; Stepper; Materials science; Fluorescence; Remote sensing; Artificial intelligence; Computer science; Computer vision; Geology; Physics","score_opus":0.010804442626618506,"score_gpt":0.26235729722003653,"score_spread":0.25155285459341803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562965105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93091327,0.00040875052,0.000094543175,0.0006388085,0.000015713087,0.000038969272,0.000005185812,0.00036614435,0.06751859],"genre_scores_gemma":[0.9966979,0.000041107538,0.0027032811,0.000054034565,0.00004601406,0.000017266091,4.890077e-7,0.000008312798,0.00043157185],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987012,0.0000052009536,0.00013289283,0.0004795369,0.00029461007,0.00038657078],"domain_scores_gemma":[0.999484,0.00014485128,0.00006603489,0.00019045119,0.00003223984,0.000082431085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013761943,0.00015507755,0.00017753626,0.000086680106,0.0002909628,0.00008549223,0.00027326596,0.000033385546,0.00022756314],"category_scores_gemma":[0.000036661822,0.00008939873,0.000021264197,0.0005631961,0.0013049319,0.00016982981,0.00004011748,0.00006768044,0.00001269093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014686383,0.000015920676,0.0073248497,0.000017386197,0.000012488312,0.0000031270272,0.00022746083,0.0000031422421,0.9839982,0.0073339413,0.00017974101,0.00086904987],"study_design_scores_gemma":[0.00033567596,0.000031348318,0.0009866882,0.00003453411,0.00003702902,0.00003042011,0.0033374606,0.00046635457,0.9931102,0.0011704679,0.00019809755,0.00026174338],"about_ca_topic_score_codex":0.000060846407,"about_ca_topic_score_gemma":0.000021583344,"teacher_disagreement_score":0.06708702,"about_ca_system_score_codex":0.000063376596,"about_ca_system_score_gemma":0.00007784031,"threshold_uncertainty_score":0.48080763},"labels":[],"label_agreement":null},{"id":"W2563348153","doi":"10.1055/s-0036-1596222","title":"Validation of chemometric methods for plant authentication","year":2016,"lang":"en","type":"article","venue":"Planta Medica","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Ginseng; Ingredient; Traditional medicine; Folk medicine; Active ingredient; Biology; Botany; Food science; Biotechnology; Chemistry; Medicine; Pharmacology","score_opus":0.033014060845294776,"score_gpt":0.35610681342965894,"score_spread":0.32309275258436415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563348153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4718721,0.0013673033,0.50918335,0.0017154124,0.0004358884,0.0002559101,0.00060911913,0.00021206979,0.014348862],"genre_scores_gemma":[0.9790085,0.0003079761,0.01814417,0.000031117295,0.00020771082,0.000049243405,0.00020710473,0.000016992572,0.002027182],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914813,0.000014361193,0.00027840026,0.00019525288,0.00018841014,0.00017543219],"domain_scores_gemma":[0.99831325,0.0011380297,0.00019420889,0.000224711,0.00005423943,0.00007555853],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037610054,0.000094273906,0.00022153716,0.00024350178,0.000035714645,0.0000058792,0.00019956182,0.0001038886,0.0014182368],"category_scores_gemma":[0.0013511874,0.00006202485,0.00007374263,0.00040957035,0.00005612311,0.00005432071,0.000022604858,0.000045018845,0.000012983893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054321314,0.00007843169,0.001708108,0.00015184023,0.00012015662,3.830663e-7,0.00007752236,4.7716828e-8,0.96016854,0.00042075754,0.002376871,0.034843],"study_design_scores_gemma":[0.00051155116,0.000021547181,0.00016374084,0.000036559995,0.00019953778,0.000007676733,0.00004285442,0.00007606397,0.9881558,0.0007207582,0.009974506,0.00008941146],"about_ca_topic_score_codex":0.000008980347,"about_ca_topic_score_gemma":1.9681534e-7,"teacher_disagreement_score":0.5071364,"about_ca_system_score_codex":0.000046957808,"about_ca_system_score_gemma":0.000040418054,"threshold_uncertainty_score":0.9994946},"labels":[],"label_agreement":null},{"id":"W2565015492","doi":"10.1016/j.aca.2016.12.009","title":"Improved modeling of multivariate measurement errors based on the Wishart distribution","year":2016,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Advanced Studies in Basic Sciences","keywords":"Interpretability; Wishart distribution; Covariance; Replicate; Algorithm; Multivariate statistics; Replication (statistics); Covariance matrix; Parameterized complexity; Statistics; Computer science; Mathematics; Artificial intelligence","score_opus":0.03701035715193438,"score_gpt":0.2648318172369338,"score_spread":0.2278214600849994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565015492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92528546,0.00003472045,0.021565367,0.027798992,0.00010563149,0.0003716468,0.0003918688,0.0004403427,0.024005987],"genre_scores_gemma":[0.9994284,0.000011615566,0.00007944005,0.000130934,0.00005502611,0.000013005415,0.000019710289,0.0000246021,0.0002372884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984926,0.000025797368,0.00035450188,0.00032609448,0.00049881707,0.00030216144],"domain_scores_gemma":[0.99867946,0.00022249043,0.0001785742,0.00061761757,0.00021640807,0.00008542292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034152414,0.0001998875,0.00027304798,0.00006712935,0.00008669266,0.000020316413,0.0003571081,0.00009920078,0.0007371635],"category_scores_gemma":[0.0010435046,0.00010754536,0.00021546171,0.00034367092,0.00008403817,0.00006373487,0.00004443484,0.00015001459,0.00000854221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104833074,0.00021676676,0.00022092383,0.000024293311,0.00021051994,4.114357e-7,0.000016384782,0.00003199822,0.997884,0.000475059,0.0007138152,0.00010099153],"study_design_scores_gemma":[0.0005813826,0.00003840866,0.00009102091,0.00008251076,0.00031895822,4.0898175e-7,0.000055236647,0.39720836,0.6008148,0.00025365912,0.00037509095,0.00018017045],"about_ca_topic_score_codex":0.000031413423,"about_ca_topic_score_gemma":0.0000062036015,"teacher_disagreement_score":0.39717636,"about_ca_system_score_codex":0.00021127195,"about_ca_system_score_gemma":0.00008155588,"threshold_uncertainty_score":0.80714226},"labels":[],"label_agreement":null},{"id":"W2565438736","doi":"","title":"Hyperspectral imaging: A potential tool for monitoring crop infestation, crop yield and macronutrient analysis, with special emphasis to Oilseed Brassica","year":2016,"lang":"en","type":"article","venue":"Journal of oilseed Brassica","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rapeseed; Agriculture; Brassica; Agronomy; Infestation; Environmental science; Hectare; Crop; Crop yield; Hyperspectral imaging; Fertilizer; Precision agriculture; Biology; Agroforestry; Geography; Remote sensing; Ecology","score_opus":0.01062944118175455,"score_gpt":0.26770092368473974,"score_spread":0.25707148250298517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565438736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9689273,0.00033402003,0.025532337,0.00403295,0.00021913611,0.00010189071,0.000056030713,0.000038282265,0.0007580166],"genre_scores_gemma":[0.98767716,0.00012338035,0.0077805193,0.00009445109,0.0030728912,0.000015202021,0.0000037058646,0.000045388555,0.0011873262],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974402,0.000018575949,0.00081886025,0.00046415892,0.0006683897,0.00058980513],"domain_scores_gemma":[0.99759924,0.0003475259,0.0006969187,0.00032077785,0.0006179316,0.00041758755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002632601,0.00037023643,0.00077692454,0.0007866972,0.0002707594,0.00029142533,0.0003734428,0.000121985104,0.0004451582],"category_scores_gemma":[0.0005292975,0.0002516643,0.00043799065,0.0011729778,0.00016041729,0.00047979818,0.0000673781,0.00026812204,0.000005563484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018437639,0.0004215452,0.069667295,0.000072139,0.0023428,0.00017565102,0.00043161455,0.00009378528,0.90841687,0.00009374902,0.0034421878,0.012998625],"study_design_scores_gemma":[0.004754117,0.0005267914,0.030731628,0.00021835255,0.004789043,0.0002795241,0.00095348555,0.00006035404,0.95284444,0.0002547348,0.0038463045,0.0007412255],"about_ca_topic_score_codex":0.000022971159,"about_ca_topic_score_gemma":0.000010811135,"teacher_disagreement_score":0.044427596,"about_ca_system_score_codex":0.00032277033,"about_ca_system_score_gemma":0.00022467517,"threshold_uncertainty_score":0.99999356},"labels":[],"label_agreement":null},{"id":"W2566467847","doi":"10.1039/c6ay02445a","title":"Elastic net wavelength interval selection based on iterative rank PLS regression coefficient screening","year":2016,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Elastic net regularization; Selection (genetic algorithm); Regression; Interval (graph theory); Rank (graph theory); Statistics; Regression analysis; Feature selection; Mathematics; Linear regression; Computer science; Artificial intelligence; Combinatorics","score_opus":0.04326273796444289,"score_gpt":0.39199722131691445,"score_spread":0.34873448335247154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566467847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023034818,0.000044688506,0.9664555,0.000574764,0.00007219176,0.00005097182,0.000010948832,0.00012409812,0.009632002],"genre_scores_gemma":[0.9163983,0.00000967877,0.07961313,0.00027873254,0.000224274,0.000013376056,0.000009288815,0.000031194784,0.0034220337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997967,0.0002584992,0.0003994732,0.00056098524,0.00037858478,0.00043544316],"domain_scores_gemma":[0.996554,0.0026500903,0.00014434989,0.0003134235,0.00013040012,0.00020773376],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006439877,0.00028774605,0.00046493096,0.00028271964,0.00015315974,0.000056487777,0.00020942505,0.00019786974,0.0043153996],"category_scores_gemma":[0.0025036298,0.0001720611,0.00024339619,0.00083032844,0.00012679421,0.00008497631,0.00005536182,0.00033087158,0.00004760575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027073922,0.0015169283,0.010258498,0.0002196728,0.0011047223,0.0000656117,0.00024721507,0.002308863,0.41819027,0.007685536,0.0051201354,0.55057514],"study_design_scores_gemma":[0.0010826867,0.00026152458,0.00047486767,0.00028996426,0.0003664734,0.0000061398464,0.000070197246,0.4985405,0.496355,0.00026894966,0.0019562729,0.00032742982],"about_ca_topic_score_codex":0.000009959663,"about_ca_topic_score_gemma":0.0000010368246,"teacher_disagreement_score":0.8933635,"about_ca_system_score_codex":0.00019135009,"about_ca_system_score_gemma":0.00004436429,"threshold_uncertainty_score":0.9965948},"labels":[],"label_agreement":null},{"id":"W2570170669","doi":"10.15353/vsnl.v1i1.52","title":"An Integrated Systems Design Framework for Computational Polarimetry","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Polarimetry; Computer science; Instrumentation (computer programming); Detector; Optics; Physics; Telecommunications; Scattering","score_opus":0.045437146177600904,"score_gpt":0.34225110513090984,"score_spread":0.2968139589533089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570170669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057632454,0.0002338906,0.9410358,0.00058559235,0.0001507599,0.000076818746,0.000026671365,0.00013280417,0.00012519571],"genre_scores_gemma":[0.8403841,0.0000010903207,0.15803207,0.0010628662,0.0002429903,0.00000929507,0.0001384992,0.000026595031,0.000102471684],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901134,0.000032702028,0.0002119805,0.00026702395,0.0002665287,0.00021041589],"domain_scores_gemma":[0.9990432,0.0003472542,0.0000986501,0.00022853467,0.000117439944,0.00016486896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022408931,0.00014316925,0.00019820344,0.00015665723,0.0000813723,0.00012274747,0.00022613822,0.00014014948,0.00013500467],"category_scores_gemma":[0.00022276198,0.00012507309,0.00006443477,0.00042026563,0.000042713298,0.0001352418,0.000014611001,0.0001778943,0.000035580524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097398716,0.0010391263,0.008753023,0.00032234017,0.00078250916,0.000042261483,0.0008275012,0.28720415,0.40359426,0.011027422,0.2801108,0.0053226203],"study_design_scores_gemma":[0.0056561027,0.0011073214,0.00081751734,0.000396239,0.000803844,0.000080714846,0.006283678,0.59570915,0.3216616,0.013725769,0.051304314,0.0024537593],"about_ca_topic_score_codex":0.00005527262,"about_ca_topic_score_gemma":1.0592854e-7,"teacher_disagreement_score":0.78300375,"about_ca_system_score_codex":0.00012678023,"about_ca_system_score_gemma":0.00005406054,"threshold_uncertainty_score":0.51003325},"labels":[],"label_agreement":null},{"id":"W2578724174","doi":"","title":"Determination of physical contaminants in wheat using hyperspectral imaging","year":2015,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Environmental science; Contamination; Remote sensing; Environmental chemistry; Geography; Chemistry; Biology; Ecology","score_opus":0.01914628208181442,"score_gpt":0.267982682625103,"score_spread":0.24883640054328857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578724174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893812,0.0002821093,0.00006821444,0.000019846895,0.000045462795,0.000048848633,0.000021660071,0.00001703523,0.010115634],"genre_scores_gemma":[0.99824005,0.000031022362,0.00070894114,0.0000012437625,0.000043973974,1.14319754e-7,0.00010927187,0.000017869124,0.00084752],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991082,0.000016874035,0.00011703791,0.00025851448,0.0003090317,0.00019032451],"domain_scores_gemma":[0.9991955,0.000043108055,0.00033115532,0.00019225263,0.00018812588,0.000049856233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006684972,0.0001806231,0.00049306656,0.0004027014,0.000050917533,0.000007611419,0.00025827737,0.00015676577,0.000027899994],"category_scores_gemma":[0.000035628826,0.00024400755,0.00015049256,0.0003762384,0.00007280723,0.00017148485,0.000034099838,0.00022778283,0.0000036285167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050805375,0.0006792283,0.12018746,0.001546608,0.00017342775,0.0001751661,0.004861653,0.00005278189,0.866681,0.00013941638,0.00024418905,0.0047510425],"study_design_scores_gemma":[0.0029494306,0.000112798065,0.07154512,0.0009102881,0.0017831948,0.000014370527,0.38366294,0.02257665,0.51478255,0.0005809996,0.00011441422,0.0009672664],"about_ca_topic_score_codex":0.0035587328,"about_ca_topic_score_gemma":0.054294664,"teacher_disagreement_score":0.3788013,"about_ca_system_score_codex":0.00038332425,"about_ca_system_score_gemma":0.00013126699,"threshold_uncertainty_score":0.99503386},"labels":[],"label_agreement":null},{"id":"W2583815362","doi":"10.1016/j.foodchem.2017.01.158","title":"A validated fast difference spectrophotometric method for 5-hydroxymethyl-2-furfural (HMF) determination in corn syrups","year":2017,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Fundação Araucária; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Foundation for Innovation","keywords":"Repeatability; Furfural; Hydroxymethyl; Chemistry; Significant difference; Accuracy and precision; Chromatography; Mathematics; Organic chemistry; Statistics","score_opus":0.02910482310029554,"score_gpt":0.3292866753216694,"score_spread":0.30018185222137383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583815362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9754127,0.00036311243,0.009060575,0.0001294487,0.000080873455,0.00016407386,0.00019991674,0.00013960393,0.014449687],"genre_scores_gemma":[0.98546433,0.00004398648,0.0085213585,0.000035295478,0.0002604747,0.00015261269,0.00014280273,0.000055703753,0.0053234575],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99762446,0.000017297072,0.0005189247,0.0007824942,0.00036919393,0.00068761694],"domain_scores_gemma":[0.9976436,0.00033892336,0.0005477496,0.0011483884,0.0001385053,0.00018286677],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025732061,0.0004249551,0.0006351016,0.0002257657,0.00034965284,0.00026567688,0.001125924,0.00040247306,0.00096108107],"category_scores_gemma":[0.001306483,0.00043756104,0.00026823653,0.0006273157,0.00011618164,0.00016766517,0.00019728977,0.00047154716,0.000011804941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008183507,0.00019363569,0.0036638563,0.0004114367,0.000096376076,0.000015594509,0.00007343012,0.000003486321,0.99173564,0.000013873518,0.00009963358,0.0036111781],"study_design_scores_gemma":[0.0015282792,0.00007021768,0.0013997512,0.00005477472,0.00017602638,0.000031877484,0.00019890352,0.0035733967,0.99153817,0.00072703784,0.00020313947,0.00049843424],"about_ca_topic_score_codex":0.00007445933,"about_ca_topic_score_gemma":0.000018233179,"teacher_disagreement_score":0.010051599,"about_ca_system_score_codex":0.0003227299,"about_ca_system_score_gemma":0.00009023549,"threshold_uncertainty_score":0.9999522},"labels":[],"label_agreement":null},{"id":"W2584880899","doi":"10.1109/icdmw.2016.0096","title":"Selecting the Top-k Discriminative Features Using Principal Component Analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Dimensionality reduction; Discriminative model; Pattern recognition (psychology); Artificial intelligence; Computer science; Leverage (statistics); Linear discriminant analysis; Feature selection; Ranking (information retrieval); Data mining; Feature extraction; Discriminant; Curse of dimensionality; Multivariate statistics; Machine learning","score_opus":0.02801157358970927,"score_gpt":0.3115671412970352,"score_spread":0.28355556770732593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584880899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96376663,0.00016769324,0.01053582,0.000553294,0.000019213141,0.00002812883,0.000008291408,0.00007412689,0.024846803],"genre_scores_gemma":[0.99092907,0.0000075589,0.0006900973,0.00006613995,0.00010020486,0.00000371364,0.000003748706,0.000009368552,0.008190089],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907404,0.000018891378,0.0001658116,0.0002523448,0.00022754626,0.00026138604],"domain_scores_gemma":[0.9991972,0.0002981119,0.000099147044,0.00029030352,0.00006240507,0.0000528299],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010725424,0.00014004046,0.00022026726,0.0001357231,0.00022760479,0.000045890938,0.00023378745,0.00005784799,0.003535827],"category_scores_gemma":[0.00011911068,0.000062570085,0.0001994487,0.0009899426,0.000079777834,0.00007292867,0.000086230575,0.00011576662,0.0000086908085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051600902,0.00018927653,0.26498193,0.00003143766,0.00509609,0.000007594477,0.0009094025,0.00015849849,0.7204665,0.0042516827,0.00046387388,0.003392156],"study_design_scores_gemma":[0.00028019527,0.0000081413245,0.025761629,0.0000101993455,0.0026277993,0.000005435843,0.0015950556,0.0006761287,0.96814036,0.00023951336,0.00040475768,0.00025079888],"about_ca_topic_score_codex":0.00026060207,"about_ca_topic_score_gemma":0.000070102295,"teacher_disagreement_score":0.24767387,"about_ca_system_score_codex":0.00013419201,"about_ca_system_score_gemma":0.000020549694,"threshold_uncertainty_score":0.9973751},"labels":[],"label_agreement":null},{"id":"W2586549354","doi":"10.1021/acs.iecr.6b04437","title":"A Bayesian Learning Approach to Modeling Pseudoreaction Networks for Complex Reacting Systems: Application to the Mild Visbreaking of Bitumen","year":2017,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Process (computing); Dynamic Bayesian network; Computer science; Bayesian probability; Feature (linguistics); Process engineering; Biological system; Chemistry; Artificial intelligence; Engineering","score_opus":0.1627615823764936,"score_gpt":0.3791989151227284,"score_spread":0.2164373327462348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586549354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3881315,0.00022364699,0.59219766,0.0006747507,0.00019753462,0.0015871848,0.000043594566,0.0003110304,0.016633116],"genre_scores_gemma":[0.9966628,0.000005919324,0.00040662877,0.000003522384,0.0019159619,0.0004683009,0.000057786703,0.00005794173,0.0004211657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778324,0.000020828385,0.000459019,0.000522555,0.0005523661,0.0006620015],"domain_scores_gemma":[0.9980589,0.0003559215,0.0002071025,0.0008513455,0.00031335294,0.00021338147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001516159,0.00022776517,0.00037831423,0.00017772845,0.000774581,0.00033076998,0.0009647335,0.0003388755,0.0000131198585],"category_scores_gemma":[0.0020520424,0.00021224866,0.0001120058,0.00067284587,0.000039731734,0.00010801007,0.00025286456,0.0011573738,0.00000208453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055177057,0.00002622577,0.00011796107,0.00015214804,0.00006251102,2.3733826e-7,0.00006537011,0.51116467,0.487479,0.000026132338,0.00014748065,0.00070311327],"study_design_scores_gemma":[0.00033031465,0.00001556296,0.0000056726394,0.00012796739,0.00004311318,0.000005484449,0.00059358217,0.7859033,0.21014147,0.0000026016883,0.002654331,0.00017664708],"about_ca_topic_score_codex":0.0006718132,"about_ca_topic_score_gemma":0.0000012414545,"teacher_disagreement_score":0.6085313,"about_ca_system_score_codex":0.00033928407,"about_ca_system_score_gemma":0.00007460654,"threshold_uncertainty_score":0.86552495},"labels":[],"label_agreement":null},{"id":"W2588750467","doi":"10.5539/ijsp.v6n2p54","title":"Invariance Test: Detecting Difference Between Latent Variables Structure in Partial Least Squares Path Modeling","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Latent variable; Mathematics; Structural equation modeling; Context (archaeology); Path (computing); Partial least squares regression; Econometrics; Causal model; Path analysis (statistics); Least-squares function approximation; Focus (optics); Measurement invariance; Statistics; Applied mathematics; Computer science; Confirmatory factor analysis","score_opus":0.03466848110872416,"score_gpt":0.30289640577763977,"score_spread":0.2682279246689156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588750467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9418067,0.000120394034,0.057254467,0.00016180496,0.00010244149,0.00002542309,0.00040949334,0.000004130793,0.00011514618],"genre_scores_gemma":[0.98826355,0.000073810865,0.011371665,0.000010677793,0.00025052734,0.0000010293246,0.00000777238,0.0000062303925,0.000014753395],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988669,0.000016087539,0.00048734993,0.00016967015,0.00031861814,0.00014136841],"domain_scores_gemma":[0.99859565,0.0003290408,0.0005041196,0.0001542069,0.0003438421,0.00007313387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002688748,0.0001174041,0.00024896022,0.000062255596,0.00012960265,0.00022731106,0.00041713665,0.000072904724,0.00013421952],"category_scores_gemma":[0.0021532793,0.00009894899,0.00003540492,0.000034236073,0.00008451185,0.00014741629,0.00010459309,0.00033447935,2.4938274e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007326074,0.0000812014,0.97999865,0.00007634043,0.00011250704,0.00003534448,0.00017659417,0.0011185982,0.0059798867,0.001372843,0.0000058077994,0.0109689655],"study_design_scores_gemma":[0.0022069118,0.00016948169,0.47579217,0.00045027764,0.000257948,0.000095529984,0.00016813441,0.1156274,0.022432549,0.382243,0.00005194284,0.0005046678],"about_ca_topic_score_codex":0.00024944294,"about_ca_topic_score_gemma":0.0000775287,"teacher_disagreement_score":0.5042065,"about_ca_system_score_codex":0.00009789619,"about_ca_system_score_gemma":0.00007945105,"threshold_uncertainty_score":0.4035023},"labels":[],"label_agreement":null},{"id":"W2604892329","doi":"10.7437/nt2236-7640/2017.01.003","title":"On a method for Rock Classification using Textural Features and Genetic Optimization","year":2017,"lang":"en","type":"article","venue":"Notas Técnicas","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"QUAD Engineering (Canada)","funders":"","keywords":"Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Naive Bayes classifier; Computer science; Mathematics; Support vector machine","score_opus":0.05702550454452048,"score_gpt":0.3796120676536003,"score_spread":0.3225865631090798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604892329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3352868,0.0012519856,0.64675224,0.0011882685,0.00014063902,0.0002946004,0.000062804655,0.00012628277,0.014896412],"genre_scores_gemma":[0.87677836,0.000044504384,0.12168627,0.00008671715,0.00015191565,0.000014134946,0.000021578982,0.000018914918,0.0011976229],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992909,0.000009468626,0.00013855881,0.00029120463,0.000109488195,0.00016042097],"domain_scores_gemma":[0.9990986,0.00015770702,0.00019527633,0.00043602998,0.000056686793,0.000055673252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054275515,0.00012439057,0.00016585623,0.00005445952,0.0004277432,0.00016351782,0.00018458453,0.00013001438,0.00018076066],"category_scores_gemma":[0.00042427192,0.000111256195,0.00006217292,0.000053716758,0.000029523735,0.00008284841,0.000037587113,0.000101761805,0.0000026040932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006829814,0.00038048273,0.007826424,0.0004324544,0.00059979624,0.000014130311,0.00034028874,0.058117535,0.87859106,0.0071090446,0.0055967197,0.04030907],"study_design_scores_gemma":[0.0015015847,0.00009044889,0.0097018285,0.00004967771,0.00066768256,0.00006903014,0.0001602366,0.74861676,0.23641892,0.001370517,0.0008708445,0.00048244942],"about_ca_topic_score_codex":0.000038374325,"about_ca_topic_score_gemma":0.0000058251926,"teacher_disagreement_score":0.69049925,"about_ca_system_score_codex":0.000054413358,"about_ca_system_score_gemma":0.000026408952,"threshold_uncertainty_score":0.45368958},"labels":[],"label_agreement":null},{"id":"W2605430898","doi":"","title":"A Comparative Study of Enumeration Methods for Listeria monocytogenes on Naturally Contaminated Ready-to-Eat Foods","year":2017,"lang":"en","type":"article","venue":"IAFP 2017","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs","funders":"","keywords":"Listeria monocytogenes; Enumeration; Food science; Food contaminant; Listeria; Biology; Contamination; Food microbiology; Biotechnology; Microbiology; Mathematics; Bacteria; Ecology; Genetics","score_opus":0.1902476815634611,"score_gpt":0.5066707376776284,"score_spread":0.31642305611416727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605430898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98705685,0.00017029724,0.0019063466,0.00008406538,0.00015239035,0.00036854707,0.000055546436,0.000035116416,0.010170865],"genre_scores_gemma":[0.99091583,0.00000763726,0.005423881,0.00003381485,0.000103054525,0.000103299,0.00003451183,0.000015835863,0.0033621346],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989769,0.00004414065,0.0002900235,0.00034764875,0.00013981143,0.00020148425],"domain_scores_gemma":[0.9982494,0.00023403892,0.00040305548,0.00081381336,0.00022518968,0.00007446938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002157073,0.00020151492,0.00053133536,0.00010675799,0.00038355042,0.00012330542,0.0005125213,0.000114814604,0.00009094017],"category_scores_gemma":[0.00034495853,0.00017444896,0.00008789184,0.00007487887,0.000056712568,0.00012427823,0.00008287881,0.00010945774,0.00001124762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009987509,0.00073777366,0.003039186,0.00009019047,0.0006578841,0.0000044821877,0.002946355,0.000023726116,0.98387593,0.00026032707,0.0017386036,0.0056267832],"study_design_scores_gemma":[0.0014817001,0.0007733131,0.0068093855,0.000027864387,0.0002563604,0.000001929562,0.0013634504,0.0004200303,0.9870927,0.00012189459,0.0014284871,0.00022291245],"about_ca_topic_score_codex":0.00020006527,"about_ca_topic_score_gemma":0.000115711184,"teacher_disagreement_score":0.0068087303,"about_ca_system_score_codex":0.000060603612,"about_ca_system_score_gemma":0.000029774184,"threshold_uncertainty_score":0.7113822},"labels":[],"label_agreement":null},{"id":"W2605780698","doi":"10.1007/s11745-017-4250-5","title":"First Application of Newly Developed FT‐NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA","year":2017,"lang":"en","type":"article","venue":"Lipids","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Olive oil; Food and drug administration; Food science; Oleic acid; Deep frying; Chemistry; Mathematics; Biochemistry; Statistics","score_opus":0.08002028738424952,"score_gpt":0.33755944242023034,"score_spread":0.2575391550359808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605780698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814715,0.00018440121,0.00467668,0.00090714206,0.00008345195,0.00011687197,0.000016298249,0.00001846909,0.0125251785],"genre_scores_gemma":[0.98538756,0.00008533663,0.01137292,0.000053573956,0.0002395254,0.000060358252,0.000008747652,0.000013402901,0.0027785911],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985807,0.00006687801,0.00044105048,0.00037766958,0.0002604689,0.00027321183],"domain_scores_gemma":[0.9980444,0.0002693945,0.00040605664,0.0011210904,0.00011046596,0.000048648555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006187926,0.00016774105,0.00043183635,0.00012871088,0.000134553,0.000026001959,0.0009594211,0.00014100695,0.0003068229],"category_scores_gemma":[0.0014700602,0.00013174869,0.00006916176,0.00044491168,0.0001996265,0.000085223524,0.00012904376,0.00021650865,0.000019777972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017807762,0.00039715413,0.31229916,0.0011518644,0.00015931744,0.000002780114,0.0034432085,0.00003685824,0.6749854,0.0023031465,0.0005224814,0.0045204964],"study_design_scores_gemma":[0.00047290558,0.000068383786,0.10800649,0.00006051444,0.00014113591,0.0000034449301,0.00032641244,0.00015241923,0.887648,0.00043420834,0.002526809,0.00015931319],"about_ca_topic_score_codex":0.0011722546,"about_ca_topic_score_gemma":0.00073558756,"teacher_disagreement_score":0.21266252,"about_ca_system_score_codex":0.00006522431,"about_ca_system_score_gemma":0.00009235656,"threshold_uncertainty_score":0.5372555},"labels":[],"label_agreement":null},{"id":"W2605967833","doi":"10.5539/ijsp.v6n3p51","title":"Geometric Views of Partial Correlation Coefficient in Regression Analysis","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Partial correlation; Regression analysis; Regression; Simple (philosophy); Mathematics; Correlation coefficient; Simple linear regression; Comprehension; Statistics; Reading (process); Simple correlation; Correlation; Computer science; Epistemology; Linguistics; Geometry; Philosophy","score_opus":0.032911176632807106,"score_gpt":0.3475992857505187,"score_spread":0.31468810911771156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605967833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8961543,0.00040137646,0.101992816,0.00014083962,0.0001655162,0.000029564468,0.00015312531,0.0000018943964,0.000960573],"genre_scores_gemma":[0.9951261,0.00022217713,0.0045144083,0.0000058813084,0.000051018123,7.829918e-7,0.000013428449,0.0000027330263,0.00006345308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988066,0.000019676198,0.00059559336,0.00011587153,0.00038377187,0.00007846793],"domain_scores_gemma":[0.99803406,0.00020967651,0.0010607206,0.00016512816,0.0004802826,0.000050161212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005002336,0.00007596876,0.00029626314,0.000383552,0.000052228657,0.00005836238,0.00028111922,0.00005201512,0.00036647206],"category_scores_gemma":[0.0013733219,0.00005940744,0.000087183886,0.00022274925,0.00011680038,0.00010177262,0.000061120954,0.00014773468,4.8854673e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023894549,0.00038746183,0.9745838,0.000058041718,0.0005588265,0.0000228778,0.00016648967,0.001969365,0.000999643,0.0017370594,0.00013238346,0.01914508],"study_design_scores_gemma":[0.0021157889,0.00017188005,0.9168197,0.00014264633,0.001305492,0.00002821895,0.0001444372,0.03439731,0.025768949,0.017763285,0.0010809085,0.00026140836],"about_ca_topic_score_codex":0.00011004825,"about_ca_topic_score_gemma":0.000032419513,"teacher_disagreement_score":0.09897182,"about_ca_system_score_codex":0.00008237154,"about_ca_system_score_gemma":0.00005403551,"threshold_uncertainty_score":0.40126118},"labels":[],"label_agreement":null},{"id":"W2606047366","doi":"10.3168/jds.2016-12102","title":"Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets","year":2017,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Dairy Commission; Dairy Farmers of Canada","keywords":"Fatty acid; Partial least squares regression; Population; Dairy cattle; Food science; Bovine milk; Chemistry; Animal science; Biology; Mathematics; Biochemistry; Statistics; Medicine","score_opus":0.06246027746546507,"score_gpt":0.2923922676582236,"score_spread":0.22993199019275856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606047366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911131,0.0001678626,0.0075370385,0.00010342782,0.00009406861,0.000043412907,0.000095342606,0.0000058543515,0.00083989295],"genre_scores_gemma":[0.99162406,0.00001574849,0.008233961,0.000016859298,0.000032085336,0.0000012721849,0.0000072403973,0.000008567519,0.00006020846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808335,0.000007889647,0.0005648818,0.00024420046,0.0006222798,0.00047739688],"domain_scores_gemma":[0.99812216,0.00001785275,0.0008058814,0.00038624238,0.00028584918,0.0003820131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041143884,0.00017029696,0.00037241308,0.0004169755,0.00043025913,0.00012442665,0.00084574823,0.0000743566,0.00005215311],"category_scores_gemma":[0.00026521747,0.0001337974,0.00006372606,0.0004205008,0.00042241244,0.00061710755,0.000071929324,0.0002811628,7.607489e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008498155,0.00010170522,0.34454215,0.000035904493,0.000049008075,0.000024973397,0.00045355601,0.0008298154,0.6537517,0.000011589329,0.00006388836,0.00005074098],"study_design_scores_gemma":[0.00081054703,0.00005130544,0.19025175,0.00015523238,0.00004899371,0.000050786308,0.00041015653,0.0056939246,0.8023049,0.00007986666,0.000019212219,0.00012332184],"about_ca_topic_score_codex":0.0054965485,"about_ca_topic_score_gemma":0.004650574,"teacher_disagreement_score":0.1542904,"about_ca_system_score_codex":0.0011652632,"about_ca_system_score_gemma":0.0022600675,"threshold_uncertainty_score":0.83091736},"labels":[],"label_agreement":null},{"id":"W2606089367","doi":"10.1139/cjps-2017-0013","title":"Development of a predictive model for ‘Lapins’ sweet cherry dry matter content using a visible/near infrared spectrometer and its potential application to other cultivars.","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wind Energy Institute of Canada; Thompson Rivers University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Dry matter; Cultivar; Horticulture; Mathematics; Chemistry; Agronomy; Biology","score_opus":0.05167347249404807,"score_gpt":0.28949721804766887,"score_spread":0.2378237455536208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606089367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9420336,0.00007490824,0.057144616,0.00008473583,0.00003929642,0.000098693636,0.00018676279,0.000001923013,0.00033545724],"genre_scores_gemma":[0.9743704,0.0000021700453,0.02528645,0.0000867267,0.00004566168,0.000004831884,0.000001348029,0.000008026282,0.000194345],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989955,0.0000029235887,0.0002973893,0.00019105646,0.00023126912,0.0002818923],"domain_scores_gemma":[0.99879915,0.000012761615,0.00038274395,0.00016408236,0.00023496375,0.00040630565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028433878,0.00010373939,0.00020824945,0.00029263165,0.0004581726,0.00014383931,0.00046251086,0.000049804297,0.00006114867],"category_scores_gemma":[0.000118760785,0.00009068648,0.000045247456,0.00014340461,0.00017991017,0.00028948692,0.000033869757,0.00009229578,0.0000016434245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070602786,0.000018242295,0.005633864,0.00004405337,0.000067770765,0.000005934381,0.0017922419,0.00054363755,0.99153006,0.000038100225,0.00007605985,0.00017944658],"study_design_scores_gemma":[0.0011267157,0.00008424051,0.026336355,0.0002512423,0.000176553,0.00011207433,0.0009459925,0.28560314,0.6842332,0.00026164955,0.00049666734,0.00037219908],"about_ca_topic_score_codex":0.00043855264,"about_ca_topic_score_gemma":0.0004929324,"teacher_disagreement_score":0.30729687,"about_ca_system_score_codex":0.00025626767,"about_ca_system_score_gemma":0.0012943726,"threshold_uncertainty_score":0.36980873},"labels":[],"label_agreement":null},{"id":"W2606153420","doi":"10.1080/10942912.2017.1312437","title":"Quantitative determination of free fatty acids in extra virgin olive oils by multivariate methods and Fourier transform infrared spectroscopy considering different absorption modes","year":2017,"lang":"en","type":"article","venue":"International Journal of Food Properties","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Partial least squares regression; Chemometrics; Chemistry; Calibration; Fourier transform infrared spectroscopy; Analytical Chemistry (journal); Infrared spectroscopy; Absorption (acoustics); Attenuated total reflection; Coefficient of determination; Oleic acid; Spectroscopy; Correlation coefficient; Fourier transform; Mean squared error; Chromatography; Materials science; Mathematics; Optics; Organic chemistry; Statistics; Biochemistry","score_opus":0.05829461190222211,"score_gpt":0.35935425745786687,"score_spread":0.30105964555564474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606153420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9655155,0.0016656957,0.031153103,0.0002911149,0.00010062975,0.000053660024,0.000046079567,0.0000069975436,0.0011672059],"genre_scores_gemma":[0.9595366,0.00052487367,0.039747458,0.0000126813975,0.000054820877,0.0000071950285,0.0000027916637,0.000013345595,0.0001002306],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986752,0.00004738022,0.00059792877,0.00015731169,0.00037566436,0.00014646578],"domain_scores_gemma":[0.99858594,0.0001455177,0.00071560696,0.00017294276,0.0003248909,0.0000551138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028180864,0.00017430143,0.00037694734,0.00026700078,0.0000796157,0.00014027058,0.0004754395,0.00009333974,0.00008185256],"category_scores_gemma":[0.0006872575,0.00013489692,0.00011152816,0.000041236508,0.0001906225,0.00066115265,0.00007394362,0.0002545413,2.7208984e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065556954,0.00013407458,0.0032996947,0.000075978285,0.0003184143,0.000007388137,0.0015033112,0.000012107165,0.98851717,0.0003881749,0.000009913238,0.0050782165],"study_design_scores_gemma":[0.0015885857,0.0002621644,0.0020783655,0.0002785225,0.000087469416,0.00002845612,0.0008942109,0.0031707622,0.983619,0.007832931,0.000018334531,0.0001412282],"about_ca_topic_score_codex":0.000089133035,"about_ca_topic_score_gemma":0.000042073858,"teacher_disagreement_score":0.008594354,"about_ca_system_score_codex":0.00013904696,"about_ca_system_score_gemma":0.00004482931,"threshold_uncertainty_score":0.5500937},"labels":[],"label_agreement":null},{"id":"W2606622195","doi":"10.1016/j.foodcont.2017.04.036","title":"Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique","year":2017,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Principal component analysis; Linear discriminant analysis; Gluten; Partial least squares regression; Near-infrared spectroscopy; Chemometrics; Avena; Food science; Mathematics; Agronomy; Chemistry; Computer science; Artificial intelligence; Biology; Statistics; Chromatography","score_opus":0.020745092648491517,"score_gpt":0.27850114502595064,"score_spread":0.25775605237745913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606622195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97415364,0.0011863433,0.011814464,0.00024134018,0.00009488473,0.00017359156,0.00032291855,0.00009042892,0.011922362],"genre_scores_gemma":[0.99593955,0.000011576326,0.003644777,0.000033956352,0.00015405494,0.000022841292,0.000017252385,0.00002592899,0.00015003038],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988556,0.000015744605,0.0003006978,0.00029727168,0.00024174232,0.0002889613],"domain_scores_gemma":[0.9982944,0.00008774694,0.00043742743,0.0009911838,0.0001182843,0.000070944276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009847278,0.0001933336,0.00041095188,0.00008192761,0.00032156767,0.00014067783,0.0007083881,0.000114821225,0.00029620805],"category_scores_gemma":[0.00050799164,0.00018848771,0.00015315223,0.00008004331,0.00018043522,0.00029653162,0.00010490678,0.00017776381,0.000003651214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012195611,0.00010768202,0.04358501,0.000045437817,0.0002567928,0.000013587244,0.0001831819,0.000004892151,0.9539677,0.00022022071,0.000099901496,0.0013936219],"study_design_scores_gemma":[0.0030890387,0.0000631634,0.013616061,0.00009180605,0.00055642903,0.0000079728625,0.0003908092,0.0046070204,0.9736039,0.0035931603,0.00009296736,0.00028766665],"about_ca_topic_score_codex":0.0008637369,"about_ca_topic_score_gemma":0.000047208003,"teacher_disagreement_score":0.02996895,"about_ca_system_score_codex":0.00011730212,"about_ca_system_score_gemma":0.00007748798,"threshold_uncertainty_score":0.76863056},"labels":[],"label_agreement":null},{"id":"W2606747436","doi":"10.1504/ijpti.2016.10004649","title":"Development of an objective freshness index for a variety of Mediterranean eggplant","year":2016,"lang":"en","type":"article","venue":"International Journal of Postharvest Technology and Innovation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Solanum; Horticulture; Mathematics; Hyperspectral imaging; Mediterranean climate; Botany; Food science; Biology; Geography; Ecology","score_opus":0.01958909260846098,"score_gpt":0.3097978358699167,"score_spread":0.29020874326145574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606747436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97526085,0.000055770404,0.02379651,0.00053415034,0.00010874785,0.0000280813,0.000041490086,0.000009730542,0.0001646521],"genre_scores_gemma":[0.9931203,0.000017149785,0.006697481,0.000021484784,0.00006912006,0.0000038644866,0.000015492737,0.0000059658614,0.00004910108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989725,0.0000045304296,0.0006344549,0.00009835736,0.00021253806,0.000077629],"domain_scores_gemma":[0.99772215,0.00007320193,0.0008376748,0.000074968724,0.0012758642,0.000016125512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024355591,0.00008067161,0.00020669379,0.0013211551,0.00002633526,0.0000065671784,0.00026909445,0.000158892,0.00007776469],"category_scores_gemma":[0.00035789981,0.000057699664,0.000030823834,0.00057714834,0.00014094597,0.00025905838,0.000036223737,0.000102610466,1.7178603e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004154163,0.00019744513,0.01930486,0.000035517463,0.00045617486,0.0000039436327,0.0002554207,7.735857e-7,0.897105,0.032960914,0.000034985977,0.049229592],"study_design_scores_gemma":[0.0011586369,0.000136063,0.0077714166,0.00011251201,0.00003221944,0.00010023477,0.00043843652,0.000024287108,0.98149353,0.0079584755,0.00070096226,0.00007324311],"about_ca_topic_score_codex":0.0000026786388,"about_ca_topic_score_gemma":0.00000876919,"teacher_disagreement_score":0.084388554,"about_ca_system_score_codex":0.0000576232,"about_ca_system_score_gemma":0.0001163126,"threshold_uncertainty_score":0.2352924},"labels":[],"label_agreement":null},{"id":"W2608620014","doi":"10.1142/9789813225701_0004","title":"A Novel Exploratory Shape Analysis Method to Identify Spatial Variation of Fungi Infection Level on Wheat Kernels","year":2017,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Variation (astronomy); Computer science; Artificial intelligence; Physics","score_opus":0.08832741964214162,"score_gpt":0.3895614472013948,"score_spread":0.30123402755925316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608620014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2893537,0.000019774496,0.6993378,0.00008487585,0.00006646829,0.000050858594,0.000041961306,0.000052241205,0.010992336],"genre_scores_gemma":[0.98679423,0.00001551114,0.010918766,0.000077727986,0.00016470187,0.000020977497,0.0000236254,0.000016198383,0.0019682352],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867153,0.000020165484,0.00035025223,0.00039991614,0.00036228672,0.00019582549],"domain_scores_gemma":[0.99853814,0.00011889338,0.00033522845,0.0007283787,0.00017597374,0.000103370294],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003390086,0.00018069195,0.00041435845,0.0005075332,0.00020523589,0.00011832329,0.00028264476,0.0001594802,0.004607206],"category_scores_gemma":[0.00028226167,0.00016995946,0.00027310004,0.00061583275,0.000024249377,0.00024664064,0.00009961063,0.00013437429,0.000060376915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006709915,0.0002526641,0.020274732,0.000042455747,0.0012259315,9.5019885e-7,0.0002060176,0.00036913026,0.96770626,0.0003236829,0.00012248648,0.009408595],"study_design_scores_gemma":[0.0003655843,0.000039402188,0.23560181,0.000011586655,0.0013651168,6.83486e-7,0.00007070296,0.005870008,0.75631505,0.00009785147,0.00007618955,0.000185986],"about_ca_topic_score_codex":0.004468179,"about_ca_topic_score_gemma":0.0005611884,"teacher_disagreement_score":0.69744056,"about_ca_system_score_codex":0.00012187674,"about_ca_system_score_gemma":0.00003719575,"threshold_uncertainty_score":0.9963027},"labels":[],"label_agreement":null},{"id":"W2612180235","doi":"10.1016/s1474-6670(17)38707-4","title":"Digital Imaging for Process Monitoring and Control with Industrial Applications","year":2004,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Process control; Principal component analysis; Process (computing); Feature (linguistics); Engineering; Monitoring and control; Process engineering; Computer science; Control engineering; Data mining; Artificial intelligence","score_opus":0.011559405383985625,"score_gpt":0.25836973239122685,"score_spread":0.24681032700724123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612180235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986784,0.0006828471,0.0041356212,0.00038159598,0.000019489675,0.00034442727,0.000051728694,0.00025062085,0.007349713],"genre_scores_gemma":[0.99805254,0.000009596803,0.0006254578,0.000014059267,0.0004997679,0.0003777936,0.0000064460905,0.000027331867,0.00038699884],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909294,2.1043942e-7,0.0001583966,0.0003201756,0.00015631107,0.00027194846],"domain_scores_gemma":[0.9995228,0.000031300293,0.000113258255,0.00006094654,0.00017508061,0.00009660811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037148402,0.0001653689,0.00019984879,0.000080758444,0.00019286037,0.0002849611,0.00013831203,0.00007300259,0.000014383624],"category_scores_gemma":[0.000081170794,0.00014658729,0.000037988055,0.00028992267,0.00009495718,0.0004512734,0.000017849228,0.0001429173,0.0000021463866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025628612,0.00020285173,0.9137362,0.0004790248,0.00028403383,0.0000018068514,0.0005357829,0.000018194201,0.04483236,0.0006881427,0.00008652273,0.03887875],"study_design_scores_gemma":[0.009734659,0.00014385587,0.0010238538,0.0002100407,0.0006968995,0.00008788286,0.008946141,0.00028680096,0.96688086,0.004830928,0.00621755,0.0009405346],"about_ca_topic_score_codex":0.000007290029,"about_ca_topic_score_gemma":2.0715221e-7,"teacher_disagreement_score":0.9220485,"about_ca_system_score_codex":0.000069633235,"about_ca_system_score_gemma":0.000056095956,"threshold_uncertainty_score":0.5977656},"labels":[],"label_agreement":null},{"id":"W2614953272","doi":"10.1016/j.saa.2017.05.038","title":"An improved partial least-squares regression method for Raman spectroscopy","year":2017,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Partial least squares regression; Sorting; Feature selection; Mathematics; Regression analysis; Genetic algorithm; Limit (mathematics); Statistics; Mean squared error; Selection (genetic algorithm); Regression; Computer science; Algorithm; Artificial intelligence; Mathematical optimization","score_opus":0.014301276346361138,"score_gpt":0.3345831321511023,"score_spread":0.32028185580474117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614953272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64845264,0.0019298317,0.33372748,0.00371642,0.0005156574,0.001209383,0.0003146184,0.00071797613,0.009415977],"genre_scores_gemma":[0.90483123,0.0003067918,0.09235928,0.00047172882,0.0009083921,0.00023041852,0.00029295642,0.00022838275,0.00037079348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99422556,0.00012520008,0.00087255525,0.0022481096,0.00066954014,0.0018590366],"domain_scores_gemma":[0.99497145,0.000097638214,0.0008719757,0.003038316,0.00018668271,0.00083391654],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005054093,0.0012002729,0.0013414721,0.00038357064,0.0019139166,0.0014242589,0.0017778287,0.0005840295,0.00071195856],"category_scores_gemma":[0.0003527446,0.0010935607,0.0007508115,0.00032007686,0.0005681107,0.00057690457,0.0003591097,0.0007429579,0.000016200307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008844641,0.0004547608,0.0004677328,0.00014919175,0.00048478166,0.00008344387,0.00008915434,0.0000013191549,0.99367845,0.0026365847,0.00060607237,0.00046401602],"study_design_scores_gemma":[0.0027802426,0.00096165534,0.00020804672,0.00007716727,0.0009478408,0.00011101341,0.000155063,0.0035058113,0.98208046,0.0032686698,0.0046075154,0.0012964929],"about_ca_topic_score_codex":0.00018996009,"about_ca_topic_score_gemma":0.0000692598,"teacher_disagreement_score":0.2563786,"about_ca_system_score_codex":0.0002235417,"about_ca_system_score_gemma":0.00021148189,"threshold_uncertainty_score":0.99961233},"labels":[],"label_agreement":null},{"id":"W2618241291","doi":"","title":"A robust statistical framework for QTL analysis","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.024928479686809855,"score_gpt":0.2808257557398236,"score_spread":0.25589727605301377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618241291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023657847,0.0006747802,0.9418183,0.0027342972,0.00010698002,0.00018233735,0.0007080051,0.0002566984,0.029860765],"genre_scores_gemma":[0.46077105,0.00024038344,0.52612406,0.00007923589,0.000082679726,0.00017797056,0.002503158,0.000065057924,0.009956368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967374,0.0005853708,0.0006320333,0.0010920033,0.0004552667,0.0004978896],"domain_scores_gemma":[0.98989725,0.0048433966,0.0006061423,0.0027072493,0.0016687083,0.00027727275],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002213491,0.00043305397,0.00079525437,0.0004153349,0.0003575561,0.0004657665,0.0014522137,0.0009253524,0.0043769567],"category_scores_gemma":[0.006144471,0.00046135724,0.0006194409,0.0010633797,0.0002944382,0.00005881727,0.0008487596,0.0016109707,0.000028404902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011691218,0.0031699578,0.017751332,0.0018903911,0.008001227,0.000018118972,0.005953737,0.0010131827,0.016339017,0.91173494,0.005897222,0.028113991],"study_design_scores_gemma":[0.0013963428,9.164715e-7,0.0034487776,0.0013199694,0.011598956,0.000011296146,0.0005788339,0.13401255,0.6567102,0.16257972,0.025628727,0.0027136754],"about_ca_topic_score_codex":0.0007799713,"about_ca_topic_score_gemma":0.0010956314,"teacher_disagreement_score":0.74915516,"about_ca_system_score_codex":0.00013819907,"about_ca_system_score_gemma":0.00025903631,"threshold_uncertainty_score":0.9997838},"labels":[],"label_agreement":null},{"id":"W2619070203","doi":"","title":"ACOUSTICAL STUDIES OF ZINC ACETATE AND ITS STATISTICAL INTERPRETATION","year":2017,"lang":"en","type":"article","venue":"International Education and Research Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Molecule; Compressibility; Aqueous solution; Chemistry; Ultrasonic sensor; Chemical physics; Volume (thermodynamics); Computational chemistry; Thermodynamics; Physical chemistry; Organic chemistry; Physics; Acoustics","score_opus":0.12385953300410613,"score_gpt":0.5318461280224077,"score_spread":0.4079865950183016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619070203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792462,0.004389958,0.000768443,0.0041456693,0.00045426213,0.00003664157,0.000027147209,0.0000064655787,0.01092523],"genre_scores_gemma":[0.9918609,0.0034058178,0.00094015535,0.000027716613,0.00028453092,0.00000545012,0.000005878388,0.000005659677,0.0034638422],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990157,0.000033796965,0.00022840797,0.00013827319,0.00044489262,0.00013893032],"domain_scores_gemma":[0.9981123,0.00033531417,0.00015169282,0.000096410084,0.0011695452,0.00013471613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005477281,0.00006793147,0.00013350301,0.00022242515,0.000400638,0.00024426213,0.00023871702,0.000046219415,0.000830472],"category_scores_gemma":[0.0050394484,0.00005436804,0.00002410777,0.000045274697,0.00037476947,0.00025199828,0.00013177177,0.000373218,0.0000070831024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012095261,0.002073366,0.061303258,0.0010959689,0.0032624868,0.00007351796,0.009661237,0.00002011276,0.2511154,0.05502605,0.026165362,0.5889937],"study_design_scores_gemma":[0.0070596864,0.0008931832,0.18672545,0.0022820556,0.0009358319,0.0037705891,0.08271977,0.040152896,0.37280366,0.2738948,0.027190253,0.0015718311],"about_ca_topic_score_codex":0.000016688387,"about_ca_topic_score_gemma":0.0000030443882,"teacher_disagreement_score":0.5874219,"about_ca_system_score_codex":0.000076625605,"about_ca_system_score_gemma":0.00019145482,"threshold_uncertainty_score":0.9093086},"labels":[],"label_agreement":null},{"id":"W2620378345","doi":"10.1177/0003702817709299","title":"A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products","year":2017,"lang":"en","type":"review","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":239,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"P&P Optica (Canada); Agriculture and Agri-Food Canada","funders":"","keywords":"Processed meat; Quality (philosophy); Meat packing industry; Near-infrared spectroscopy; Quality assurance; Biochemical engineering; Process engineering; Food science; Business; Environmental science; Computer science; Chemistry; Marketing; Engineering; Physics","score_opus":0.061225739210338775,"score_gpt":0.3391597857062665,"score_spread":0.2779340464959277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620378345","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011160974,0.97301817,0.00009214588,0.0001371823,0.000043901997,0.0022424508,0.00036953273,0.00006358783,0.023921432],"genre_scores_gemma":[0.00009519103,0.99283016,0.0036837303,0.00013994217,0.00034733542,0.00076669065,0.00012270182,0.000118291035,0.00189597],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.996125,0.000049159633,0.0014387978,0.001250427,0.0005328261,0.00060378126],"domain_scores_gemma":[0.9944525,0.00019656362,0.0021066586,0.0028456012,0.00016477267,0.00023393071],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003156965,0.00090441044,0.0037356403,0.00021564603,0.00038235798,0.0001302536,0.0014541205,0.00038270553,0.0002770109],"category_scores_gemma":[0.00031304537,0.0006570963,0.00044081974,0.0010944611,0.00066755165,0.00008237118,0.0007285416,0.0007080848,0.000018302095],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108793414,0.0005920205,0.00020529404,0.7301327,0.0022601325,0.0000044682124,0.00026225572,1.2616137e-7,0.14465365,0.012802692,0.0058371658,0.10314069],"study_design_scores_gemma":[0.0002140856,0.00003754548,0.000030060432,0.01952963,0.0028136906,0.000025376112,0.000017268949,3.3811145e-7,0.1504432,0.00017975994,0.82617825,0.0005307769],"about_ca_topic_score_codex":0.00003753816,"about_ca_topic_score_gemma":0.000008357396,"teacher_disagreement_score":0.8203411,"about_ca_system_score_codex":0.00012259814,"about_ca_system_score_gemma":0.00045136537,"threshold_uncertainty_score":0.999588},"labels":[],"label_agreement":null},{"id":"W2621140902","doi":"10.1007/s11222-017-9758-2","title":"Supervised functional principal component analysis","year":2017,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Functional principal component analysis; Principal component analysis; Functional data analysis; Computer science; Independent component analysis; Regression analysis; Regression; Artificial intelligence; Variable (mathematics); Principal component regression; Pattern recognition (psychology); Linear regression; Component (thermodynamics); Machine learning; Mathematics; Statistics","score_opus":0.02890103905285795,"score_gpt":0.29445801474683597,"score_spread":0.265556975693978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621140902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.752703,0.00017282098,0.23642078,0.000067835295,0.00006907814,0.000018253542,0.00012575768,0.00004017554,0.010382305],"genre_scores_gemma":[0.9899752,0.000018443483,0.0091461195,0.000032146003,0.0001290014,8.2977e-7,0.00008500245,0.0000074113964,0.0006058428],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99923193,0.0000053697863,0.00017655222,0.00023293355,0.00016586285,0.00018733359],"domain_scores_gemma":[0.9993123,0.000110653695,0.00014512875,0.0002863563,0.00006132762,0.00008426726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008297245,0.00011077506,0.00022654737,0.00007698051,0.0007216146,0.00020822979,0.0001531422,0.000040351064,0.00081299344],"category_scores_gemma":[0.00008454977,0.00010693548,0.00005830891,0.000096447846,0.000093093804,0.00003198324,0.00014697699,0.00011396883,0.000006591989],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000670075,0.0002681547,0.8896593,0.0002568217,0.00417467,0.00007860962,0.00041079483,0.001251873,0.013858811,0.0516281,0.0014579364,0.036887944],"study_design_scores_gemma":[0.0010212046,0.00002913917,0.54490364,0.000016105201,0.0024641065,0.000011132975,0.0003255229,0.4412401,0.005890371,0.0023530924,0.0012241682,0.00052139733],"about_ca_topic_score_codex":0.00013910697,"about_ca_topic_score_gemma":0.000020747335,"teacher_disagreement_score":0.43998823,"about_ca_system_score_codex":0.000022389957,"about_ca_system_score_gemma":0.000017590832,"threshold_uncertainty_score":0.8901708},"labels":[],"label_agreement":null},{"id":"W2623069288","doi":"10.1007/s11694-017-9573-7","title":"Determination of total phenolic content and antioxidant capacity of blueberries using Fourier transformed infrared (FT-IR) spectroscopy and Raman spectroscopy","year":2017,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Oxygen radical absorbance capacity; Chemistry; DPPH; Raman spectroscopy; Absorbance; Partial least squares regression; Trolox; Analytical Chemistry (journal); Fourier transform infrared spectroscopy; Antioxidant; Chemometrics; Spectroscopy; Infrared spectroscopy; Chromatography; Organic chemistry; Mathematics; Chemical engineering","score_opus":0.07909947734907757,"score_gpt":0.2757762487625119,"score_spread":0.19667677141343431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623069288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98506635,0.00022819555,0.014166729,0.0000954528,0.00010743096,0.00011641446,0.000040929986,0.0000071287363,0.00017133981],"genre_scores_gemma":[0.99513876,0.00032519508,0.004339181,0.00001104871,0.00014006204,0.0000019044294,0.0000062940608,0.00001877467,0.000018764405],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803483,0.000031777654,0.00084071513,0.00018459478,0.0006953684,0.00021270638],"domain_scores_gemma":[0.9969893,0.00002215795,0.0020241358,0.00024538452,0.0006117218,0.0001072913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045728192,0.00022008618,0.0006123708,0.0002523436,0.00019485361,0.0001069939,0.0001367111,0.00014532392,0.000044806682],"category_scores_gemma":[0.00029283052,0.000199947,0.0001278173,0.00011282246,0.00021722837,0.00072476687,0.00002864143,0.00022664387,7.458972e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024036532,0.00015906461,0.011383996,0.0003086636,0.00021520632,0.0000021006922,0.00061931205,7.0418844e-7,0.98654133,0.000049220223,0.0000014958865,0.00047852268],"study_design_scores_gemma":[0.0013980182,0.00039674493,0.10726616,0.00027280865,0.00046295702,0.00006082333,0.00022899144,0.00031269516,0.88928235,0.00015881662,0.000008547729,0.00015108203],"about_ca_topic_score_codex":0.000022059616,"about_ca_topic_score_gemma":0.000013252842,"teacher_disagreement_score":0.09725899,"about_ca_system_score_codex":0.00015516196,"about_ca_system_score_gemma":0.0001275596,"threshold_uncertainty_score":0.8153602},"labels":[],"label_agreement":null},{"id":"W2626942767","doi":"10.17713/ajs.v27i1&2.530","title":"Higher Order Cumulants and Inference for a Class of Filtered Poisson Processes","year":2016,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mathematics; Cumulant; Estimator; Poisson distribution; Point process; Applied mathematics; Gaussian; Bootstrapping (finance); Statistical physics; Algorithm; Statistics; Econometrics; Physics","score_opus":0.2619669503074761,"score_gpt":0.5566710602753584,"score_spread":0.2947041099678823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626942767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796605,0.013437243,0.0010344008,0.00028836017,0.000102380545,0.00020399128,0.00022110387,0.000034899338,0.0050171264],"genre_scores_gemma":[0.9887472,0.0072236187,0.00039049098,0.00008031302,0.000112468,0.00004600004,0.000008645501,0.000038354992,0.0033529464],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981343,0.000029823312,0.00067348545,0.0004101678,0.00040423355,0.00034796263],"domain_scores_gemma":[0.99700975,0.00093342375,0.0008482489,0.0003699127,0.0006614883,0.00017715705],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028809777,0.00028300617,0.000782719,0.00042407995,0.000112447175,0.0002972341,0.0013939298,0.00014974132,0.016595509],"category_scores_gemma":[0.0012626386,0.00019708065,0.000106793486,0.0009782292,0.00018018136,0.001121219,0.00040813177,0.00014541895,0.0000033574152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003252072,0.00022813,0.32821697,0.0007567859,0.00034518327,0.00000434604,0.00006349496,0.0000031970737,0.6567394,0.00007822296,0.008363198,0.004875855],"study_design_scores_gemma":[0.0012440229,0.00001593098,0.061996188,0.00065311254,0.00024537038,0.0000052196415,0.000058379846,0.000014088242,0.92401016,0.0054634498,0.0059186835,0.0003753946],"about_ca_topic_score_codex":0.00024614198,"about_ca_topic_score_gemma":0.00002815822,"teacher_disagreement_score":0.26727074,"about_ca_system_score_codex":0.000053606054,"about_ca_system_score_gemma":0.00017581531,"threshold_uncertainty_score":0.9843035},"labels":[],"label_agreement":null},{"id":"W2626947650","doi":"","title":"Detection of fungal infection in pulses using near infrared (NIR) hyperspectral imaging","year":2015,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Quadratic classifier; Linear discriminant analysis; Penicillium; Aspergillus; Biology; Pattern recognition (psychology); Artificial intelligence; Mathematics; Microbiology; Food science; Computer science; Support vector machine","score_opus":0.01698208621944436,"score_gpt":0.24604593024232035,"score_spread":0.229063844022876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626947650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896525,0.0005111003,0.00011595381,0.000011373653,0.0001339817,0.000063937456,0.000018662444,0.00004647104,0.009445978],"genre_scores_gemma":[0.99846774,0.00013483985,0.00050863775,0.0000016880311,0.0000577347,2.2433207e-7,0.00010955145,0.00002532938,0.0006942788],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893546,0.000027024711,0.00017571278,0.00030628126,0.0003274648,0.00022803606],"domain_scores_gemma":[0.99896264,0.000032428903,0.0004603304,0.00023205999,0.00024984457,0.000062701474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001085877,0.00022155263,0.00046085264,0.00058652787,0.00012591333,0.000021169384,0.00019415855,0.00029369624,0.00006884387],"category_scores_gemma":[0.000072611496,0.0003174045,0.00018230136,0.0007980785,0.00009005012,0.00028245934,0.00003983439,0.00038367018,0.000005391999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007453928,0.00032412683,0.42935076,0.0012368829,0.0003482553,0.000068514266,0.0015640808,0.0005838034,0.5634099,0.000019634814,0.00023113431,0.0021175013],"study_design_scores_gemma":[0.0024193132,0.00012994219,0.30367678,0.000539566,0.0017416363,0.000032877084,0.11625163,0.0082704425,0.5649499,0.0004372764,0.0005186546,0.0010319598],"about_ca_topic_score_codex":0.018708972,"about_ca_topic_score_gemma":0.14521685,"teacher_disagreement_score":0.1265079,"about_ca_system_score_codex":0.0005933811,"about_ca_system_score_gemma":0.00019110614,"threshold_uncertainty_score":0.9999278},"labels":[],"label_agreement":null},{"id":"W2729489092","doi":"","title":"On Testing for Serial Correlation with a Wavelet-Based Spectral Density Estimator in Multivariate Time Series","year":2005,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Autocorrelation; Estimator; Multivariate statistics; Multivariate kernel density estimation; Wavelet; Statistics; Series (stratigraphy); Smoothing; Econometrics; Variable kernel density estimation; Kernel method; Artificial intelligence; Computer science","score_opus":0.02217163785892677,"score_gpt":0.2986555542493748,"score_spread":0.276483916390448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729489092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9820905,0.0000061672536,0.00006316251,0.00018793168,0.000018447368,0.0002076094,0.000020845833,0.000045483263,0.017359879],"genre_scores_gemma":[0.97611094,0.00000756701,0.02213734,0.000032527903,0.00016423792,0.00007049445,0.000038847378,0.000036550224,0.0014015279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985168,0.000031016647,0.00031620034,0.00044841543,0.00014448853,0.0005430572],"domain_scores_gemma":[0.998383,0.0010707136,0.0000995626,0.00028355425,0.00006991562,0.00009328078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045670848,0.00017785326,0.0003008469,0.00028753668,0.00014353154,0.000061652834,0.00016940084,0.0001496022,0.00023726466],"category_scores_gemma":[0.0011888482,0.00017543748,0.000052723826,0.00030503937,0.000119103875,0.00014989474,0.000033416924,0.000423562,0.000014391535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01847294,0.0028376859,0.22484265,0.0006698233,0.00043952503,0.00018441347,0.0009129525,0.30469537,0.28896368,0.0027541437,0.00019867138,0.15502815],"study_design_scores_gemma":[0.0066465405,0.00069772324,0.018459925,0.00020785433,0.000038565802,0.000024803816,0.00039199227,0.73231614,0.23935455,0.0006667673,0.0004550499,0.000740116],"about_ca_topic_score_codex":0.00006914665,"about_ca_topic_score_gemma":0.0003035316,"teacher_disagreement_score":0.42762074,"about_ca_system_score_codex":0.00087672233,"about_ca_system_score_gemma":0.000256744,"threshold_uncertainty_score":0.71541333},"labels":[],"label_agreement":null},{"id":"W2734766137","doi":"","title":"Sequential determination of sample size for robust linear regression: application to microarray experimental design","year":2006,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Linear regression; Sample size determination; Computer science; Sample (material); Regression analysis; Statistics; Artificial intelligence; Mathematics; Machine learning; Chromatography","score_opus":0.029450211040082178,"score_gpt":0.2885029463104181,"score_spread":0.25905273527033595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734766137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03720096,0.00058437657,0.9565619,0.00076394377,0.00007053354,0.0005439523,0.00025570177,0.00013583526,0.0038827965],"genre_scores_gemma":[0.41028747,0.000037204536,0.5847524,0.000028801314,0.00006682487,0.00035478,0.00083747524,0.000052360847,0.003582714],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731296,0.000538735,0.0006684994,0.00080514245,0.00036733737,0.00030730173],"domain_scores_gemma":[0.9937361,0.0025281797,0.0007520954,0.001447284,0.0014022129,0.00013412094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014883002,0.00035438393,0.00048134426,0.0001964666,0.00023922675,0.000118346354,0.00093282043,0.00039535938,0.00033616583],"category_scores_gemma":[0.0018937248,0.00038062295,0.00028555116,0.0004043414,0.00013490327,0.000075130345,0.00052366906,0.00030261275,0.0000068614236],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010802178,0.0011630041,0.00063108956,0.0005457055,0.000104225524,9.2565574e-7,0.0022343085,0.0010375925,0.9771488,0.0011001457,0.0017253547,0.014200872],"study_design_scores_gemma":[0.0004506758,8.666081e-7,0.000057151483,0.0004039875,0.00012026003,0.0000022644085,0.000102892205,0.024571262,0.97182506,0.0007268493,0.0014042504,0.00033450246],"about_ca_topic_score_codex":0.001129367,"about_ca_topic_score_gemma":0.00014569426,"teacher_disagreement_score":0.37308648,"about_ca_system_score_codex":0.00026599984,"about_ca_system_score_gemma":0.00019416217,"threshold_uncertainty_score":0.9998646},"labels":[],"label_agreement":null},{"id":"W2735578862","doi":"10.1002/cjce.22931","title":"Long‐term monitoring of metalworking fluid emulsion aging using a spectroscopic sensor","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade de São Paulo","keywords":"Emulsion; Metalworking; Process engineering; Environmental science; Materials science; Machining; Chemical engineering; Engineering; Metallurgy","score_opus":0.025279086439614663,"score_gpt":0.27229322962620767,"score_spread":0.247014143186593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735578862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973006,0.0016450904,0.00042515688,0.0001192298,0.00021486118,0.000019309251,0.0000021026115,0.000009164943,0.0002644561],"genre_scores_gemma":[0.9977425,0.000012857065,0.0014237299,0.0000048894653,0.0007380342,4.5693764e-7,4.607787e-7,0.000029109367,0.000047999147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884206,0.0000052852715,0.00040637437,0.00011900027,0.00023995165,0.00038735318],"domain_scores_gemma":[0.9987064,0.00007837716,0.0004067926,0.0003977546,0.00010048464,0.0003102086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017615083,0.00017745014,0.00038108483,0.00019236583,0.00023510346,0.00014777393,0.0006804013,0.00010063539,0.000127409],"category_scores_gemma":[0.00039219265,0.00014526251,0.00019792994,0.00014756311,0.00010337099,0.0001646006,0.000044001452,0.00048785572,0.0000010043319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055108485,0.000004907319,0.02975728,0.00008016025,0.00014789391,0.000058013455,0.00010869215,0.0009008193,0.9687525,0.000021187978,0.000002574802,0.00016042957],"study_design_scores_gemma":[0.00023964865,0.000006661323,0.0016387309,0.0003794994,0.00023949628,0.00012475968,0.000030017574,0.0011228118,0.9960351,0.000029727978,0.00001367616,0.00013989881],"about_ca_topic_score_codex":0.00077483477,"about_ca_topic_score_gemma":0.000019638828,"teacher_disagreement_score":0.02811855,"about_ca_system_score_codex":0.00034524486,"about_ca_system_score_gemma":0.00014756637,"threshold_uncertainty_score":0.59236336},"labels":[],"label_agreement":null},{"id":"W2738348898","doi":"10.1021/acs.analchem.7b02400","title":"Hadamard-Transform Fluorescence Excitation-Emission-Matrix Spectroscopy","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gastops (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Fluorescence; Excitation; Analytical Chemistry (journal); Spectrometer; Fluorescence spectroscopy; Emission spectrum; Hadamard transform; Spectroscopy; Spectral line; Optics; Physics","score_opus":0.016452962840720075,"score_gpt":0.3253155938839988,"score_spread":0.3088626310432787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738348898","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42146334,0.00041605343,0.0007350925,0.002529592,0.00006784672,0.000061568804,0.000048156653,0.00026167926,0.57441664],"genre_scores_gemma":[0.95493454,0.00014039742,0.0010776126,0.000059497404,0.00047751112,0.000012988846,0.000050296883,0.000040885243,0.04320628],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976212,0.000004972432,0.00046501495,0.00067691976,0.0005676512,0.0006642443],"domain_scores_gemma":[0.9976186,0.00016049929,0.0002471315,0.0013950741,0.00013130895,0.0004473687],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014221441,0.00038014463,0.00050233846,0.000048624584,0.0007400055,0.0003525508,0.0011697679,0.00033800633,0.012353671],"category_scores_gemma":[0.000953075,0.00035480328,0.00034314973,0.00020902768,0.0004359568,0.0002882533,0.00013765026,0.0005771628,0.00015872478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009219794,0.0002717231,0.021628976,0.0004369912,0.00031748266,0.00010379598,0.00005027672,0.000003926695,0.96599656,0.0005933319,0.009939351,0.0005654122],"study_design_scores_gemma":[0.00057201926,0.000011871391,0.0009656626,0.00006201376,0.0003626081,0.000017259508,0.00017806221,0.0019329508,0.9874556,0.0027602883,0.0051751547,0.000506474],"about_ca_topic_score_codex":0.0000677384,"about_ca_topic_score_gemma":0.0000023216699,"teacher_disagreement_score":0.53347117,"about_ca_system_score_codex":0.0001886297,"about_ca_system_score_gemma":0.00014662604,"threshold_uncertainty_score":0.9998904},"labels":[],"label_agreement":null},{"id":"W2742879289","doi":"10.1017/s1431927617006699","title":"Determining the Number of Components for Multivariate Curve Resolution: Case Study Using Raman Mapping of Pharmaceutical Tablets","year":2017,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"","keywords":"Library science; Art history; History; Classics; Computer science","score_opus":0.09389399733659257,"score_gpt":0.4052037097177047,"score_spread":0.31130971238111216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742879289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99605477,0.0003469147,0.0031480303,0.000027216734,0.000037261358,0.00015637526,0.00009096906,0.0000098617675,0.00012862924],"genre_scores_gemma":[0.99568063,0.000027388813,0.004024062,0.000018639914,0.000050431772,0.000011151362,0.0000137807765,0.000019264458,0.00015462942],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985727,0.0000468349,0.0005575337,0.00037323558,0.00014483773,0.00030486958],"domain_scores_gemma":[0.9982596,0.00021182334,0.00065418717,0.0006158904,0.00018858607,0.00006988506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041416808,0.00022177931,0.0006153019,0.000115109775,0.0009223904,0.000104174964,0.00036757905,0.000098388395,0.00013665431],"category_scores_gemma":[0.00013788404,0.00017805239,0.00023517896,0.00021807296,0.00032968054,0.0001228013,0.00022741912,0.00016477819,7.894301e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006931159,0.00032052782,0.2590269,0.0001459729,0.00096672104,0.00002664301,0.0007009207,0.000010019064,0.7383994,0.0000044889384,0.000018448127,0.0003106383],"study_design_scores_gemma":[0.0035427082,0.000032969703,0.0056329276,0.000076739,0.004770793,0.00020739959,0.0053337384,0.009157799,0.97054607,0.000030390795,0.00031583794,0.0003526012],"about_ca_topic_score_codex":0.0018751548,"about_ca_topic_score_gemma":0.000046729943,"teacher_disagreement_score":0.253394,"about_ca_system_score_codex":0.000045994402,"about_ca_system_score_gemma":0.000027735941,"threshold_uncertainty_score":0.72607654},"labels":[],"label_agreement":null},{"id":"W2745255717","doi":"10.1093/fqsafe/fyx021","title":"Quantification of browning in apples using colour and textural features by image analysis","year":2017,"lang":"en","type":"article","venue":"Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Browning; Hue; Artificial intelligence; Digital image analysis; Image processing; Computer vision; Mathematics; Computer science; Horticulture; Biology; Image (mathematics)","score_opus":0.0631451605205623,"score_gpt":0.36900531518436275,"score_spread":0.30586015466380045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745255717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963316,0.0013568373,0.0010536575,0.00020524664,0.0000054015845,0.000028077791,0.00012829593,0.000009425731,0.0008814747],"genre_scores_gemma":[0.99905175,0.00015806014,0.0005909082,0.000013161625,0.000014571537,0.0000015311815,0.000040540912,0.000004280395,0.00012520958],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99921113,0.000032094173,0.0002906126,0.00022649378,0.00011334212,0.00012630623],"domain_scores_gemma":[0.9992723,0.00012041486,0.00029450093,0.00023824125,0.000034311317,0.000040251725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032961552,0.000098675046,0.00033994974,0.00008965629,0.00024473784,0.00007358707,0.000120601646,0.000099722085,0.00005538382],"category_scores_gemma":[0.00019081128,0.000091418435,0.00006549417,0.00019342922,0.00017353274,0.00013610415,0.000057022793,0.0001152973,1.7347904e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017296105,0.000093943396,0.24172164,0.0002660115,0.0005310219,7.4496126e-7,0.0005087223,0.000018207153,0.7542937,0.0014363326,0.00004268965,0.0009140285],"study_design_scores_gemma":[0.00061916024,0.000020090725,0.61176974,0.000021179341,0.00058028393,0.0000019732925,0.0017622429,0.0005481051,0.3840202,0.00036876003,0.000077362834,0.0002108963],"about_ca_topic_score_codex":0.0012117962,"about_ca_topic_score_gemma":0.00035834528,"teacher_disagreement_score":0.37027347,"about_ca_system_score_codex":0.000021654354,"about_ca_system_score_gemma":0.000012112988,"threshold_uncertainty_score":0.37279356},"labels":[],"label_agreement":null},{"id":"W2747848761","doi":"10.1016/j.aca.2017.08.021","title":"Application of a sparseness constraint in multivariate curve resolution – Alternating least squares","year":2017,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"FP7 Ideas: European Research Council; Agence Nationale de la Recherche; Government of Canada; European Research Council; Seventh Framework Programme; Irene W. and C.B. Pennington Foundation","keywords":"Chemistry; Multivariate statistics; Constraint (computer-aided design); Resolution (logic); Least-squares function approximation; Statistics; Artificial intelligence; Geometry","score_opus":0.02847695653012741,"score_gpt":0.3167451960098837,"score_spread":0.2882682394797563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747848761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766645,0.000015104281,0.00048462529,0.00044476922,0.000019852188,0.00007243751,0.000034424054,0.000034254484,0.022230044],"genre_scores_gemma":[0.99913317,0.000030171308,0.00051496766,0.000015943335,0.00008312195,0.000012969823,0.000034784483,0.000015520847,0.0001593391],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872166,0.000016741587,0.00041497755,0.00035756992,0.00021745615,0.00027159505],"domain_scores_gemma":[0.99845433,0.00012137577,0.00052410125,0.0007525765,0.00007940923,0.000068222835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018997985,0.00016452848,0.00035042333,0.00017045221,0.0001580017,0.000065598935,0.0005258487,0.00011818097,0.00017458887],"category_scores_gemma":[0.00054895884,0.0001625799,0.00011167815,0.00018762794,0.00022152298,0.00015805538,0.00011957274,0.00021183833,0.000011860007],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008089594,0.00030798698,0.12783574,0.00012942964,0.00018126702,0.0000055502633,0.00015460467,0.000034302288,0.8674123,0.0029171861,0.000049937706,0.00089074805],"study_design_scores_gemma":[0.0019954122,0.000039751598,0.12445858,0.00020559436,0.00040428198,0.000015174714,0.00073966786,0.17311893,0.6958411,0.0021786205,0.00039923357,0.0006036999],"about_ca_topic_score_codex":0.00095278723,"about_ca_topic_score_gemma":0.0001626349,"teacher_disagreement_score":0.17308463,"about_ca_system_score_codex":0.00009432417,"about_ca_system_score_gemma":0.00004829026,"threshold_uncertainty_score":0.6629816},"labels":[],"label_agreement":null},{"id":"W2748837252","doi":"10.1021/acs.iecr.7b01849","title":"Self-Modeling Multivariate Curve Resolution Model for Online Monitoring of Bitumen Conversion Using Infrared Spectroscopy","year":2017,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Infrared spectroscopy; Resolution (logic); Biological system; Chemometrics; Computer science; Infrared; Spectral line; Asphalt; Spectroscopy; Process (computing); Process engineering; Chemistry; Analytical Chemistry (journal); Materials science; Artificial intelligence; Machine learning; Chromatography; Optics; Engineering","score_opus":0.22426998141725327,"score_gpt":0.41593468929483957,"score_spread":0.1916647078775863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748837252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934319,0.00018946166,0.0049445857,0.00004090868,0.00013846901,0.00020231023,0.00015302064,0.00013750946,0.0007618361],"genre_scores_gemma":[0.98714745,0.000062344116,0.010406092,6.8336396e-7,0.0014189245,0.0000283569,0.000060527975,0.00006903687,0.0008065736],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99744934,0.000013833173,0.00053205783,0.0005341354,0.00066556194,0.0008050814],"domain_scores_gemma":[0.9979582,0.00023551213,0.00025453596,0.0008863086,0.00045465206,0.0002107996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075570284,0.00029679286,0.00048367554,0.00018933488,0.0005239731,0.00016046414,0.0008635096,0.0005773638,0.000056615856],"category_scores_gemma":[0.001953384,0.00033458232,0.0001804823,0.000311629,0.00009447359,0.00027315927,0.00032962536,0.0011044173,0.0000011757484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000159414,0.00012137393,0.00097373297,0.00045028195,0.00014934913,0.0000031982322,0.000065173364,0.10186445,0.8961384,0.000007804077,0.000042435422,0.000024334422],"study_design_scores_gemma":[0.0009777419,0.000011737745,0.0000039638217,0.00013261003,0.00005641614,0.000001517007,0.00007689044,0.49892777,0.49956557,0.000024459736,0.00007071047,0.00015061679],"about_ca_topic_score_codex":0.00024388179,"about_ca_topic_score_gemma":3.6699205e-7,"teacher_disagreement_score":0.39706331,"about_ca_system_score_codex":0.00068876555,"about_ca_system_score_gemma":0.00035436175,"threshold_uncertainty_score":0.9999106},"labels":[],"label_agreement":null},{"id":"W2749331433","doi":"10.1016/b978-0-444-53868-0.50011-3","title":"Canonical analysis","year":2012,"lang":"en","type":"book-chapter","venue":"Developments in environmental modelling","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Canonical correlation; Correspondence analysis; Linear discriminant analysis; Canonical analysis; Mathematics; Canonical correspondence analysis; Multiple correspondence analysis; Canonical form; Algebra over a field; Statistics; Pure mathematics","score_opus":0.025182963047354665,"score_gpt":0.2277397389401757,"score_spread":0.20255677589282106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749331433","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010308574,0.005489072,0.004633604,0.000008539954,0.00007126773,0.000090014895,0.00011020085,0.00006093357,0.9792278],"genre_scores_gemma":[0.6304278,0.0027415445,0.004220661,0.00005710932,0.00009540249,0.000015710939,0.0006566208,0.00009041407,0.36169472],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976853,0.000004573361,0.0006443532,0.0006365893,0.000513282,0.0005159136],"domain_scores_gemma":[0.9991439,0.00004997362,0.00023184782,0.00040111638,0.000003499393,0.00016966253],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011116922,0.00050933537,0.00073353166,0.00057430967,0.00008324751,0.000022860348,0.00034003775,0.0005603331,0.015351044],"category_scores_gemma":[0.0000022732415,0.0005622117,0.00029055995,0.00013401621,0.000103270395,0.000090567744,0.00019823405,0.00065020303,0.00037151325],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062265334,0.004172074,0.4759742,0.0013038418,0.07683577,0.0011925121,0.0073941494,0.30384263,0.013231483,0.04512856,0.0008429247,0.069459215],"study_design_scores_gemma":[0.0056848307,0.000076186916,0.005343766,0.0008401197,0.039139036,0.00009293552,0.00082898926,0.041932985,0.060784433,0.022778066,0.80479866,0.017700007],"about_ca_topic_score_codex":0.000027516968,"about_ca_topic_score_gemma":0.000012708382,"teacher_disagreement_score":0.80395573,"about_ca_system_score_codex":0.0011550398,"about_ca_system_score_gemma":0.000046025656,"threshold_uncertainty_score":0.99968296},"labels":[],"label_agreement":null},{"id":"W2758158523","doi":"10.1007/s00216-017-0628-8","title":"Estimation of start and stop numbers for cluster resolution feature selection algorithm: an empirical approach using null distribution analysis of Fisher ratios","year":2017,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Natural Sciences and Engineering Research Council of Canada; Cystic Fibrosis Foundation; Innovative Research Group Project of the National Natural Science Foundation of China; Genome Canada; Genome Alberta","keywords":"Feature selection; Mathematics; Range (aeronautics); Principal component analysis; Algorithm; Selection (genetic algorithm); Feature (linguistics); Statistics; Cluster (spacecraft); Constant (computer programming); Computer science; Pattern recognition (psychology); Artificial intelligence","score_opus":0.03474418780196063,"score_gpt":0.3373741921611618,"score_spread":0.3026300043592012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758158523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8626695,0.00010881083,0.13573636,0.0002809187,0.000008662901,0.00009370781,0.00029947207,0.000025199684,0.0007773103],"genre_scores_gemma":[0.9890894,0.000021610567,0.009621596,0.000021818361,0.000091666276,0.0000059622157,0.0006593749,0.000012717414,0.00047581588],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983558,0.0000216634,0.0004702489,0.0005444754,0.0003169487,0.00029086924],"domain_scores_gemma":[0.99867696,0.00016127327,0.00033788357,0.0003825451,0.00022126993,0.00022007471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032566793,0.0002436973,0.0006906946,0.000094994095,0.00027513938,0.000106816384,0.000173987,0.00040452922,0.0001164985],"category_scores_gemma":[0.0005253198,0.00021154605,0.00025662992,0.0005160319,0.00055599224,0.00024330555,0.00007887952,0.00023023682,1.438296e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004246987,0.0071380106,0.7040701,0.008239085,0.030062241,0.000013394669,0.0008756595,0.012286363,0.18298885,0.003314413,0.0038615714,0.042903345],"study_design_scores_gemma":[0.0005473743,0.000044739383,0.0032991061,0.000018055749,0.0063579693,0.000008165868,0.00022159558,0.946599,0.042448644,0.00017131402,0.000059076545,0.00022495589],"about_ca_topic_score_codex":0.000120717115,"about_ca_topic_score_gemma":0.000009489778,"teacher_disagreement_score":0.93431264,"about_ca_system_score_codex":0.00009819888,"about_ca_system_score_gemma":0.00005326976,"threshold_uncertainty_score":0.86265975},"labels":[],"label_agreement":null},{"id":"W2762884884","doi":"10.1007/s12247-017-9299-4","title":"Calibration Transfer of a Quantitative Transmission Raman PLS Model: Direct Transfer vs. Global Modeling","year":2017,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Innovation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mallinckrodt Pharmaceuticals","keywords":"Calibration; Partial least squares regression; Microcrystalline cellulose; Process analytical technology; Computer science; Chemometrics; Engineering; Mathematics; Statistics; Machine learning; Work in process","score_opus":0.12009216261693546,"score_gpt":0.42032278553592467,"score_spread":0.3002306229189892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762884884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46745172,0.00019006492,0.52931106,0.00058170303,0.000044218694,0.00004046905,0.000021781132,0.000012424252,0.0023465762],"genre_scores_gemma":[0.9971782,0.00019872718,0.002314122,0.00012514679,0.000103497674,0.0000021806463,0.000010442913,0.00001721947,0.00005047947],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99798477,0.000030304293,0.0010481909,0.00018234512,0.0005355561,0.00021884184],"domain_scores_gemma":[0.9988038,0.00005717875,0.0002324227,0.00018277123,0.0006219855,0.00010187545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049834856,0.00019066918,0.00047054992,0.00020948776,0.00018370347,0.00008199759,0.00033525095,0.00017937557,0.0003324319],"category_scores_gemma":[0.00016750357,0.00016087753,0.00020273712,0.0005201238,0.00011146187,0.00077371,0.0000120713485,0.00040443914,7.848432e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017229181,0.00041046538,0.00078118325,0.00026385914,0.00035249678,0.000010300224,0.00035689468,0.027282055,0.9430007,0.021122353,0.000083212035,0.004613615],"study_design_scores_gemma":[0.0012471188,0.0000720088,0.00003019959,0.0000750905,0.0003153388,0.000008892816,0.00006615602,0.48722547,0.5086492,0.0021093441,0.000082542145,0.00011870721],"about_ca_topic_score_codex":0.00001367075,"about_ca_topic_score_gemma":0.0000012684922,"teacher_disagreement_score":0.52972645,"about_ca_system_score_codex":0.00011391812,"about_ca_system_score_gemma":0.0001433445,"threshold_uncertainty_score":0.6560396},"labels":[],"label_agreement":null},{"id":"W2765786590","doi":"10.1111/ijfs.13581","title":"Red–Green–Blue (<scp>RGB</scp>) colour system approach to study the segregation and percolation in a mixture of white wheat flour and bleached wheat bran","year":2017,"lang":"en","type":"article","venue":"International Journal of Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Bran; Wheat flour; Food science; RGB color model; Chemistry; Homogeneous; Mathematics; Materials science; Chromaticity; Raw material; Computer science; Combinatorics; Organic chemistry; Artificial intelligence","score_opus":0.01871752874703773,"score_gpt":0.29055154271301503,"score_spread":0.2718340139659773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765786590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957776,0.00025178146,0.00095864054,0.001757549,0.00009074629,0.0001315405,0.000005513804,0.000015467169,0.0010111347],"genre_scores_gemma":[0.9992544,0.000020863188,0.0005592428,0.000017980958,0.000056428304,0.000009164433,5.16936e-7,0.0000066447656,0.00007476216],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848217,0.0000233955,0.00043828104,0.00026925752,0.00060185324,0.00018504926],"domain_scores_gemma":[0.99851686,0.00007037303,0.0005408337,0.00029416708,0.00051177584,0.00006599991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007908646,0.00013028394,0.00028256417,0.0010484938,0.00034175307,0.0001413949,0.0012365506,0.00013839504,0.0000029795283],"category_scores_gemma":[0.00073512754,0.00009594811,0.00004155697,0.00063980365,0.00042674036,0.00038019122,0.00031188605,0.00035563673,2.935246e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103169405,0.00050282985,0.5228094,0.000054063854,0.00033109495,0.000028708997,0.0076219337,0.00035374338,0.46169963,0.0025748888,0.00003975872,0.0038807706],"study_design_scores_gemma":[0.0044418024,0.0015782209,0.22877821,0.0003536166,0.00032500917,0.002126349,0.08566342,0.008810944,0.6648208,0.0026676322,0.00015272714,0.00028129053],"about_ca_topic_score_codex":0.000044225606,"about_ca_topic_score_gemma":0.00007391238,"teacher_disagreement_score":0.2940312,"about_ca_system_score_codex":0.00018794759,"about_ca_system_score_gemma":0.00009455216,"threshold_uncertainty_score":0.39126503},"labels":[],"label_agreement":null},{"id":"W2765973328","doi":"10.1038/s41598-017-15389-3","title":"Detection and quantification of offal content in ground beef meat using vibrational spectroscopic-based chemometric analysis","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Food science; Food products; Meat packing industry; Processed meat; Chemometrics; Chemistry; Mathematics; Chromatography","score_opus":0.0634048690695288,"score_gpt":0.31290100446969404,"score_spread":0.24949613540016524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765973328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98708403,0.00023190123,0.011327076,0.000044704757,0.00034990624,0.00007265588,0.000003892936,0.000018615438,0.0008672246],"genre_scores_gemma":[0.99819565,0.000004632992,0.0010879933,0.000003836595,0.000036921756,0.0000061088977,0.000042587344,0.000008503313,0.0006137781],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979834,0.000014916882,0.0006105317,0.0006661091,0.00050864316,0.00021641074],"domain_scores_gemma":[0.99783456,0.000053091087,0.00090637757,0.0009315574,0.00020110719,0.00007332629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006555894,0.0001435635,0.00036387332,0.0011842318,0.00045644154,0.00039937202,0.00016370062,0.00010340617,0.00037355625],"category_scores_gemma":[0.00055744784,0.00014205101,0.00014753487,0.0017274376,0.0003505393,0.00024919512,0.00004942668,0.000112033835,0.0000012683593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012941296,0.000083222156,0.25209424,0.000040065326,0.00009183567,0.000012667772,0.000020878935,0.00016994083,0.7472719,0.000025738462,0.0000058213095,0.00017074034],"study_design_scores_gemma":[0.00019357441,0.000008805366,0.13518707,0.000013191143,0.00038053468,0.000010502954,0.00008363419,0.0090644015,0.85460275,0.0003094845,0.000025849107,0.000120189376],"about_ca_topic_score_codex":0.00086327485,"about_ca_topic_score_gemma":0.00039934117,"teacher_disagreement_score":0.116907164,"about_ca_system_score_codex":0.00013291958,"about_ca_system_score_gemma":0.000099332836,"threshold_uncertainty_score":0.5792672},"labels":[],"label_agreement":null},{"id":"W2765975960","doi":"10.1139/cjas-2017-0106","title":"Guaranteeing Canadian lamb meat quality using near-infrared spectroscopy on intact rack","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Agriculture Food and Rural Development; Alberta Crop Industry Development Fund","funders":"Alberta Livestock and Meat Agency","keywords":"Quality (philosophy); Near-infrared spectroscopy; Rack; Mathematics; Food science; Animal science; Environmental science; Biology; Engineering; Physics","score_opus":0.05955062228827517,"score_gpt":0.3507016464700949,"score_spread":0.2911510241818197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765975960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93964255,0.0001776242,0.000027972108,0.00056514476,0.00021621301,0.000027969409,0.000029476709,0.000006244096,0.059306785],"genre_scores_gemma":[0.9979438,0.000010131907,0.0012748877,0.00025604005,0.0002904091,2.5549667e-7,6.6304375e-7,0.000015651814,0.0002081581],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99770826,0.000018848526,0.0004454972,0.00033039856,0.00053569983,0.0009612871],"domain_scores_gemma":[0.9965792,0.00005124789,0.00057125004,0.00057469076,0.0003232772,0.0019003493],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008044322,0.00021152067,0.00037730986,0.000540317,0.002682998,0.0012344323,0.0017220578,0.000108038184,0.0015991994],"category_scores_gemma":[0.0015680321,0.00019727116,0.00015995723,0.00055748643,0.0010322692,0.00082367775,0.00003386019,0.0005215562,0.000024895451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009879697,0.000020028618,0.093110435,0.000024572117,0.00006155614,0.00062632374,0.0005182365,0.000045859655,0.902999,0.0012259695,0.00087021844,0.00039900022],"study_design_scores_gemma":[0.0008237984,0.0004150064,0.24122989,0.00026523398,0.0001568718,0.0003851373,0.0012874431,0.0011049213,0.7468754,0.0006597181,0.006017681,0.0007788765],"about_ca_topic_score_codex":0.4512581,"about_ca_topic_score_gemma":0.39018014,"teacher_disagreement_score":0.1561236,"about_ca_system_score_codex":0.001666509,"about_ca_system_score_gemma":0.00883953,"threshold_uncertainty_score":0.9998024},"labels":[],"label_agreement":null},{"id":"W2766364639","doi":"10.1109/icinfa.2017.8078954","title":"Intelligent estimate of chemical compositions based on NIR spectra analysis","year":2017,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Partial least squares regression; Support vector machine; Chemical composition; Near-infrared spectroscopy; Computer science; Least squares support vector machine; Pattern recognition (psychology); Biological system; Artificial intelligence; Chemistry; Machine learning","score_opus":0.02365603120105898,"score_gpt":0.33340383269790436,"score_spread":0.3097478014968454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766364639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6465087,0.000031018473,0.020962078,0.0004761098,0.00001914728,0.000036067297,0.000048230413,0.00008461976,0.33183402],"genre_scores_gemma":[0.99511284,0.0000049683504,0.003917218,0.000052327803,0.00003402617,0.00000503273,0.000051581377,0.000008491653,0.0008135084],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991379,0.0000039284996,0.00022767381,0.00024315785,0.00021584886,0.00017153421],"domain_scores_gemma":[0.9987512,0.000095659154,0.00017271025,0.00084418827,0.00005199989,0.000084205414],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000057122255,0.0001309188,0.00033590532,0.00018920588,0.00012319643,0.00005372308,0.00039387937,0.00007578368,0.009940086],"category_scores_gemma":[0.00009356284,0.00011286747,0.00030264742,0.000296271,0.00013326136,0.00003849072,0.000043031694,0.00012391391,0.00004084184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010829367,0.0011635947,0.16896412,0.000097782424,0.0025728894,0.000015496573,0.00003145799,0.0020565554,0.82056606,0.0031270275,0.000846714,0.0004500155],"study_design_scores_gemma":[0.00015626442,0.000014409859,0.0029903285,0.000008918167,0.0012997035,5.637544e-7,0.00001784321,0.031258725,0.9639504,0.00015298135,0.000028855213,0.000121030665],"about_ca_topic_score_codex":0.00017139186,"about_ca_topic_score_gemma":0.000007724479,"teacher_disagreement_score":0.3486041,"about_ca_system_score_codex":0.000060930404,"about_ca_system_score_gemma":0.00002047741,"threshold_uncertainty_score":0.99096495},"labels":[],"label_agreement":null},{"id":"W2766418130","doi":"10.1016/j.scitotenv.2017.10.277","title":"Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network","year":2017,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Probabilistic neural network; Pattern recognition (psychology); Artificial neural network; Artificial intelligence; Computer science; Wavelet packet decomposition; Feature vector; Network packet; Wavelet; Support vector machine; Probabilistic logic; Wavelet transform; Time delay neural network","score_opus":0.012713990564651487,"score_gpt":0.2360080326317448,"score_spread":0.2232940420670933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766418130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99759036,0.00014556997,0.00008398154,0.001008813,0.000043196633,0.00007800844,0.000006529008,0.000018915189,0.001024635],"genre_scores_gemma":[0.99807423,0.000039305993,0.0014666999,0.000008847798,0.000053616815,0.000009416697,0.0000012514201,0.000009890324,0.00033676016],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986637,0.000017434484,0.00019010491,0.00033026346,0.00038242055,0.00041604222],"domain_scores_gemma":[0.9986802,0.000047421097,0.00037669568,0.0008116069,0.000019815896,0.0000642758],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002513056,0.00016153001,0.00019360539,0.000026233383,0.001193945,0.00010437776,0.0007536105,0.000053410316,0.00013668576],"category_scores_gemma":[0.00010948577,0.000093453404,0.00003973308,0.0002254747,0.004151764,0.00016338065,0.00034400276,0.0001977266,0.0000039658416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017066789,0.00015714712,0.0019909022,0.00006857592,0.00006589062,0.000003238418,0.0001937109,0.02585217,0.96589667,0.00062599237,0.000017353754,0.0049576713],"study_design_scores_gemma":[0.00083610107,0.0002523223,0.0029968584,0.00012391672,0.00034953756,0.00012467222,0.0005062331,0.080938436,0.91049165,0.0030212393,0.000009190966,0.00034983607],"about_ca_topic_score_codex":0.0000404366,"about_ca_topic_score_gemma":0.000001348476,"teacher_disagreement_score":0.05540502,"about_ca_system_score_codex":0.00017760176,"about_ca_system_score_gemma":0.00006317908,"threshold_uncertainty_score":0.99855834},"labels":[],"label_agreement":null},{"id":"W2767505813","doi":"10.1214/21-aoas1471","title":"Improving exoplanet detection power: Multivariate Gaussian process models for stellar activity","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Astrophysics Division; Pennsylvania Space Grant Consortium; Natural Sciences and Engineering Research Council of Canada; European Commission; National Aeronautics and Space Administration; University of Pennsylvania; Simons Foundation Autism Research Initiative; Pennsylvania State University; Simons Foundation; Institute for Computational and Data Sciences, Pennsylvania State University; Division of Mathematical Sciences; National Science Foundation","keywords":"Exoplanet; Radial velocity; Physics; Planet; Astrophysics; Astronomy; Astron; Gaussian process; Gaussian; Stars","score_opus":0.05106795244628423,"score_gpt":0.325273856738226,"score_spread":0.2742059042919418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767505813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25022984,0.00017497469,0.7337,0.000108876004,0.00008098932,0.0004855993,0.004407696,0.00012003883,0.010691998],"genre_scores_gemma":[0.9976347,0.00001586091,0.0018226232,0.00007655605,0.000041708343,0.00012213469,0.00007425691,0.000030021252,0.00018213438],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888295,0.000014434563,0.00024545318,0.0002526723,0.00030979663,0.0002947194],"domain_scores_gemma":[0.9988648,0.00032593153,0.00036025833,0.0003101082,0.00008621602,0.00005265048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002851996,0.00016442456,0.00026679764,0.000071782924,0.00039950426,0.000024903737,0.000316632,0.000053785596,0.00030374303],"category_scores_gemma":[0.000051866467,0.00014182425,0.000055694494,0.00029255226,0.00006343035,0.000044332966,0.00008906421,0.0002524073,0.0000014085649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030563793,0.00060476956,0.000014696493,0.0009940441,0.00059920416,0.0000049805744,0.0025792837,0.049721412,0.88203526,0.017993815,0.0011172723,0.041278873],"study_design_scores_gemma":[0.0005378397,0.00012514585,0.00002164116,0.0000029793528,0.00018551531,0.0000044304024,0.0019194251,0.09982383,0.8625377,0.034193706,0.00039395,0.0002538324],"about_ca_topic_score_codex":0.000086510896,"about_ca_topic_score_gemma":0.00000718726,"teacher_disagreement_score":0.7474049,"about_ca_system_score_codex":0.000033044955,"about_ca_system_score_gemma":0.000060190312,"threshold_uncertainty_score":0.5783425},"labels":[],"label_agreement":null},{"id":"W2769157021","doi":"10.3390/s17122739","title":"Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency","funders":"National Natural Science Foundation of China","keywords":"Fourier transform; Hyperplane; k-nearest neighbors algorithm; Pattern recognition (psychology); Colorectal cancer; Fourier transform infrared spectroscopy; Entropy (arrow of time); Artificial intelligence; Mathematics; Support vector machine; Physics; Cancer; Medicine; Computer science; Internal medicine; Optics","score_opus":0.02698894129614037,"score_gpt":0.3195053316165202,"score_spread":0.29251639032037985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769157021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883331,0.00047198232,0.000013682013,0.00028633734,0.000081529164,0.0001137449,0.00015153195,0.000061102735,0.010486996],"genre_scores_gemma":[0.99506474,0.0010230219,0.0014142554,0.000060155915,0.00032914183,0.00003846005,0.000009096101,0.00004508364,0.0020160621],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986109,0.000013799616,0.00022915193,0.0004879013,0.00018139697,0.00047683733],"domain_scores_gemma":[0.9990131,0.00008219817,0.00017081547,0.00039274967,0.000063202395,0.00027792095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000790385,0.0002782406,0.00039097894,0.00009499877,0.0006988164,0.00034909503,0.00020145655,0.0002294044,0.0008443782],"category_scores_gemma":[0.00013103183,0.00026838094,0.000074562755,0.00010232152,0.00038119117,0.00030421026,0.000060259044,0.00026487236,0.0000018571625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009127924,0.00030829653,0.5573283,0.00043449723,0.0008642786,0.00014193328,0.0022308407,0.0000211187,0.4241911,0.0002550357,0.0007169462,0.012594916],"study_design_scores_gemma":[0.0016613476,0.00013664147,0.07027423,0.000059802547,0.00049556274,0.000040612096,0.0010163873,0.014349535,0.9084806,0.00079087756,0.0020391592,0.0006552453],"about_ca_topic_score_codex":0.0021755206,"about_ca_topic_score_gemma":0.00085895375,"teacher_disagreement_score":0.48705405,"about_ca_system_score_codex":0.00012613031,"about_ca_system_score_gemma":0.000075528136,"threshold_uncertainty_score":0.9999768},"labels":[],"label_agreement":null},{"id":"W2770571241","doi":"10.1016/j.talanta.2017.11.051","title":"Determination of the optimal number of components in independent components analysis","year":2017,"lang":"en","type":"article","venue":"Talanta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Conseil National de la Recherche Scientifique; Université Libanaise; Agence Universitaire de la Francophonie","keywords":"Independent component analysis; Computation; Blind signal separation; Algorithm; Generalization; Residual; Reliability (semiconductor); Principal component analysis; Pattern recognition (psychology); Chemistry; Computer science; Artificial intelligence; Mathematics","score_opus":0.03137633766917391,"score_gpt":0.31206576954506315,"score_spread":0.28068943187588924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770571241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902879,0.000016012897,0.000039867627,0.000021781767,0.00002228471,0.000024561856,0.00002711898,0.00000433195,0.009556126],"genre_scores_gemma":[0.99922806,0.000010756993,0.00006912132,0.0000039143747,0.000011372969,0.0000017210116,0.000021598107,0.0000051348006,0.00064832927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921,0.000012481693,0.00025860424,0.00013549328,0.0002698417,0.00011359868],"domain_scores_gemma":[0.9989651,0.000034249435,0.00040608822,0.0005357746,0.0000358586,0.000022920995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008382572,0.00008340911,0.00027794766,0.00009689955,0.000060746293,0.000015083698,0.000520195,0.000068126385,0.00058329257],"category_scores_gemma":[0.000046444584,0.00006472575,0.00014931256,0.00022645066,0.0001002121,0.00006540137,0.00016238805,0.00010086903,0.000006061744],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020889736,0.00013556544,0.8428608,0.00003101271,0.00015113164,0.0000023401838,0.00007934458,0.000019148381,0.15657912,0.000025614616,0.000005979674,0.000089029374],"study_design_scores_gemma":[0.00034947696,0.0000024435203,0.5930773,0.000015705376,0.0003308669,0.0000026173655,0.00005846833,0.0016088265,0.4044519,0.000036295318,0.000008802584,0.00005730414],"about_ca_topic_score_codex":0.00095238397,"about_ca_topic_score_gemma":0.000097933145,"teacher_disagreement_score":0.24978353,"about_ca_system_score_codex":0.00003805534,"about_ca_system_score_gemma":0.000009761879,"threshold_uncertainty_score":0.6386645},"labels":[],"label_agreement":null},{"id":"W2772555171","doi":"","title":"Fingerprinting of Olive Oil from Spectral Data","year":2016,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Olive oil; Principal component analysis; Sunflower oil; Edible oil; Detection limit; Food science; Chemistry; Chromatography; Environmental science; Mathematics; Statistics","score_opus":0.03466763258748155,"score_gpt":0.27596169823982697,"score_spread":0.2412940656523454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772555171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9137345,0.00056675606,0.0000680054,0.00013134681,0.000031584965,0.000008965288,0.000085789434,0.00008755184,0.0852855],"genre_scores_gemma":[0.99346346,0.0003630961,0.0018892293,0.000028068536,0.000376883,0.0000031015204,0.000015169037,0.00002107909,0.0038399296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988365,9.796411e-7,0.00026186192,0.00042468536,0.00020715591,0.00026881244],"domain_scores_gemma":[0.99921244,0.00010991349,0.00019431027,0.00031389948,0.00009918848,0.00007026477],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008854476,0.0001478969,0.00025802245,0.000073835305,0.00005263682,0.000031190593,0.00073283014,0.00009541461,0.0023439287],"category_scores_gemma":[0.0005030555,0.00010719719,0.000060575887,0.0002511859,0.000110784385,0.00029844829,0.0003189118,0.000108590095,0.000052356605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021100419,0.00004717225,0.052658107,0.00017308256,0.00010911547,0.0000013831586,0.00016187137,2.7824967e-8,0.9400112,0.0005339982,0.002052424,0.0042305235],"study_design_scores_gemma":[0.0003285987,0.0000105272065,0.0008914731,0.00021466572,0.0001316118,0.0000032050393,0.00048329536,0.000051872077,0.9949385,0.0007150972,0.002053694,0.00017748491],"about_ca_topic_score_codex":0.000122010126,"about_ca_topic_score_gemma":0.0000034449738,"teacher_disagreement_score":0.081445575,"about_ca_system_score_codex":0.00004578161,"about_ca_system_score_gemma":0.000027370077,"threshold_uncertainty_score":0.99856806},"labels":[],"label_agreement":null},{"id":"W2775346378","doi":"10.1177/0960336017707869","title":"FT-NIR the method of choice to verify the authenticity of extra virgin olive oils","year":2017,"lang":"en","type":"article","venue":"NIR news","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Olive oil; Calibration; Fraction (chemistry); Biological system; Fourier transform infrared spectroscopy; Fingerprint (computing); Matrix (chemical analysis); Chemistry; Chromatography; Environmental science; Mathematics; Materials science; Food science; Computer science; Artificial intelligence; Statistics; Chemical engineering; Biology","score_opus":0.04127020296308803,"score_gpt":0.36548500122011485,"score_spread":0.3242147982570268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775346378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91269726,0.00043622375,0.003901834,0.0016780427,0.000105733314,0.000121344354,0.00004498564,0.000025178402,0.08098938],"genre_scores_gemma":[0.99290615,0.000049147257,0.0018500311,0.00021683399,0.00019341207,0.000015807957,0.0000016944888,0.000014905145,0.0047520096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989517,0.000044119286,0.00028329916,0.00022031173,0.00027759402,0.00022301677],"domain_scores_gemma":[0.99772114,0.00047508487,0.0003678201,0.0012886715,0.00008203934,0.000065255655],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003105272,0.00014611539,0.00031511337,0.000043152828,0.00024940647,0.00004860869,0.0011364804,0.00010352414,0.001160111],"category_scores_gemma":[0.00089824724,0.00008425632,0.00018484663,0.00019284854,0.00017907881,0.00006486947,0.00018010807,0.00023753247,0.000025501347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062846266,0.00014929222,0.008981965,0.000129371,0.0002890743,0.0000020578539,0.0016696695,0.000020335,0.97334844,0.0009942027,0.0047533545,0.009599407],"study_design_scores_gemma":[0.0002606925,0.00002320521,0.0063948147,0.000024304763,0.00021960438,0.0000024892995,0.0006891568,0.0000796102,0.9814981,0.0004165233,0.010288274,0.00010318943],"about_ca_topic_score_codex":0.0025707723,"about_ca_topic_score_gemma":0.0001945212,"teacher_disagreement_score":0.08020888,"about_ca_system_score_codex":0.00003242742,"about_ca_system_score_gemma":0.00004673716,"threshold_uncertainty_score":0.99975294},"labels":[],"label_agreement":null},{"id":"W2783831679","doi":"10.1007/s13197-018-3033-1","title":"Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques","year":2018,"lang":"en","type":"article","venue":"Journal of Food Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Infrared; Aflatoxin; Infrared Spectrophotometry; Infrared spectroscopy; Brown rice; Environmental science; Materials science; Environmental chemistry; Chemistry; Food science; Optics; Physics; Chromatography; Organic chemistry","score_opus":0.02036086527721531,"score_gpt":0.30916184379860046,"score_spread":0.28880097852138514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783831679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963396,0.0020089636,0.000097841155,0.00012025138,0.000022733015,0.000057904024,0.0000024345609,0.000012295968,0.0013379537],"genre_scores_gemma":[0.9971712,0.00022595689,0.002547917,0.000012988692,0.000026554644,0.0000016482559,1.6232828e-7,0.0000050009667,0.000008549719],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883175,0.000010714544,0.00033202843,0.00021966547,0.00039430318,0.00021156335],"domain_scores_gemma":[0.9988282,0.00003362465,0.00037935915,0.00014687091,0.0005520646,0.000059879225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007121598,0.00011148201,0.00029881566,0.00074715365,0.00014636047,0.00004704255,0.00021333291,0.00016036694,0.00002680943],"category_scores_gemma":[0.00041048435,0.00009717263,0.000018179784,0.0016889266,0.0017369137,0.00032984355,0.00009063508,0.00023055999,9.437818e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019315856,0.000031670985,0.015101739,0.000019530547,0.00002323182,0.0000014231972,0.00017473633,8.391548e-8,0.9803915,0.000037289577,0.000019581581,0.00417991],"study_design_scores_gemma":[0.0005773252,0.0006020869,0.0027408279,0.000058178673,0.00009512438,0.00014217585,0.0009223967,0.00061854144,0.98710483,0.0070246775,0.000022022112,0.00009183016],"about_ca_topic_score_codex":0.000012732876,"about_ca_topic_score_gemma":0.0000040606624,"teacher_disagreement_score":0.012360912,"about_ca_system_score_codex":0.00010555543,"about_ca_system_score_gemma":0.00030568743,"threshold_uncertainty_score":0.6399731},"labels":[],"label_agreement":null},{"id":"W2786519145","doi":"10.5539/jas.v10n3p81","title":"Determination of Iron in Vegetable Oil by Fourier Transform Mid-Infrared Spectroscopy","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Attenuated total reflection; Partial least squares regression; Fourier transform infrared spectroscopy; Calibration; Analytical Chemistry (journal); Fourier transform; Coefficient of determination; Chemistry; Spectroscopy; Vegetable oil; Diffuse reflectance infrared fourier transform; Palm oil; Mathematics; Fourier transform spectroscopy; Mean squared error; Infrared spectroscopy; Chromatography; Statistics; Optics; Food science; Physics","score_opus":0.007560628574082031,"score_gpt":0.2592924243166226,"score_spread":0.25173179574254057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786519145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97435534,0.00035017505,0.00012887036,0.00023262987,0.00008721292,0.000013019641,0.0000028070526,0.0000056051626,0.024824344],"genre_scores_gemma":[0.99414045,0.00011653587,0.0033787256,0.000018132216,0.00015481602,7.5221163e-7,0.0000010293846,0.0000031046252,0.0021864611],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851507,0.0000070669303,0.00043853963,0.00015028908,0.0006043458,0.0002846903],"domain_scores_gemma":[0.99905753,0.000036883677,0.00038307172,0.000095842595,0.00031906835,0.00010760878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032369865,0.000113048125,0.0002453951,0.00016336035,0.00009790612,0.00004960622,0.00046035004,0.00006291729,0.00033873593],"category_scores_gemma":[0.00012810476,0.00006868003,0.00009106366,0.0014020989,0.00031666807,0.0007102037,0.000021201435,0.00017686591,0.0000035922926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020986952,0.00007217768,0.0013585881,0.000022205206,0.0000046148075,0.0000018849951,0.00029051045,0.000002477743,0.9965677,0.0000076632905,0.00045390945,0.0011972256],"study_design_scores_gemma":[0.00036663137,0.00014525588,0.008357554,0.00005000829,0.000036451915,0.00004687408,0.00049745775,0.000048612288,0.9898752,0.00011257665,0.0003611993,0.00010219515],"about_ca_topic_score_codex":0.000031706273,"about_ca_topic_score_gemma":0.000016102931,"teacher_disagreement_score":0.022637881,"about_ca_system_score_codex":0.0002476874,"about_ca_system_score_gemma":0.0000909379,"threshold_uncertainty_score":0.37089208},"labels":[],"label_agreement":null},{"id":"W2790797326","doi":"10.1255/jsi.2018.a5","title":"Concentration monitoring with near infrared chemical imaging in a tableting press","year":2018,"lang":"en","type":"article","venue":"Journal of Spectral Imaging","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); University of Ottawa; Université de Sherbrooke","funders":"","keywords":"Process analytical technology; Near-infrared spectroscopy; Tableting; Microcrystalline cellulose; Materials science; Spectroscopy; Chemometrics; Calibration; Process engineering; Analytical Chemistry (journal); Chemistry; Chromatography; Cellulose; Mathematics; Chemical engineering; Optics; Engineering","score_opus":0.010425868099258535,"score_gpt":0.2707072574152601,"score_spread":0.26028138931600153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790797326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9843014,0.0012324253,0.00076813623,0.00026966666,0.00009311788,0.000026576898,9.661621e-7,0.000028762874,0.013278947],"genre_scores_gemma":[0.9918755,0.000037832146,0.00682378,0.000035366164,0.0010857219,0.0000012167792,8.6981686e-7,0.000022342674,0.0001173379],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985982,0.0000111784,0.00048062185,0.00018070228,0.0003285842,0.00040070692],"domain_scores_gemma":[0.9991187,0.000059595695,0.000392585,0.0001309646,0.00019243213,0.00010572183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019697772,0.00016594339,0.00028366197,0.0000978689,0.00008969043,0.00021479213,0.00022474163,0.000033239256,0.00024294258],"category_scores_gemma":[0.00011713729,0.0001434323,0.000074359436,0.0003630343,0.00015951431,0.0006225838,0.000031951935,0.00044287232,0.0000024448311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012951907,0.00006226609,0.32368168,0.000030024505,0.000041305422,0.0001559867,0.00056662475,0.000043295382,0.67373616,0.00001479806,0.00021206378,0.001326264],"study_design_scores_gemma":[0.0010571639,0.000022009512,0.0025023054,0.0002563429,0.000091644564,0.0003599111,0.0010393475,0.0025621962,0.9914524,0.00012119649,0.00034464418,0.000190849],"about_ca_topic_score_codex":0.00006507847,"about_ca_topic_score_gemma":0.0000010529941,"teacher_disagreement_score":0.3211794,"about_ca_system_score_codex":0.0002662814,"about_ca_system_score_gemma":0.00009423022,"threshold_uncertainty_score":0.5849},"labels":[],"label_agreement":null},{"id":"W2790831645","doi":"10.3390/s18020377","title":"Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quality (philosophy); Quality assessment; Hyperspectral imaging; Iodine value; Biochemical engineering; Process engineering; Food science; Computer science; Biotechnology; Chemistry; Artificial intelligence; Engineering; Reliability engineering; Biology; Evaluation methods; Physics","score_opus":0.06325614674195124,"score_gpt":0.4354867959694505,"score_spread":0.37223064922749927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790831645","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037509333,0.9935917,0.000042379135,0.00003152684,0.000020873062,0.00093236275,0.00016965458,0.000039379618,0.0014211447],"genre_scores_gemma":[0.0006481456,0.99072564,0.0076279636,0.000012422906,0.00016746396,0.00016956312,0.00015734625,0.00004365517,0.00044781165],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970881,0.00010673947,0.0012392466,0.0009694274,0.00024728084,0.00034921383],"domain_scores_gemma":[0.99746615,0.00033980748,0.0012263083,0.0006974756,0.00018400866,0.00008625133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006674179,0.0005465389,0.0037366329,0.0005778247,0.000059875918,0.000028977192,0.00022494073,0.00034323052,0.0000920053],"category_scores_gemma":[0.00038339355,0.00044390242,0.00045823172,0.0022143633,0.00022695858,0.000054219487,0.00011580137,0.00036806107,6.88491e-7],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007145057,0.00064924115,0.011487049,0.8627933,0.0139156375,0.000040341587,0.00028818526,5.006764e-7,0.0008158677,0.00074872945,0.0004204001,0.10876927],"study_design_scores_gemma":[0.0034669756,0.001061974,0.0034192624,0.22521187,0.15777676,0.0001655495,0.0011810925,0.00012072636,0.03770622,0.0020804903,0.56053513,0.007273969],"about_ca_topic_score_codex":0.00022039101,"about_ca_topic_score_gemma":0.000019638168,"teacher_disagreement_score":0.63758147,"about_ca_system_score_codex":0.00022656955,"about_ca_system_score_gemma":0.00015163003,"threshold_uncertainty_score":0.9998013},"labels":[],"label_agreement":null},{"id":"W2792036082","doi":"10.5539/jas.v10n4p351","title":"Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chemometrics; Principal component analysis; Cultivar; Spectroscopy; Near-infrared spectroscopy; Mathematics; Analytical Chemistry (journal); Chemistry; Biological system; Chromatography; Statistics; Botany; Optics; Biology; Physics","score_opus":0.02329376918490428,"score_gpt":0.37283903896865717,"score_spread":0.3495452697837529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792036082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99113876,0.0004289381,0.0039265566,0.000047463513,0.00010291298,0.000028907967,0.0000066424154,0.0000070018737,0.0043128347],"genre_scores_gemma":[0.91274256,0.00004155705,0.086755,0.000011524463,0.00015083018,2.5330542e-7,0.0000012430105,0.0000042494894,0.00029280284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980779,0.000026607087,0.00068150926,0.00018639996,0.0007487201,0.0002788565],"domain_scores_gemma":[0.9972878,0.000111147325,0.0012518484,0.00015418864,0.0010548152,0.00014021837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070645346,0.00014772886,0.00042568825,0.0002906132,0.0001755037,0.00006259031,0.00059934956,0.000081305974,0.0003455919],"category_scores_gemma":[0.00069257827,0.000090389454,0.00017386902,0.0051217303,0.0007612105,0.00064913783,0.000068105895,0.00019210523,0.0000016410601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020570089,0.00008212298,0.0021684256,0.000020503134,0.000041569714,4.277803e-7,0.00028330565,0.000035179346,0.99624074,0.000014670259,0.00090516434,0.00018735215],"study_design_scores_gemma":[0.00025198376,0.00018731783,0.010731294,0.000036805162,0.00012103757,0.000055943274,0.0007733828,0.00028673597,0.98722684,0.00006391629,0.00014740492,0.000117350355],"about_ca_topic_score_codex":0.00004213676,"about_ca_topic_score_gemma":6.857063e-7,"teacher_disagreement_score":0.08282844,"about_ca_system_score_codex":0.00022708479,"about_ca_system_score_gemma":0.0001915066,"threshold_uncertainty_score":0.37839887},"labels":[],"label_agreement":null},{"id":"W2792353639","doi":"10.1016/j.fitote.2018.02.006","title":"FT-NIR characterization with chemometric analyses to differentiate goldenseal from common adulterants","year":2018,"lang":"en","type":"article","venue":"Fitoterapia","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Partial least squares regression; Coptis chinensis; Principal component analysis; Mathematics; Chemometrics; Traditional medicine; Artificial intelligence; Computer science; Machine learning; Statistics; Medicine; Traditional Chinese medicine","score_opus":0.029289458406345517,"score_gpt":0.3195821214269986,"score_spread":0.2902926630206531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792353639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99510896,0.00008565317,0.002239051,0.00013116926,0.000119867866,0.00007574712,0.00020853845,0.00016407741,0.0018669311],"genre_scores_gemma":[0.9948302,0.000026339647,0.0004250812,0.00037200202,0.00088898075,0.000022129918,0.0006729995,0.00004665808,0.0027156142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99824303,0.000021329131,0.00034042355,0.0005685903,0.00039564993,0.00043098922],"domain_scores_gemma":[0.9987865,0.000078250145,0.0001778415,0.0005740014,0.00016163968,0.00022176013],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000373563,0.00033092167,0.00047497544,0.0004720058,0.00017056633,0.00016962491,0.00038163995,0.00014291031,0.006363437],"category_scores_gemma":[0.00004185666,0.0002707506,0.00010302041,0.0017695493,0.00007496951,0.00014753728,0.000087950815,0.00018680481,0.00033803636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028134603,0.00014658873,0.061922934,0.000034484354,0.00040903856,0.000014332642,0.00054101547,0.0000011955974,0.9321191,0.0000045337824,0.0006276755,0.0038977782],"study_design_scores_gemma":[0.0007460364,0.00014494624,0.20198306,0.000065256885,0.0002480207,0.00000401453,0.000065584456,0.00023478537,0.7947568,0.00003126737,0.0013361328,0.0003840376],"about_ca_topic_score_codex":0.00034275252,"about_ca_topic_score_gemma":0.00008368412,"teacher_disagreement_score":0.14006013,"about_ca_system_score_codex":0.00008631184,"about_ca_system_score_gemma":0.00002261682,"threshold_uncertainty_score":0.9999745},"labels":[],"label_agreement":null},{"id":"W2793919979","doi":"10.1016/j.foodres.2018.01.041","title":"Inter-correlation of apple firmness determinations and development of cross-validated regression models for prediction of sensory attributes from instrumental and compositional analyses","year":2018,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University; Agriculture and Agri-Food Canada","funders":"Kimberly-Clark","keywords":"Sensory system; Correlation; Regression analysis; Regression; Food science; Mathematics; Chemistry; Statistics; Psychology; Cognitive psychology","score_opus":0.20167316576605002,"score_gpt":0.45181937603990663,"score_spread":0.25014621027385664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793919979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9699843,0.00011084048,0.026221227,0.000033675056,0.000044591434,0.00008845157,0.003237023,0.000008837879,0.00027106065],"genre_scores_gemma":[0.9912346,0.000017624177,0.0069627888,0.0000015468719,0.00007090948,0.000025138963,0.00158948,0.0000074711643,0.00009043851],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987423,0.000020536934,0.0004293809,0.00021370278,0.00047860417,0.0001154964],"domain_scores_gemma":[0.9982981,0.00034003763,0.00021350062,0.00009789585,0.0010051166,0.00004533769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023593029,0.000087655106,0.00017770827,0.00034307854,0.00013249971,0.000025084475,0.0001414144,0.000090825386,0.00023412175],"category_scores_gemma":[0.00016771024,0.000080391415,0.000039751485,0.00018121816,0.0004254514,0.0002232105,0.00013597764,0.00009983171,3.9866444e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004256208,0.0002763701,0.061372407,0.00015029359,0.0004979988,2.219748e-7,0.00074799074,0.00007892412,0.93391335,0.0005083387,0.00010905164,0.0019194491],"study_design_scores_gemma":[0.00082789565,0.00014933244,0.012121071,0.00013423731,0.000030083786,0.0000026909624,0.00044538538,0.032663643,0.9520773,0.0014377941,0.00005360409,0.000056924895],"about_ca_topic_score_codex":0.000044520828,"about_ca_topic_score_gemma":0.000016436243,"teacher_disagreement_score":0.049251337,"about_ca_system_score_codex":0.00007699096,"about_ca_system_score_gemma":0.00007588729,"threshold_uncertainty_score":0.32782668},"labels":[],"label_agreement":null},{"id":"W2799287231","doi":"10.1093/fqsafe/fyy018","title":"Detection of the adulteration of extra virgin olive oil by near-infrared spectroscopy and chemometric techniques","year":2018,"lang":"en","type":"article","venue":"Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Canola; Chemometrics; Chemistry; Food science; Olive oil; Adulterant; Principal component analysis; Sunflower oil; Edible oil; Sample (material); Sunflower; Detection limit; Mathematics; Chromatography; Statistics","score_opus":0.01820827180380327,"score_gpt":0.287610406668751,"score_spread":0.26940213486494774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799287231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885673,0.001054659,0.0020176237,0.00009557584,0.000026067497,0.000046824847,0.000111030145,0.000036741134,0.008044206],"genre_scores_gemma":[0.998138,0.00059381156,0.0006921513,0.00004605979,0.00006465986,0.0000057972707,0.000009779715,0.000008606032,0.00044111762],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896806,0.00005200844,0.0004073015,0.00022663674,0.00020446075,0.00014150997],"domain_scores_gemma":[0.9991608,0.00012463996,0.00029010547,0.00026230482,0.00011788468,0.000044212644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029046973,0.00013218993,0.00028246603,0.00007737091,0.0001746714,0.000024007926,0.00011497569,0.00014873489,0.00013219843],"category_scores_gemma":[0.0002217932,0.000104426865,0.00007095748,0.0006910576,0.0003683475,0.00009968977,0.000058160575,0.00014527187,5.519677e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019068984,0.00008325757,0.0022978634,0.00034720002,0.000076665,4.706582e-8,0.0005161012,6.891655e-8,0.9895627,0.00025206362,0.00007765236,0.0065957247],"study_design_scores_gemma":[0.00033296872,0.00021705125,0.004495992,0.00003875931,0.00006615013,0.000002777395,0.00031433985,0.00004298669,0.99316454,0.0005165768,0.00069371634,0.000114127855],"about_ca_topic_score_codex":0.00014901512,"about_ca_topic_score_gemma":0.000033861503,"teacher_disagreement_score":0.009570744,"about_ca_system_score_codex":0.0000389351,"about_ca_system_score_gemma":0.000025779238,"threshold_uncertainty_score":0.42584038},"labels":[],"label_agreement":null},{"id":"W2800213381","doi":"10.1002/cem.3034","title":"Using elastic net regression to perform spectrally relevant variable selection","year":2018,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"University of Delaware; Center for Hierarchical Manufacturing, National Science Foundation; National Science Foundation","keywords":"Elastic net regularization; Partial least squares regression; Feature selection; Regression analysis; Interpretability; Statistics; Regression; Variable elimination; Linear regression; Variables; Mathematics; Multivariate statistics; Selection (genetic algorithm); Segmented regression; Computer science; Artificial intelligence; Bayesian multivariate linear regression; Inference","score_opus":0.033253832887902346,"score_gpt":0.31919158980396073,"score_spread":0.2859377569160584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800213381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9380895,0.00059638516,0.047510974,0.00022753472,0.00044483718,0.000046323592,0.000004655301,0.00004328587,0.0130365025],"genre_scores_gemma":[0.915418,0.000079142876,0.08051113,0.00018245805,0.0021313205,7.105341e-7,0.0000020258146,0.00004138249,0.001633823],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783194,0.0000136211,0.00070930726,0.00023968992,0.0007588942,0.00044653864],"domain_scores_gemma":[0.9978044,0.00018028876,0.0006773504,0.00022447431,0.000852977,0.00026052908],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005189119,0.00023693863,0.00049081806,0.0017288536,0.00019560989,0.000105399035,0.00038371916,0.00020635167,0.0026593863],"category_scores_gemma":[0.002079133,0.00019121842,0.00016920205,0.0069573824,0.000052948013,0.00028994534,0.00007141877,0.00053577725,0.00003568454],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029218648,0.0002566422,0.009123162,0.000108247696,0.00021923971,0.00002294194,0.00013368922,0.00029016245,0.9802476,0.000146545,0.0076217894,0.0015377547],"study_design_scores_gemma":[0.00073370576,0.00056427135,0.0006039276,0.00015406258,0.00045496997,0.0006246838,0.00017481734,0.0028899473,0.9784656,0.0005748297,0.014432412,0.00032674227],"about_ca_topic_score_codex":0.000015441521,"about_ca_topic_score_gemma":0.0000016692069,"teacher_disagreement_score":0.033000153,"about_ca_system_score_codex":0.0006430138,"about_ca_system_score_gemma":0.00024890056,"threshold_uncertainty_score":0.99825233},"labels":[],"label_agreement":null},{"id":"W2800408211","doi":"10.1080/10408398.2018.1471446","title":"Connection of inherent structure with nutrient profiles and bioavailability of different co-products and by-products after processing using advanced grading and vibrational molecular spectroscopy","year":2018,"lang":"en","type":"review","venue":"Critical Reviews in Food Science and Nutrition","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Brookhaven National Laboratory","keywords":"Molecular spectroscopy; Fourier transform infrared spectroscopy; Bioavailability; Chemistry; Spectroscopy; Molecule; Organic chemistry; Chemical engineering; Bioinformatics; Physics","score_opus":0.04452845306373175,"score_gpt":0.3590530620891788,"score_spread":0.31452460902544704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800408211","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2917837,0.70755213,0.0000908225,0.00003058101,0.000014337116,0.00045784574,0.000059190766,0.00000471278,0.000006687255],"genre_scores_gemma":[0.263553,0.7335265,0.0027769231,0.00000685669,0.000039337618,0.000061993225,0.000023648896,0.000011171008,5.263551e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99782544,0.00006500879,0.00064216263,0.00084720657,0.00036474626,0.00025542796],"domain_scores_gemma":[0.99900734,0.000099745404,0.00029505498,0.00021222276,0.00028062274,0.000104992745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003848155,0.0003212567,0.0011750234,0.00024585857,0.00012752834,0.00008151214,0.00009911309,0.00013606394,0.000012279306],"category_scores_gemma":[0.0009941036,0.00023030922,0.00003566048,0.00096088834,0.0019720148,0.00030824947,0.00007033909,0.00022071376,4.177115e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022497796,0.00066862104,0.0005449068,0.34590346,0.000050852315,0.000003104166,0.00016055508,2.4565031e-8,0.56905967,0.0010226052,0.000021115478,0.08234013],"study_design_scores_gemma":[0.0005843114,0.00091184716,0.000037298953,0.034526505,0.00070433866,0.000093045586,0.00016009904,0.000043397587,0.95757174,0.002154706,0.0026769273,0.0005357492],"about_ca_topic_score_codex":0.000001952023,"about_ca_topic_score_gemma":0.0000012835575,"teacher_disagreement_score":0.38851213,"about_ca_system_score_codex":0.00010883039,"about_ca_system_score_gemma":0.00012726343,"threshold_uncertainty_score":0.93917376},"labels":[],"label_agreement":null},{"id":"W2803049642","doi":"","title":"Parameter estimation of milk Mid-infrared spectra in Canadian Holsteins","year":2018,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Genetics Applied to Livestock Production","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimation; Statistics; Mathematics; Environmental science; Economics","score_opus":0.01328200296337179,"score_gpt":0.2551452188075152,"score_spread":0.2418632158441434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803049642","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9724596,0.000064629625,0.000007700553,0.00061587454,0.0002443597,0.00038661706,0.00000856097,0.000025642716,0.026187023],"genre_scores_gemma":[0.99106646,0.000012073655,0.004553774,0.00006424602,0.0002145828,0.00006845603,0.0000029724151,0.000029124814,0.003988309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984131,0.0000031253444,0.0004291649,0.00046560954,0.00032716696,0.0003618608],"domain_scores_gemma":[0.9989119,0.000030666455,0.0003294118,0.0003222644,0.00026882757,0.00013691079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001629008,0.00021473462,0.00030258665,0.00064224616,0.00010388609,0.000034341065,0.00046462618,0.000093973926,0.00012569003],"category_scores_gemma":[0.0002565803,0.00019343162,0.000066691915,0.0015302663,0.00019531482,0.000054241616,0.000072500254,0.0002067669,0.000011953072],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033018884,0.00027996776,0.018219918,0.0003596228,0.000136057,2.443987e-7,0.000947817,0.001862948,0.96595055,0.0044272416,0.0035538762,0.003931582],"study_design_scores_gemma":[0.00022050082,0.000085282954,0.0049720015,0.0001114361,0.00008200345,0.0000018174735,0.00015354942,0.0002696465,0.9904277,0.002614478,0.0008703419,0.00019129743],"about_ca_topic_score_codex":0.0011268068,"about_ca_topic_score_gemma":0.0035625817,"teacher_disagreement_score":0.024477107,"about_ca_system_score_codex":0.00022166123,"about_ca_system_score_gemma":0.000089268346,"threshold_uncertainty_score":0.7887913},"labels":[],"label_agreement":null},{"id":"W2803125750","doi":"10.1016/j.infrared.2018.05.018","title":"Geographic origin identification of coal using near-infrared spectroscopy combined with improved random forest method","year":2018,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Postdoctoral Science Foundation of Jiangsu Province; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Random forest; Support vector machine; Computer science; Pattern recognition (psychology); Coal; Artificial intelligence; Algorithm; Remote sensing; Environmental science; Geology; Chemistry","score_opus":0.012969896500688645,"score_gpt":0.2942102642141289,"score_spread":0.28124036771344024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803125750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70973325,0.0001336413,0.287205,0.000053230873,0.000074639,0.00020364372,0.000044958528,0.00036821442,0.0021833829],"genre_scores_gemma":[0.9664028,0.000017478846,0.032954935,0.000025383999,0.00014587639,0.00005120662,0.00006225644,0.00005794603,0.00028210343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980036,0.000027055748,0.0006177431,0.00056795136,0.00027018448,0.00051348185],"domain_scores_gemma":[0.99765605,0.00012259661,0.0007010655,0.0010397318,0.00040950527,0.00007105729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016359494,0.00035497147,0.0007175863,0.0003805986,0.00025141108,0.00006663002,0.0006224404,0.00039607016,0.00016415631],"category_scores_gemma":[0.00009836866,0.00033295256,0.00016104059,0.0023956785,0.001030296,0.00019239177,0.00012480922,0.00043260213,0.000011222429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070970063,0.00024763047,0.034041513,0.00009409919,0.0004961513,0.000004078625,0.00009795313,0.000084836975,0.9616096,0.0021815058,0.00006745371,0.0003654682],"study_design_scores_gemma":[0.0029029844,0.00032440198,0.0005542278,0.00003393786,0.0004561,0.0000137693805,0.00021498468,0.019186381,0.9396267,0.036138143,0.0002063802,0.00034197],"about_ca_topic_score_codex":0.00013689461,"about_ca_topic_score_gemma":0.0000178463,"teacher_disagreement_score":0.25666955,"about_ca_system_score_codex":0.00011043762,"about_ca_system_score_gemma":0.00018159169,"threshold_uncertainty_score":0.99991226},"labels":[],"label_agreement":null},{"id":"W2803165918","doi":"10.1139/cjc-2017-0730","title":"Implications of measurement error structure on the visualization of multivariate chemical data: hazards and alternatives","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; International Tropical Timber Organization","keywords":"Principal component analysis; Multivariate statistics; Projection pursuit; Visualization; Exploratory data analysis; Projection (relational algebra); Chemometrics; Dimensionality reduction; Data mining; Multivariate analysis; Hierarchical clustering; Pattern recognition (psychology); Curse of dimensionality; Statistics; Computer science; Chemistry; Artificial intelligence; Cluster analysis; Algorithm; Mathematics; Machine learning","score_opus":0.054193874579074805,"score_gpt":0.32473723454826947,"score_spread":0.27054335996919465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803165918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948389,0.00042911703,0.00024302598,0.0005345191,0.000028054441,0.000023686727,0.00021907786,0.000002500036,0.0036811016],"genre_scores_gemma":[0.99953663,0.000012332885,0.00012962353,0.000040501414,0.00020497217,5.730368e-7,0.00001723196,0.000009923725,0.00004821088],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991194,0.00000975572,0.00035402394,0.00013791349,0.00024247411,0.00013643758],"domain_scores_gemma":[0.99839526,0.000060331924,0.00046441072,0.0003703502,0.00052765606,0.00018198953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018170827,0.000108118234,0.000213355,0.00005533656,0.00006461441,0.000017855506,0.00049782667,0.00008611735,0.0008413685],"category_scores_gemma":[0.0006872463,0.00007994464,0.000046397996,0.00019862976,0.0003103699,0.00006381826,0.000029063243,0.00015822069,2.1885403e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016757864,0.000019710724,0.002246767,0.000071446586,0.00017512409,0.0000010701151,0.00021933924,0.0000025622005,0.995984,0.0001724708,0.0008000884,0.00029064287],"study_design_scores_gemma":[0.00023713493,0.000016256372,0.0010407199,0.0000792403,0.000103946484,0.00002435045,0.00026433423,0.00006685101,0.99706644,0.00041233771,0.0006163388,0.0000720446],"about_ca_topic_score_codex":0.00029758748,"about_ca_topic_score_gemma":0.00020158706,"teacher_disagreement_score":0.004697713,"about_ca_system_score_codex":0.00011043601,"about_ca_system_score_gemma":0.0005956404,"threshold_uncertainty_score":0.9212395},"labels":[],"label_agreement":null},{"id":"W2803602290","doi":"10.1016/j.sab.2018.05.011","title":"Detecting trace levels of heavy metals in pharmaceutical raw materials with wavelength-dispersive X-ray fluorescence spectroscopy and curve-fitting regression","year":2018,"lang":"en","type":"article","venue":"Spectrochimica Acta Part B Atomic Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Pfizer (Canada); Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer Canada","keywords":"Partial least squares regression; Ordinary least squares; Bootstrapping (finance); Linear regression; Statistics; Regression; Regression analysis; Analytical Chemistry (journal); Mathematics; Chemistry; Econometrics; Environmental chemistry","score_opus":0.024499458712938373,"score_gpt":0.30506451613760655,"score_spread":0.2805650574246682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803602290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994199,0.00075196655,0.0004589899,0.00046111768,0.0002071483,0.00043856355,0.00010923179,0.00022246178,0.0031515711],"genre_scores_gemma":[0.98475003,0.0006356302,0.013363903,0.000093840354,0.00072025665,0.0000443581,0.000024877516,0.00016134829,0.00020574522],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99377006,0.00020304586,0.001514393,0.0017782935,0.00086625665,0.0018679389],"domain_scores_gemma":[0.9967737,0.0005590302,0.0009810193,0.0010479867,0.00019257411,0.00044564894],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008686049,0.0010936868,0.0019063841,0.00066951715,0.00044806232,0.00019995541,0.0008541139,0.00042614684,0.004358531],"category_scores_gemma":[0.00043038462,0.0009318647,0.00023340544,0.0015059657,0.0011668219,0.000541899,0.00029526892,0.0011176196,0.000042043914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018492303,0.0003023914,0.0035400158,0.0002250007,0.00031797047,0.000038828268,0.00074972055,0.0000012238419,0.9922123,0.00048644005,0.0001565861,0.00012029759],"study_design_scores_gemma":[0.00278292,0.00053338136,0.0020007188,0.0005145356,0.00046920235,0.000152782,0.0012273358,0.000621596,0.9900577,0.0005300676,0.00013660085,0.00097314874],"about_ca_topic_score_codex":0.00014253795,"about_ca_topic_score_gemma":0.00012342099,"teacher_disagreement_score":0.012904914,"about_ca_system_score_codex":0.0006163161,"about_ca_system_score_gemma":0.00029729665,"threshold_uncertainty_score":0.9993132},"labels":[],"label_agreement":null},{"id":"W2803685237","doi":"10.5539/jfr.v7n4p80","title":"Evaluation of Carrot Quality Using Visible-Near Infrared Spectroscopy and Multivariate Analysis","year":2018,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Partial least squares regression; Carotene; Chemistry; Near-infrared spectroscopy; Mathematics; Carotenoid; Analytical Chemistry (journal); Coefficient of determination; Food science; Correlation coefficient; Multivariate statistics; Linear regression; Horticulture; Chromatography; Statistics; Biology","score_opus":0.259180185825709,"score_gpt":0.5189201616413861,"score_spread":0.2597399758156771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803685237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927681,0.0012385261,0.00080174446,0.000047800593,0.000028072755,0.000053690652,0.000014746797,0.00000446504,0.005042857],"genre_scores_gemma":[0.9956973,0.00005618741,0.0038972022,0.0000050056224,0.0002516689,0.0000012062693,0.000001734112,0.000010871482,0.00007879543],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958234,0.0004474173,0.0007009643,0.000204903,0.00250315,0.00032019665],"domain_scores_gemma":[0.9946432,0.00034798146,0.00053787517,0.000327852,0.0039890627,0.00015400733],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008523612,0.00011644416,0.0005113414,0.00096705667,0.000212627,0.00009011096,0.0002820889,0.00012736575,0.0012942427],"category_scores_gemma":[0.0023920573,0.000096704134,0.00021013345,0.0027048958,0.00037133045,0.00017363777,0.00009842852,0.0004932866,0.0000019731754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024945734,0.0001754507,0.016155155,0.00007177513,0.0027942574,0.0000023651742,0.000656867,0.00014875355,0.9791574,0.000048115435,0.000077505116,0.00046289922],"study_design_scores_gemma":[0.0011427413,0.0004711708,0.007916747,0.000038488906,0.0021523233,0.000010409622,0.0008050599,0.011798932,0.973675,0.0018436614,0.000039726718,0.00010572198],"about_ca_topic_score_codex":0.00029119887,"about_ca_topic_score_gemma":0.00004666148,"teacher_disagreement_score":0.011650179,"about_ca_system_score_codex":0.00031704863,"about_ca_system_score_gemma":0.0005867902,"threshold_uncertainty_score":0.9996187},"labels":[],"label_agreement":null},{"id":"W2804898279","doi":"10.2316/journal.206.2018.3.206-5440","title":"CLASSIFICATION OF COOKED BEEF, LAMB, AND PORK USING HYPERSPECTRAL IMAGING","year":2018,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Food science; Computer science; Artificial intelligence; Chemistry","score_opus":0.022191028672218366,"score_gpt":0.31172409895476383,"score_spread":0.2895330702825455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804898279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98223186,0.00037072174,0.015821014,0.0005562061,0.00015770365,0.000011698272,0.0000037564712,0.000008143277,0.0008388717],"genre_scores_gemma":[0.9886236,0.00009568675,0.010868823,0.000025861162,0.00033982578,1.17990695e-7,0.000003470601,0.000005708792,0.00003685746],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993165,0.0000068768477,0.00030980544,0.00007335971,0.000229324,0.00006413571],"domain_scores_gemma":[0.99899757,0.0000414774,0.00043829327,0.00004589816,0.00044142004,0.000035332378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114920804,0.00006382566,0.00012500252,0.00017914856,0.00003994057,0.00004939621,0.00009438293,0.000034416058,0.00006708563],"category_scores_gemma":[0.00007862806,0.000058679223,0.00003717884,0.00008926454,0.000094088886,0.00018328923,0.000020901094,0.00007182041,4.7589447e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006138209,0.000097076736,0.051274125,0.00003322034,0.00024947818,0.000008061531,0.0004229619,0.00043955512,0.9354716,0.0060062115,0.00009900521,0.0058373003],"study_design_scores_gemma":[0.0022704245,0.00015254901,0.12083048,0.00035469673,0.00060295826,0.0009470808,0.0023273912,0.47116718,0.39410752,0.006586715,0.00028228672,0.00037071735],"about_ca_topic_score_codex":0.000012057002,"about_ca_topic_score_gemma":0.000001058663,"teacher_disagreement_score":0.5413641,"about_ca_system_score_codex":0.00005833065,"about_ca_system_score_gemma":0.000033358352,"threshold_uncertainty_score":0.23928693},"labels":[],"label_agreement":null},{"id":"W2805993325","doi":"10.1016/j.chemosphere.2018.05.111","title":"Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis","year":2018,"lang":"en","type":"article","venue":"Chemosphere","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Asphaltene; Petroleum; Petroleum product; Principal component analysis; Chemistry; Environmental chemistry; Environmental science; Mass spectrometry; Chromatography; Computer science; Organic chemistry","score_opus":0.0210249750606047,"score_gpt":0.2606674114401381,"score_spread":0.23964243637953342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805993325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951153,0.000684319,0.0020598534,0.00005091886,0.00002348508,0.00008647582,0.000009317951,0.00007504351,0.0018953081],"genre_scores_gemma":[0.96393883,0.00006438354,0.035431687,0.000015154479,0.00017603127,0.000005760739,0.000014366574,0.000032134332,0.00032162256],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805564,0.000013125418,0.00042377185,0.0006621447,0.0003840808,0.00046122496],"domain_scores_gemma":[0.9985631,0.00006176481,0.00040326698,0.0005994526,0.00023689822,0.00013552234],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011111001,0.00033358097,0.0007414735,0.00007777161,0.00016969691,0.00005576127,0.0002802058,0.000118516844,0.0023407254],"category_scores_gemma":[0.00010809046,0.00027983275,0.00012701933,0.0012040874,0.00040433035,0.00010575667,0.00012850505,0.00022554804,0.0000056295335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025809082,0.00016866825,0.12627888,0.00027807927,0.0011924619,0.00000772085,0.00027410025,0.00012429482,0.871197,0.00002144984,0.000018043083,0.00018123435],"study_design_scores_gemma":[0.0008744801,0.00009236335,0.0113134105,0.00005527753,0.001166056,0.000010437811,0.00040362356,0.02592224,0.9596989,0.000008801478,0.000095076044,0.000359341],"about_ca_topic_score_codex":0.00016974784,"about_ca_topic_score_gemma":0.000026798469,"teacher_disagreement_score":0.11496547,"about_ca_system_score_codex":0.000111954476,"about_ca_system_score_gemma":0.000097236356,"threshold_uncertainty_score":0.99996537},"labels":[],"label_agreement":null},{"id":"W2810207001","doi":"10.1016/j.isatra.2018.06.009","title":"Probabilistic PCR based near-infrared modeling with temperature compensation","year":2018,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Probabilistic logic; Compensation (psychology); Set (abstract data type); Biological system; Principal component analysis; Computer science; Mathematics; Artificial intelligence","score_opus":0.019486612422463825,"score_gpt":0.2519710605131887,"score_spread":0.23248444809072485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810207001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53429353,0.000072143775,0.4452644,0.00040304422,0.00007592609,0.00012613149,0.000058892685,0.00035763145,0.01934829],"genre_scores_gemma":[0.99353236,0.0000026245123,0.004638806,0.00011360161,0.00011086265,0.000031222928,0.000038464255,0.000024082987,0.0015079638],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991486,0.000009618537,0.00016695801,0.00026943046,0.00019327232,0.00021209501],"domain_scores_gemma":[0.9993719,0.00004963619,0.000040529638,0.00029005823,0.0001673249,0.00008055009],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000038932572,0.0001562722,0.00016422367,0.00007139474,0.0004077929,0.00009173855,0.00011357802,0.000121578494,0.0045691268],"category_scores_gemma":[0.000015279405,0.00013422481,0.0000720312,0.00055233086,0.00013686468,0.00012487601,0.0000015520926,0.000233289,0.000042956348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012173144,0.0020437043,0.0015347417,0.0008595912,0.0012515008,0.00003309983,0.0034518011,0.41331625,0.5715838,0.0009651594,0.0013408978,0.0024021133],"study_design_scores_gemma":[0.0013941753,0.00014328239,0.000096223666,0.00007682887,0.00067031296,0.00002399327,0.00065001345,0.69976807,0.29506236,0.00041036977,0.0011969461,0.00050746015],"about_ca_topic_score_codex":0.00006715251,"about_ca_topic_score_gemma":0.0001123552,"teacher_disagreement_score":0.45923883,"about_ca_system_score_codex":0.000086139524,"about_ca_system_score_gemma":0.00012721044,"threshold_uncertainty_score":0.9963408},"labels":[],"label_agreement":null},{"id":"W2810562980","doi":"10.1016/j.jcs.2018.06.011","title":"Application of low-intensity ultrasound as a rapid, cost-effective tool to wheat screening: A systematic frequency selection","year":2018,"lang":"en","type":"article","venue":"Journal of Cereal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Agriculture and Agri-Food Canada; Keysight Technologies","keywords":"Selection (genetic algorithm); Intensity (physics); Computer science; Statistics; Mathematics; Artificial intelligence; Optics; Physics","score_opus":0.01146398393080761,"score_gpt":0.30052071855982143,"score_spread":0.2890567346290138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810562980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93447256,0.00007841068,0.06012885,0.00006757043,0.00007072209,0.0003538795,0.0000028104298,0.000015850594,0.004809366],"genre_scores_gemma":[0.9971437,0.000009230043,0.0024728985,0.00007094162,0.00020003258,0.000020699998,3.7310468e-7,0.0000062817135,0.00007585272],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99828416,0.000023224167,0.0005121041,0.00021977072,0.0007117921,0.0002489251],"domain_scores_gemma":[0.99734205,0.0002002221,0.0006475125,0.00020402615,0.0014296505,0.00017651105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095396774,0.00012318844,0.00040871178,0.00025744128,0.00018732945,0.00006348496,0.0004332552,0.000063934436,0.00016788686],"category_scores_gemma":[0.0016569241,0.00009541721,0.00011391422,0.0019460705,0.00028816468,0.00034809645,0.000033493237,0.0001784903,0.000018836989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117624775,0.00008610312,0.006959692,0.00051207625,0.000055441753,0.0000015829916,0.00030382123,0.000016769589,0.9904899,0.00014995804,0.00006973733,0.0012372681],"study_design_scores_gemma":[0.00027186386,0.00042043577,0.007709898,0.0005229877,0.00015750302,0.00036438476,0.00045078402,0.00050584006,0.9890956,0.0003582658,0.000018944247,0.00012346817],"about_ca_topic_score_codex":0.00020161933,"about_ca_topic_score_gemma":0.000016521668,"teacher_disagreement_score":0.06267115,"about_ca_system_score_codex":0.00035005593,"about_ca_system_score_gemma":0.00017640438,"threshold_uncertainty_score":0.38910007},"labels":[],"label_agreement":null},{"id":"W2810946894","doi":"10.1080/10837450.2018.1492617","title":"Specificity of process analytical tools in the monitoring of multicomponent pharmaceutical powders","year":2018,"lang":"en","type":"article","venue":"Pharmaceutical Development and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Pfizer (Canada); Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Process analytical technology; Active ingredient; Component (thermodynamics); Process engineering; Computer science; Biochemical engineering; Process (computing); Factorial experiment; Ascorbic acid; Work in process; Chemistry; Machine learning; Engineering","score_opus":0.09417254451777804,"score_gpt":0.3940318824635554,"score_spread":0.29985933794577735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810946894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960918,0.0005370088,0.000110311026,0.000658494,0.000035082056,0.00008492957,0.0000027653025,0.00004222345,0.0024374018],"genre_scores_gemma":[0.99887395,0.00014400172,0.00085399655,0.00002693599,0.000049392354,0.000018028431,0.0000023531506,0.000008997793,0.000022360784],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998552,0.000017982156,0.0004960219,0.00028699997,0.00028736176,0.00035960096],"domain_scores_gemma":[0.99937487,0.00018133882,0.000091329865,0.00017199364,0.00011089031,0.000069575035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023222779,0.0001716685,0.00035328788,0.0003179413,0.00006625005,0.000012121575,0.00037873152,0.00017298889,0.00027698415],"category_scores_gemma":[0.00018233333,0.0001280287,0.000037503283,0.0010317512,0.0008349027,0.000056672947,0.00011764435,0.00040412214,0.0000055200494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002662469,0.0016897076,0.7023825,0.0005936766,0.000371211,0.000035485828,0.0013165901,0.000001562687,0.21918495,0.009046095,0.000029834926,0.06508212],"study_design_scores_gemma":[0.0007435826,0.000032848766,0.008087165,0.000036319278,0.00009711184,0.000013411607,0.0015026295,0.0006757795,0.9864591,0.0003174588,0.0018861189,0.00014849732],"about_ca_topic_score_codex":0.0000020782475,"about_ca_topic_score_gemma":8.3105806e-7,"teacher_disagreement_score":0.76727414,"about_ca_system_score_codex":0.000045288343,"about_ca_system_score_gemma":0.00005438823,"threshold_uncertainty_score":0.5220859},"labels":[],"label_agreement":null},{"id":"W284454431","doi":"","title":"Applied real-time acoustic chemometrics for measurements of Canadian Standard Freeness","year":2005,"lang":"en","type":"article","venue":"Paperi ja puu","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fast Fourier transform; Chemometrics; Computer science; Signal processing; Wavelet; Partial least squares regression; Wavelet transform; Acoustics; Artificial intelligence; Digital signal processing; Algorithm; Machine learning; Computer hardware","score_opus":0.0314824761316917,"score_gpt":0.26688413461228006,"score_spread":0.23540165848058836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W284454431","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33565062,0.0008747758,0.0011730457,0.0003844918,0.00008734284,0.0003753576,0.0006824021,0.00017684342,0.6605951],"genre_scores_gemma":[0.99176055,0.00008967183,0.004147614,0.00008929572,0.00020768757,0.000056325363,0.00008231903,0.000043554486,0.0035229565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843544,0.0000046234804,0.0003487632,0.0003136171,0.00041227578,0.0004852867],"domain_scores_gemma":[0.99890333,0.000117906886,0.00014587432,0.00038360571,0.00018851706,0.0002607645],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021881206,0.00021123822,0.0004224691,0.0006623331,0.0001213399,0.000030776097,0.00033104818,0.00017161641,0.0032560406],"category_scores_gemma":[0.00025687885,0.00021605352,0.00012294693,0.0016938021,0.000057746118,0.000055974437,0.000027891258,0.00010201053,0.00003878265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008636026,0.00007368052,0.0022087547,0.00018585403,0.00020134848,0.0000010381393,0.000086123255,0.00016526226,0.98921126,0.00005787441,0.0024332884,0.005289141],"study_design_scores_gemma":[0.00088021485,0.000029183011,0.00035724288,0.00001240901,0.00026534006,0.0000015561286,0.00013553766,0.00010460153,0.97466224,0.000037995247,0.02323515,0.00027853745],"about_ca_topic_score_codex":0.004683891,"about_ca_topic_score_gemma":0.004249522,"teacher_disagreement_score":0.6570722,"about_ca_system_score_codex":0.0004190438,"about_ca_system_score_gemma":0.00025926557,"threshold_uncertainty_score":0.9976551},"labels":[],"label_agreement":null},{"id":"W2883935080","doi":"10.21611/qirt.2018.097","title":"Infrared and Terahertz time-domain imaging for evaluation of impacted thick homogeneous particleboards of sugarcane bagasse","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Terahertz radiation; Bagasse; Homogeneous; Materials science; Infrared; Composite material; Time domain; Optoelectronics; Optics; Pulp and paper industry; Computer science; Engineering; Physics","score_opus":0.03954581470239454,"score_gpt":0.3316458836066078,"score_spread":0.29210006890421325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883935080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9532318,0.00015433929,0.00008631988,0.00021476114,0.000058119072,0.00022318079,0.00023852507,0.000018604174,0.04577431],"genre_scores_gemma":[0.9977903,0.000026468148,0.0017766055,0.000035519206,0.0000423729,0.000038866805,0.000012710848,0.000018696375,0.00025845622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983982,0.000020174275,0.00047820195,0.00026765018,0.00065195694,0.00018381837],"domain_scores_gemma":[0.9956494,0.00020244718,0.0007920776,0.00014935304,0.0031573,0.000049427552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005899348,0.00019211312,0.00031901838,0.0003359297,0.00007881758,0.00003952566,0.00050267787,0.00007509319,0.00081406446],"category_scores_gemma":[0.00077844755,0.00014822547,0.00018460587,0.00048749588,0.0007135393,0.00019626833,0.00008762298,0.0001092406,0.0000014031402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074173784,0.00017245105,0.016567212,0.00009502805,0.0005982808,6.212221e-8,0.0016529574,0.0000014961655,0.94497204,0.033713095,0.0004866374,0.0009989908],"study_design_scores_gemma":[0.0014613854,0.00032077005,0.0064028,0.00023549741,0.00027486475,0.0000028916854,0.0021454338,0.009229179,0.8767205,0.10293884,0.000089179506,0.00017866159],"about_ca_topic_score_codex":0.000025116746,"about_ca_topic_score_gemma":0.0000019156407,"teacher_disagreement_score":0.06922575,"about_ca_system_score_codex":0.00004586778,"about_ca_system_score_gemma":0.00008820072,"threshold_uncertainty_score":0.8913435},"labels":[],"label_agreement":null},{"id":"W2885007850","doi":"10.14311/nnw.2018.28.013","title":"AUTOMATIC CLASSIFICATION OF AGRICULTURAL GRAINS: COMPARISON OF NEURAL NETWORKS","year":2018,"lang":"en","type":"article","venue":"Neural Network World","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Agriculture; Artificial intelligence; Computer science; Pattern recognition (psychology); Biology; Ecology","score_opus":0.031759851089693376,"score_gpt":0.3041091928121343,"score_spread":0.2723493417224409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885007850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879575,0.0011317722,0.0001548156,0.00017274545,0.00027176144,0.000085806874,0.00000519962,0.00013081328,0.0100896275],"genre_scores_gemma":[0.9974521,0.000010712155,0.00037612458,0.00006525907,0.0010607225,0.000010541211,0.000055057135,0.000021051444,0.0009484337],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813527,0.00004225768,0.0007656432,0.00030712286,0.0003140507,0.00043562922],"domain_scores_gemma":[0.9984409,0.00022190201,0.00065368286,0.00040879843,0.00017687495,0.0000978023],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010191754,0.0002462389,0.0005958211,0.00011252769,0.00012986771,0.000024966292,0.00039820667,0.00011847687,0.0012749926],"category_scores_gemma":[0.00005000066,0.00019654167,0.00020935688,0.0018280288,0.0003055977,0.000114970266,0.00007600571,0.00030618475,0.000007727781],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002371534,0.0009328809,0.68377805,0.0005529344,0.0007067883,0.0000036449105,0.0006547245,0.044706285,0.13077737,0.003005187,0.09758518,0.037059825],"study_design_scores_gemma":[0.00058947824,0.00017843801,0.2090777,0.000100813944,0.00042509855,0.000007126283,0.00039431805,0.7281305,0.060063627,0.00013700961,0.00047790902,0.00041796832],"about_ca_topic_score_codex":0.000030207915,"about_ca_topic_score_gemma":0.0001493436,"teacher_disagreement_score":0.68342423,"about_ca_system_score_codex":0.000046066354,"about_ca_system_score_gemma":0.000012028859,"threshold_uncertainty_score":0.99963796},"labels":[],"label_agreement":null},{"id":"W2885666589","doi":"10.1139/cjfas-2018-0112","title":"A transformative approach to ageing fish otoliths using Fourier transform near infrared spectroscopy: a case study of eastern Bering Sea walleye pollock (<i>Gadus chalcogrammus</i>)","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Marine Fisheries Service; National Oceanic and Atmospheric Administration","keywords":"Pollock; Otolith; Fish <Actinopterygii>; Repeatability; Gadus; Near-infrared spectroscopy; Fishery; Absorbance; Oceanography; Environmental science; Mathematics; Geology; Biology; Chemistry; Statistics; Chromatography","score_opus":0.036573425234456156,"score_gpt":0.27369785140509334,"score_spread":0.23712442617063717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885666589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99131364,0.00017906107,0.0018056694,0.00020837285,0.000071876995,0.0001527472,0.000015701084,0.0000065267627,0.006246422],"genre_scores_gemma":[0.9938922,0.000008961595,0.0057669026,0.0001102579,0.00011791617,0.000004002764,5.566672e-7,0.000013658222,0.000085548694],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9981852,0.000031710355,0.00062208256,0.000246601,0.000386752,0.00052764104],"domain_scores_gemma":[0.99877626,0.0000681352,0.00027640333,0.00015755038,0.000107025844,0.000614651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044483843,0.00022631792,0.00050250243,0.0003957895,0.0006640058,0.0003538702,0.0003613779,0.00007141447,0.00013775808],"category_scores_gemma":[0.000108703614,0.00018892712,0.00010676559,0.0011785114,0.0007135356,0.0004925485,0.000017005137,0.00020863616,4.3046444e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043725627,0.00082448503,0.29657885,0.0007740837,0.0017118937,0.0034731084,0.66506404,0.0005763649,0.004371308,0.000053455904,0.00070144795,0.025433715],"study_design_scores_gemma":[0.0038904578,0.0052170814,0.002368208,0.00059734203,0.0012365468,0.0077878926,0.87033534,0.080036215,0.024213726,0.00061436574,0.0023126625,0.0013901725],"about_ca_topic_score_codex":0.035357945,"about_ca_topic_score_gemma":0.0363875,"teacher_disagreement_score":0.29421067,"about_ca_system_score_codex":0.00010885349,"about_ca_system_score_gemma":0.00079017883,"threshold_uncertainty_score":0.9811959},"labels":[],"label_agreement":null},{"id":"W2886899156","doi":"10.13031/aea.12421","title":"Determination of Maize Seed Purity Based on Multi-Step Clustering","year":2018,"lang":"en","type":"article","venue":"Applied Engineering in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shandong Agricultural University; Ryerson University","keywords":"Cluster analysis; Hue; RGB color model; Artificial intelligence; Mathematics; Horticulture; Biology; Computer science","score_opus":0.007841109372957155,"score_gpt":0.22389457959835665,"score_spread":0.2160534702253995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886899156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8427241,0.0000813429,0.13019827,0.00006332444,0.00014002067,0.0002605908,0.000022372811,0.0004164548,0.026093546],"genre_scores_gemma":[0.9849215,0.0000029066468,0.014682037,0.000024981213,0.00012422571,0.00002706405,0.000021123558,0.000014739404,0.00018142903],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920654,0.0000024592723,0.00020668315,0.00022052629,0.0001587282,0.00020505089],"domain_scores_gemma":[0.99961203,0.000055685356,0.00006856463,0.00018339479,0.000036084402,0.000044232143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005831165,0.00017288938,0.0002122775,0.00011951769,0.00002657035,0.000013748054,0.00015996776,0.00017661044,0.00011571308],"category_scores_gemma":[0.000040699364,0.00014147632,0.000052469088,0.0005235985,0.000021473645,0.000029879384,0.000027762168,0.00020476955,0.000010122759],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003549844,0.00017944352,0.0018920869,0.0002840101,0.00002141409,0.0000039432834,0.00015107612,0.012538618,0.98405015,0.00012421249,0.00009545392,0.0006241156],"study_design_scores_gemma":[0.0009875109,0.00002932552,0.012674569,0.000066796165,0.00003731434,0.0000021110243,0.00009793842,0.14795555,0.83759046,0.000004421446,0.00029426222,0.00025971927],"about_ca_topic_score_codex":0.00002036236,"about_ca_topic_score_gemma":0.000024984638,"teacher_disagreement_score":0.14645965,"about_ca_system_score_codex":0.000098108176,"about_ca_system_score_gemma":0.000006771955,"threshold_uncertainty_score":0.57692367},"labels":[],"label_agreement":null},{"id":"W2886974613","doi":"10.1016/j.foodchem.2018.08.075","title":"Evaluation of near-infrared (NIR) and Fourier transform mid-infrared (ATR-FT/MIR) spectroscopy techniques combined with chemometrics for the determination of crude protein and intestinal protein digestibility of wheat","year":2018,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Agricultural Development Fund; Saskatchewan Forage Network","keywords":"Chemometrics; Near-infrared spectroscopy; Spectroscopy; Infrared spectroscopy; Chemistry; Fourier transform; Fourier transform infrared spectroscopy; Infrared; Analytical Chemistry (journal); Attenuated total reflection; Biological system; Chromatography; Mathematics; Optics; Organic chemistry; Biology","score_opus":0.021775490722268403,"score_gpt":0.28558165262818647,"score_spread":0.26380616190591805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886974613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911048,0.0004547334,0.00447867,0.00008763049,0.0000063654525,0.0010168223,0.000087784036,0.000046421686,0.0027167853],"genre_scores_gemma":[0.9767245,0.000011547823,0.022582728,0.000003494458,0.00005692905,0.0002977758,0.00003092865,0.000029542027,0.00026253852],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979728,0.000022707967,0.0005960805,0.00041617965,0.00073216326,0.0002600972],"domain_scores_gemma":[0.99744874,0.00023011696,0.00055412325,0.0005239425,0.0011622235,0.0000808425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008303403,0.0002869164,0.00050350453,0.000093913244,0.00012504979,0.000034818124,0.0002676554,0.0002267413,0.00014432598],"category_scores_gemma":[0.0012027741,0.00022331382,0.00009490592,0.0007221083,0.0008261604,0.00012959084,0.00005626551,0.00019975846,7.6282014e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007884073,0.00021660098,0.0011052003,0.0016520771,0.00013353555,2.1527603e-7,0.00023820705,2.3983895e-7,0.99068254,0.0000073475767,0.000015549283,0.005160105],"study_design_scores_gemma":[0.0017214761,0.0012843251,0.00032945274,0.000289066,0.00066028774,0.000010143196,0.00031329785,0.0013381474,0.99160016,0.002202236,0.000029190518,0.00022221242],"about_ca_topic_score_codex":0.000029590505,"about_ca_topic_score_gemma":0.000009969833,"teacher_disagreement_score":0.018104058,"about_ca_system_score_codex":0.00012472301,"about_ca_system_score_gemma":0.00027395829,"threshold_uncertainty_score":0.91064733},"labels":[],"label_agreement":null},{"id":"W2890309898","doi":"10.3390/jmmp2030062","title":"Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0","year":2018,"lang":"en","type":"article","venue":"Journal of Manufacturing and Materials Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Machining; Computer science; Automation; Process (computing); Artificial intelligence; Key (lock); Machine learning; Fractal; Industrial engineering; Engineering; Mechanical engineering","score_opus":0.011671516544670281,"score_gpt":0.284049960743609,"score_spread":0.2723784441989387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890309898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918038,0.00061618286,0.0072975513,0.000054131324,0.000059834743,0.000062432446,0.000008157663,0.000022420672,0.00007545908],"genre_scores_gemma":[0.9966924,0.000076806864,0.0025889683,0.0000125331235,0.0005231476,0.000012096848,0.0000033156653,0.00002362229,0.000067099456],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864113,0.0000103547,0.00058963243,0.0002848936,0.00017624511,0.00029773355],"domain_scores_gemma":[0.998931,0.000060551014,0.0007154414,0.0001074933,0.00009654651,0.00008898519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044689918,0.00020817132,0.00051413325,0.0004068274,0.00019286563,0.00022405064,0.000151985,0.000110479756,0.00002607013],"category_scores_gemma":[0.00007318547,0.00017346184,0.00006192783,0.00013617396,0.000071376395,0.0004097676,0.00004909353,0.00016452192,2.773048e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005687236,0.000096510856,0.0044814376,0.0029034014,0.00035276607,0.000013881313,0.0011507627,0.00005617021,0.8581716,0.000004091605,0.0000029715634,0.13219772],"study_design_scores_gemma":[0.000625804,0.00004307853,0.0041596787,0.0001292774,0.00051864336,0.000048748163,0.0005395255,0.00025624275,0.9930633,0.0003794761,0.00004817819,0.000188095],"about_ca_topic_score_codex":0.000057574318,"about_ca_topic_score_gemma":0.0000072549033,"teacher_disagreement_score":0.1348917,"about_ca_system_score_codex":0.000070664835,"about_ca_system_score_gemma":0.000031362822,"threshold_uncertainty_score":0.7073568},"labels":[],"label_agreement":null},{"id":"W2890555081","doi":"10.1109/tim.2018.2866277","title":"A Hyperspectral Imaging Instrumentation Architecture Based on Accessible Optical Disc Technology and Frequency-Domain Analyses","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Instrumentation (computer programming); Frequency domain; Architecture; Optical imaging; Computer science; Remote sensing; Engineering; Optics; Artificial intelligence; Computer vision; Physics; Geology","score_opus":0.0358191334745056,"score_gpt":0.31603539634424505,"score_spread":0.28021626286973944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890555081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7894881,0.00008628971,0.20045553,0.0021368598,0.00013970745,0.00016966392,0.000026086456,0.00015329177,0.007344486],"genre_scores_gemma":[0.9898183,0.000030571784,0.009575717,0.00037640397,0.000051886844,0.00007003609,0.000007904867,0.000020410125,0.000048814836],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984251,0.000026664073,0.0002938389,0.00046969607,0.0005023648,0.00028233154],"domain_scores_gemma":[0.9994187,0.00003090761,0.00010056481,0.00021918767,0.000100343634,0.0001303015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013450783,0.00025505514,0.00021878726,0.0007769837,0.00041192528,0.00011223723,0.000105485844,0.000098774886,0.00061534764],"category_scores_gemma":[0.000012813951,0.00023001336,0.00006826481,0.0007119503,0.0003095828,0.00016451156,0.0000021142707,0.00026896023,0.000008608548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002611624,0.0004328341,0.0028804657,0.00007200696,0.00022141104,0.0000041138624,0.00040531871,0.00016509749,0.93279433,0.0007553671,0.00002098127,0.061986923],"study_design_scores_gemma":[0.0019290777,0.00027547753,0.0008154656,0.00007582874,0.00027984977,0.000019168287,0.002596271,0.00096278026,0.99112594,0.0015973651,0.000044551285,0.0002782306],"about_ca_topic_score_codex":0.000055479282,"about_ca_topic_score_gemma":0.00009808482,"teacher_disagreement_score":0.20033018,"about_ca_system_score_codex":0.00028063002,"about_ca_system_score_gemma":0.00007170486,"threshold_uncertainty_score":0.9379673},"labels":[],"label_agreement":null},{"id":"W2891359098","doi":"10.1021/acs.jafc.8b03985","title":"New Method for Accurate Determination of Polyphenol Oxidase Activity Based on Reduction in SERS Intensity of Catechol","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Catechol; Polyphenol oxidase; Chemistry; Catechol oxidase; Polyphenol; Intensity (physics); Reduction (mathematics); Chromatography; Biochemistry; Enzyme; Mathematics; Antioxidant; Peroxidase; Optics","score_opus":0.018712209432864873,"score_gpt":0.2947158612930769,"score_spread":0.27600365186021203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891359098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972064,0.000059077905,0.0021731225,0.00019562688,0.00003076757,0.000035432167,0.000013832114,0.0000049463456,0.0002807871],"genre_scores_gemma":[0.994847,0.000011596446,0.004764618,0.0000071860063,0.00020968288,0.0000013091577,0.0000045346683,0.0000045406596,0.0001495091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922216,0.0000067538645,0.00035185297,0.00013416934,0.00016603526,0.00011902723],"domain_scores_gemma":[0.99874586,0.00007404349,0.00067190797,0.000087350985,0.00034401627,0.000076832424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013078924,0.0001299723,0.0003389244,0.000061057486,0.000031803134,0.0000108638915,0.00011159416,0.00013702924,0.000046824538],"category_scores_gemma":[0.00014351662,0.000087107226,0.00013460487,0.0002738551,0.000047492733,0.00012834558,0.000016061013,0.00018822777,7.911689e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041866596,0.00011259029,0.00019273932,0.0001957749,0.000046221896,4.079548e-7,0.00009303433,0.000014962175,0.99595284,0.0000011795235,0.000105838604,0.002865751],"study_design_scores_gemma":[0.0009048479,0.00031030687,0.0041232877,0.00010267719,0.00013301102,0.000055914064,0.00066611025,0.00020520306,0.9933232,0.00006706549,0.0000075964786,0.00010075076],"about_ca_topic_score_codex":0.00003336626,"about_ca_topic_score_gemma":0.000003254868,"teacher_disagreement_score":0.0039305487,"about_ca_system_score_codex":0.0000801709,"about_ca_system_score_gemma":0.000053637414,"threshold_uncertainty_score":0.35521296},"labels":[],"label_agreement":null},{"id":"W2892421788","doi":"10.3390/s18103222","title":"Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Support vector machine; Genetic algorithm; Principal component analysis; Near-infrared spectroscopy; Pattern recognition (psychology); Computer science; Identification (biology); Artificial intelligence; Data mining; Algorithm; Machine learning; Biology","score_opus":0.019828658309922153,"score_gpt":0.30295132539008746,"score_spread":0.2831226670801653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892421788","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9227681,0.00096347346,0.052742805,0.000234351,0.00024133424,0.0003921258,0.011206524,0.00031985182,0.011131443],"genre_scores_gemma":[0.31008568,0.00038283493,0.6487577,0.00025135072,0.0007900464,0.000037385347,0.008611184,0.00021614695,0.030867686],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974189,0.00003194094,0.00066810776,0.0008685957,0.00041624386,0.0005962557],"domain_scores_gemma":[0.99723965,0.00025737935,0.0003781421,0.0017206878,0.00020489049,0.0001992436],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020799646,0.00036843048,0.000981084,0.00032797168,0.00017824942,0.00006827143,0.0008002292,0.00020957235,0.008230738],"category_scores_gemma":[0.00024791458,0.00035406035,0.000369502,0.0018137316,0.00035402575,0.00008365328,0.00016634894,0.00017653017,0.000040583134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016826806,0.0019558256,0.01543366,0.0005057788,0.040668175,0.00009025231,0.0015511462,0.0025447805,0.7395541,0.000047700683,0.18502137,0.010944538],"study_design_scores_gemma":[0.0011924799,0.00014578542,0.00024640313,0.000005496613,0.005285975,0.0000061168384,0.00016595048,0.43993017,0.54514897,0.000027251232,0.0074534263,0.00039196116],"about_ca_topic_score_codex":0.00031690905,"about_ca_topic_score_gemma":0.00001740948,"teacher_disagreement_score":0.6126824,"about_ca_system_score_codex":0.00008558139,"about_ca_system_score_gemma":0.00009091115,"threshold_uncertainty_score":0.99989116},"labels":[],"label_agreement":null},{"id":"W2895055902","doi":"10.1007/s11694-018-9937-7","title":"Discrimination of organic and conventional rice by chemometric analysis of NIR spectra: a pilot study","year":2018,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Heilongjiang Academy of Agricultural Sciences; Northeast Agricultural University; McGill University","keywords":"Partial least squares regression; Principal component analysis; Near-infrared spectroscopy; Calibration; Principal component regression; Multivariate statistics; Correlation coefficient; Coefficient of determination; Cross-validation; Regression analysis; Standard error; Linear regression; Mean squared error; Statistics; Pattern recognition (psychology); Mathematics; Analytical Chemistry (journal); Artificial intelligence; Chemistry; Computer science; Chromatography; Optics","score_opus":0.04339456119470271,"score_gpt":0.2705328224056802,"score_spread":0.22713826121097752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895055902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914788,0.00032289096,0.007808141,0.0000480621,0.00005622204,0.00007488816,0.000034929035,0.000006424958,0.00016962527],"genre_scores_gemma":[0.99958825,0.00011055441,0.00006851612,0.0000111389945,0.00011283232,0.0000019648835,0.00004727964,0.000012038409,0.000047449324],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977604,0.000041017844,0.0008665655,0.00017041691,0.0010355875,0.0001259801],"domain_scores_gemma":[0.99678487,0.000038394395,0.0016129259,0.00015111118,0.0013434624,0.000069248636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000672468,0.00014645877,0.00053383317,0.0011079672,0.000054098135,0.000024794075,0.00016082679,0.000043987504,0.00053611497],"category_scores_gemma":[0.0003260161,0.00013203455,0.000120089775,0.00285087,0.00006455489,0.00024484977,0.000024359008,0.000110049594,5.785592e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017701296,0.0017983726,0.050847907,0.00009903589,0.0025740243,4.055353e-7,0.00023803485,4.2699352e-7,0.94404656,0.000011764403,0.000020557432,0.00018588353],"study_design_scores_gemma":[0.0008304428,0.0018566977,0.36256886,0.000030211977,0.0035590057,0.000003199857,0.00029520624,0.000034799712,0.6307131,0.0000132892255,0.000010888991,0.000084314765],"about_ca_topic_score_codex":0.0000066906737,"about_ca_topic_score_gemma":0.000013323077,"teacher_disagreement_score":0.31333348,"about_ca_system_score_codex":0.00015944698,"about_ca_system_score_gemma":0.00007101403,"threshold_uncertainty_score":0.5870083},"labels":[],"label_agreement":null},{"id":"W2895784174","doi":"10.1016/j.rse.2018.10.014","title":"Discrimination of liana and tree leaves from a Neotropical Dry Forest using visible-near infrared and longwave infrared reflectance spectra","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Inter-American Institute for Global Change Research","keywords":"Liana; Remote sensing; Longwave; Environmental science; Near-infrared spectroscopy; Geology; Botany; Physics; Optics; Radiative transfer; Biology","score_opus":0.020416193413698387,"score_gpt":0.2602222012958118,"score_spread":0.23980600788211343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895784174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860884,0.00082848105,0.010294062,0.000042344953,0.00001808242,0.00005029272,0.00001151654,0.000014147164,0.0026526747],"genre_scores_gemma":[0.9295135,0.00036210028,0.06971518,0.000016958453,0.00010391407,3.565403e-8,0.000010801438,0.000020669366,0.00025685408],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988678,0.000019782368,0.000337358,0.00033616164,0.00024205084,0.00019681208],"domain_scores_gemma":[0.9992463,0.00009286103,0.00023916505,0.0003246362,0.000019945472,0.00007707903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040597217,0.00018103732,0.0003470936,0.0000869241,0.00008918241,0.000025561372,0.00006658124,0.00012529865,0.000083832056],"category_scores_gemma":[0.00007339354,0.00018015817,0.00005593388,0.00012915678,0.0005616134,0.00008125335,0.00008455582,0.00012038802,0.0000014580057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118749,0.00004892366,0.0056868275,0.000107447995,0.00012811304,0.000009028136,0.00095042185,0.000036122157,0.97645134,0.000018405373,0.00001430059,0.016430352],"study_design_scores_gemma":[0.000696112,0.00015012236,0.036390137,0.00022929737,0.00031741612,0.000011212157,0.00055719464,0.05389914,0.90443206,0.002973461,0.00009723477,0.00024662743],"about_ca_topic_score_codex":0.00033317137,"about_ca_topic_score_gemma":0.00006877132,"teacher_disagreement_score":0.072019264,"about_ca_system_score_codex":0.00010263128,"about_ca_system_score_gemma":0.000020158066,"threshold_uncertainty_score":0.73466367},"labels":[],"label_agreement":null},{"id":"W2895974930","doi":"10.1117/12.2325661","title":"Autoregressive model for multi-pass SAR change detection based on image stacks","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Autoregressive model; Synthetic aperture radar; Change detection; Computer science; Artificial intelligence; Computer vision; Image (mathematics); Stack (abstract data type); Radar imaging; Pixel; Inverse synthetic aperture radar; Remote sensing; Pattern recognition (psychology); Radar; Mathematics; Geology; Telecommunications","score_opus":0.1015061378745668,"score_gpt":0.3603342441834177,"score_spread":0.2588281063088509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895974930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014672887,0.0001753097,0.9688109,0.00023996567,0.00032355078,0.0005973343,0.00083721045,0.0005830069,0.013759841],"genre_scores_gemma":[0.87351865,0.000043906486,0.10650876,0.0005562197,0.0013528042,0.00079583866,0.00054030004,0.00015749055,0.016526004],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979432,0.000012903283,0.0003423314,0.0009233799,0.00032523583,0.00045293028],"domain_scores_gemma":[0.997878,0.00010824544,0.00036999604,0.0012018115,0.00030388686,0.0001380178],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012318637,0.00051602983,0.0005378733,0.00030525078,0.00016291774,0.00010800244,0.0006037869,0.00078480714,0.0015381777],"category_scores_gemma":[0.00028519589,0.0004597929,0.0004325659,0.00015573422,0.00009925489,0.00007560032,0.00031073112,0.00057408185,0.000047500645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005986683,0.009405227,0.0025374852,0.021092111,0.0058327415,0.00010627625,0.0047050132,0.022321137,0.83166534,0.00041626202,0.0625293,0.03340245],"study_design_scores_gemma":[0.00054109696,0.000035378067,0.000018038405,0.000058816033,0.00021224628,3.2581573e-7,0.000039358776,0.5839592,0.4143891,0.00027559424,0.0001497315,0.00032107285],"about_ca_topic_score_codex":0.00015615587,"about_ca_topic_score_gemma":0.0000649706,"teacher_disagreement_score":0.8623021,"about_ca_system_score_codex":0.00045037342,"about_ca_system_score_gemma":0.00012625339,"threshold_uncertainty_score":0.99978536},"labels":[],"label_agreement":null},{"id":"W2897457301","doi":"10.23919/chicc.2018.8483742","title":"Near-Infrared Spectrum of Coal Origin Identification Based on SVM Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Principal component analysis; Gaussian; Pattern recognition (psychology); Kernel (algebra); Artificial intelligence; Gaussian function; Coal; Computer science; Kernel principal component analysis; Ranking SVM; Algorithm; Mathematics; Engineering; Chemistry; Kernel method","score_opus":0.01631232055185095,"score_gpt":0.2884163530973854,"score_spread":0.27210403254553445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897457301","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3824719,0.00007879447,0.11060109,0.0005680942,0.0002754832,0.0001229519,0.000094879455,0.00036120962,0.5054256],"genre_scores_gemma":[0.9757056,0.0000041184453,0.0040265154,0.00012605987,0.00022845237,0.000005474543,0.000045591718,0.000015683398,0.019842502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989775,0.000008260178,0.0002712077,0.00026328058,0.00028124845,0.00019849125],"domain_scores_gemma":[0.99920106,0.000071292845,0.00013454776,0.00045420416,0.00007512581,0.00006374264],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009487364,0.00012752206,0.00018844345,0.00010876764,0.000088588444,0.000043911587,0.00021455991,0.00009656348,0.015774807],"category_scores_gemma":[0.00005888927,0.00011492444,0.0000883791,0.00047480332,0.00015449857,0.000059391332,0.000018409455,0.00009877049,0.00028895936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062085973,0.0024643226,0.069723874,0.00043662204,0.00078240794,0.000030185889,0.00047354415,0.00027604334,0.8164695,0.005419564,0.08753675,0.015766364],"study_design_scores_gemma":[0.0003730243,0.000066821114,0.0012462485,0.000008398613,0.00006113198,0.0000010611965,0.00007732608,0.030789154,0.96355176,0.00042842462,0.003264529,0.00013211934],"about_ca_topic_score_codex":0.00011327935,"about_ca_topic_score_gemma":0.00000723525,"teacher_disagreement_score":0.5932337,"about_ca_system_score_codex":0.00007006666,"about_ca_system_score_gemma":0.00006901467,"threshold_uncertainty_score":0.9851249},"labels":[],"label_agreement":null},{"id":"W2897594239","doi":"10.15353/vsnl.v3i1.193","title":"A Comprehensive Spectral Analysis of the Auto-fluorescence Characteristics of Three Algae Species at Twelve Discrete Excitation Wavelengths","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algae; Chlorophyta; Fluorescence; Phylum; Biological system; Botany; Fluorescence spectroscopy; Biology; Computer science; Pattern recognition (psychology); Artificial intelligence; Optics; Physics; Gene","score_opus":0.017912940040178262,"score_gpt":0.292263605993652,"score_spread":0.27435066595347374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897594239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98945624,0.0006038101,0.008879766,0.00030301395,0.00018706791,0.000043335396,0.00006309916,0.0000045247025,0.00045916793],"genre_scores_gemma":[0.9990811,0.00006110825,0.0005657606,0.000012739259,0.00010940623,4.72054e-7,0.000011659286,0.000007790846,0.00015001367],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99836284,0.000033668024,0.0007667706,0.00012180433,0.0005968691,0.000118025804],"domain_scores_gemma":[0.99654526,0.00031882076,0.0021952363,0.00023686067,0.00064325053,0.00006058259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001911108,0.00013882038,0.0005861611,0.00025480337,0.00025192305,0.0001168545,0.00033421177,0.000037143982,0.00006785365],"category_scores_gemma":[0.00017151963,0.00009317422,0.000296016,0.00025403604,0.0003365378,0.00021040314,0.0000981365,0.0001490597,5.758419e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029690994,0.00019619925,0.7756077,0.0006530256,0.0032160196,0.000019144516,0.0014060144,0.0055262367,0.20702681,0.003071301,0.0006029856,0.0023776735],"study_design_scores_gemma":[0.00044715716,0.000029837303,0.8845854,0.00021529716,0.0008323402,0.000044653472,0.0005008838,0.10869591,0.004033141,0.00039738033,0.00011725122,0.00010078614],"about_ca_topic_score_codex":0.000034663306,"about_ca_topic_score_gemma":0.0000020998084,"teacher_disagreement_score":0.20299366,"about_ca_system_score_codex":0.00007224578,"about_ca_system_score_gemma":0.00005491572,"threshold_uncertainty_score":0.37995344},"labels":[],"label_agreement":null},{"id":"W2898057311","doi":"10.1021/acs.analchem.8b04626","title":"A Hierarchical Multivariate Curve Resolution Methodology To Identify and Map Compounds in Spectral Images","year":2018,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Pfizer Canada; Mitacs","keywords":"Multivariate statistics; Chemistry; Sample (material); Pixel; Chemical imaging; Pattern recognition (psychology); Noise (video); Multivariate analysis; TRACE (psycholinguistics); Resolution (logic); Artificial intelligence; Data mining; Biological system; Analytical Chemistry (journal); Computer science; Hyperspectral imaging; Chromatography; Machine learning; Image (mathematics)","score_opus":0.05711844577168148,"score_gpt":0.38928235384939497,"score_spread":0.3321639080777135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898057311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96271366,0.00027313523,0.0037536905,0.0016131548,0.00003990614,0.000047308313,0.000028542981,0.000091145805,0.031439483],"genre_scores_gemma":[0.9891932,0.000018129358,0.0066590714,0.00018569808,0.0004835674,0.00001460589,0.000026711956,0.000022107655,0.0033968994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998061,0.00004999575,0.0004168766,0.00065725157,0.00024075777,0.00057411636],"domain_scores_gemma":[0.9987313,0.0004467636,0.000068404355,0.0003703836,0.000076390745,0.0003067493],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040393445,0.0002508088,0.0004749586,0.00012592149,0.00009592288,0.000069610054,0.00028312896,0.0002857255,0.0024371576],"category_scores_gemma":[0.000920359,0.00024814188,0.00010756472,0.0006079384,0.00046836585,0.0000769986,0.00021354588,0.0005291656,0.00007363037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002731972,0.00021687575,0.022677587,0.00017118649,0.0001573002,0.0000702337,0.00017822998,0.000004282292,0.97188383,0.00045983304,0.0036636142,0.00024382172],"study_design_scores_gemma":[0.0009692763,0.00005690464,0.016392063,0.000055908615,0.000211029,0.00006249575,0.00032827325,0.0031535581,0.9721807,0.0040592183,0.0020316062,0.0004989289],"about_ca_topic_score_codex":0.00025068014,"about_ca_topic_score_gemma":0.00002451287,"teacher_disagreement_score":0.028042585,"about_ca_system_score_codex":0.00015700166,"about_ca_system_score_gemma":0.00004614919,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W2899248844","doi":"10.1155/2018/4706147","title":"Assessment of Ripening Degree of Avocado by Electrical Impedance Spectroscopy and Support Vector Machine","year":2018,"lang":"en","type":"article","venue":"Journal of Food Quality","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Ripening; Postharvest; Climacteric; Electrical impedance; Dielectric spectroscopy; Chemistry; Phase angle (astronomy); Biological system; Ethylene; Horticulture; Food science; Electrode; Biology; Engineering; Electrical engineering; Physics; Biochemistry; Optics","score_opus":0.040083507624965806,"score_gpt":0.3765332029250064,"score_spread":0.3364496953000406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899248844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98884714,0.0015101295,0.004183637,0.0000957406,0.000051924257,0.000027894035,0.000055726134,0.000008093909,0.0052197166],"genre_scores_gemma":[0.99489313,0.00015146016,0.004590904,0.00003444591,0.00018811403,6.692973e-7,0.0000035407888,0.000012958768,0.00012479672],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978537,0.000067847854,0.0010633274,0.00018437057,0.0005767416,0.0002539949],"domain_scores_gemma":[0.99763584,0.0002638252,0.0014137718,0.00023964923,0.0003039267,0.00014297424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090801134,0.00017139148,0.0007354392,0.00013226338,0.00005555872,0.00001700583,0.0002751202,0.00011403866,0.00067733193],"category_scores_gemma":[0.00035863966,0.00014289489,0.00019645493,0.00040194887,0.00019560463,0.00012364313,0.000048980044,0.00041398956,6.2793293e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017680663,0.0002947879,0.06236341,0.00013953655,0.00034220124,0.00000281897,0.00009689084,2.529554e-7,0.93477947,0.00056921924,0.0006304424,0.00060417573],"study_design_scores_gemma":[0.0009042128,0.001553503,0.031119417,0.000030226061,0.00022231162,0.000047830305,0.0000938919,0.000068158064,0.9653014,0.00035746867,0.00016430061,0.00013726916],"about_ca_topic_score_codex":0.000040418825,"about_ca_topic_score_gemma":0.0000089064015,"teacher_disagreement_score":0.03124399,"about_ca_system_score_codex":0.00011234896,"about_ca_system_score_gemma":0.00019425595,"threshold_uncertainty_score":0.741631},"labels":[],"label_agreement":null},{"id":"W2900517774","doi":"10.5539/ijsp.v8n1p40","title":"A Sub-Model Theorem for Ordinary Least Squares","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ordinary least squares; Mathematics; Selection (genetic algorithm); Variable (mathematics); Least-squares function approximation; Feature selection; Mathematical optimization; Model selection; Applied mathematics; Generalized least squares; Statistics; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.023470829037210924,"score_gpt":0.3194291431579394,"score_spread":0.2959583141207285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900517774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5565318,0.00032385834,0.43889374,0.0005913586,0.00022098875,0.000056822988,0.0008162749,0.000010725736,0.0025544679],"genre_scores_gemma":[0.9491777,0.000058653924,0.05013015,0.00007037818,0.00038896495,0.0000028261481,0.000012830492,0.000007638988,0.00015087229],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99917996,0.00000827244,0.00033853474,0.00012285564,0.00024177002,0.000108584296],"domain_scores_gemma":[0.99827164,0.00025185404,0.00024813833,0.000081070204,0.0010783296,0.00006898881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027481868,0.00008980382,0.00016613184,0.00006868796,0.000060244252,0.00005656851,0.0002152284,0.00004643872,0.00028831678],"category_scores_gemma":[0.0005407506,0.00007255052,0.0000637585,0.000050003702,0.00020476863,0.00008386482,0.000038589016,0.00010525626,0.0000013039107],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072379354,0.002205522,0.06617407,0.0008573549,0.0030977873,0.00008532829,0.0020460044,0.00047309895,0.120179676,0.58838,0.04116952,0.16809374],"study_design_scores_gemma":[0.0013876188,0.0004293267,0.001696887,0.000055224707,0.00023221309,0.00014270535,0.00014653093,0.018287243,0.060665622,0.9132212,0.0034985247,0.00023691996],"about_ca_topic_score_codex":0.0000072096122,"about_ca_topic_score_gemma":0.0000090122085,"teacher_disagreement_score":0.3926459,"about_ca_system_score_codex":0.00006862667,"about_ca_system_score_gemma":0.000090501446,"threshold_uncertainty_score":0.31568667},"labels":[],"label_agreement":null},{"id":"W2901723422","doi":"10.1177/0142331218798429","title":"Multivariate Gaussian process regression for nonlinear modelling with colored noise","year":2018,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Autoregressive model; Colors of noise; White noise; Gaussian process; Noise (video); Autocorrelation; Multivariate statistics; Colored; Kriging; Gaussian noise; Covariance; Nonlinear system; Computer science; Mathematics; Gaussian; Algorithm; Artificial intelligence; Statistics; Physics","score_opus":0.03396358364129978,"score_gpt":0.2705435489783464,"score_spread":0.23657996533704664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901723422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31534857,0.00028255908,0.682699,0.0003360254,0.00008446547,0.00030223824,0.00005935926,0.000024223307,0.00086353533],"genre_scores_gemma":[0.99610704,0.000012808388,0.0036335257,0.0000126870245,0.00005157815,0.000035380966,0.0000015054173,0.000009347029,0.00013610735],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992049,0.0000063496527,0.00022330658,0.00015267929,0.000289907,0.00012289385],"domain_scores_gemma":[0.9992064,0.000017909322,0.00017545754,0.00019263757,0.00036894402,0.00003860677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014124365,0.00012095531,0.00023978861,0.000053398377,0.00020252613,0.000008783918,0.00015813332,0.00006115218,0.000032361633],"category_scores_gemma":[0.000013413774,0.00007249009,0.000087070475,0.0001703073,0.00022622428,0.00010271272,0.000002310599,0.000079568585,1.8984683e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050915643,0.00079166563,0.00096496154,0.0009616589,0.0013934101,4.907307e-7,0.00085582817,0.01991129,0.9666379,0.00021716992,0.000015996937,0.0031580466],"study_design_scores_gemma":[0.003926281,0.00016356968,0.00004649456,0.00025621202,0.0009810643,0.0000021117146,0.00018148175,0.03970596,0.9539355,0.00011355199,0.0005589103,0.00012884279],"about_ca_topic_score_codex":0.00009246248,"about_ca_topic_score_gemma":0.000082541796,"teacher_disagreement_score":0.6807585,"about_ca_system_score_codex":0.000028809058,"about_ca_system_score_gemma":0.00010394731,"threshold_uncertainty_score":0.295606},"labels":[],"label_agreement":null},{"id":"W2902531571","doi":"10.1130/abs/2018am-324851","title":"CRYSTAL SIZE DISTRIBUTION OF OLIVINE IN THE BLACK STURGEON SILL, ONTARIO","year":2018,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sill; Geology; Olivine; Sturgeon; Crystal (programming language); Geochemistry; Mineralogy; Fishery; Fish <Actinopterygii>; Computer science; Biology","score_opus":0.01731688700663292,"score_gpt":0.2605968102450718,"score_spread":0.2432799232384389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902531571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99377906,0.00007245208,0.00021131916,0.00048514424,0.0000081843145,0.00014258847,0.00001026087,0.000037811573,0.0052531906],"genre_scores_gemma":[0.99687725,0.000039443967,0.0024121364,0.00019077162,0.00006702308,0.000018884071,0.00008016021,0.000007775048,0.00030653275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99834067,0.000023963534,0.0004631389,0.00031558005,0.00042527885,0.0004313543],"domain_scores_gemma":[0.99865836,0.00034696187,0.00045183473,0.00030889828,0.00016527297,0.000068685724],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022958682,0.00021520228,0.00044588142,0.000008737908,0.00008384748,0.00002056649,0.00036465545,0.00019903248,0.0012020144],"category_scores_gemma":[0.00013318482,0.00012540666,0.00022763011,0.00069492916,0.002096543,0.00007320588,0.00006779492,0.00042906622,0.0000069339767],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009480586,0.009352991,0.92751163,0.0005249796,0.0011956961,0.000030513407,0.012516132,0.0003019132,0.02055876,0.000112691916,0.0060470477,0.020899577],"study_design_scores_gemma":[0.0015365569,0.0034061186,0.92178226,0.000113121096,0.00037160734,0.000022437818,0.013302006,0.00014509182,0.034135524,0.00044382474,0.024175188,0.00056629203],"about_ca_topic_score_codex":0.014984427,"about_ca_topic_score_gemma":0.0009364462,"teacher_disagreement_score":0.020333285,"about_ca_system_score_codex":0.00009815309,"about_ca_system_score_gemma":0.00007986948,"threshold_uncertainty_score":0.99971104},"labels":[],"label_agreement":null},{"id":"W2902576147","doi":"10.1002/btpr.2762","title":"Raw material quality assessment approaches comparison in pectin production","year":2018,"lang":"en","type":"article","venue":"Biotechnology Progress","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Innovationsfonden","keywords":"Raw material; Production (economics); Pectin; Quality (philosophy); Pulp and paper industry; Environmental science; Food science; Chemistry; Biotechnology; Biology; Engineering; Organic chemistry; Economics; Physics","score_opus":0.09126944377857234,"score_gpt":0.3762459173460806,"score_spread":0.28497647356750827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902576147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943181,0.00027884936,0.0001674007,0.0019796148,0.00023977863,0.00012336246,0.0000050410017,0.00043298016,0.0024548292],"genre_scores_gemma":[0.9959874,0.000013656112,0.0032707972,0.0000107918295,0.00033259243,0.00008127389,0.000027726997,0.000017045926,0.00025870284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984344,0.0000368664,0.00040086627,0.0005599672,0.00019395172,0.0003739204],"domain_scores_gemma":[0.9991685,0.000016951813,0.00021530158,0.0005229566,0.00004261506,0.000033670884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032774525,0.00018982575,0.00037427802,0.00025940925,0.0001206395,0.00004358777,0.0003702192,0.0005578238,0.00046177147],"category_scores_gemma":[0.000098965385,0.00017803404,0.00004874203,0.0007204311,0.00075530494,0.000070589565,0.00011584399,0.00036445126,0.000025130405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002402689,0.0015047109,0.4811887,0.00023483088,0.00017399219,0.00000847718,0.00023117875,0.0000023442458,0.48339158,0.0068470133,0.00070870377,0.025468163],"study_design_scores_gemma":[0.0003044648,0.000078746794,0.011394,0.00001363261,0.000035948848,0.000010194578,0.0008724831,0.00010982249,0.9852993,0.00035312853,0.0013205004,0.00020778517],"about_ca_topic_score_codex":0.00005792814,"about_ca_topic_score_gemma":0.000068945585,"teacher_disagreement_score":0.5019077,"about_ca_system_score_codex":0.0001800348,"about_ca_system_score_gemma":0.000041572937,"threshold_uncertainty_score":0.72600174},"labels":[],"label_agreement":null},{"id":"W2902969155","doi":"10.21273/jashs04504-18","title":"Evaluation of Carbohydrate Concentrations in Phalaenopsis Using Near-infrared Spectroscopy","year":2018,"lang":"en","type":"article","venue":"Journal of the American Society for Horticultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Taiwan University; Ministry of Science and Technology, Taiwan; National Science Council","keywords":"Calibration; Starch; Sucrose; Fructose; Coefficient of determination; Chemistry; Phalaenopsis; Derivative (finance); Analytical Chemistry (journal); Correlation coefficient; Near-infrared spectroscopy; Second derivative; Mathematics; Botany; Chromatography; Food science; Biology; Physics; Statistics; Optics","score_opus":0.04218505941825031,"score_gpt":0.36264650157306383,"score_spread":0.32046144215481354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902969155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99887496,0.00013175662,0.0002086117,0.00021163958,0.00008352231,0.00007289162,0.000006008186,0.0000034771258,0.00040711058],"genre_scores_gemma":[0.9915617,0.000021215485,0.008175688,0.00007228598,0.00013259589,0.0000021249127,3.0914504e-7,0.000004088569,0.00003000379],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982842,0.000022463431,0.00039402078,0.00013372353,0.0009136078,0.00025196024],"domain_scores_gemma":[0.9972408,0.000052944197,0.0012218163,0.00015910345,0.0012607083,0.00006464377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010565212,0.00009007509,0.00024608494,0.00003187198,0.00029304106,0.000060988863,0.00045418905,0.000021575528,0.000053432632],"category_scores_gemma":[0.0006223133,0.000054514272,0.00033012824,0.0021512285,0.0019279217,0.00028476462,0.00004233633,0.00011905491,2.467915e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013744942,0.000037118105,0.006763536,0.0000040570203,0.000062514286,6.425183e-8,0.00089678733,0.00044021718,0.99137676,0.000008609353,0.00014409673,0.00025249232],"study_design_scores_gemma":[0.00039351045,0.00012424657,0.010519059,0.000027849188,0.0005961052,0.000009563776,0.0043120654,0.04416739,0.93946403,0.0002536536,0.000039737846,0.00009281096],"about_ca_topic_score_codex":0.00011387183,"about_ca_topic_score_gemma":0.000014647811,"teacher_disagreement_score":0.051912755,"about_ca_system_score_codex":0.0006275776,"about_ca_system_score_gemma":0.000627068,"threshold_uncertainty_score":0.7103508},"labels":[],"label_agreement":null},{"id":"W2904043173","doi":"10.1007/s10489-018-1300-5","title":"Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy","year":2018,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea","keywords":"Computer science; Classifier (UML); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Fourier transform; Radial basis function; Materials science; Fuzzy logic; Process engineering; Mathematics; Engineering","score_opus":0.14245398593497746,"score_gpt":0.3590531760838978,"score_spread":0.21659919014892035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904043173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15597335,0.0003166585,0.84082127,0.00002020936,0.00012888429,0.00031225913,0.000032617034,0.00004441055,0.002350357],"genre_scores_gemma":[0.9847836,0.000071125265,0.014754914,0.000055080345,0.00021388687,0.000027718834,0.00006617915,0.000011577999,0.000015941561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859196,0.000018274777,0.00056909217,0.0003530206,0.00021790423,0.00024973697],"domain_scores_gemma":[0.99715513,0.0016902519,0.000500606,0.00041643452,0.00019099793,0.000046581783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032276637,0.00021025274,0.00043233336,0.00012209482,0.00015970196,0.00003717604,0.00028063534,0.00009804342,0.000061075305],"category_scores_gemma":[0.00034973372,0.00018935764,0.000038042355,0.00048089997,0.00045396734,0.0002552644,0.00008048371,0.00014351576,0.0000059616996],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.022803,0.0006405392,0.005880832,0.005505061,0.0022436143,8.8934496e-7,0.012413006,0.4675772,0.43363887,0.0033861017,0.008160855,0.037750054],"study_design_scores_gemma":[0.00027853044,0.00032546214,0.00006805013,0.00012524852,0.0002657695,3.4719585e-7,0.0032913461,0.3488143,0.6455427,0.0010782683,0.000044158423,0.0001658124],"about_ca_topic_score_codex":0.0000060730413,"about_ca_topic_score_gemma":0.000004132396,"teacher_disagreement_score":0.8288102,"about_ca_system_score_codex":0.000053166103,"about_ca_system_score_gemma":0.000055310273,"threshold_uncertainty_score":0.77217805},"labels":[],"label_agreement":null},{"id":"W2906106607","doi":"10.5539/ijsp.v8n1p113","title":"On the Error Rate Comparison of the Quadratic Discriminant Function, Euclidean Distance Classifier, Fisher’s Linear Discriminant Function and the Vine Copulas","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Linear discriminant analysis; Discriminant function analysis; Kernel Fisher discriminant analysis; Discriminant; Quadratic classifier; Statistics; Euclidean distance; Word error rate; Optimal discriminant analysis; Covariance; Classifier (UML); Pattern recognition (psychology); Gaussian; Artificial intelligence; Quadratic equation; Computer science","score_opus":0.0370092993692735,"score_gpt":0.3160317181469758,"score_spread":0.2790224187777023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906106607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9129561,0.00044382745,0.0780093,0.0065309852,0.0008404706,0.00016300574,0.00023536333,0.000006131125,0.000814824],"genre_scores_gemma":[0.99887097,0.00006822838,0.0003805153,0.00017965904,0.00020984394,0.000004539325,0.0000070269366,0.000007826747,0.00027136825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99852127,0.00012055581,0.0006458657,0.00016147285,0.00043460642,0.00011620121],"domain_scores_gemma":[0.9974114,0.0010011708,0.00073010026,0.00022318563,0.0005872143,0.00004694514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071959506,0.00014248356,0.00027810497,0.000033846056,0.00023878044,0.000077128956,0.00031048467,0.000046132474,0.00026688463],"category_scores_gemma":[0.0012961788,0.0000624025,0.00008460465,0.00011586497,0.00092913007,0.0000738714,0.000079166304,0.00030891373,0.0000010462574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01350981,0.001697841,0.19336647,0.0005292379,0.0019529557,0.000008365554,0.003933645,0.00021769735,0.009399105,0.74337745,0.020405816,0.011601607],"study_design_scores_gemma":[0.0042039193,0.0012146877,0.2620887,0.000416485,0.00177542,0.000055079137,0.0038267905,0.026010882,0.013861594,0.68030113,0.0058322772,0.00041304808],"about_ca_topic_score_codex":0.00008205804,"about_ca_topic_score_gemma":0.00023097526,"teacher_disagreement_score":0.0859149,"about_ca_system_score_codex":0.00006717285,"about_ca_system_score_gemma":0.000059031132,"threshold_uncertainty_score":0.34234184},"labels":[],"label_agreement":null},{"id":"W2909618363","doi":"10.3166/i2m.17.663-674","title":"Identification of pests hidden in wheat kernels based on support vector machine classifier","year":2018,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Henan University; Henan University of Technology; National Natural Science Foundation of China; National Science Foundation","keywords":"Support vector machine; Classifier (UML); Artificial intelligence; Identification (biology); Structured support vector machine; Pattern recognition (psychology); Margin classifier; Computer science; Quadratic classifier; Machine learning; Biology; Botany","score_opus":0.026825212621029382,"score_gpt":0.3237382023290476,"score_spread":0.2969129897080182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909618363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908852,0.00002871006,0.0008197643,0.0007656358,0.0001550687,0.00009875385,0.000060654806,0.000072882445,0.007113328],"genre_scores_gemma":[0.99846846,0.000012596893,0.00035099534,0.00024264437,0.00010052946,0.000031041534,0.00013643953,0.000017206286,0.00064006745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984354,0.000052433166,0.00055355375,0.00035646182,0.00035563853,0.00024653907],"domain_scores_gemma":[0.99905705,0.00012888208,0.00029584856,0.0003657238,0.00009937272,0.000053108895],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031994612,0.0001771385,0.0002667525,0.0003098989,0.000056312674,0.000028899985,0.00024457782,0.00017092051,0.003461112],"category_scores_gemma":[0.00020762283,0.00016617065,0.00008626911,0.00053293287,0.0001460582,0.00013433208,0.000023372828,0.00017677406,0.000083694256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035382016,0.00031409334,0.31772292,0.00007121879,0.000060031816,0.000006454602,0.00017530192,0.000048570833,0.6749744,0.00020490907,0.00078117434,0.0052871034],"study_design_scores_gemma":[0.0012073065,0.00019272775,0.11169933,0.000012797649,0.0000708888,0.0000017568815,0.00013654098,0.0022911835,0.88378316,0.00033406838,0.00012124513,0.0001489708],"about_ca_topic_score_codex":0.00005801763,"about_ca_topic_score_gemma":0.00005613114,"teacher_disagreement_score":0.20880878,"about_ca_system_score_codex":0.00017873524,"about_ca_system_score_gemma":0.000098263976,"threshold_uncertainty_score":0.9974499},"labels":[],"label_agreement":null},{"id":"W2910548408","doi":"10.5539/mas.v13n2p157","title":"Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taguchi methods; Fuzzy logic; Computer science; Artificial neural network; Process (computing); Quality (philosophy); Product (mathematics); Backpropagation; Range (aeronautics); Data mining; Machine learning; Mathematical optimization; Artificial intelligence; Mathematics; Engineering","score_opus":0.01667363421174541,"score_gpt":0.2514000047679802,"score_spread":0.23472637055623477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910548408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9090451,0.00012292847,0.06127564,0.000042446565,0.000040366387,0.00013072083,0.0000020087468,0.000042989122,0.029297812],"genre_scores_gemma":[0.9964025,0.000014892592,0.0023184086,0.00011791704,0.00005999128,0.00002419819,0.000005757519,0.000008974261,0.0010473623],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988955,0.0000012015199,0.00012666442,0.0004226636,0.00020987661,0.0003440919],"domain_scores_gemma":[0.99955493,0.000035058172,0.000069080095,0.00022409514,0.000042632233,0.00007422354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017196697,0.00011784016,0.00014172366,0.00006848506,0.00020231504,0.000092258604,0.0002824615,0.000059216523,0.00015333916],"category_scores_gemma":[0.000010862678,0.00010128306,0.000025306368,0.0003911885,0.00022053604,0.00017710624,0.00008301003,0.00011071843,0.000028642922],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007267568,0.000030281131,0.0033091104,0.000088056775,0.000007891789,1.914789e-7,0.000118033415,0.023048943,0.9621785,0.0013766453,0.000034858705,0.00973479],"study_design_scores_gemma":[0.00028196917,0.000025586849,0.0004238792,0.000002801572,0.000014673819,0.000003116228,0.000056702745,0.89013624,0.10845713,0.0004215403,0.000035976074,0.00014036443],"about_ca_topic_score_codex":0.0000017575986,"about_ca_topic_score_gemma":2.2544508e-7,"teacher_disagreement_score":0.8670873,"about_ca_system_score_codex":0.00004824455,"about_ca_system_score_gemma":0.00002785948,"threshold_uncertainty_score":0.41302034},"labels":[],"label_agreement":null},{"id":"W2910820632","doi":"10.1016/j.saa.2019.01.046","title":"Application of infrared spectroscopy as Process Analytics Technology (PAT) approach in biodiesel production process utilizing Multivariate Curve Resolution Alternative Least Square (MCR-ALS)","year":2019,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Process analytical technology; Chemometrics; Process (computing); Process engineering; Process control; Spectroscopy; Computer science; Materials science; Analytical Chemistry (journal); Biological system; Chemistry; Work in process; Engineering; Machine learning; Physics; Chromatography","score_opus":0.011186549024751333,"score_gpt":0.28867813483819976,"score_spread":0.2774915858134484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910820632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9694777,0.0011772863,0.017703645,0.000477826,0.00009296047,0.0010935994,0.00004848714,0.00024342051,0.009685076],"genre_scores_gemma":[0.9941899,0.0002873102,0.0046493,0.00007176355,0.000120995785,0.00020941024,0.00023991705,0.00011373937,0.00011764146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9948416,0.00009961329,0.0011006647,0.00193601,0.0008668216,0.0011553399],"domain_scores_gemma":[0.99735105,0.00003917975,0.000877742,0.0011602202,0.0003577179,0.00021406171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035093084,0.0008585194,0.0012019933,0.0014597138,0.0001943486,0.000116029616,0.00077601837,0.00054999086,0.00015665771],"category_scores_gemma":[0.00025014736,0.0008748048,0.00027737726,0.0036756066,0.00046122415,0.00035171132,0.00019806967,0.00093438925,0.000024470655],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005795765,0.00085696444,0.012953156,0.000620442,0.0003586127,0.000022445016,0.0004235408,0.00021279401,0.9816381,0.0022688932,0.00001945791,0.000046026824],"study_design_scores_gemma":[0.001735828,0.00040103804,0.00039407102,0.00016944746,0.00032981325,0.000096519725,0.002488408,0.009508751,0.97843575,0.0055411058,0.00006182699,0.0008374523],"about_ca_topic_score_codex":0.00016275713,"about_ca_topic_score_gemma":0.00002400884,"teacher_disagreement_score":0.024712222,"about_ca_system_score_codex":0.00041513355,"about_ca_system_score_gemma":0.0002620952,"threshold_uncertainty_score":0.9993703},"labels":[],"label_agreement":null},{"id":"W2912754481","doi":"10.1016/j.forsciint.2019.01.045","title":"A random object-oriented population study of household paints measured by infrared spectroscopy","year":2019,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Centre for Comparative Criminology; Université du Québec à Trois-Rivières; Université du Québec à Montréal; Innovation and Economic Development Trois Rivières","funders":"Fondation de l’UQTR","keywords":"Principal component analysis; Population; Infrared spectroscopy; Computer science; Artificial intelligence; Chemistry; Environmental health; Medicine","score_opus":0.014970184496463168,"score_gpt":0.2765546818723039,"score_spread":0.2615844973758408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912754481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98587066,0.000029854673,0.00030487223,0.000032349115,0.00045503303,0.00015807693,0.000042899486,0.000063984444,0.013042257],"genre_scores_gemma":[0.997449,0.0000031663353,0.00047984926,0.00003764837,0.000072059534,0.000013634153,0.000057577938,0.000012944929,0.001874137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974556,0.000015000127,0.00040706768,0.0004670508,0.0013800162,0.00027525018],"domain_scores_gemma":[0.9988965,0.00008693312,0.00028572703,0.00036974248,0.00028064588,0.000080493766],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040574765,0.00016238075,0.00028201035,0.00036575316,0.0000951593,0.00006557784,0.0006054233,0.000056997807,0.0013638055],"category_scores_gemma":[0.0004317609,0.00014657588,0.00008413838,0.0010643349,0.00017544566,0.00034566098,0.00010166972,0.00014720805,0.000020240312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037911846,0.00066024374,0.4923375,0.000013810423,0.00015314743,0.0000022838806,0.0006793818,0.000202217,0.50351036,0.00023991239,0.0015540512,0.00026796764],"study_design_scores_gemma":[0.005797278,0.00030837583,0.0457052,0.00004133703,0.00009502219,0.000008490296,0.0026723065,0.0035583673,0.9404792,0.0006120906,0.00037157277,0.00035076868],"about_ca_topic_score_codex":0.00035344876,"about_ca_topic_score_gemma":0.000026824995,"teacher_disagreement_score":0.4466323,"about_ca_system_score_codex":0.00027134726,"about_ca_system_score_gemma":0.00007258006,"threshold_uncertainty_score":0.9995491},"labels":[],"label_agreement":null},{"id":"W2914231058","doi":"10.1208/s12249-018-1286-7","title":"Methods to Assess Mixing of Pharmaceutical Powders","year":2019,"lang":"en","type":"article","venue":"AAPS PharmSciTech","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Process engineering; Process analytical technology; Pharmaceutical manufacturing; Computer science; Quality by Design; Process (computing); Biochemical engineering; Quality (philosophy); New product development; Manufacturing engineering; Work in process; Engineering; Operations management; Business","score_opus":0.07468100929846573,"score_gpt":0.44547091339643535,"score_spread":0.37078990409796964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914231058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9112101,0.0006047679,0.015501285,0.00034827858,0.00021433693,0.00012597248,0.00001838285,0.00015467496,0.07182218],"genre_scores_gemma":[0.9624409,0.000027153237,0.033241738,0.00046569432,0.00008448779,0.000012764801,0.000007891888,0.000031411506,0.0036879338],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985707,0.00003304799,0.00031903246,0.0003884796,0.00030452618,0.00038426055],"domain_scores_gemma":[0.9989024,0.00032834525,0.00009787932,0.00040335683,0.0000829314,0.00018510912],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038003706,0.00018961298,0.00038489187,0.00023481702,0.000039639715,0.000028145072,0.00044108403,0.000117001226,0.010606096],"category_scores_gemma":[0.00015949926,0.00018064822,0.00015334082,0.0010511635,0.00004638041,0.00010717707,0.0001379318,0.00028452484,0.00019469533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035372872,0.000089305395,0.008191603,0.0001342136,0.00013316987,0.0000032354528,0.00007898509,0.000018809495,0.9846184,0.00028191312,0.00069927104,0.0057157036],"study_design_scores_gemma":[0.00046369978,0.000022296239,0.00009552412,0.000019796507,0.00014237525,0.000007452467,0.00026415178,0.0002885774,0.97807306,0.00019191114,0.020218357,0.00021278439],"about_ca_topic_score_codex":0.000023182389,"about_ca_topic_score_gemma":3.3514974e-7,"teacher_disagreement_score":0.06813425,"about_ca_system_score_codex":0.00007378004,"about_ca_system_score_gemma":0.0000425862,"threshold_uncertainty_score":0.99029833},"labels":[],"label_agreement":null},{"id":"W2919670103","doi":"10.1109/tcbb.2019.2903094","title":"A Novel Edge Effect Detection Method for Real-Time Cellular Analyzer Using Functional Principal Component Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Principal component analysis; Enhanced Data Rates for GSM Evolution; Spectrum analyzer; Point (geometry); Time point; Computer science; Set (abstract data type); Biological system; Mathematics; Pattern recognition (psychology); Algorithm; Statistics; Artificial intelligence; Geometry; Physics; Telecommunications","score_opus":0.020928439962683357,"score_gpt":0.3014782696383845,"score_spread":0.28054982967570113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919670103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2823994,0.000017213439,0.71686524,0.000028788565,0.00011552478,0.00015144848,0.00024662513,0.000054165692,0.00012158873],"genre_scores_gemma":[0.8361118,0.0000075476323,0.16278371,0.000079501646,0.00007865186,0.000031314554,0.00056440895,0.000016065658,0.00032698718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866843,0.000036658003,0.00051055895,0.00032292242,0.00018655846,0.00027489883],"domain_scores_gemma":[0.9980895,0.0011736122,0.0002389145,0.00024657577,0.00015327978,0.00009813245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003192791,0.0002740588,0.00049995026,0.00062201446,0.0003455591,0.000042610296,0.00014040065,0.00027671797,0.00049365294],"category_scores_gemma":[0.000024425826,0.00023986402,0.00036334805,0.0008017207,0.00008560024,0.0001335118,0.000008646114,0.00022900125,0.00003494864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050913467,0.0002811704,0.0006575491,0.00024221944,0.003657544,3.8120126e-7,0.000117908814,0.6136789,0.37408936,0.00009334915,0.000009561149,0.0066629476],"study_design_scores_gemma":[0.0011987571,0.00018548999,0.00038137272,0.000009893469,0.0018486847,0.000021979718,0.00006125257,0.8999275,0.09572771,0.00030368674,0.000055430184,0.00027822237],"about_ca_topic_score_codex":0.000044486107,"about_ca_topic_score_gemma":0.0000060138896,"teacher_disagreement_score":0.55408156,"about_ca_system_score_codex":0.000129967,"about_ca_system_score_gemma":0.000057122466,"threshold_uncertainty_score":0.9781371},"labels":[],"label_agreement":null},{"id":"W2921099479","doi":"10.3390/app9061111","title":"Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Postdoctoral Science Foundation","keywords":"Coal; Near-infrared spectroscopy; Particle swarm optimization; Computer science; Identification (biology); Pattern recognition (psychology); Data mining; Artificial intelligence; Biological system; Process engineering; Machine learning; Engineering; Physics; Ecology; Biology; Waste management","score_opus":0.021116047839219478,"score_gpt":0.29395002612386595,"score_spread":0.27283397828464645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921099479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98018223,0.000017435103,0.00056719914,0.00002319805,0.00003108307,0.000044413813,0.0000027902033,0.000040942337,0.019090714],"genre_scores_gemma":[0.99876094,0.000002296026,0.0008630865,0.000045384728,0.00002567033,0.0000030974406,0.0000051589373,0.0000058150554,0.00028857312],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988173,0.00001066156,0.00022870033,0.00031483316,0.00043066771,0.00019783054],"domain_scores_gemma":[0.9994,0.0001567055,0.00018529873,0.0001848643,0.00003095559,0.000042143187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029306224,0.00009810334,0.00016930464,0.0001670654,0.00020330658,0.00007903401,0.00024114884,0.0000763646,0.0006662452],"category_scores_gemma":[0.000051529292,0.00008645954,0.000060323346,0.0010223404,0.00032514852,0.00005724387,0.00002261163,0.00015749704,0.00004234717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024927842,0.0000627604,0.08696863,0.000038987877,0.00001434871,2.700313e-7,0.00005975496,0.048890967,0.863246,0.00054500333,0.000017478242,0.0001308737],"study_design_scores_gemma":[0.00039173767,0.000050045768,0.0026422988,0.000017939832,0.000055282086,9.03661e-7,0.0011099065,0.20987281,0.7849759,0.0001983264,0.00047239964,0.00021248177],"about_ca_topic_score_codex":0.000042253658,"about_ca_topic_score_gemma":4.6771933e-7,"teacher_disagreement_score":0.16098185,"about_ca_system_score_codex":0.000024230641,"about_ca_system_score_gemma":0.00007585707,"threshold_uncertainty_score":0.7294918},"labels":[],"label_agreement":null},{"id":"W2924325937","doi":"10.1016/j.infrared.2019.03.033","title":"Single kernel wheat hardness estimation using near infrared hyperspectral imaging","year":2019,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canada Foundation for Innovation","keywords":"Hyperspectral imaging; Partial least squares regression; Principal component analysis; Calibration; Kernel (algebra); Mathematics; Smoothing; Mean squared error; Pattern recognition (psychology); Biological system; Artificial intelligence; Computer science; Materials science; Statistics","score_opus":0.014160684690811547,"score_gpt":0.26273580862090856,"score_spread":0.248575123930097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924325937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9603259,0.00058584387,0.012216561,0.00018003944,0.00019915159,0.00011835313,0.000024353205,0.00085408764,0.025495714],"genre_scores_gemma":[0.98598194,0.000008933981,0.012301731,0.00008639804,0.0001210235,0.00001552786,0.000054587515,0.00006609881,0.0013637482],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998272,0.000009921395,0.0003456718,0.0005433695,0.0002534098,0.00057563675],"domain_scores_gemma":[0.99870384,0.000046552188,0.00022510052,0.00081422256,0.00014127184,0.000068995876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000047228095,0.00032878065,0.00047163313,0.00025054906,0.00018003672,0.00012706034,0.0005005628,0.00030577305,0.00065554015],"category_scores_gemma":[0.000078021585,0.00036783717,0.00014592797,0.0014018615,0.00025522272,0.00039994132,0.0001802821,0.0005041236,0.00017783965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028199707,0.00023746832,0.023998784,0.00011933705,0.00015294665,0.000016467062,0.0002452749,0.0011278517,0.96542513,0.002286196,0.00030998385,0.0060523404],"study_design_scores_gemma":[0.0007509094,0.000032988162,0.00009763631,0.00006237319,0.00017302623,0.000043530184,0.0007760445,0.072140485,0.8868823,0.03773629,0.0008087858,0.0004956146],"about_ca_topic_score_codex":0.000047277234,"about_ca_topic_score_gemma":5.172355e-7,"teacher_disagreement_score":0.078542836,"about_ca_system_score_codex":0.0003980033,"about_ca_system_score_gemma":0.000118778145,"threshold_uncertainty_score":0.99987733},"labels":[],"label_agreement":null},{"id":"W2930748308","doi":"10.1177/0967033519840742","title":"Temperature and moisture insensitive prediction of biomass calorific value from near infrared spectra using external parameter orthogonalization","year":2019,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations","funders":"FPInnovations; Canadian Forest Service; Government of Canada","keywords":"Orthogonalization; Heat of combustion; Calibration; Water content; Partial least squares regression; Biomass (ecology); Moisture; Environmental science; Mathematics; Process engineering; Biological system; Materials science; Statistics; Algorithm; Chemistry; Engineering","score_opus":0.012429948521437811,"score_gpt":0.2526515805098399,"score_spread":0.24022163198840207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930748308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944167,0.0019108925,0.001626286,0.000038836104,0.00035220882,0.00011948115,0.0002338258,0.000032987715,0.001268755],"genre_scores_gemma":[0.9671669,0.00023993175,0.031393204,0.00009179198,0.0005945487,0.0000014034371,0.000061435385,0.00005868733,0.00039209696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974284,0.00008000224,0.0009832482,0.00040305604,0.00073972595,0.00036553465],"domain_scores_gemma":[0.99768615,0.00021633737,0.0011094598,0.00039989327,0.0003879081,0.00020022897],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002179892,0.00038123375,0.0008449436,0.00028621854,0.00016710033,0.00025830042,0.0002646558,0.000426132,0.0009228532],"category_scores_gemma":[0.00020509874,0.0003402055,0.0002905113,0.0007481117,0.00021761372,0.0005341403,0.000062884625,0.00082171947,0.000007093912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050078117,0.00013200233,0.0679277,0.00009489108,0.00043520937,0.000053870877,0.0006672462,0.00031381595,0.9294564,0.00005656522,0.00033737853,0.000024141207],"study_design_scores_gemma":[0.0019297632,0.0003863744,0.03525716,0.00032549302,0.0004989157,0.00022537589,0.00059876643,0.0042946395,0.9532895,0.0023707268,0.0004938747,0.00032942818],"about_ca_topic_score_codex":0.00013775477,"about_ca_topic_score_gemma":0.0000032204243,"teacher_disagreement_score":0.032670546,"about_ca_system_score_codex":0.0002888707,"about_ca_system_score_gemma":0.00032487212,"threshold_uncertainty_score":0.99999046},"labels":[],"label_agreement":null},{"id":"W2938945452","doi":"10.7451//cbe.2018.60.7.1","title":"Non-destructive and rapid discrimination of hard-to-cook beans using hyperspectral imaging","year":2018,"lang":"en","type":"article","venue":"Canadian Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Hyperspectral imaging; Near-infrared spectroscopy; Relative humidity; Spectral imaging; Materials science; Remote sensing; Environmental science; Optics; Physics; Geology; Meteorology","score_opus":0.010946237686582925,"score_gpt":0.2215960590498268,"score_spread":0.21064982136324387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938945452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99573183,0.00042707013,0.0011849143,0.00004386189,0.00013108301,0.00006199617,0.000043249995,0.000032507283,0.0023434907],"genre_scores_gemma":[0.99783176,0.000003881511,0.0017760076,0.000022566828,0.00027327248,0.0000035624464,0.0000048413335,0.000027944065,0.00005618544],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991529,0.0000026538328,0.00019048828,0.00023109457,0.00009774116,0.0003251124],"domain_scores_gemma":[0.9993764,0.000016322236,0.000051349412,0.00017871306,0.0000826316,0.0002945735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006437158,0.00015214551,0.00021868112,0.000448231,0.000079430574,0.000035453104,0.000112536894,0.00006083884,0.00008461093],"category_scores_gemma":[0.00005018597,0.00016911782,0.000042483247,0.00039950386,0.000045218854,0.0001027859,0.000015281516,0.000082240505,0.0000032836185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029968598,0.000003648883,0.007017283,0.00019263827,0.00006750376,0.0000073623273,0.000843412,0.00007863407,0.9912675,0.0002743124,0.000050766394,0.00019391472],"study_design_scores_gemma":[0.00025407295,0.000025193469,0.004315895,0.00024197529,0.00014427266,0.00007574979,0.002398713,0.018988261,0.97258794,0.00000995721,0.00057089987,0.00038708738],"about_ca_topic_score_codex":0.019519342,"about_ca_topic_score_gemma":0.0027536575,"teacher_disagreement_score":0.018909628,"about_ca_system_score_codex":0.00041200014,"about_ca_system_score_gemma":0.00008863565,"threshold_uncertainty_score":0.98700976},"labels":[],"label_agreement":null},{"id":"W2942216804","doi":"10.1186/s13007-019-0411-2","title":"Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds","year":2019,"lang":"en","type":"article","venue":"Plant Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"FP7 People: Marie-Curie Actions; Institut National de la Recherche Agronomique; AgreenSkills; Agence Universitaire de la Francophonie; European Commission; Science and Technology Development Fund; Institut de Recherche pour le Développement","keywords":"Vigna; Multispectral image; Computer science; Biology; Agronomy; Artificial intelligence","score_opus":0.042757603294046785,"score_gpt":0.3932963199551017,"score_spread":0.35053871666105496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942216804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23147815,0.00051790016,0.7660447,0.000034787136,0.000046321937,0.00016409982,0.00008801712,0.00006065602,0.0015653858],"genre_scores_gemma":[0.6578113,0.00005648776,0.3419054,0.00000998759,0.000031406726,0.0000059885406,0.00010775146,0.0000094962315,0.00006222916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992799,0.000034416258,0.00027269704,0.00020032993,0.0001014845,0.0001111536],"domain_scores_gemma":[0.99909866,0.0003974957,0.00022057936,0.00017636915,0.00008045689,0.000026458176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035336512,0.000102420825,0.00027264253,0.00013394577,0.000023574195,0.0000105105355,0.00009002861,0.00007471755,0.00006560653],"category_scores_gemma":[0.00005824759,0.00008928686,0.000060630686,0.00018486177,0.000043306543,0.00007161809,0.000029473415,0.00006150792,3.3411018e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050751,0.000046333807,0.014631076,0.00031969443,0.000034783872,1.9795937e-7,0.000074551754,0.000003713825,0.9518549,0.0007583008,0.00008486396,0.032140847],"study_design_scores_gemma":[0.00023680335,0.000039472754,0.0048240093,0.000057337315,0.00008448599,0.0000071435275,0.00009249482,0.081722945,0.91205156,0.00023661567,0.000556918,0.00009023533],"about_ca_topic_score_codex":0.00002239704,"about_ca_topic_score_gemma":5.960567e-7,"teacher_disagreement_score":0.42633313,"about_ca_system_score_codex":0.000022482458,"about_ca_system_score_gemma":0.000012704777,"threshold_uncertainty_score":0.36410126},"labels":[],"label_agreement":null},{"id":"W2943222241","doi":"10.1016/j.postharvbio.2019.04.005","title":"Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging","year":2019,"lang":"en","type":"article","venue":"Postharvest Biology and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; South China University of Technology","keywords":"PEAR; Alternaria alternata; Hyperspectral imaging; Black spot; Alternaria; Horticulture; Biology; Artificial intelligence; Computer science","score_opus":0.007971359293416726,"score_gpt":0.2672049333542392,"score_spread":0.2592335740608225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943222241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938584,0.0056349523,0.00009799889,0.000107944616,0.000054544344,0.00007009722,0.00003364455,0.00008046528,0.00006198017],"genre_scores_gemma":[0.99952763,0.0002559419,0.000064581385,0.000005680542,0.000046340203,0.000005441504,0.000006809858,0.000018424233,0.00006917316],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899817,0.000013624013,0.00021089317,0.00042908653,0.000056265086,0.0002919786],"domain_scores_gemma":[0.9994435,0.000035576635,0.00015305278,0.00023936262,0.000052073257,0.00007643478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005501443,0.00018059721,0.0002858503,0.00024993526,0.00006907181,0.00001824363,0.00017151567,0.00019247217,0.000031383523],"category_scores_gemma":[0.0000546743,0.00017594032,0.000031237327,0.00028404896,0.0003810498,0.00009053275,0.00009094119,0.00023533452,0.0000027600759],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022001928,0.000018897472,0.4844276,0.000049684437,0.000033157794,0.0000047227945,0.000023415123,8.043479e-7,0.5150802,0.00006172216,1.4123997e-7,0.00027765796],"study_design_scores_gemma":[0.0005312048,0.00007792611,0.044402927,0.00003274131,0.00014183021,0.00003813885,0.00035182256,0.0003575538,0.9529512,0.0008996102,0.000015612708,0.00019943596],"about_ca_topic_score_codex":0.00010976302,"about_ca_topic_score_gemma":0.00000299128,"teacher_disagreement_score":0.44002467,"about_ca_system_score_codex":0.000027104894,"about_ca_system_score_gemma":0.00002173086,"threshold_uncertainty_score":0.7174638},"labels":[],"label_agreement":null},{"id":"W2943991194","doi":"10.1016/j.foodchem.2019.05.060","title":"Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers","year":2019,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Detector; Artificial neural network; Pattern recognition (psychology); Artificial intelligence; Cow milk; Biological system; Chemistry; Computer science; Food science; Physics; Optics; Biology","score_opus":0.0920350037923193,"score_gpt":0.2833387620410171,"score_spread":0.1913037582486978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943991194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973884,0.0010432131,0.00014053803,0.00004945486,0.00006587756,0.00007171607,0.00028007652,0.000030123003,0.0009305956],"genre_scores_gemma":[0.9978959,0.000015814101,0.0014379742,0.000013492921,0.00036718932,0.0000021008786,0.00019969992,0.00002761611,0.000040217907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998438,0.0000121171815,0.0004715548,0.00047907926,0.00029650325,0.00030272428],"domain_scores_gemma":[0.9980731,0.00024099037,0.00053316593,0.0010263259,0.000062437226,0.000063985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010892144,0.00022211691,0.00040245897,0.000024073403,0.000066114924,0.000035508103,0.00067727215,0.00016482969,0.0008497446],"category_scores_gemma":[0.00012716485,0.00018767087,0.000084403786,0.00047434558,0.00019089376,0.00008930933,0.00021676571,0.0002456652,0.0000023230452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011261038,0.000062913045,0.021072397,0.000111906105,0.00021557575,8.510353e-7,0.000035704834,0.00004104646,0.97812366,0.000010968158,0.000050298142,0.00016209325],"study_design_scores_gemma":[0.00031259493,0.00003871883,0.0035683573,0.00004315454,0.0002497039,0.000007011039,0.0003414126,0.0043957424,0.9904925,0.00030460866,0.000045595905,0.0002005509],"about_ca_topic_score_codex":0.00009691113,"about_ca_topic_score_gemma":0.000042888314,"teacher_disagreement_score":0.01750404,"about_ca_system_score_codex":0.00006825911,"about_ca_system_score_gemma":0.00008143156,"threshold_uncertainty_score":0.93041074},"labels":[],"label_agreement":null},{"id":"W2945640850","doi":"10.1002/polb.24837","title":"Classifying formulations of crosslinked polyethylene pipe by applying machine‐learning concepts to infrared spectra","year":2019,"lang":"en","type":"article","venue":"Journal of Polymer Science Part B Polymer Physics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Principal component analysis; Cluster analysis; Polyethylene; Support vector machine; Chemometrics; Infrared spectroscopy; Computer science; Pattern recognition (psychology); Biological system; Artificial intelligence; Materials science; Chemistry; Machine learning; Composite material; Organic chemistry","score_opus":0.018017606975494496,"score_gpt":0.30654879596097273,"score_spread":0.28853118898547825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945640850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97966784,0.006077396,0.004725467,0.00020483113,0.00033428427,0.000104006984,0.000036871777,0.00003844428,0.008810856],"genre_scores_gemma":[0.99513865,0.00004375805,0.00019415522,0.00017310581,0.00034543077,0.000005210691,0.000006558944,0.000032996333,0.0040601147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99685067,0.000027869648,0.0009059849,0.00038105968,0.0011657646,0.0006686254],"domain_scores_gemma":[0.9976145,0.00021228596,0.0010894097,0.00042408225,0.0002776265,0.00038211027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041101055,0.000302498,0.00065177353,0.00035510195,0.0003563402,0.00012059801,0.0008982921,0.000104360755,0.0008227532],"category_scores_gemma":[0.00011866891,0.00027470017,0.00030351477,0.0023378665,0.00039177004,0.0008784065,0.00016505898,0.00060696487,0.000033415796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043652097,0.00012931714,0.017722096,0.000032281216,0.00008605646,0.0000016135681,0.00071921427,0.000060086077,0.9786534,0.000746167,0.000109184984,0.0016969508],"study_design_scores_gemma":[0.0006529632,0.00012965602,0.00012142265,0.0000779451,0.000119044846,0.000016886755,0.00063842593,0.0010175053,0.99633586,0.00014778164,0.00044165875,0.00030085052],"about_ca_topic_score_codex":0.000084227206,"about_ca_topic_score_gemma":0.0000011184843,"teacher_disagreement_score":0.017682478,"about_ca_system_score_codex":0.00015561793,"about_ca_system_score_gemma":0.00043421483,"threshold_uncertainty_score":0.9999705},"labels":[],"label_agreement":null},{"id":"W2948559690","doi":"10.1201/9781315209203-8","title":"Linear Discriminant Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Linear discriminant analysis; Optimal discriminant analysis; Mathematics; Statistics; Artificial intelligence; Pattern recognition (psychology); Computer science","score_opus":0.02792143203881798,"score_gpt":0.2843426882529681,"score_spread":0.25642125621415013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948559690","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015259032,0.0005685831,0.0008925386,0.000050173385,0.000034392724,0.00001945354,0.00006829055,0.0001325845,0.9980814],"genre_scores_gemma":[0.0054901987,0.00027385954,0.0006824149,0.00009637284,0.00063884957,0.0000028465934,0.00028001147,0.00005518051,0.9924803],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850947,0.0000013955133,0.00035787295,0.0005475752,0.000323722,0.00025994383],"domain_scores_gemma":[0.99872965,0.000050361057,0.00020642535,0.000782747,0.00010555709,0.00012527742],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004792384,0.00039130554,0.0007472467,0.0004896721,0.00007765856,0.00003337846,0.00035490707,0.00047911963,0.34913093],"category_scores_gemma":[0.000022941978,0.00030972375,0.00077314005,0.00020991536,0.00014871972,0.000032852662,0.000115851304,0.00030223737,0.0016797568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000313362,0.0007033738,0.0028841053,0.0018222027,0.1445522,0.0005838053,0.00089971523,0.000031546424,0.018756595,0.32543707,0.49727783,0.006738199],"study_design_scores_gemma":[0.00027219797,0.000055411358,0.000021328167,0.00004996881,0.04379896,0.000009249494,0.00013748292,0.00034361353,0.076375686,0.011130224,0.86629313,0.0015127182],"about_ca_topic_score_codex":0.00005519407,"about_ca_topic_score_gemma":0.00007386016,"teacher_disagreement_score":0.36901534,"about_ca_system_score_codex":0.00009257578,"about_ca_system_score_gemma":0.00004019239,"threshold_uncertainty_score":0.9999355},"labels":[],"label_agreement":null},{"id":"W2950421003","doi":"10.1080/09593330.2019.1626913","title":"Rapid fingerprinting technology of heavy oil spill by mid-infrared spectroscopy","year":2019,"lang":"en","type":"article","venue":"Environmental Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Chemometrics; Infrared spectroscopy; Spectroscopy; Partial least squares regression; Principal component analysis; Petroleum; Petroleum product; Analytical Chemistry (journal); Chemistry; Near-infrared spectroscopy; Fourier transform infrared spectroscopy; Infrared; Environmental science; Environmental chemistry; Chromatography; Artificial intelligence; Computer science; Engineering; Optics; Machine learning","score_opus":0.004155731567222854,"score_gpt":0.2108881396028533,"score_spread":0.20673240803563045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950421003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96812403,0.0043504895,0.000095632895,0.00050151197,0.00006566742,0.000052018306,0.00003917113,0.0003560409,0.026415415],"genre_scores_gemma":[0.9911843,0.0008405606,0.002168785,0.000045266956,0.000026670661,0.000025485057,0.000036892652,0.00004817718,0.00562389],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99812603,0.000007525402,0.00045759807,0.00061750785,0.00022379335,0.000567527],"domain_scores_gemma":[0.9987254,0.000045109504,0.00030239014,0.00086577656,0.000006445205,0.000054868637],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006738099,0.00031365769,0.0005655619,0.00056596065,0.000076855555,0.0000090398835,0.00066847616,0.0006547255,0.008085719],"category_scores_gemma":[0.00005032724,0.00033513387,0.00013093288,0.00077932316,0.0005425804,0.000057902922,0.00037927474,0.00056491006,0.00038565177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024038594,0.00018555472,0.05998226,0.000049550985,0.000102178856,0.000005463093,0.000018353037,0.0000015690741,0.9336998,0.00020875133,0.0002833228,0.0054391567],"study_design_scores_gemma":[0.0006270881,0.00014434477,0.00012641001,0.000022497987,0.000060422895,0.000030802286,0.0010197794,0.0000167164,0.9803932,0.0009147746,0.016346892,0.0002971169],"about_ca_topic_score_codex":0.0000085478505,"about_ca_topic_score_gemma":3.5339852e-7,"teacher_disagreement_score":0.05985585,"about_ca_system_score_codex":0.00023041524,"about_ca_system_score_gemma":0.000016102491,"threshold_uncertainty_score":0.99991006},"labels":[],"label_agreement":null},{"id":"W2951884821","doi":"10.1002/cem.3137","title":"Simulation of <b>1</b>/<i>f</i><sup><i>α</i></sup> noise for analytical measurements","year":2019,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Noise (video); Gradient noise; Noise spectral density; Noise measurement; Value noise; Noise power; Algorithm; Gaussian noise; Impulse noise; Mathematics; Noise reduction; Computer science; Physics; Noise floor; Power (physics); Noise figure; Acoustics; Artificial intelligence","score_opus":0.0520505971519172,"score_gpt":0.32529337846300566,"score_spread":0.2732427813110885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951884821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97831684,0.001147027,0.012727884,0.00014335394,0.00012113909,0.00011653276,0.000025115361,0.000018185407,0.007383916],"genre_scores_gemma":[0.9957395,0.000047057503,0.0025510346,0.000071885195,0.00025402557,0.0000013954542,0.0000071309973,0.00003202248,0.0012959432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99742234,0.000013338772,0.0010170508,0.00020508822,0.0010288572,0.00031333597],"domain_scores_gemma":[0.99629885,0.0009548686,0.0010116675,0.00031728926,0.001241319,0.00017602221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007071889,0.00021036614,0.00074438064,0.0012703182,0.000037044163,0.00003530022,0.00043275714,0.00017252243,0.00090930547],"category_scores_gemma":[0.0030938308,0.00018544206,0.00052344805,0.0028292576,0.00004604196,0.00024635182,0.000043444623,0.00029195557,0.000013112788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017539047,0.0025348477,0.3183646,0.0024564862,0.0028850273,0.00001816771,0.00044167336,0.42455864,0.23346685,0.00026078706,0.0053002443,0.007958772],"study_design_scores_gemma":[0.006198022,0.0006150988,0.0007438463,0.00014013857,0.001734754,0.00003710189,0.00064476964,0.13881305,0.8393311,0.0005716357,0.010599558,0.0005709197],"about_ca_topic_score_codex":0.0000020292812,"about_ca_topic_score_gemma":7.637341e-8,"teacher_disagreement_score":0.6058643,"about_ca_system_score_codex":0.00021504883,"about_ca_system_score_gemma":0.00013890887,"threshold_uncertainty_score":0.9956257},"labels":[],"label_agreement":null},{"id":"W2954836870","doi":"10.1016/j.ifacol.2019.06.061","title":"A GMM-MRF Based Image Segmentation approach for Interface Level Estimation","year":2019,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov random field; Interface (matter); Computer science; Process (computing); Segmentation; Maximum a posteriori estimation; Image segmentation; Artificial intelligence; Image (mathematics); Scale (ratio); Layer (electronics); Mixture model; Computer vision; Pattern recognition (psychology); Maximum likelihood; Mathematics; Materials science; Statistics","score_opus":0.027576901654284114,"score_gpt":0.3138629903581034,"score_spread":0.2862860887038193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954836870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33936584,0.0002136563,0.6512179,0.0005180156,0.00007517292,0.0003567398,0.00030672626,0.00018430549,0.007761662],"genre_scores_gemma":[0.29594246,0.0000045138613,0.6971408,0.00016069396,0.00009555241,0.000055940618,0.0008940505,0.00003194088,0.005673997],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988184,0.000010171392,0.00026516098,0.0004026507,0.00022665634,0.000276937],"domain_scores_gemma":[0.9992492,0.00013116407,0.00015809377,0.0002998629,0.00009043356,0.00007121328],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011678963,0.00021999427,0.00027225062,0.000112305126,0.00007684496,0.00006121195,0.00019877058,0.000131258,0.0018752487],"category_scores_gemma":[0.00011949888,0.0002072154,0.00014489243,0.0002922205,0.000036933572,0.00019544583,0.000026442412,0.00014915936,0.00008970364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013353756,0.00026638698,0.00084403565,0.00042206718,0.00010011621,6.61785e-7,0.00017302966,0.0017805866,0.9936673,0.000030668594,0.000057893467,0.0025237259],"study_design_scores_gemma":[0.001501481,0.00006009883,0.00011245058,0.000019129262,0.00014978199,0.0000030163942,0.0009096122,0.28470048,0.7121782,0.000027042464,0.00009507589,0.0002436473],"about_ca_topic_score_codex":0.000034960092,"about_ca_topic_score_gemma":0.000003378653,"teacher_disagreement_score":0.2829199,"about_ca_system_score_codex":0.00015555814,"about_ca_system_score_gemma":0.000054778862,"threshold_uncertainty_score":0.99903715},"labels":[],"label_agreement":null},{"id":"W2955734110","doi":"10.1109/dsw.2019.8755562","title":"Long-Range Dependence Parameter Estimation For Mixed Spectra Gaussian Processes","year":2019,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Range (aeronautics); Gaussian; Statistical physics; Gaussian process; Spectral line; Estimation; Computer science; Estimation theory; Algorithm; Physics; Materials science; Engineering; Quantum mechanics","score_opus":0.01747554417381114,"score_gpt":0.28515937725543117,"score_spread":0.26768383308162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955734110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78940326,0.0006060104,0.14836027,0.0003772117,0.00010613576,0.00031600497,0.00002494217,0.00030773788,0.060498454],"genre_scores_gemma":[0.969718,0.000022366266,0.01646683,0.00008539974,0.00006720877,0.000051816707,0.000038879763,0.000020180642,0.013529322],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990255,0.0000032002688,0.000189271,0.00032247155,0.00018755942,0.00027196435],"domain_scores_gemma":[0.9992414,0.00026998395,0.000085742664,0.00027337886,0.00006448122,0.00006498015],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000059584352,0.0001551,0.00020837913,0.00007806345,0.000052906562,0.00006929821,0.00020345477,0.00010802567,0.0059185526],"category_scores_gemma":[0.0002693835,0.00012868791,0.00008153452,0.00035053564,0.000024822573,0.00019725514,0.00001960073,0.00009166352,0.00021020323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008828818,0.0018773094,0.7199918,0.014460037,0.0014620931,0.000038660142,0.0009235239,0.00086558907,0.21421929,0.0100569865,0.015570663,0.019651199],"study_design_scores_gemma":[0.0006330183,0.00006132239,0.0020506962,0.000033141492,0.00016333739,0.000013043126,0.00020647938,0.0041604717,0.9896401,0.0023276133,0.00038574496,0.00032499564],"about_ca_topic_score_codex":0.000026616848,"about_ca_topic_score_gemma":0.0000424102,"teacher_disagreement_score":0.77542084,"about_ca_system_score_codex":0.000050927465,"about_ca_system_score_gemma":0.00005683192,"threshold_uncertainty_score":0.99499017},"labels":[],"label_agreement":null},{"id":"W2955831236","doi":"10.1016/j.visres.2019.05.002","title":"The processing of compound radial frequency patterns","year":2019,"lang":"en","type":"article","venue":"Vision Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Channel (broadcasting); Sensitivity (control systems); Noise (video); Biological system; Window (computing); Component (thermodynamics); Point (geometry); Algorithm; Computer science; Mathematics; Acoustics; Topology (electrical circuits); Physics; Electronic engineering; Artificial intelligence; Telecommunications; Engineering; Geometry","score_opus":0.04806918463011959,"score_gpt":0.4131071403660925,"score_spread":0.36503795573597286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955831236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9422127,0.0010658168,0.000033829267,0.00027794676,0.000027197892,0.00005500428,0.000005900657,0.0000164252,0.05630519],"genre_scores_gemma":[0.9951194,0.00014370205,0.000055548775,0.000009682533,0.000075649674,0.000005467879,0.0000048759766,0.000010865337,0.0045748134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854887,0.00004007381,0.00018875381,0.00019395305,0.00071372744,0.00031461794],"domain_scores_gemma":[0.99882776,0.00048650624,0.000056046538,0.00036167869,0.0002108558,0.0000571485],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005849807,0.00006763616,0.00013108758,0.00012602101,0.00022789821,0.00008945252,0.00043939787,0.00006950544,0.0026698615],"category_scores_gemma":[0.00015226314,0.000043444088,0.00005404336,0.0006053318,0.00013783098,0.00006447107,0.00010938064,0.00036143628,0.00012351245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008183981,0.00017136142,0.118603915,0.0003844256,0.000051865325,0.000008049438,0.0002206484,0.0000035211267,0.8538583,0.0009330877,0.0023309763,0.023352],"study_design_scores_gemma":[0.0009050816,0.00021579581,0.010948275,0.00017731203,0.000023819453,0.000007766126,0.0026155268,0.0013364084,0.9663659,0.0026045186,0.014569059,0.00023056564],"about_ca_topic_score_codex":0.00021242787,"about_ca_topic_score_gemma":0.000012258862,"teacher_disagreement_score":0.11250756,"about_ca_system_score_codex":0.000055569384,"about_ca_system_score_gemma":0.00009650031,"threshold_uncertainty_score":0.99824184},"labels":[],"label_agreement":null},{"id":"W2963857932","doi":"10.1109/access.2018.2889017","title":"The Feasibility of Automated Identification of Six Algae Types Using Feed-Forward Neural Networks and Fluorescence-Based Spectral-Morphological Features","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Spectral signature; Artificial intelligence; Computer science; Pattern recognition (psychology); Identification (biology); Fluorescence; Artificial neural network; Spectral bands; Algae; Fluorescence microscope; Remote sensing; Biological system; Optics; Biology; Geography; Ecology; Physics","score_opus":0.045328890331718975,"score_gpt":0.3552518967731205,"score_spread":0.3099230064414015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963857932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99836504,0.00042789042,0.0006564797,0.000083024635,0.00011248039,0.00007337628,0.0000123118025,0.00008058171,0.00018881304],"genre_scores_gemma":[0.99960977,0.000027584063,0.0001324254,0.000027023165,0.00014160757,0.0000029446153,0.000007913687,0.0000097198745,0.00004102387],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890757,0.000036520094,0.00037062276,0.00026750524,0.00020128151,0.00021647474],"domain_scores_gemma":[0.99883735,0.00020946888,0.00033520345,0.00037599407,0.00019302193,0.00004897437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002159781,0.00013637668,0.00024759132,0.000060309856,0.00016828276,0.000090622976,0.00044614662,0.0001371112,0.00009137664],"category_scores_gemma":[0.0001987395,0.000093233524,0.000080796235,0.00055007415,0.00058246584,0.00012200133,0.00005339751,0.00014634329,6.743214e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026879998,0.00012321786,0.09964809,0.00009383156,0.00009085582,0.0000033037795,0.00003506817,0.0015939836,0.89693207,0.000037529542,0.0006423483,0.00053090876],"study_design_scores_gemma":[0.00022662018,0.000036096986,0.058759164,0.000013216136,0.00011849016,0.0000058800547,0.00003378009,0.10154361,0.83905745,0.000107312335,0.0000026593325,0.00009574284],"about_ca_topic_score_codex":0.00019615376,"about_ca_topic_score_gemma":0.000029485778,"teacher_disagreement_score":0.09994963,"about_ca_system_score_codex":0.00004072846,"about_ca_system_score_gemma":0.00003292128,"threshold_uncertainty_score":0.38019526},"labels":[],"label_agreement":null},{"id":"W2963940906","doi":"10.5539/jas.v11n13p304","title":"Fast and Non-Destructive Determination of N, P, and K in Sorghum, Oat, and Corn Residue Using Near-Infrared Spectroscopy","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Calibration; Partial least squares regression; Coefficient of determination; Sorghum; Residual; Near infrared reflectance spectroscopy; Mean squared error; Residue (chemistry); Near-infrared spectroscopy; Spectroscopy; Potassium; Replicate; Analytical Chemistry (journal); Chemistry; Cross-validation; Infrared spectroscopy; Mathematics; Agronomy; Chromatography; Statistics; Algorithm; Optics","score_opus":0.008909945354958298,"score_gpt":0.2637355896969858,"score_spread":0.2548256443420275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963940906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99877083,0.00019791714,0.000035002937,0.000035660672,0.000024365878,0.000032814834,0.0000016955042,0.0000018526273,0.00089985185],"genre_scores_gemma":[0.99472374,0.00006752179,0.005113002,0.0000055439023,0.000030387248,1.6884155e-7,2.1630686e-7,0.000002123734,0.0000573162],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991638,0.00000735904,0.0002541583,0.00015653572,0.00026256868,0.00015556901],"domain_scores_gemma":[0.9993122,0.00005680518,0.00033863744,0.00005485491,0.00015524987,0.00008225594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018857537,0.0000924724,0.00022985552,0.00011994575,0.00009314483,0.00009166839,0.00012281472,0.000046263704,0.0000238173],"category_scores_gemma":[0.00008748611,0.000058610924,0.000023330647,0.00060465006,0.00030872758,0.00059063383,0.000059001894,0.00015283262,1.9966215e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023063292,0.000009417506,0.111107655,0.000027354323,0.0000045896927,0.0000026754933,0.00035988577,0.000008757048,0.88826066,0.0000067328833,0.0000029978537,0.00018620024],"study_design_scores_gemma":[0.00029812273,0.000065631466,0.5585344,0.000060902097,0.000023960853,0.00018569647,0.0015111534,0.0004595006,0.43869096,0.000102813116,5.6047014e-7,0.00006633461],"about_ca_topic_score_codex":0.00004670729,"about_ca_topic_score_gemma":0.000010339649,"teacher_disagreement_score":0.44956973,"about_ca_system_score_codex":0.00008845073,"about_ca_system_score_gemma":0.000054185566,"threshold_uncertainty_score":0.23900841},"labels":[],"label_agreement":null},{"id":"W2967703797","doi":"10.1007/s12161-019-01609-1","title":"Potential of Near-Infrared (NIR) Spectroscopy and Hyperspectral Imaging for Quality and Safety Assessment of Fruits: an Overview","year":2019,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":135,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Maple Leaf Foods","funders":"","keywords":"Hyperspectral imaging; Chemometrics; Chemical imaging; Computer science; Environmental science; Food quality; Remote sensing; Quality (philosophy); Near-infrared spectroscopy; Process engineering; Pattern recognition (psychology); Artificial intelligence; Chemistry; Machine learning; Food science; Engineering","score_opus":0.06851248476272241,"score_gpt":0.46148490825789557,"score_spread":0.39297242349517314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967703797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91056377,0.0032603724,0.07869671,0.00025573908,0.000061221384,0.0002162779,0.00018900856,0.000042786334,0.0067140986],"genre_scores_gemma":[0.64981014,0.0001623627,0.34976542,0.000044622597,0.0000537639,0.0000050263816,0.000017116445,0.000020103975,0.000121467696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99807954,0.00014622335,0.0006600907,0.0005026138,0.00027249593,0.0003390601],"domain_scores_gemma":[0.9983747,0.0005985572,0.0002784853,0.0004363255,0.00013304384,0.00017890646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010244277,0.00023304447,0.00091657793,0.0001036016,0.00006891313,0.000037340465,0.00019014595,0.00012330162,0.0006391348],"category_scores_gemma":[0.00031905135,0.0002110541,0.00022257704,0.00033909906,0.00024241282,0.00014951147,0.00010156117,0.0002113717,3.0471003e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042786813,0.0004720087,0.07354651,0.0018716565,0.0008679922,0.0000015820614,0.00014157644,0.000025780995,0.87108415,0.046363603,0.000020979427,0.005176314],"study_design_scores_gemma":[0.0045914836,0.0014240668,0.12845127,0.00015449297,0.0022603737,0.000024717698,0.00225113,0.09785043,0.7358311,0.025812758,0.00043572127,0.00091248896],"about_ca_topic_score_codex":0.00003839018,"about_ca_topic_score_gemma":0.0000014868556,"teacher_disagreement_score":0.2710687,"about_ca_system_score_codex":0.00006534559,"about_ca_system_score_gemma":0.00009699173,"threshold_uncertainty_score":0.86065364},"labels":[],"label_agreement":null},{"id":"W2972687923","doi":"10.1016/j.forsciint.2019.109946","title":"Batch-to-batch variation in domestic paints: Insights into the newly commercialized recycled paints","year":2019,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Pulp and paper industry; Environmental science; Production (economics); Alkyd; Materials science; Process engineering; Waste management; Composite material; Engineering","score_opus":0.011066377116661148,"score_gpt":0.29791049495249566,"score_spread":0.2868441178358345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972687923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9761073,0.00003871776,0.0026974755,0.003068185,0.0008091932,0.000113279835,0.0000036113197,0.00004837926,0.017113864],"genre_scores_gemma":[0.9956744,0.000015348625,0.0012083461,0.0012991723,0.00018450995,0.00003057577,0.000017387734,0.000012137868,0.0015581315],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.997811,0.00003296262,0.00038069047,0.00050657574,0.00093715836,0.00033158256],"domain_scores_gemma":[0.9986156,0.000417202,0.00015661363,0.0004274873,0.0002735053,0.000109570574],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006493178,0.00017334754,0.00021710219,0.00048701026,0.00016782481,0.00017640307,0.0011733834,0.000080551596,0.0021880588],"category_scores_gemma":[0.001233966,0.00012901692,0.00007565358,0.0018707087,0.0002806906,0.0003608833,0.00021332888,0.00021461539,0.00025313167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008948879,0.0009963401,0.18484426,0.00007883527,0.0004053444,0.000075933545,0.038162325,0.0028486005,0.6102807,0.07049318,0.006800851,0.084118776],"study_design_scores_gemma":[0.008982652,0.00040439298,0.3362499,0.00056999456,0.00023066926,0.00008727313,0.008537482,0.1650676,0.24935845,0.1859438,0.04212107,0.002446739],"about_ca_topic_score_codex":0.0012231782,"about_ca_topic_score_gemma":0.00037358273,"teacher_disagreement_score":0.36092225,"about_ca_system_score_codex":0.0005898071,"about_ca_system_score_gemma":0.00019268562,"threshold_uncertainty_score":0.9987241},"labels":[],"label_agreement":null},{"id":"W2972918700","doi":"10.3390/pr7090614","title":"A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance","year":2019,"lang":"en","type":"article","venue":"Processes","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cluster analysis; Hierarchical clustering; Random forest; Computer science; Outcome (game theory); Data mining; Artificial intelligence; Machine learning; Feature selection; Biochemical engineering; Bioreactor; Set (abstract data type); Mathematics; Engineering; Biology","score_opus":0.06544163702627569,"score_gpt":0.4064067179162489,"score_spread":0.3409650808899732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972918700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845191,0.0068078935,0.0073371753,0.000007311792,0.000038485385,0.00008793873,0.000008790602,0.00006691665,0.0011263923],"genre_scores_gemma":[0.98348814,0.0003077055,0.0159901,0.0000062454187,0.00006620563,0.000022761375,0.000016442182,0.000010605001,0.0000917704],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930525,0.0000068663035,0.00020884546,0.00024108087,0.00007480954,0.00016312738],"domain_scores_gemma":[0.9991841,0.0004299327,0.00016746741,0.000097496435,0.000087775385,0.00003323543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014647523,0.000117736105,0.00029946145,0.00004028253,0.000056490455,0.0000310219,0.00010985774,0.000116304174,0.000081616476],"category_scores_gemma":[0.0004991255,0.000095446005,0.0000407436,0.00019026535,0.000038439204,0.00014389258,0.00006280029,0.000108592234,6.121145e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066164386,0.000052032003,0.12247472,0.0054107467,0.00005350531,3.046202e-8,0.0005571708,0.0000068232234,0.8666623,0.0000029114963,0.000011097271,0.004702441],"study_design_scores_gemma":[0.00024483458,0.000057404042,0.0012753379,0.000100599675,0.00007565184,0.0000049844807,0.0012964967,0.0019507309,0.994712,0.000033934728,0.00014470522,0.000103317325],"about_ca_topic_score_codex":0.000009527049,"about_ca_topic_score_gemma":0.0000023262037,"teacher_disagreement_score":0.12804966,"about_ca_system_score_codex":0.000029798357,"about_ca_system_score_gemma":0.000016541222,"threshold_uncertainty_score":0.3892175},"labels":[],"label_agreement":null},{"id":"W2974037954","doi":"10.1016/j.foodchem.2019.125585","title":"High throughput nutritional profiling of pea seeds using Fourier transform mid-infrared spectroscopy","year":2019,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Canadian Light Source (Canada)","funders":"Western Grains Research Foundation; Ministry of Agriculture - Saskatchewan","keywords":"Phytic acid; Starch; Partial least squares regression; Carotenoid; Food science; Correlation coefficient; Chemistry; Mathematics; Statistics","score_opus":0.015698936914852003,"score_gpt":0.25770621468007193,"score_spread":0.24200727776521994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974037954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9335306,0.00083638326,0.00056059664,0.00006578174,0.00008043437,0.00009864131,0.00022825718,0.000109137414,0.06449016],"genre_scores_gemma":[0.9809488,0.00004518516,0.015221858,0.000030440355,0.0004829992,0.000015093644,0.00025121323,0.00005797041,0.002946453],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99784154,0.0000060308535,0.00055043196,0.0005322087,0.0005458416,0.0005239678],"domain_scores_gemma":[0.9988069,0.00007865337,0.0002618646,0.00057841686,0.00013970502,0.00013444254],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008818945,0.00036400248,0.00059088646,0.00006981125,0.00009391332,0.00003825053,0.00040828006,0.0003503502,0.008040427],"category_scores_gemma":[0.000038871036,0.0003849573,0.0003277138,0.00054330524,0.00011940675,0.0001611139,0.00006148912,0.00042382933,0.000022299246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001065806,0.00022462278,0.001985337,0.0011932302,0.00031454075,0.000003998877,0.00006270877,0.000048250353,0.9957802,0.00009913452,0.00016450105,0.000016889178],"study_design_scores_gemma":[0.00139477,0.000059151203,0.000015651334,0.00010960148,0.00023811002,0.000033916447,0.0005733345,0.00026545656,0.99487656,0.0015529337,0.00047533683,0.00040518164],"about_ca_topic_score_codex":0.000021608046,"about_ca_topic_score_gemma":4.6058258e-7,"teacher_disagreement_score":0.061543707,"about_ca_system_score_codex":0.00023051974,"about_ca_system_score_gemma":0.00023396622,"threshold_uncertainty_score":0.9998602},"labels":[],"label_agreement":null},{"id":"W2974331494","doi":"10.1007/s10812-019-00874-6","title":"A Novel Two-Step Spectral Recovery Framework for Coal Quality Assessment by Near-Infrared Spectroscopy","year":2019,"lang":"en","type":"article","venue":"Journal of Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Smoothing; Computer science; Noise (video); Coal; Particle swarm optimization; Partial least squares regression; Algorithm; Biological system; Artificial intelligence; Chemistry; Computer vision","score_opus":0.01942287290614959,"score_gpt":0.33503031793423627,"score_spread":0.3156074450280867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974331494","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46498284,0.0005970919,0.43886715,0.0005202013,0.0008785933,0.0006023344,0.00039972283,0.00014586966,0.093006186],"genre_scores_gemma":[0.44650728,0.00025085715,0.5481122,0.0007219706,0.0016923405,0.000048303114,0.0001105426,0.00018593756,0.0023705275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945472,0.000033561533,0.001985681,0.00085731875,0.0012845166,0.0012917386],"domain_scores_gemma":[0.99513465,0.0010079175,0.0021445449,0.00097467477,0.0002590335,0.00047920426],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010845957,0.0007790584,0.0018998596,0.00030644462,0.00031907222,0.00046066093,0.0011152967,0.00048829877,0.0055265366],"category_scores_gemma":[0.00016942366,0.00073341135,0.0009647482,0.0007642458,0.00019811922,0.0003657152,0.00011781775,0.0018212217,0.00007619596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003234994,0.0008737588,0.002180712,0.00020616951,0.00087225484,0.000007108489,0.00010293999,0.00018969008,0.96182567,0.019228313,0.011179947,0.000098413744],"study_design_scores_gemma":[0.0068141776,0.0009643861,0.00068585866,0.00011082208,0.0006123053,0.00006618009,0.0009735542,0.00041122606,0.9495905,0.028598914,0.010173759,0.0009983346],"about_ca_topic_score_codex":0.000040703304,"about_ca_topic_score_gemma":0.00000546416,"teacher_disagreement_score":0.10924508,"about_ca_system_score_codex":0.0014012505,"about_ca_system_score_gemma":0.0007245691,"threshold_uncertainty_score":0.9995117},"labels":[],"label_agreement":null},{"id":"W2976808484","doi":"10.1002/slct.201901659","title":"Recycling Control of Histological Xylol: A Chemometric Approach","year":2019,"lang":"en","type":"article","venue":"ChemistrySelect","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"","keywords":"Chemistry; Chromatography; Distillation; Sample (material); Analytical Chemistry (journal)","score_opus":0.011824437242226697,"score_gpt":0.238550371281223,"score_spread":0.2267259340389963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976808484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75360316,0.0021570814,0.0012821201,0.000027002185,0.0000279324,0.0000701139,0.000013756707,0.00012935216,0.24268948],"genre_scores_gemma":[0.9922282,0.000045075896,0.0008395192,0.00004966218,0.00011723536,0.000021562133,0.000044653098,0.000031538337,0.006622562],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981015,0.000010301682,0.00047819313,0.0005701014,0.00036181332,0.0004780914],"domain_scores_gemma":[0.9985941,0.00024944657,0.00032103187,0.00057405286,0.00012808111,0.00013326669],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017595809,0.00028966198,0.00072814117,0.00016244705,0.000049212133,0.000020461563,0.0005108286,0.0003357938,0.004898893],"category_scores_gemma":[0.00050408085,0.00026550624,0.0002863565,0.0015334449,0.00010909071,0.00005478808,0.000051329625,0.00042870897,0.00005573995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077186334,0.00025310882,0.02770096,0.0004676904,0.00019503481,0.0000027157505,0.000023199807,0.000026263491,0.97046894,0.00005297964,0.00033895345,0.00039295384],"study_design_scores_gemma":[0.0012567415,0.000032951943,0.00020302796,0.000015925481,0.00020604474,0.000038722825,0.00012354205,0.00079180364,0.9958593,0.00007080219,0.0010868905,0.00031420097],"about_ca_topic_score_codex":0.000013793452,"about_ca_topic_score_gemma":8.265915e-8,"teacher_disagreement_score":0.23862502,"about_ca_system_score_codex":0.00017449312,"about_ca_system_score_gemma":0.00008494148,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W2979260266","doi":"","title":"Texture classification using improved 2D local discriminant basis","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Workshop on Statistical Signal Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Basis (linear algebra); Orthonormal basis; Artificial intelligence; Feature extraction; Wavelet transform; Mathematics; Discriminant; Divergence (linguistics); Computer science; Wavelet packet decomposition; Euclidean distance; Feature (linguistics); Contextual image classification; Wavelet; Image (mathematics)","score_opus":0.04756998926865358,"score_gpt":0.3258723116971454,"score_spread":0.27830232242849184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979260266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011354126,0.0016507838,0.97673947,0.000119177275,0.000076976416,0.00017348403,0.00006508462,0.00031259793,0.009508322],"genre_scores_gemma":[0.9852138,0.000021044745,0.013016773,0.0003139453,0.0003885524,0.000050026745,0.00006377759,0.00016237126,0.0007697144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946865,0.00013109384,0.0011524641,0.0015206663,0.0011282788,0.0013810071],"domain_scores_gemma":[0.9972722,0.00068023166,0.00066039944,0.00039206858,0.00041578853,0.00057929306],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00051660795,0.00089858536,0.0009240222,0.0003520796,0.0012609435,0.00078249717,0.0005323403,0.0006078307,0.0025742482],"category_scores_gemma":[0.00030231502,0.00078611984,0.00020932204,0.0016855085,0.00075220445,0.0005925998,0.000047823178,0.001460847,0.000047542802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005153572,0.0010831329,0.00020170299,0.0013667245,0.00013463935,0.00009859694,0.00035691334,0.0021488164,0.33529633,0.0013539016,0.0004545223,0.6569894],"study_design_scores_gemma":[0.0016084145,0.00019384694,0.00009394286,0.0014465416,0.0012981341,0.00013845188,0.004544823,0.7388181,0.24286425,0.0056889434,0.00119851,0.0021060174],"about_ca_topic_score_codex":0.000027479613,"about_ca_topic_score_gemma":0.000005604515,"teacher_disagreement_score":0.97385967,"about_ca_system_score_codex":0.0006227932,"about_ca_system_score_gemma":0.000745322,"threshold_uncertainty_score":0.99945897},"labels":[],"label_agreement":null},{"id":"W2979845538","doi":"10.18280/i2m.180302","title":"A Novel Apple Size and Surface Quality Detection and Grading System","year":2019,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Grading (engineering); Software portability; Computer science; Engineering","score_opus":0.02495402960902774,"score_gpt":0.30199274908561635,"score_spread":0.2770387194765886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979845538","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962564,0.00020726913,0.0015969347,0.00009354188,0.00008322828,0.00009498246,0.000024346102,0.00014405859,0.0014992303],"genre_scores_gemma":[0.99811846,0.000033319568,0.0014774043,0.000052460946,0.000033557943,0.000009408151,0.000010884458,0.000011347451,0.00025315472],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990242,0.000029804616,0.00025227788,0.00032965926,0.00017321907,0.00019084885],"domain_scores_gemma":[0.99930555,0.00027748093,0.00016307132,0.00016154071,0.000033810993,0.0000585718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002911486,0.00014645005,0.0002435801,0.00004592276,0.00010552738,0.000064799984,0.000070828624,0.00013175285,0.00012582667],"category_scores_gemma":[0.0000978002,0.00013827776,0.000038136415,0.00020596525,0.000047926238,0.00015881179,0.00004134486,0.00014172647,0.000010150675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006123026,0.000020543559,0.14959435,0.0002233452,0.000072266615,5.9806956e-7,0.00013613702,0.000025250818,0.8484401,0.0002705444,0.000006625483,0.0011490055],"study_design_scores_gemma":[0.003285118,0.00011535853,0.08798023,0.000031457814,0.00023625724,0.000073087314,0.009980962,0.0018509488,0.8955676,0.00023127263,0.00019975365,0.00044794145],"about_ca_topic_score_codex":0.00018651069,"about_ca_topic_score_gemma":0.000020470565,"teacher_disagreement_score":0.06161412,"about_ca_system_score_codex":0.00011218623,"about_ca_system_score_gemma":0.00001563233,"threshold_uncertainty_score":0.5638803},"labels":[],"label_agreement":null},{"id":"W2980850947","doi":"","title":"FIELD HYPERSPECTRAL VISIBLE IMAGING SPECTROSCOPY OF SERPENTINE DEPOSITS IN QUEBEC: IMPLICATIONS FOR THE VALUE OF VISIBLE SPECTROSCOPY IN PLANETARY EXPLORATION","year":2012,"lang":"en","type":"article","venue":"2012 GSA Annual Meeting in Charlotte","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Spectroscopy; Remote sensing; Field (mathematics); Imaging spectroscopy; Geology; Materials science; Optics; Physics; Astronomy; Mathematics","score_opus":0.01718580918927929,"score_gpt":0.2976094999292318,"score_spread":0.2804236907399525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980850947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9884158,0.00502188,0.0011395924,0.0022253222,0.00013230991,0.00035176106,0.000081113394,0.000037226,0.0025950198],"genre_scores_gemma":[0.99519825,0.00031949047,0.0037380036,0.0000939512,0.00026305308,0.0000947268,0.000057496964,0.00003451594,0.00020050022],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99789107,0.000048259644,0.0008010193,0.00034655552,0.00021624511,0.000696848],"domain_scores_gemma":[0.9983351,0.0007560509,0.00035631575,0.0004214838,0.000058840385,0.00007218148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121253,0.00025925454,0.00049263076,0.00042102122,0.00007527646,0.000027491396,0.00040121944,0.00012850194,0.00011271362],"category_scores_gemma":[0.00025324605,0.00023527499,0.000122027996,0.0009370673,0.000071391034,0.0007672873,0.00007621645,0.00034278736,0.0000036376935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017837527,0.00035147788,0.4651862,0.00018203299,0.000037351347,9.3324394e-7,0.0018073124,0.0003184176,0.5303442,0.0007883059,0.0006320861,0.00017330088],"study_design_scores_gemma":[0.00087710813,0.00006493553,0.07855563,0.00018520207,0.000085261716,0.0000047974654,0.0032275345,0.0008678602,0.91532785,0.00039185735,0.00015650815,0.00025548952],"about_ca_topic_score_codex":0.009395515,"about_ca_topic_score_gemma":0.004655341,"teacher_disagreement_score":0.3866306,"about_ca_system_score_codex":0.0001708247,"about_ca_system_score_gemma":0.00007024452,"threshold_uncertainty_score":0.997201},"labels":[],"label_agreement":null},{"id":"W2981355802","doi":"10.1016/j.biosystemseng.2019.09.014","title":"A novel hyperspectral line-scan imaging method for whole surfaces of round shaped agricultural products","year":2019,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Agricultural Statistics Service; Agricultural Research Service; Rural Development Administration; National Academy of Agricultural Sciences; U.S. Department of Agriculture","keywords":"Hyperspectral imaging; Optics; Line (geometry); Computer vision; Surface (topology); Multispectral image; Artificial intelligence; Aperture (computer memory); Optical path; Computer science; Remote sensing; Mathematics; Physics; Geology; Geometry; Acoustics","score_opus":0.013689783337790917,"score_gpt":0.2518886828148443,"score_spread":0.23819889947705336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981355802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98669255,0.0022261513,0.009252171,0.00014708235,0.0003374661,0.00026565354,0.000093680595,0.00019429496,0.0007909595],"genre_scores_gemma":[0.97247386,0.000004179751,0.025388861,0.0000041658313,0.00033099548,0.000022286087,0.000031686366,0.000035291734,0.001708664],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875957,0.0000039857423,0.00034088752,0.00036123008,0.00018502522,0.00034931308],"domain_scores_gemma":[0.99925864,0.00012782704,0.00014814796,0.00026329802,0.00013926456,0.000062839295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001697337,0.00023270883,0.000439048,0.00011946638,0.000032321175,0.000047894275,0.00022195297,0.00007982972,0.00003717355],"category_scores_gemma":[0.00013022935,0.0001962775,0.00014319246,0.00046639508,0.000009263422,0.00013567219,0.000033711793,0.00011720375,0.000006445403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008949991,0.000037424023,0.0006363798,0.0010061623,0.00013154693,4.2149827e-7,0.000119087475,0.0018835972,0.9959083,0.00018647143,0.000052507916,0.000029102432],"study_design_scores_gemma":[0.0005461052,0.000022428689,0.00038011972,0.00011701765,0.0001075699,0.00002824221,0.0009307087,0.033486202,0.96299094,0.0000013477547,0.0011032165,0.00028612377],"about_ca_topic_score_codex":0.0001363957,"about_ca_topic_score_gemma":0.0000026926668,"teacher_disagreement_score":0.032917432,"about_ca_system_score_codex":0.00012440141,"about_ca_system_score_gemma":0.00003117458,"threshold_uncertainty_score":0.8003964},"labels":[],"label_agreement":null},{"id":"W2981437533","doi":"10.4095/287963","title":"Soil sample preparation, analytical techniques and QA/QC","year":2011,"lang":"en","type":"report","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Sample (material); Sample preparation; Soil test; Computer science; Environmental science; Chromatography; Chemistry; Soil science; Soil water","score_opus":0.0572242289641695,"score_gpt":0.3593805133763374,"score_spread":0.3021562844121679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981437533","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00201049,0.0018758398,0.00065539073,0.000032321594,0.000040003055,0.00008237258,0.00007613646,0.0003664217,0.994861],"genre_scores_gemma":[0.15508734,0.019422907,0.00978582,0.00012315404,0.0029219731,0.00028918553,0.0013219565,0.00020965093,0.81083804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818534,0.000007500494,0.00047271393,0.0005693185,0.0004825536,0.0002825917],"domain_scores_gemma":[0.9986364,0.00014473134,0.00022732993,0.0005506689,0.00030029274,0.00014053955],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020850037,0.0003381565,0.0006073506,0.00022421642,0.00008297225,0.000068529305,0.00021317758,0.000658853,0.01571675],"category_scores_gemma":[0.0004152293,0.000285548,0.00017104241,0.000281822,0.00013166663,0.00006928852,0.00012284066,0.00038038293,0.000023184473],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021410025,0.0007949421,0.04673268,0.0030141296,0.0034424523,0.00014739893,0.00026710852,2.681356e-7,0.0023334557,0.0042298976,0.91243345,0.026390135],"study_design_scores_gemma":[0.00012748165,0.00008033024,0.000054687865,0.00006189216,0.0012311465,0.00018246172,0.00008576126,0.000047588524,0.26337197,0.0013828565,0.732692,0.0006818239],"about_ca_topic_score_codex":0.0024230394,"about_ca_topic_score_gemma":0.00014038694,"teacher_disagreement_score":0.26103854,"about_ca_system_score_codex":0.00017640102,"about_ca_system_score_gemma":0.00043698604,"threshold_uncertainty_score":0.99995965},"labels":[],"label_agreement":null},{"id":"W2981545378","doi":"10.4095/219853","title":"Validation of a Hyperspectral Curve-Fitting Technique for Mapping Crop Water Status","year":2001,"lang":"en","type":"report","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; Crop; Mathematics; Remote sensing; Computer science; Environmental science; Artificial intelligence; Geography; Forestry","score_opus":0.05489350316557037,"score_gpt":0.3351983639531334,"score_spread":0.280304860787563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981545378","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061601162,0.0015912658,0.06993897,0.00009910595,0.0002327131,0.00069959176,0.00023592447,0.00038529723,0.86521596],"genre_scores_gemma":[0.6615966,0.0042764856,0.0555287,0.00004921831,0.0026417456,0.00084804057,0.0038512943,0.00038610402,0.27082184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99740773,0.000010951785,0.0008240051,0.00053821463,0.0005644966,0.00065457035],"domain_scores_gemma":[0.9982216,0.00012097518,0.00047401764,0.00046032734,0.00063453283,0.00008855373],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005211396,0.00037805884,0.0007898689,0.00044235395,0.00009609645,0.000047771573,0.00024866982,0.00060898747,0.004634099],"category_scores_gemma":[0.000401285,0.00029513618,0.00045504418,0.0003713355,0.000058235695,0.00008145801,0.00007573713,0.00038652302,0.000006899142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022398877,0.00011997088,0.002792214,0.001867789,0.00047843612,0.0000075464063,0.00011624528,0.000008387572,0.989923,0.000032151504,0.004201869,0.0004300185],"study_design_scores_gemma":[0.00022651494,0.000027065402,0.000008811823,0.00013032384,0.00037116843,0.000043095926,0.0004111845,0.000011915263,0.9608962,0.0002564903,0.037244458,0.00037280572],"about_ca_topic_score_codex":0.0008193807,"about_ca_topic_score_gemma":0.0000051447764,"teacher_disagreement_score":0.59999543,"about_ca_system_score_codex":0.000589738,"about_ca_system_score_gemma":0.00038748005,"threshold_uncertainty_score":0.99995005},"labels":[],"label_agreement":null},{"id":"W2982041772","doi":"10.4095/215623","title":"Dispersal train identification algorithm (DTIA)","year":2004,"lang":"en","type":"report","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Identification (biology); Biological dispersal; Algorithm; Computer science; Computational biology; Biology; Ecology; Sociology; Demography","score_opus":0.034865943256584896,"score_gpt":0.3302345224804042,"score_spread":0.2953685792238193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982041772","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004560765,0.0042377845,0.027308546,0.00018567588,0.0005348268,0.00009314429,0.00028625233,0.000447522,0.96645015],"genre_scores_gemma":[0.09152165,0.004498117,0.0033753228,0.000081120954,0.003152585,0.00008292042,0.0031402092,0.00019224884,0.8939558],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973012,0.0000063013454,0.00060230424,0.00067121664,0.0010526731,0.0003662705],"domain_scores_gemma":[0.9985275,0.00004777101,0.00036836724,0.00066654076,0.00025828238,0.0001315296],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023901708,0.00038277602,0.00055680133,0.00025900477,0.00009873975,0.00011781855,0.0004078991,0.000667048,0.0193725],"category_scores_gemma":[0.00021266173,0.00035753674,0.00038832726,0.000495843,0.00008961052,0.00008480216,0.000054353306,0.0005410109,0.00028265125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040779105,0.002549509,0.0014714323,0.006049634,0.0073089595,0.0005492626,0.0006897152,0.00004804496,0.14459258,0.0017380283,0.42348254,0.41147953],"study_design_scores_gemma":[0.0010003701,0.000037232472,0.00039084564,0.0002557199,0.0034539865,0.00033589225,0.0011930545,0.00044236207,0.6762604,0.0013672303,0.31295884,0.0023040697],"about_ca_topic_score_codex":0.0012954677,"about_ca_topic_score_gemma":0.00005582061,"teacher_disagreement_score":0.5316678,"about_ca_system_score_codex":0.0011010647,"about_ca_system_score_gemma":0.0010005006,"threshold_uncertainty_score":0.99988765},"labels":[],"label_agreement":null},{"id":"W2984871496","doi":"10.1016/j.saa.2019.117539","title":"Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach","year":2019,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Artificial neural network; Absorbance; Mean squared error; Feature selection; Zeolite; Fourier transform infrared spectroscopy; Biological system; Mathematics; Analytical Chemistry (journal); Chemistry; Materials science; Pattern recognition (psychology); Computer science; Artificial intelligence; Statistics; Chromatography; Engineering; Catalysis; Chemical engineering","score_opus":0.011793469250990788,"score_gpt":0.2570517603101195,"score_spread":0.24525829105912872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984871496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9813687,0.0020751504,0.0077363187,0.00007084984,0.000099683384,0.0004094648,0.000054743356,0.00010887734,0.008076165],"genre_scores_gemma":[0.9821414,0.00011143492,0.016930878,0.00012305685,0.000122033416,0.000022543813,0.00032302568,0.000099950674,0.00012572648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955184,0.00013482552,0.0010619466,0.0012795416,0.000810342,0.0011949816],"domain_scores_gemma":[0.9980079,0.000090643225,0.00067603757,0.00082387007,0.000120377095,0.00028116084],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036872015,0.0007677935,0.001274518,0.0011209467,0.00013458061,0.00015909052,0.000595304,0.00045341032,0.00070685876],"category_scores_gemma":[0.00017332655,0.0007962791,0.00039653832,0.004966632,0.00028324084,0.00030327388,0.00025960096,0.00072589365,0.0000132567075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023240384,0.00052322284,0.00449208,0.00016414131,0.00021357244,0.00004729953,0.00003941823,0.000035645935,0.99299765,0.001166741,0.000043830736,0.00004400225],"study_design_scores_gemma":[0.0011286223,0.00034123444,0.00026141485,0.000040595438,0.00037036097,0.00010442909,0.00014180611,0.006587924,0.98532057,0.004866578,0.000056895635,0.0007795422],"about_ca_topic_score_codex":0.0001316152,"about_ca_topic_score_gemma":0.0000058438964,"teacher_disagreement_score":0.00919456,"about_ca_system_score_codex":0.00031862262,"about_ca_system_score_gemma":0.00016403194,"threshold_uncertainty_score":0.99944884},"labels":[],"label_agreement":null},{"id":"W2986010898","doi":"10.1109/19.850391","title":"Signal processing support for the evaluation of blood toxic oxidation activity","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Signal processing; SIGNAL (programming language); Digital signal processing; Biological system; Electronic engineering; Engineering; Computer hardware","score_opus":0.0827299516613772,"score_gpt":0.3269764005735668,"score_spread":0.2442464489121896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986010898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.868081,0.00011478995,0.1297129,0.00014687043,0.000043549884,0.00034560298,0.000022012096,0.00003067706,0.001502624],"genre_scores_gemma":[0.9991988,0.000080730664,0.00021015681,0.00003961522,0.000022226359,0.00018928992,0.0000052650075,0.000008378357,0.00024551578],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883896,0.000022862148,0.00020017347,0.00017102335,0.0006587104,0.000108256354],"domain_scores_gemma":[0.9995155,0.000035382196,0.00010099514,0.00010001697,0.00021366292,0.000034446766],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050715177,0.00010188065,0.00010529868,0.00007115469,0.0002426715,0.000030197489,0.000053515185,0.000047391437,0.0033124525],"category_scores_gemma":[0.000004683841,0.00008302775,0.00006267426,0.00017674277,0.00004265277,0.00014564813,2.4305953e-7,0.00007374644,0.0000022635907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013490186,0.00023880231,0.000018944744,0.00005313161,0.00012797213,2.2537462e-8,0.00016784448,0.001156706,0.3815279,0.0000030378665,0.000009479762,0.61656123],"study_design_scores_gemma":[0.0015247944,0.000098081415,0.00037130452,0.000020102287,0.0014184016,0.0000023999592,0.0003544279,0.0076015317,0.98837686,0.000033041008,0.00011340477,0.00008564355],"about_ca_topic_score_codex":0.000025907686,"about_ca_topic_score_gemma":0.000027665128,"teacher_disagreement_score":0.61647564,"about_ca_system_score_codex":0.000103778904,"about_ca_system_score_gemma":0.00010809032,"threshold_uncertainty_score":0.99759865},"labels":[],"label_agreement":null},{"id":"W2986584427","doi":"10.18280/i2m.180411","title":"Determination Characteristic and Classification the Types of Orange Using UV-Vis Spectrophotometer by K-Nearest Neighbor Algorithm","year":2019,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Udayana","keywords":"k-nearest neighbors algorithm; Orange (colour); Pattern recognition (psychology); Algorithm; Mathematics; Computer science; Artificial intelligence; Physics; Optics","score_opus":0.02750720707999678,"score_gpt":0.30294670107262633,"score_spread":0.27543949399262957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986584427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99640036,0.00022857671,0.0023016545,0.00018861357,0.0000759984,0.00012680884,0.00007701888,0.00003405393,0.0005668939],"genre_scores_gemma":[0.99726915,0.0001068758,0.002136971,0.00010505997,0.000048271468,0.000014077829,0.00012943796,0.000014390883,0.0001757805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990356,0.000041690095,0.0002841661,0.00024995848,0.00021800512,0.00017056862],"domain_scores_gemma":[0.999229,0.00014442917,0.0002971546,0.00022333837,0.00007433942,0.00003174263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015377568,0.00014342161,0.00021252682,0.000103917926,0.00007609527,0.000052023275,0.00014729146,0.00011177564,0.0007931782],"category_scores_gemma":[0.00006286031,0.00010946195,0.000050902694,0.00029532632,0.00009838803,0.00017355323,0.00003032746,0.0001338366,0.00002026817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052106836,0.000057057852,0.06048348,0.000069427784,0.000083717314,0.0000010196097,0.00012596128,0.0000036506049,0.9190474,0.00004696966,0.000091897775,0.019937266],"study_design_scores_gemma":[0.0016563416,0.00023888431,0.14534403,0.00003048797,0.0005646087,0.000027752516,0.0008632949,0.04152436,0.8081813,0.0003821228,0.0007694873,0.00041732268],"about_ca_topic_score_codex":0.000049513015,"about_ca_topic_score_gemma":0.0000023766686,"teacher_disagreement_score":0.110866144,"about_ca_system_score_codex":0.000089226094,"about_ca_system_score_gemma":0.000028325463,"threshold_uncertainty_score":0.86847454},"labels":[],"label_agreement":null},{"id":"W2987499710","doi":"10.1093/mnras/stz3100","title":"Application of convolutional neural networks for stellar spectral classification","year":2019,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Smithsonian Astrophysical Observatory; University of Colorado Boulder; Office of Science; Max-Planck-Institut für Astronomie; Ministério da Ciência, Tecnologia e Inovação; Department of Atomic Energy, Government of India; University of Oxford; York University; Carnegie Institution for Science; Leibniz-Gemeinschaft; University of Notre Dame; Instituto de Astrofísica de Canarias; Carnegie Mellon University; Universidad Nacional Autónoma de México; Alfred P. Sloan Foundation; University of Washington; Johns Hopkins University; Carnegie Institution of Washington; University of Utah; Ohio State University; U.S. Department of Energy; Smithsonian Institution; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; Max-Planck-Institut für Astrophysik","keywords":"Stellar classification; Convolutional neural network; Artificial intelligence; Physics; Artificial neural network; Deep learning; Pattern recognition (psychology); LAMOST; Astronomical spectroscopy; Spectral line; Random forest; Astrophysics; Computer science; Machine learning; Stars; Astronomy","score_opus":0.010355302883253524,"score_gpt":0.22917342307438332,"score_spread":0.21881812019112978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987499710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99591565,0.0001568478,0.0028051084,0.00017083703,0.0000698184,0.00017135676,0.00006212826,0.000015619402,0.0006326493],"genre_scores_gemma":[0.9975163,1.7456684e-7,0.0018776212,0.000014175754,0.00017295757,0.000017169225,0.000052871095,0.000011814114,0.00033696697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909186,0.000011054944,0.00032698244,0.00022437434,0.00014476005,0.00020096547],"domain_scores_gemma":[0.99903846,0.00019749915,0.00036577007,0.0002970722,0.00006176509,0.000039454048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001142879,0.00011949483,0.0002506803,0.000009737278,0.000058345762,0.000010261776,0.00037067547,0.00012679459,0.00015617971],"category_scores_gemma":[0.000018783838,0.000095038915,0.00045630717,0.00010423239,0.0001530691,0.000042402848,0.00006447645,0.00015027537,0.0000024875274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000710063,0.00011293362,0.14591552,0.00007465129,0.00014340138,1.6026693e-9,0.000027563116,0.8486378,0.0041287295,0.00035379682,0.00025525628,0.00027930626],"study_design_scores_gemma":[0.0004095592,0.000026245143,0.060229383,0.000004905263,0.00014524975,4.088722e-9,0.00019608259,0.92163426,0.017070103,0.000019271005,0.00018148065,0.000083457606],"about_ca_topic_score_codex":0.000053938336,"about_ca_topic_score_gemma":0.00000225451,"teacher_disagreement_score":0.08568615,"about_ca_system_score_codex":0.00010216316,"about_ca_system_score_gemma":0.000023044451,"threshold_uncertainty_score":0.38755745},"labels":[],"label_agreement":null},{"id":"W2992267701","doi":"10.52151/jae2014511.1538","title":"Determination of Main Constituents in Green Gram Using Near- Infrared Hyperspectral Imaging","year":2014,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering (India)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Gram; Veterinary medicine; Mathematics; Remote sensing; Biology; Medicine; Geography","score_opus":0.007124770539024223,"score_gpt":0.2282488761643765,"score_spread":0.22112410562535229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992267701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99761313,0.00023107015,0.0014317324,0.00003374125,0.000095663665,0.000024734552,0.0000034473792,0.000014896645,0.00055158674],"genre_scores_gemma":[0.98890436,0.000013758208,0.010828377,0.000008286616,0.00017878458,6.1661075e-7,0.0000033142485,0.000009565893,0.000052931984],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989163,0.000010781169,0.0004747155,0.0001045417,0.00026425303,0.00022936414],"domain_scores_gemma":[0.9993089,0.000076416545,0.00033967968,0.00008201683,0.00010799261,0.000085008585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015941254,0.00015368851,0.0003411782,0.00021120757,0.000033038792,0.000040175128,0.00019085917,0.000075364886,0.0000436013],"category_scores_gemma":[0.00018605212,0.00011833817,0.00014821993,0.0004691708,0.00003428116,0.00027575542,0.00002398211,0.000300004,8.685974e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017391121,0.000079291465,0.02826733,0.00015516064,0.00007486221,0.000038874852,0.000355993,0.010439773,0.95957255,0.000077253586,0.000030650353,0.00089087477],"study_design_scores_gemma":[0.0026310852,0.00011105204,0.3335733,0.00064056925,0.0004217173,0.0014060909,0.0011968103,0.04377485,0.61525357,0.00013521059,0.00014473865,0.00071103225],"about_ca_topic_score_codex":0.00003644214,"about_ca_topic_score_gemma":0.000001405134,"teacher_disagreement_score":0.344319,"about_ca_system_score_codex":0.00023134821,"about_ca_system_score_gemma":0.00003306014,"threshold_uncertainty_score":0.48256904},"labels":[],"label_agreement":null},{"id":"W2994236510","doi":"10.20431/2349-4050.0602002","title":"A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample","year":2019,"lang":"en","type":"review","venue":"International Journal of Innovative Research in Electronics and Communications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Sample (material); Image processing; Agriculture; Computer science; Agricultural engineering; Artificial intelligence; Process engineering; Image (mathematics); Engineering; Geography; Chromatography; Chemistry","score_opus":0.3058526918161472,"score_gpt":0.5512048456984996,"score_spread":0.24535215388235238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994236510","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000079709476,0.9949399,0.0024019098,0.0012719546,0.000009626,0.000662394,0.00025137447,0.0000057120983,0.0003773946],"genre_scores_gemma":[0.008823899,0.982459,0.008023368,0.00001958019,0.00007620855,0.00028189056,0.00027235833,0.00001837117,0.000025306283],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.997896,0.00018752804,0.0010572239,0.0002542197,0.00040967733,0.0001953607],"domain_scores_gemma":[0.99288535,0.0012034882,0.0011451405,0.00035090168,0.004376336,0.000038792456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028296576,0.00019207822,0.00085350126,0.00061163027,0.00012397494,0.00013787008,0.000717494,0.00027716934,0.0000010511546],"category_scores_gemma":[0.0008855325,0.00014184238,0.000101839505,0.0010101562,0.0001246241,0.00017792363,0.0001764455,0.0019013498,2.571471e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000952624,0.000488735,0.000081377875,0.11048129,0.0008782171,9.715081e-7,0.00017365013,0.0000010944926,0.001977654,0.06846835,0.00022060706,0.8171328],"study_design_scores_gemma":[0.0022290528,0.001418611,0.000046844812,0.5667891,0.0010628748,0.00036962033,0.0033543285,0.0059657395,0.0026504977,0.01208244,0.4027765,0.0012543786],"about_ca_topic_score_codex":0.0000067498886,"about_ca_topic_score_gemma":0.000003973859,"teacher_disagreement_score":0.8158784,"about_ca_system_score_codex":0.00055769435,"about_ca_system_score_gemma":0.0007677914,"threshold_uncertainty_score":0.8260524},"labels":[],"label_agreement":null},{"id":"W2994869058","doi":"10.1002/bit.27256","title":"Noninvasive tool for optical online monitoring of individual biomass concentrations in a defined coculture","year":2019,"lang":"en","type":"article","venue":"Biotechnology and Bioengineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Bundesministerium für Bildung und Forschung","keywords":"Biomass (ecology); Environmental chemistry; Chemistry; Computational biology; Biochemical engineering; Environmental science; Biology; Ecology; Engineering","score_opus":0.01312710581988149,"score_gpt":0.2531944415268207,"score_spread":0.2400673357069392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994869058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99762195,0.0010196072,0.0008367352,0.00022597321,0.000041846626,0.00007709885,0.00008432747,0.000066660716,0.00002577501],"genre_scores_gemma":[0.9916297,0.00011909472,0.008123678,0.000004414175,0.00003221077,0.000011773067,0.00003949323,0.000007732357,0.000031871343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994006,0.0000010803072,0.00017868297,0.00017578076,0.000057085792,0.0001867755],"domain_scores_gemma":[0.9997213,0.00007742132,0.00004291399,0.00011582267,0.000022028438,0.000020539384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003802873,0.00010755362,0.00020853577,0.00015242802,0.000020260888,0.000008879278,0.000120130055,0.00036733237,0.000023310826],"category_scores_gemma":[0.00007494095,0.00009910043,0.000037903817,0.0003168978,0.00006112074,0.000037347636,0.000043938922,0.00016434217,8.7614933e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013076629,0.000051741285,0.052920707,0.0001446417,0.00008651133,0.0000026863777,0.000040281455,0.000030340727,0.94433564,0.0019757757,0.00000534241,0.00039325643],"study_design_scores_gemma":[0.000572729,0.000050332914,0.0033599774,0.000043411896,0.000059100217,0.000011638211,0.00049666484,0.00034585604,0.99477106,0.000029779349,0.00014205098,0.00011737063],"about_ca_topic_score_codex":0.000003488442,"about_ca_topic_score_gemma":0.0000021403177,"teacher_disagreement_score":0.050435446,"about_ca_system_score_codex":0.000023377448,"about_ca_system_score_gemma":0.000015388501,"threshold_uncertainty_score":0.40411982},"labels":[],"label_agreement":null},{"id":"W2995705012","doi":"10.3847/1538-3881/acd75c","title":"Automated SpectroPhotometric Image REDuction (ASPIRED)","year":2023,"lang":"en","type":"article","venue":"The Astronomical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Comisión Nacional de Investigación Científica y Tecnológica; National Science Foundation; Ministério da Ciência e Tecnologia; European Southern Observatory; Horizon 2020 Framework Programme; United States-Israel Binational Science Foundation; Canadian Institute for Advanced Research; Israel Science Foundation; Science and Technology Facilities Council; Instituto de Astrofísica de Canarias; Narodowe Centrum Nauki; European Commission; Liverpool John Moores University; Council for Higher Education","keywords":"Python (programming language); Data reduction; Reduction (mathematics); Workflow; Correctness; Automation; Software; Computer science; Data processing; Physics; Suite; Computational science; Algorithm; Data mining; Programming language; Database","score_opus":0.014324237662848441,"score_gpt":0.28272194926615435,"score_spread":0.2683977116033059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995705012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941865,0.00011865302,0.00017137131,0.00073459256,0.00042974166,0.000027220442,0.0000056701156,0.0005123787,0.0038138276],"genre_scores_gemma":[0.99452674,0.00006359254,0.00056546746,0.000012046173,0.0027121515,0.0000049372857,0.000010853246,0.000026725747,0.0020774992],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872357,0.000028366694,0.00032766574,0.00018770811,0.00026746464,0.00046522735],"domain_scores_gemma":[0.9992639,0.00008423446,0.00017713947,0.00026841514,0.000053738357,0.00015258807],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00032701477,0.00015642917,0.00021665537,0.00039647962,0.0003160421,0.00017485049,0.0004544757,0.00007916431,0.0037406897],"category_scores_gemma":[0.00011047841,0.00010911359,0.00020210374,0.001617465,0.00012659705,0.0001398077,0.000086101565,0.0006446016,0.0010165676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010072617,0.00009571826,0.002655784,0.000010776906,0.00032924398,0.000011673238,0.00008932336,0.0005858081,0.8857947,0.00006832528,0.10854089,0.0017170208],"study_design_scores_gemma":[0.002222027,0.0001739885,0.047072425,0.000030656243,0.0006821121,0.0011405586,0.0028320593,0.0147813875,0.92024064,0.0016847168,0.00838751,0.0007519348],"about_ca_topic_score_codex":0.000011442395,"about_ca_topic_score_gemma":1.4643513e-7,"teacher_disagreement_score":0.10015338,"about_ca_system_score_codex":0.00022726454,"about_ca_system_score_gemma":0.00006540012,"threshold_uncertainty_score":0.9997613},"labels":[],"label_agreement":null},{"id":"W2996196789","doi":"10.1021/acs.analchem.9b03166","title":"Sparse Projection Pursuit Analysis: An Alternative for Exploring Multivariate Chemical Data","year":2019,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Projection pursuit; Interpretability; Principal component analysis; Multivariate statistics; Projection (relational algebra); Pattern recognition (psychology); Artificial intelligence; Chemometrics; Exploratory data analysis; Univariate; Feature selection; Data mining; Computer science; Machine learning; Algorithm","score_opus":0.15286129409927252,"score_gpt":0.36563464595280676,"score_spread":0.21277335185353424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996196789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9812743,0.000080880454,0.0050350004,0.00011674055,0.000059082686,0.00014524133,0.00028612037,0.00020419458,0.01279845],"genre_scores_gemma":[0.9910262,0.000018626391,0.0016714772,0.00005233497,0.00060246856,0.00007593418,0.0020872094,0.00004465848,0.0044210884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99722326,0.000010723322,0.00048378864,0.0012419171,0.00047190962,0.000568403],"domain_scores_gemma":[0.9975382,0.00023201208,0.00019100508,0.0015978871,0.00015846854,0.00028239554],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028082047,0.00034929888,0.0006533685,0.00012717965,0.000082155355,0.00013656159,0.0012060786,0.00024702816,0.0031233523],"category_scores_gemma":[0.0004232971,0.0003377438,0.00031254752,0.0011193294,0.00008968256,0.0005360029,0.0003195461,0.00039502303,0.00006825474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037531072,0.0008594864,0.010767794,0.0004992218,0.005539364,0.000014554082,0.00014781649,0.0004024471,0.97967345,0.0003334436,0.00053693756,0.00085017644],"study_design_scores_gemma":[0.0008869856,0.00002338644,0.000100252095,0.000018119097,0.003487826,0.00000589207,0.0005828517,0.25121695,0.7413966,0.00021503754,0.0015732588,0.00049286225],"about_ca_topic_score_codex":0.00012651834,"about_ca_topic_score_gemma":0.000004139845,"teacher_disagreement_score":0.2508145,"about_ca_system_score_codex":0.00018693668,"about_ca_system_score_gemma":0.00007631442,"threshold_uncertainty_score":0.99990743},"labels":[],"label_agreement":null},{"id":"W2996896630","doi":"10.1007/s11694-019-00360-9","title":"Rapid assessment of pork freshness using miniaturized NIR spectroscopy","year":2020,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"TBARS; Partial least squares regression; Chemometrics; Mathematics; Chemistry; Food science; Thiobarbituric acid; Linear regression; Near-infrared spectroscopy; Statistics; Chromatography; Biochemistry; Biology; Lipid peroxidation","score_opus":0.08317053818401246,"score_gpt":0.29718842785700156,"score_spread":0.2140178896729891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996896630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789483,0.00042780733,0.019238282,0.00047210697,0.00019206801,0.00008040843,0.000030237934,0.000020472184,0.00059034844],"genre_scores_gemma":[0.99425524,0.00017676296,0.004729212,0.00015704018,0.00061644067,0.0000016557377,0.000027312766,0.00002764232,0.000008679961],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973664,0.00004971222,0.000979744,0.00019714155,0.0011839726,0.00022297792],"domain_scores_gemma":[0.99718887,0.000024280878,0.0017451163,0.00017550038,0.0006947735,0.00017149068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042590202,0.00021729345,0.00061544584,0.00019132982,0.00006667597,0.000050847717,0.00027826198,0.000112103095,0.0008016818],"category_scores_gemma":[0.0002094885,0.00019760082,0.00024608432,0.0006063622,0.000033016197,0.00029994998,0.000031732605,0.00029446182,9.573669e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017681519,0.00023207294,0.0036252663,0.00020151246,0.00042626623,0.0000048653746,0.00016213373,0.000015543683,0.9948492,0.000020965846,0.00004175552,0.00024358595],"study_design_scores_gemma":[0.0013171713,0.0003866755,0.013805821,0.00013569463,0.0005396804,0.000013781903,0.00014962844,0.00041770766,0.9824813,0.000018556831,0.0005571147,0.00017689183],"about_ca_topic_score_codex":0.0000015228336,"about_ca_topic_score_gemma":3.8223965e-7,"teacher_disagreement_score":0.015306984,"about_ca_system_score_codex":0.00028098043,"about_ca_system_score_gemma":0.00032304568,"threshold_uncertainty_score":0.8777853},"labels":[],"label_agreement":null},{"id":"W3000557037","doi":"10.5539/jas.v12n2p38","title":"Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images","year":2020,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Research Partnership for Sustainable Development; Japan International Cooperation Agency","keywords":"Spectroradiometer; Red edge; Blight; Crop; Infestation; Horticulture; Vegetation (pathology); Correlation coefficient; Agronomy; Biology; Botany; Canopy; Reflectivity; Mathematics; Medicine","score_opus":0.04628873610813442,"score_gpt":0.29451576516274613,"score_spread":0.24822702905461172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000557037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979947,0.0005170916,0.0008450432,0.0004504809,0.00008792537,0.000029363564,0.000011448462,0.000012240037,0.00005165978],"genre_scores_gemma":[0.988983,0.000043497013,0.010503602,0.00005181788,0.00039047076,2.5565762e-7,0.0000051202564,0.000004634925,0.000017646493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985478,0.000021667902,0.00040412162,0.00032247792,0.0004284003,0.00027551322],"domain_scores_gemma":[0.99906677,0.000065170985,0.0003811451,0.0001380358,0.00015018771,0.00019869357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003267183,0.00015510117,0.00027888792,0.00019458242,0.00015394643,0.0003213631,0.0006474774,0.000056518908,0.000099884426],"category_scores_gemma":[0.00032984288,0.0000952329,0.000057609417,0.0020029584,0.000226262,0.0013812608,0.00013189526,0.0002770591,0.0000019471174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004511243,0.0000437,0.006761909,0.00002505835,0.000012735713,0.000018238441,0.000119786724,0.000017209,0.9926881,4.6728135e-7,0.000018237894,0.00024941153],"study_design_scores_gemma":[0.00059975474,0.000080339356,0.09694322,0.000023820425,0.00007073169,0.00014557313,0.00058543414,0.0012930279,0.90001583,0.0000036013062,0.00007494375,0.00016371364],"about_ca_topic_score_codex":0.00009959308,"about_ca_topic_score_gemma":0.000021478521,"teacher_disagreement_score":0.092672296,"about_ca_system_score_codex":0.00018347912,"about_ca_system_score_gemma":0.00009089238,"threshold_uncertainty_score":0.3883485},"labels":[],"label_agreement":null},{"id":"W3002180218","doi":"10.221751/rmc2016.089","title":"Real Time Prediction of Backfat Composition and Iodine Value by Portable Near Infrared Spectroscopy in a Diverse Population of Pigs","year":2017,"lang":"en","type":"article","venue":"Meat and Muscle Biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Agriculture and Agri-Food Canada","funders":"","keywords":"Iodine value; Animal science; Population; Polyunsaturated fatty acid; Partial least squares regression; Chemistry; Near infrared reflectance spectroscopy; Analytical Chemistry (journal); Mathematics; Fatty acid; Near-infrared spectroscopy; Food science; Biology; Chromatography; Statistics; Medicine","score_opus":0.012344547156865877,"score_gpt":0.2628929853305249,"score_spread":0.250548438173659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002180218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99495155,0.00017427103,0.000017597582,0.000024358678,0.00002142796,0.00004099955,0.000133615,0.000011008215,0.0046251914],"genre_scores_gemma":[0.99876463,0.00034054232,0.00030112008,0.000004317158,0.000027296996,0.0000021110575,0.00029312092,0.000004624108,0.00026221309],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994709,0.000013595857,0.00019201364,0.00016503286,0.000046754976,0.00011173409],"domain_scores_gemma":[0.99958265,0.00002443589,0.00018022087,0.0001578087,0.000023243976,0.000031652937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066406916,0.00008136697,0.00024233888,0.00005815714,0.00009209305,0.000012791331,0.00006305889,0.00012770253,0.00017304523],"category_scores_gemma":[0.000027558663,0.00007688931,0.000024108573,0.00005326627,0.00014963593,0.00008761023,0.00004488834,0.00005670698,6.619611e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056123627,0.000037911694,0.31618002,0.000030747393,0.000016763903,3.7133358e-7,0.000046093042,7.509695e-7,0.6832962,0.00006296765,0.00014107111,0.00013094599],"study_design_scores_gemma":[0.0012689994,0.0001447061,0.5555157,0.000029703542,0.000094004536,0.0000024899584,0.000061704064,0.00068387465,0.44151637,0.00043483326,0.0001522872,0.00009529965],"about_ca_topic_score_codex":0.0020446964,"about_ca_topic_score_gemma":0.000017992583,"teacher_disagreement_score":0.24177986,"about_ca_system_score_codex":0.000021015168,"about_ca_system_score_gemma":0.000008133988,"threshold_uncertainty_score":0.3135455},"labels":[],"label_agreement":null},{"id":"W3002506388","doi":"10.37358/rc.19.7.7339","title":"ATR-FTIR Spectroscopy Coupled with Chemical and Chemometric Analysis to Distinguish Between Some Sweet Wines","year":2019,"lang":"en","type":"article","venue":"Revista de Chimie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Fourier transform infrared spectroscopy; Sugar; Chemistry; Chemical composition; Chemometrics; Spectroscopy; Analytical Chemistry (journal); Polyphenol; Infrared spectroscopy; Chemical structure; Food science; Chromatography; Mathematics; Organic chemistry; Chemical engineering","score_opus":0.00843771202395251,"score_gpt":0.25850107914200793,"score_spread":0.25006336711805544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002506388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99516773,0.000982932,0.00035210093,0.00030551475,0.000012111435,0.00014063231,0.00005278798,0.00014888518,0.0028373152],"genre_scores_gemma":[0.9947969,0.00010433979,0.0023763606,0.00018270855,0.0004991772,0.00001701508,0.00014060434,0.000057026926,0.0018258693],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976108,0.00001518446,0.00043903256,0.0008145647,0.00045389868,0.00066651555],"domain_scores_gemma":[0.99821436,0.0003100424,0.00021975118,0.0007260672,0.000100824866,0.00042893377],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021689133,0.00042638776,0.0011020225,0.0006700032,0.000096026575,0.00023422136,0.00042739094,0.00017871361,0.001219191],"category_scores_gemma":[0.00040707656,0.00036477757,0.00024279664,0.004663339,0.00009471607,0.00011081032,0.00015396845,0.00034428044,0.000071849056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010315071,0.000102761216,0.6772927,0.00043749294,0.0021087676,0.000016341388,0.00009057463,0.000017936925,0.31893116,0.0002154959,0.0005389211,0.00014467568],"study_design_scores_gemma":[0.0022151936,0.0002601382,0.18086669,0.0001905311,0.010535664,0.000037605267,0.00029772468,0.0011787289,0.79859114,0.00019090048,0.0036960565,0.0019396181],"about_ca_topic_score_codex":0.00007937236,"about_ca_topic_score_gemma":0.0000027383494,"teacher_disagreement_score":0.49642602,"about_ca_system_score_codex":0.00027198912,"about_ca_system_score_gemma":0.00007419319,"threshold_uncertainty_score":0.99988043},"labels":[],"label_agreement":null},{"id":"W3005970599","doi":"10.1002/aocs.12335","title":"Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld Near‐Infrared Spectrometer","year":2020,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian International Grains Institute","funders":"","keywords":"Canola; Spectrometer; Mobile device; Infrared; Nondestructive testing; Near-infrared spectroscopy; Environmental science; Materials science; Remote sensing; Chemistry; Computer science; Optics; Medicine; Physics; Geography; Food science; World Wide Web","score_opus":0.020439836386556946,"score_gpt":0.27134460118511894,"score_spread":0.250904764798562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005970599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961148,0.0005202555,0.00018803775,0.00043449563,0.000028416656,0.0000117144955,0.000037610782,0.0000145682,0.002650095],"genre_scores_gemma":[0.9921215,0.0002914226,0.006404368,0.00065554207,0.00024080047,7.722903e-7,0.000003107395,0.00002750629,0.00025493404],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99810046,0.000031691172,0.0006777121,0.00025329905,0.00062534295,0.00031152245],"domain_scores_gemma":[0.9967931,0.0001696743,0.0021532879,0.00038403788,0.00029211442,0.00020776938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017639711,0.0002614198,0.0011063153,0.00008027429,0.0001354225,0.000058544563,0.0007366576,0.00009552136,0.00066416],"category_scores_gemma":[0.0002784088,0.00019315242,0.0019918464,0.00405226,0.0005636921,0.00011831643,0.00014964763,0.0006879636,6.608761e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001494493,0.0000858534,0.011820668,0.00009901595,0.008693941,0.000006041932,0.0025810124,0.00025151155,0.97471756,0.0000014567612,0.0007545538,0.00083893427],"study_design_scores_gemma":[0.0006212566,0.000093949064,0.0019115595,0.000038069775,0.007333319,0.000046861616,0.005412024,0.0033811661,0.98036957,0.000019669023,0.00049126754,0.0002813054],"about_ca_topic_score_codex":0.000164346,"about_ca_topic_score_gemma":0.0000014012519,"teacher_disagreement_score":0.009909109,"about_ca_system_score_codex":0.00040805843,"about_ca_system_score_gemma":0.00038102025,"threshold_uncertainty_score":0.7876527},"labels":[],"label_agreement":null},{"id":"W3006003209","doi":"10.1021/acs.cgd.9b01482","title":"Simultaneous Measurement of Solution Concentration and Slurry Density by Raman Spectroscopy with Artificial Neural Network","year":2020,"lang":"en","type":"article","venue":"Crystal Growth & Design","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Partial least squares regression; Artificial neural network; Mean squared error; Principal component regression; Principal component analysis; Biological system; Raman spectroscopy; Analytical Chemistry (journal); Linear regression; Chemistry; Mathematics; Artificial intelligence; Statistics; Chromatography; Computer science; Optics; Physics","score_opus":0.026592238188165543,"score_gpt":0.22776565822853978,"score_spread":0.20117342004037425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006003209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71204203,0.0011362002,0.28561574,0.00037125172,0.00003081899,0.00018914642,0.000013485167,0.00010768611,0.0004936203],"genre_scores_gemma":[0.9976795,0.000027663496,0.0018972763,0.00012455188,0.0002114291,0.00000553996,0.000020199213,0.000019781248,0.000014068024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858016,0.00004809871,0.0002857849,0.00034644146,0.0004175903,0.0003219161],"domain_scores_gemma":[0.99925834,0.00011413978,0.00018185943,0.00012080373,0.00016302153,0.0001618556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014669738,0.00020573198,0.00030841262,0.000014376521,0.00016062843,0.00005173196,0.00010535294,0.000097989854,0.000099455094],"category_scores_gemma":[0.00017404687,0.00018928661,0.000048200254,0.00034015893,0.00015427213,0.000098457334,0.000026363688,0.00017814929,0.0000018132719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007493255,0.000065178036,0.0024249651,0.00009046263,0.00009330544,0.000015493431,0.00015701063,0.0011107927,0.993523,0.000091692884,0.001535965,0.0001428329],"study_design_scores_gemma":[0.00048478702,0.00035281538,0.0000777513,0.000019224097,0.00026152612,0.000009578261,0.00014756806,0.026053486,0.9720688,0.00025230186,0.000041205076,0.00023094956],"about_ca_topic_score_codex":0.000033803768,"about_ca_topic_score_gemma":0.0000051022225,"teacher_disagreement_score":0.28563744,"about_ca_system_score_codex":0.00008781092,"about_ca_system_score_gemma":0.00006291448,"threshold_uncertainty_score":0.77188843},"labels":[],"label_agreement":null},{"id":"W3006349826","doi":"","title":"Handheld Near Infrared spectroscopy for cannabis analysis: from the analytical problem to the chemometric solution","year":2020,"lang":"fr","type":"article","venue":"ORBi (University of Liège)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mobile device; Spectroscopy; Near-infrared spectroscopy; Infrared; Analytical Chemistry (journal); Computer science; Chemistry; Chromatography; Physics; Optics; World Wide Web","score_opus":0.02332699167780036,"score_gpt":0.2445119002520997,"score_spread":0.22118490857429934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006349826","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27041277,0.008862628,0.20371422,0.5046899,0.00023803825,0.0010218106,0.002082614,0.00014640478,0.00883167],"genre_scores_gemma":[0.9469993,0.00039936573,0.015189246,0.0017546322,0.00071168784,0.000003883692,0.00020259545,0.00003875573,0.03470053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782133,0.00007694302,0.000325763,0.00067172776,0.00050352036,0.0006006974],"domain_scores_gemma":[0.9979322,0.0004072151,0.00032865247,0.00068756694,0.0002781577,0.00036621958],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028085502,0.00032355613,0.00075106823,0.00024125664,0.00074370735,0.00013283205,0.0012497547,0.00030689163,0.0044195675],"category_scores_gemma":[0.00016840368,0.00028428307,0.00080805155,0.008748514,0.0004372891,0.00020200408,0.00034092215,0.00047963334,0.00014753615],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016263482,0.0007524825,0.067427896,0.0005643557,0.017925425,0.00004685409,0.016932046,0.0022385356,0.018458284,0.0021956947,0.8696091,0.0022229399],"study_design_scores_gemma":[0.005162133,0.0013489968,0.040121134,0.0001934834,0.06492957,0.000006938826,0.031914122,0.171934,0.08309779,0.0014273403,0.5977852,0.0020793024],"about_ca_topic_score_codex":0.011914053,"about_ca_topic_score_gemma":0.0010082668,"teacher_disagreement_score":0.6765865,"about_ca_system_score_codex":0.000313486,"about_ca_system_score_gemma":0.0002773361,"threshold_uncertainty_score":0.99996096},"labels":[],"label_agreement":null},{"id":"W3008064155","doi":"10.1016/j.foodchem.2020.126471","title":"Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Subcutaneous fat; Near-infrared spectroscopy; Quality assessment; Food science; Mathematics; Veterinary medicine; Biology; Medicine; Pathology; External quality assessment; Adipose tissue","score_opus":0.04298907016788285,"score_gpt":0.2906282188740534,"score_spread":0.24763914870617054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008064155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98210806,0.00029193293,0.00044007562,0.00020355108,0.000037762846,0.00005690039,0.00012859027,0.00012011583,0.016613042],"genre_scores_gemma":[0.9966815,0.000012394188,0.0021546423,0.00006623171,0.00030897764,0.000008529928,0.00017322262,0.000035165882,0.00055934855],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980304,0.000011935264,0.00068850274,0.00046903413,0.00041604342,0.000384077],"domain_scores_gemma":[0.9986563,0.00003805628,0.0005039831,0.00047543237,0.000112186564,0.00021408654],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009257547,0.00028587188,0.00052057806,0.000025463074,0.00014689626,0.00007187493,0.00036708033,0.00023337563,0.0033282912],"category_scores_gemma":[0.00023145691,0.00031519547,0.00018507979,0.0006377475,0.00018435034,0.000115333816,0.00011688235,0.00027972303,0.000010177239],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012679558,0.00015799995,0.0027283272,0.00063204154,0.00022229041,0.0000025306142,0.00065510574,0.00004037439,0.9950604,0.00010961843,0.00022454973,0.0000399839],"study_design_scores_gemma":[0.0005147124,0.000046448014,0.00012338451,0.000022662109,0.00021417558,0.0000035621747,0.0013617373,0.00044452946,0.9955069,0.0003636342,0.0010967614,0.00030152887],"about_ca_topic_score_codex":0.00010783255,"about_ca_topic_score_gemma":0.0000024090832,"teacher_disagreement_score":0.016053693,"about_ca_system_score_codex":0.00015694484,"about_ca_system_score_gemma":0.0003315502,"threshold_uncertainty_score":0.99993},"labels":[],"label_agreement":null},{"id":"W3009962338","doi":"10.18433/jpps30649","title":"Raman Spectroscopy for Quantitative Analysis in the Pharmaceutical Industry","year":2020,"lang":"en","type":"review","venue":"Journal of Pharmacy & Pharmaceutical Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Process analytical technology; Pharmaceutical industry; Pharmaceutical manufacturing; Drug development; Process (computing); Biochemical engineering; Food and drug administration; Nanotechnology; Risk analysis (engineering); Computer science; Management science; Data science; Engineering; Drug; Business; Work in process; Medicine; Materials science; Pharmacology; Operations management","score_opus":0.2628821180382175,"score_gpt":0.542143510587476,"score_spread":0.2792613925492585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009962338","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003674399,0.9937023,0.0005317189,0.0019505649,0.00021219625,0.00039873036,0.00012904349,0.000026812095,0.0026811764],"genre_scores_gemma":[0.0054578288,0.9908423,0.0015274864,0.0009046468,0.0010958568,0.000057634792,0.000016627579,0.000041616247,0.000055999644],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99391484,0.0004708183,0.002233302,0.0007966173,0.0015324095,0.001051987],"domain_scores_gemma":[0.99357575,0.0038382232,0.0015259483,0.0002630221,0.0001967306,0.0006003519],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030799536,0.0007853022,0.0032186443,0.0012758693,0.0003526759,0.00039463496,0.003135198,0.00038368217,0.0029145721],"category_scores_gemma":[0.0008894625,0.0004737468,0.002562306,0.008855576,0.00095029426,0.0004231704,0.00017108937,0.004494478,0.000025865462],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001073789,0.0030423342,0.0020430156,0.017919818,0.018208934,0.0012210559,0.0012068589,0.0003577598,0.0028017266,0.004762248,0.009284237,0.9380782],"study_design_scores_gemma":[0.0012069597,0.00014690356,0.0000017136542,0.00045940612,0.028189888,0.00017628561,0.0007202878,0.0032240886,0.0024522194,0.00010794041,0.9627965,0.0005178161],"about_ca_topic_score_codex":0.000014055358,"about_ca_topic_score_gemma":0.0000035903286,"teacher_disagreement_score":0.95351225,"about_ca_system_score_codex":0.00038623647,"about_ca_system_score_gemma":0.0010532107,"threshold_uncertainty_score":0.9997714},"labels":[],"label_agreement":null},{"id":"W3011808279","doi":"10.1002/cjs.11545","title":"Nonparametric change point detection for periodic time series","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autocorrelation; Sequence (biology); Series (stratigraphy); Nonparametric statistics; Matrix (chemical analysis); Statistics; Change detection; Time series; Variance (accounting); Computer science; Distribution (mathematics); Mathematics; Independent and identically distributed random variables; Algorithm; Econometrics; Artificial intelligence; Random variable; Mathematical analysis","score_opus":0.030419048400011937,"score_gpt":0.24075123938263326,"score_spread":0.21033219098262132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011808279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23794264,0.011499424,0.71842384,0.012367373,0.0012888658,0.00063234736,0.010147273,0.00011690022,0.007581336],"genre_scores_gemma":[0.98534673,0.00005905642,0.012851641,0.0005411046,0.00069691543,0.0000053029016,0.000024125387,0.000026908354,0.00044820606],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992518,0.000006342213,0.00028335734,0.00009950521,0.00011906857,0.00023992937],"domain_scores_gemma":[0.9987627,0.0000987168,0.0002223414,0.000071269365,0.00024286557,0.0006021044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006794575,0.00011016784,0.00024632653,0.00023634138,0.00013068444,0.00007251491,0.0001530034,0.00007088242,0.0019329996],"category_scores_gemma":[0.0009027174,0.00011056364,0.00007773497,0.00046375062,0.000066562345,0.00012765607,0.000005040181,0.00017992129,0.000025594616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001855133,0.0002957669,0.030819224,0.004789994,0.004061298,0.003600254,0.022882145,0.00029909486,0.21047547,0.006673663,0.3585124,0.35573557],"study_design_scores_gemma":[0.00548801,0.004384903,0.0048954636,0.00019327656,0.0036193507,0.0014744325,0.0064777047,0.009342461,0.56161594,0.0066518933,0.39352176,0.0023347747],"about_ca_topic_score_codex":0.00044997083,"about_ca_topic_score_gemma":0.0010254814,"teacher_disagreement_score":0.7474041,"about_ca_system_score_codex":0.00017207794,"about_ca_system_score_gemma":0.0003688873,"threshold_uncertainty_score":0.9989794},"labels":[],"label_agreement":null},{"id":"W3012317866","doi":"10.18280/ijdne.150114","title":"Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging","year":2020,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Test (biology); Cherry tomato; Remote sensing; Artificial intelligence; Computer science; Horticulture; Biology; Geography; Botany","score_opus":0.014152130088823638,"score_gpt":0.27214255042255625,"score_spread":0.2579904203337326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012317866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.899242,0.002462032,0.02947743,0.03407915,0.0010640721,0.00013138745,0.00016352008,0.00015813716,0.03322226],"genre_scores_gemma":[0.99387103,0.00012486459,0.0022490907,0.0031441858,0.0005343906,6.594041e-7,0.0000052722576,0.000016220672,0.000054262167],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896103,0.000012639609,0.00027984145,0.00017480753,0.00042907128,0.00014260403],"domain_scores_gemma":[0.9989296,0.00041030726,0.0002547883,0.00008069487,0.0001922375,0.00013233178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012410419,0.00016042928,0.00021420278,0.00018729715,0.00004858921,0.000097109565,0.0003768259,0.0001293192,0.0002517444],"category_scores_gemma":[0.00053815066,0.00013458091,0.00013320592,0.00015786481,0.000035207242,0.00013076632,0.000024711755,0.0008431169,0.000006465304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0066179754,0.003538221,0.14973137,0.00026061776,0.0028765078,0.0036089276,0.0014648673,0.021071738,0.7732989,0.008247868,0.011470471,0.017812496],"study_design_scores_gemma":[0.0063248747,0.0013774447,0.012391111,0.00046321703,0.0006919748,0.000741647,0.0014887031,0.66672015,0.30126992,0.004349349,0.002887219,0.0012944185],"about_ca_topic_score_codex":0.000002381826,"about_ca_topic_score_gemma":7.089433e-7,"teacher_disagreement_score":0.6456484,"about_ca_system_score_codex":0.00013253037,"about_ca_system_score_gemma":0.0000734594,"threshold_uncertainty_score":0.54880506},"labels":[],"label_agreement":null},{"id":"W3013386452","doi":"10.1139/cjas-2019-0144","title":"Benefit-cost analysis of near-infrared spectroscopy technology adoption by Alberta hog producers","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Alberta Pork (Canada); University of Alberta","funders":"Alberta Crop Industry Development Fund","keywords":"Business; Agricultural science; Incentive; Livestock; Investment (military); Production (economics); Total cost; Usability; Environmental economics; Environmental science; Agricultural economics; Computer science; Economics; Accounting","score_opus":0.013263783886099856,"score_gpt":0.253373152807753,"score_spread":0.24010936892165316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013386452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909897,0.00063567964,0.00012030554,0.0025310463,0.000032380205,0.00003294335,0.000045453296,0.000007745302,0.005604771],"genre_scores_gemma":[0.9986746,0.000028558346,0.00095720054,0.00016008761,0.000056095945,8.7343915e-7,0.00000455092,0.0000083576815,0.00010968219],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843,0.0000050861518,0.00042714103,0.0003090552,0.000368782,0.00045991872],"domain_scores_gemma":[0.9982107,0.00003175209,0.00042930435,0.00021537498,0.00032982387,0.00078300916],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018852117,0.00014192039,0.0004226744,0.00084393844,0.00021819958,0.000101123514,0.0008784105,0.00009690588,0.0014315371],"category_scores_gemma":[0.00067851745,0.00013402602,0.00016560678,0.008304005,0.0007797426,0.00028671863,0.000027137288,0.00028751223,0.0000064265346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041996645,0.000016129072,0.03939704,0.000011958502,0.00021321127,0.000015642892,0.00029139905,0.00011813726,0.95805985,0.00022568651,0.0013720781,0.00023690076],"study_design_scores_gemma":[0.00025485505,0.0004108799,0.010411593,0.000018477345,0.00097063516,0.000025333977,0.0010092884,0.0014028676,0.98030496,0.00007460537,0.004881637,0.00023485627],"about_ca_topic_score_codex":0.0031868608,"about_ca_topic_score_gemma":0.0021685155,"teacher_disagreement_score":0.028985446,"about_ca_system_score_codex":0.00032472904,"about_ca_system_score_gemma":0.0014105346,"threshold_uncertainty_score":0.99948126},"labels":[],"label_agreement":null},{"id":"W3015573605","doi":"10.1016/j.lwt.2020.109368","title":"Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study","year":2020,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Physiotherapy Association; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Chemometrics; Linear discriminant analysis; Partial least squares regression; Common wheat; Mathematics; Near-infrared spectroscopy; Spectroscopy; Winter wheat; Analytical Chemistry (journal); Food science; Chemistry; Statistics; Chromatography; Agronomy; Biology; Physics","score_opus":0.011753956743884457,"score_gpt":0.25301285788993955,"score_spread":0.24125890114605508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015573605","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99601835,0.0005382019,0.0014418376,0.00015796164,0.00001452829,0.00015008094,0.000030089943,0.00007904829,0.0015698773],"genre_scores_gemma":[0.9991623,0.000046209694,0.00026563636,0.00009006938,0.00007890268,0.000019660438,0.000021023547,0.000024118295,0.0002920797],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988656,0.00002158262,0.0002940302,0.0003625375,0.00025222776,0.00020400702],"domain_scores_gemma":[0.9993269,0.00007238429,0.00014623789,0.00024420032,0.000071313094,0.00013896693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036935453,0.00020478245,0.00036582595,0.00015019634,0.00011134417,0.000066832654,0.00009968403,0.00008987352,0.0001840144],"category_scores_gemma":[0.00006429935,0.00018125278,0.000041226336,0.001211437,0.00005364766,0.00016241596,0.000050401137,0.0002139288,0.0000032574774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029769825,0.0003479765,0.060941655,0.00018846137,0.00017466546,0.00019063281,0.0019544773,0.0000043145565,0.93445885,0.0000020468437,0.0006608732,0.0007783639],"study_design_scores_gemma":[0.001513389,0.0008186908,0.0003128998,0.000012918487,0.00023330396,0.0001826988,0.0073130243,0.0005224732,0.9886738,0.000010393522,0.00020661879,0.0001997751],"about_ca_topic_score_codex":0.00044544283,"about_ca_topic_score_gemma":0.000110118584,"teacher_disagreement_score":0.060628757,"about_ca_system_score_codex":0.000059337457,"about_ca_system_score_gemma":0.000008856299,"threshold_uncertainty_score":0.7391274},"labels":[],"label_agreement":null},{"id":"W3015710780","doi":"10.1039/d0ay00521e","title":"Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy","year":2020,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Near-infrared spectroscopy; Fermentation; Quality (philosophy); Food science; Chemistry; Biology; Physics","score_opus":0.12131923237928677,"score_gpt":0.43527886595267334,"score_spread":0.3139596335733866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015710780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8961666,0.00048738837,0.091970585,0.0001865588,0.000039094077,0.00006695322,0.000022038577,0.000074381525,0.010986407],"genre_scores_gemma":[0.8824176,0.000027757282,0.11713106,0.000116653966,0.00009456491,0.000002133824,0.000006225496,0.000022298353,0.00018170198],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785924,0.00024327885,0.00082910457,0.00039899166,0.00036500028,0.0003043864],"domain_scores_gemma":[0.9982233,0.0006208805,0.00038837668,0.0003986889,0.00016042576,0.0002083224],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000632481,0.00021861859,0.00086051907,0.00009601227,0.00006810217,0.000021732296,0.00025996275,0.0001937214,0.0020513004],"category_scores_gemma":[0.0023665405,0.00020669127,0.00036766817,0.0012089881,0.00025809734,0.00008612372,0.00009245283,0.0003010102,0.0000048641896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002512144,0.00011980796,0.0009561914,0.0002857946,0.0003158746,0.0000017452558,0.0003065003,0.00003100985,0.9962911,0.00007612824,0.000017589058,0.0013470489],"study_design_scores_gemma":[0.0004546411,0.00010572431,0.0006301102,0.000014750979,0.00048569418,0.0000020773316,0.000780106,0.048468634,0.94847393,0.00028698717,0.00011262082,0.00018474944],"about_ca_topic_score_codex":0.00028086544,"about_ca_topic_score_gemma":0.000001781652,"teacher_disagreement_score":0.04843762,"about_ca_system_score_codex":0.00008650188,"about_ca_system_score_gemma":0.00006915813,"threshold_uncertainty_score":0.99886096},"labels":[],"label_agreement":null},{"id":"W3016378332","doi":"","title":"Nonlinear principal component analysis of climate data","year":2000,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Component (thermodynamics); Nonlinear system; Environmental science; Mathematics; Computer science; Statistics; Physics","score_opus":0.007368619941596323,"score_gpt":0.18981345658891435,"score_spread":0.18244483664731803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016378332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6812384,0.00033812382,0.000011255792,0.00042162513,0.000016409462,0.000028613891,0.0021134478,0.000008661072,0.31582344],"genre_scores_gemma":[0.9929077,0.0008598874,0.0010234202,0.00023680503,0.000032331784,0.0000013512143,0.00020162179,0.000012164708,0.004724724],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979803,0.000014976609,0.00036365964,0.00028930104,0.0010924398,0.00025932418],"domain_scores_gemma":[0.99895585,0.00016632889,0.00015992187,0.0005446289,4.692421e-7,0.00017281892],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000010114179,0.0001532048,0.00041369456,0.000045825946,0.000086344226,0.000010915339,0.00047273777,0.000025110217,0.002143068],"category_scores_gemma":[0.0000026820767,0.0001452824,0.000048565467,0.00034006077,0.0000778481,0.00018332453,0.00020593774,0.00010247826,3.6097876e-9],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024757148,0.00073525903,0.6662008,0.001497312,0.010542095,0.00023834733,0.00020703152,0.0021990691,0.26073048,0.015597266,0.0011055699,0.03847106],"study_design_scores_gemma":[0.0007436531,0.000050580435,0.070939,0.000066756635,0.0025086752,0.000005273667,0.0018053068,0.06599397,0.803602,0.00007746254,0.053639483,0.000567868],"about_ca_topic_score_codex":0.003716134,"about_ca_topic_score_gemma":0.01330994,"teacher_disagreement_score":0.5952618,"about_ca_system_score_codex":0.000005192308,"about_ca_system_score_gemma":0.00041264886,"threshold_uncertainty_score":0.9987691},"labels":[],"label_agreement":null},{"id":"W3016659017","doi":"10.1130/abs/2020nc-347100","title":"THE IDENTIFICATION, DETECTION AND ANALYSIS OF THROMBOLITES USING SPECTROSCOPY TO FURTHER UNDERSTAND THE EVOLUTION, ADAPTATIONS, AND PRESERVATION OF EARLY LIFE","year":2020,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Identification (biology); Geology; Paleontology; Evolutionary biology; Biology; Ecology","score_opus":0.03783148864752962,"score_gpt":0.2811089031603239,"score_spread":0.24327741451279428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016659017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9654716,0.0008420509,0.031589303,0.001858917,0.0000028378606,0.00015145894,0.00000754575,0.000019511044,0.00005674912],"genre_scores_gemma":[0.993964,0.00019808825,0.0057090335,0.00007737988,0.000016296724,0.000010058321,0.000007834539,0.000005877906,0.0000113881915],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989052,0.000027093713,0.00042620677,0.00022693654,0.0002607288,0.00015387648],"domain_scores_gemma":[0.9986853,0.00027827098,0.0005622865,0.0001722359,0.00022545076,0.00007646093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017316184,0.00011229931,0.00029231276,0.000026696132,0.00019370018,0.000039040915,0.00013933341,0.00007353401,0.000027668093],"category_scores_gemma":[0.00021214072,0.000064948035,0.00010388617,0.001500851,0.00082272873,0.00009906882,0.00004394516,0.00012282234,1.942411e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045866153,0.0003876748,0.52298933,0.00024865172,0.005554421,2.160777e-7,0.014308675,0.006473637,0.44248658,0.00029545915,0.00007040876,0.006726255],"study_design_scores_gemma":[0.00028215453,0.00050351536,0.9067943,0.000016298576,0.0020245542,7.7864695e-7,0.033788353,0.014131928,0.041583065,0.0006014627,0.00010896393,0.00016464038],"about_ca_topic_score_codex":0.0011013111,"about_ca_topic_score_gemma":0.000025685764,"teacher_disagreement_score":0.40090352,"about_ca_system_score_codex":0.000027735587,"about_ca_system_score_gemma":0.000037542748,"threshold_uncertainty_score":0.3031378},"labels":[],"label_agreement":null},{"id":"W3018757111","doi":"10.1016/j.pdpdt.2020.101757","title":"Polarization gating technique extracts depth resolved fluorescence redox ratio in oral cancer diagnostics","year":2020,"lang":"en","type":"article","venue":"Photodiagnosis and Photodynamic Therapy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Board of Research in Nuclear Sciences","keywords":"Gating; Fluorescence; Chemistry; Polarization (electrochemistry); Fluorescence anisotropy; Redox; Analytical Chemistry (journal); Biophysics; Materials science; Nuclear magnetic resonance; Optics; Biology; Chromatography","score_opus":0.023684466837843456,"score_gpt":0.2937462923295972,"score_spread":0.2700618254917537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018757111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99213177,0.004953953,0.0008241178,0.00068687845,0.00004076699,0.00041457624,0.00008042704,0.00013899675,0.0007285157],"genre_scores_gemma":[0.9748891,0.022463573,0.0008711702,0.0010384833,0.000101187914,0.00047866913,0.00007120534,0.00004835754,0.000038227787],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831784,0.000047320926,0.00044756665,0.00055655587,0.00024160062,0.00038909746],"domain_scores_gemma":[0.99894243,0.00037799965,0.00020182284,0.00023439073,0.00006367719,0.00017967398],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015388097,0.0003169977,0.0004252591,0.00012931436,0.00014631645,0.000104439365,0.0002581596,0.00021571212,0.0009891825],"category_scores_gemma":[0.0003143477,0.00030794085,0.00008291985,0.00083886844,0.00008907432,0.00018223123,0.00005032233,0.00042708105,0.0000032569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106895546,0.00015477646,0.1118975,0.000067985064,0.00004940066,0.000018207098,0.0005560348,0.000047394107,0.88088155,0.00004647813,0.00018151508,0.005992276],"study_design_scores_gemma":[0.0010177719,0.00008161419,0.011492253,0.00011236763,0.000048171085,0.0000040348446,0.00020831844,0.0055768425,0.9798745,0.00011389018,0.0010550726,0.00041515837],"about_ca_topic_score_codex":0.0018977389,"about_ca_topic_score_gemma":0.0004405003,"teacher_disagreement_score":0.100405246,"about_ca_system_score_codex":0.00011227167,"about_ca_system_score_gemma":0.000080677506,"threshold_uncertainty_score":0.9999373},"labels":[],"label_agreement":null},{"id":"W3021624673","doi":"10.1007/s11694-020-00476-3","title":"Quality assessment of frying oil using short-chain fatty acid profile and infrared spectrum coupled with partial least squares","year":2020,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Education Department of Sichuan Province","keywords":"Partial least squares regression; Food science; Quality (philosophy); Fatty acid; Chemistry; Spectrum (functional analysis); Infrared; Mathematics; Statistics; Analytical Chemistry (journal); Chromatography; Organic chemistry; Physics; Optics","score_opus":0.09614444350993027,"score_gpt":0.3085091272141066,"score_spread":0.21236468370417633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021624673","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9527178,0.00015813997,0.046532,0.00025266537,0.000047920203,0.00005768004,0.000028973238,0.000017252423,0.00018761262],"genre_scores_gemma":[0.99806577,0.000064184525,0.0014913821,0.000052467592,0.00026649475,0.0000034519826,0.00002980844,0.000020701586,0.0000057386906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976935,0.000059478018,0.0008120957,0.00020708505,0.0010277488,0.0002001137],"domain_scores_gemma":[0.99824,0.000017892296,0.001036221,0.0001337198,0.00041636341,0.00015580126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050401426,0.00019978537,0.0005240134,0.00009839781,0.000092918264,0.00006896577,0.00013019949,0.00008147042,0.00018305182],"category_scores_gemma":[0.0001536226,0.0001690243,0.00009726637,0.00036260113,0.000047187015,0.0003294437,0.00003131274,0.0002477028,1.7940468e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000302737,0.00015297747,0.05204218,0.000342767,0.00033464676,0.0000037296902,0.00023711572,0.00006283927,0.94597584,0.0000196397,0.0000020560833,0.00052348396],"study_design_scores_gemma":[0.0010917205,0.00044906532,0.057690896,0.00018997172,0.00034596925,0.000015348249,0.00045476604,0.0045773117,0.9349477,0.000008599444,0.000027825392,0.00020081642],"about_ca_topic_score_codex":0.000004699813,"about_ca_topic_score_gemma":0.0000030298888,"teacher_disagreement_score":0.04534802,"about_ca_system_score_codex":0.00018847828,"about_ca_system_score_gemma":0.00023864272,"threshold_uncertainty_score":0.6892611},"labels":[],"label_agreement":null},{"id":"W3022425266","doi":"10.1177/0003702820928358","title":"Improved Infrared Optical Constants from Pressed Pellets: II. Ellipsometric <i>n</i> and <i>k</i> Values for Ammonium Sulfate with Variability Analysis","year":2020,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Analytical Chemistry (journal); Infrared; Refractive index; Ellipsometry; Wavenumber; Spectral line; Pellets; Chemistry; Yield (engineering); Infrared spectroscopy; Amplitude; Optics; Materials science; Physics; Thermodynamics; Thin film; Nanotechnology","score_opus":0.010588934221880054,"score_gpt":0.23188268990535596,"score_spread":0.2212937556834759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022425266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85961634,0.00068483414,0.109626055,0.00067638577,0.000032464977,0.00069257215,0.0007335154,0.00047006848,0.027467756],"genre_scores_gemma":[0.9629537,0.00014494975,0.03531652,0.00055685727,0.0002519959,0.00016128477,0.00028685026,0.00006937073,0.00025845628],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99673605,0.00002305703,0.0006360261,0.0013964546,0.00043582733,0.000772595],"domain_scores_gemma":[0.9974657,0.00087995396,0.0003133261,0.0007221638,0.000123976,0.0004948364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022176089,0.00057782873,0.001235264,0.0002634054,0.0003567026,0.0001810979,0.00047180091,0.00029575496,0.00058962364],"category_scores_gemma":[0.00024590836,0.00050759444,0.00024940455,0.003113532,0.00037502134,0.00013343795,0.00019666678,0.00045586273,0.000007796673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002633994,0.0003426259,0.0067257155,0.00016132226,0.0029788036,0.0000051587895,0.00032798696,0.000044767763,0.9845938,0.001221597,0.00083321735,0.00013103163],"study_design_scores_gemma":[0.0028567482,0.00027859453,0.0004347437,0.000006710318,0.0047168676,0.0000010885733,0.00037419642,0.003677862,0.98424876,0.0020603721,0.0006954368,0.00064864306],"about_ca_topic_score_codex":0.0000717285,"about_ca_topic_score_gemma":0.000010640244,"teacher_disagreement_score":0.10333737,"about_ca_system_score_codex":0.00012572494,"about_ca_system_score_gemma":0.00012366664,"threshold_uncertainty_score":0.99973756},"labels":[],"label_agreement":null},{"id":"W3033323769","doi":"10.3168/jds.2019-17758","title":"Short communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples","year":2020,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ste. Anne's Hospital; Université Laval; Cégep de Sherbrooke; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Canadian Dairy Commission; Ministère de la Santé; Ministère de la Santé et des Services sociaux; Dairy Farmers of Canada; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Université Laval","keywords":"Herd; Vitamin B12; Mathematics; Cutoff; Statistics; Mahalanobis distance; Linear discriminant analysis; Outlier; Animal science; Chemistry; Biology; Physics","score_opus":0.06581730340275578,"score_gpt":0.3079510441855164,"score_spread":0.24213374078276062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033323769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98507947,0.00021589869,0.0132103395,0.00056147634,0.00016489289,0.000068435744,0.00014251996,0.000017949142,0.00053902605],"genre_scores_gemma":[0.9916056,0.000048440295,0.007872899,0.0001551159,0.00023977106,5.921107e-7,0.000051383322,0.0000084200965,0.000017781013],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99776447,0.000030516969,0.0006925687,0.00029706315,0.0009832691,0.00023209168],"domain_scores_gemma":[0.998271,0.00007917286,0.0005673377,0.00065982866,0.0002391171,0.00018356992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006049424,0.00014255194,0.00028024646,0.00013385265,0.00021305964,0.00009804365,0.0013110285,0.00006434977,0.00019163569],"category_scores_gemma":[0.0003326131,0.00012946624,0.00009625743,0.00089204015,0.00034511337,0.00083098444,0.00015684895,0.00029878263,8.2589196e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015221254,0.00018983154,0.004783455,0.000047741763,0.000040845105,0.0000073614833,0.00025146824,0.0028129134,0.99056876,0.00000820409,0.0004997498,0.00063748466],"study_design_scores_gemma":[0.000786789,0.00033625166,0.003572244,0.00009375567,0.00018289592,0.000018005652,0.0014306478,0.094823994,0.8985034,0.000025718165,0.00010504993,0.000121227036],"about_ca_topic_score_codex":0.00001498694,"about_ca_topic_score_gemma":0.0000010608416,"teacher_disagreement_score":0.09206531,"about_ca_system_score_codex":0.00020579092,"about_ca_system_score_gemma":0.00045616945,"threshold_uncertainty_score":0.52794796},"labels":[],"label_agreement":null},{"id":"W3034042189","doi":"10.1007/s13197-020-04552-w","title":"Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging","year":2020,"lang":"en","type":"article","venue":"Journal of Food Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Aflatoxin; Hyperspectral imaging; Principal component analysis; Partial least squares regression; Aspergillus flavus; Chemometrics; Linear discriminant analysis; Mathematics; Pattern recognition (psychology); Artificial intelligence; Statistics; Chemistry; Chromatography; Computer science; Food science","score_opus":0.02352596879398639,"score_gpt":0.2738689099179126,"score_spread":0.2503429411239262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034042189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99748266,0.00045342278,0.00048020607,0.0011828917,0.000024292804,0.000030967465,0.000003541085,0.000011030353,0.00033098986],"genre_scores_gemma":[0.99916804,0.00012684455,0.00064104947,0.000019715151,0.00003891046,5.808745e-7,2.458355e-7,0.000003598784,0.0000010112673],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912536,0.0000046083164,0.00030928824,0.00018102846,0.00022377072,0.00015592219],"domain_scores_gemma":[0.99929637,0.00001865536,0.00033598352,0.00007099106,0.00021684547,0.0000611415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018010847,0.0000770936,0.0002004204,0.00033286776,0.00007423036,0.000028735652,0.00015349637,0.00007896658,0.000007975428],"category_scores_gemma":[0.00015516339,0.00007045338,0.00002131367,0.0013624443,0.00070062827,0.0003758824,0.000032429434,0.00023499024,9.708268e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021060301,0.000018607501,0.09530492,0.000014642143,0.000012816055,0.000003238348,0.00022805597,0.000008042433,0.90242535,0.0010945803,0.000002082425,0.0008665911],"study_design_scores_gemma":[0.00054217,0.00018261118,0.017122017,0.000034175435,0.000054739394,0.00016430892,0.0046724686,0.00845224,0.96690553,0.0018003283,0.000004903488,0.00006448676],"about_ca_topic_score_codex":0.0000050396798,"about_ca_topic_score_gemma":8.00088e-7,"teacher_disagreement_score":0.0781829,"about_ca_system_score_codex":0.00011816563,"about_ca_system_score_gemma":0.00017348482,"threshold_uncertainty_score":0.28730056},"labels":[],"label_agreement":null},{"id":"W3035774099","doi":"10.5539/jas.v12n7p105","title":"Rapid and Green Method Forensic Authentication of Rice Using Near-Infrared Spectroscopy (NIRS)","year":2020,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade Federal do Acre","keywords":"Principal component analysis; Terroir; Authentication (law); Fingerprint (computing); Chemometrics; Computer science; Spectroscopy; Forensic science; Pattern recognition (psychology); Agricultural engineering; Environmental science; Biotechnology; Artificial intelligence; Geography; Chemistry; Food science; Archaeology; Engineering; Machine learning; Physics; Biology; Computer security","score_opus":0.028405383742241698,"score_gpt":0.3006619538591608,"score_spread":0.27225657011691906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035774099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959072,0.00059419323,0.0015595829,0.0007562467,0.0000355088,0.000025410769,0.0000029627988,0.000008578977,0.0011103313],"genre_scores_gemma":[0.9439733,0.000053080235,0.05568309,0.00006210882,0.00015743214,2.0539609e-7,5.9055594e-7,0.0000032954802,0.00006689486],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874026,0.000014825874,0.0003761826,0.00017731437,0.0004950279,0.00019638374],"domain_scores_gemma":[0.99873185,0.000073390394,0.0005843313,0.00008421154,0.00032915894,0.00019705077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002767914,0.000110153305,0.0002776146,0.000066127075,0.00015390156,0.00008367294,0.00034872736,0.00004345341,0.00013165842],"category_scores_gemma":[0.00027980265,0.000066481225,0.00009636154,0.0014183267,0.00025654715,0.00048335496,0.00006961172,0.00017683381,0.0000011558019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017359576,0.000017548831,0.00076497043,0.00004003817,0.00002761747,0.0000018127724,0.0005935878,0.00002215717,0.9979354,0.000029214258,0.0000958891,0.00045438376],"study_design_scores_gemma":[0.0002522326,0.00010469068,0.03226253,0.000025617359,0.00019111764,0.00012103268,0.0012381668,0.0014870564,0.96396464,0.00017137069,0.00007272561,0.00010880174],"about_ca_topic_score_codex":0.000039036193,"about_ca_topic_score_gemma":4.7512887e-7,"teacher_disagreement_score":0.05412351,"about_ca_system_score_codex":0.00008360083,"about_ca_system_score_gemma":0.0001006667,"threshold_uncertainty_score":0.27110258},"labels":[],"label_agreement":null},{"id":"W3037149934","doi":"10.1002/cche.10318","title":"Application of chemometrics to prediction of some wheat quality factors by near‐infrared spectroscopy","year":2020,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Island University","funders":"","keywords":"Farinograph; Chemometrics; Reliability (semiconductor); Near-infrared spectroscopy; Random forest; Quality (philosophy); Chemistry; Statistics; Pattern recognition (psychology); Computer science; Wheat flour; Mathematics; Artificial intelligence; Food science; Chromatography","score_opus":0.023817689597589384,"score_gpt":0.28287936553176435,"score_spread":0.25906167593417495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037149934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9788142,0.00043694704,0.0024634504,0.00016217204,0.000015898937,0.000093205075,0.00074153155,0.00012543942,0.0171472],"genre_scores_gemma":[0.9980104,0.000052627674,0.00048880925,0.00007344847,0.00015883306,0.000017165867,0.000522861,0.00003200531,0.0006438717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980106,0.000008406895,0.00068502553,0.0005028751,0.0004964101,0.0002967226],"domain_scores_gemma":[0.9985151,0.00011447702,0.000397305,0.000498003,0.00014961859,0.00032547096],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000088934175,0.00026619542,0.0005802778,0.00005218269,0.0000589764,0.000022262908,0.0004248659,0.00028441526,0.00096196134],"category_scores_gemma":[0.00044486258,0.0002801856,0.00018812944,0.0013981244,0.000121857986,0.00010630701,0.0000976396,0.00027003864,0.000008534059],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008060641,0.00015297852,0.03293438,0.0008369926,0.000087709195,2.277074e-7,0.00018781693,0.00001583897,0.9608466,0.000011910594,0.004724585,0.00012035743],"study_design_scores_gemma":[0.0004298972,0.000041651594,0.001909888,0.000011574656,0.000109140805,6.18277e-7,0.00039982173,0.00017704631,0.9952047,0.00007148266,0.0014482542,0.00019591035],"about_ca_topic_score_codex":0.00015119633,"about_ca_topic_score_gemma":2.7973593e-7,"teacher_disagreement_score":0.034358118,"about_ca_system_score_codex":0.00016668667,"about_ca_system_score_gemma":0.00008152005,"threshold_uncertainty_score":0.999965},"labels":[],"label_agreement":null},{"id":"W3038498690","doi":"10.1017/s143192760002657x","title":"3D Visualization of Maize Stem by MRI Technology","year":2001,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Materials science; Magnetic resonance imaging; Scanner; Imaging phantom; Artifact (error); Biomedical engineering; Visualization; Voxel; Pixel; Nuclear magnetic resonance; Nuclear medicine; Computer science; Optics; Physics; Artificial intelligence; Medicine; Radiology","score_opus":0.0073160082013868305,"score_gpt":0.2719820234597898,"score_spread":0.26466601525840294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038498690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9665325,0.012106025,0.017800847,0.00018282047,0.000018676783,0.00004336456,0.00006115112,0.00009825035,0.0031563363],"genre_scores_gemma":[0.9856643,0.003269815,0.0011388656,0.000070829294,0.000026317442,0.0000089741425,0.00008715338,0.00002662228,0.009707115],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987031,0.000013993988,0.00042719956,0.00041966434,0.00013197563,0.0003040222],"domain_scores_gemma":[0.9991866,0.000040041265,0.00024019898,0.00036128119,0.0001015885,0.00007033349],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000096862444,0.00022276312,0.00051506667,0.00046344582,0.00013001387,0.000036412453,0.00022689729,0.000249974,0.0015352595],"category_scores_gemma":[0.000019210938,0.00021656639,0.00012913471,0.0018398161,0.00020785592,0.00008190184,0.00007761243,0.0001278875,0.000016012638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030563402,0.00011880884,0.052632336,0.000062679115,0.0003806098,0.0000033951644,0.00005836873,0.0000026059968,0.943863,0.00011238459,0.0014308598,0.0013044159],"study_design_scores_gemma":[0.00039701562,0.000024511568,0.0000422488,0.000017597427,0.0008366693,0.000021746537,0.0004706689,0.000099402816,0.9809378,0.00007998691,0.01685743,0.00021493359],"about_ca_topic_score_codex":0.000103010745,"about_ca_topic_score_gemma":0.000015330526,"teacher_disagreement_score":0.052590087,"about_ca_system_score_codex":0.000053610238,"about_ca_system_score_gemma":0.000022195174,"threshold_uncertainty_score":0.9993775},"labels":[],"label_agreement":null},{"id":"W3038599339","doi":"10.1007/s12161-020-01810-7","title":"Ultra-Low-Cost Self-Referencing Multispectral Detector for Non-Destructive Measurement of Fruit Quality","year":2020,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Centennial College","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Detector; Spectrometer; Partial least squares regression; Calibration; Mean squared error; Optics; Chemistry; Analytical Chemistry (journal); Materials science; Remote sensing; Mathematics; Chromatography; Physics; Statistics","score_opus":0.1402991479178672,"score_gpt":0.41313072625726877,"score_spread":0.2728315783394015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038599339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46787882,0.00018532568,0.5231472,0.0003662006,0.00009413939,0.00043501615,0.0002199279,0.00023485786,0.0074384506],"genre_scores_gemma":[0.83596027,0.000006254473,0.16362144,0.00007733283,0.00022760377,0.00004560977,0.000008147211,0.00003201436,0.000021359376],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736863,0.00013213513,0.0007788508,0.00063734595,0.00056234794,0.00052068994],"domain_scores_gemma":[0.997612,0.0009554377,0.0002980076,0.00036823255,0.0003826389,0.00038370476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00087074976,0.00033405947,0.0009256976,0.00010138502,0.00009790305,0.000025395226,0.00039628783,0.00023170302,0.0005082487],"category_scores_gemma":[0.00394821,0.00030199837,0.00051488954,0.0007600342,0.000096327705,0.00008208873,0.00005052338,0.00038066396,0.0000055746677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033457394,0.0002928333,0.0024568008,0.0009824707,0.0016674241,0.0000019512436,0.0005352493,0.000025344432,0.98657525,0.0015394081,0.000048484897,0.005540192],"study_design_scores_gemma":[0.0011070415,0.00027291832,0.0011797886,0.000031670574,0.0008487385,0.0000017044853,0.0006095021,0.005605012,0.9890392,0.0008042626,0.00014190008,0.000358252],"about_ca_topic_score_codex":0.00004134843,"about_ca_topic_score_gemma":0.00001204094,"teacher_disagreement_score":0.36808142,"about_ca_system_score_codex":0.000269475,"about_ca_system_score_gemma":0.00016676744,"threshold_uncertainty_score":0.9999432},"labels":[],"label_agreement":null},{"id":"W3039963140","doi":"10.1016/j.foodchem.2020.127445","title":"Retraction notice to “Statistical and chemometric view of the variation in the concentration of selected organophosphates in peeled unwashed and unpeeled washed fruits and vegetables” [Food Chem. 300 (2019) 125220]","year":2020,"lang":"en","type":"retraction","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":true,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Notice; Chemistry; Food science; Statistical analysis; Mathematics; Statistics","score_opus":0.00988392448075071,"score_gpt":0.2359794115983655,"score_spread":0.2260954871176148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039963140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864252,0.010517069,0.00005386593,0.000518057,0.00012456342,0.00047866,0.00034570586,0.000032444434,0.0015044082],"genre_scores_gemma":[0.9956495,0.0032593103,0.00022575758,0.000049190196,0.00016563603,0.00003320233,0.00035182587,0.000042414205,0.00022312046],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973552,0.00007834351,0.00090572133,0.0006959221,0.00061568245,0.00034913255],"domain_scores_gemma":[0.99767715,0.0008304419,0.0007341511,0.00037929107,0.00026254443,0.00011644952],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003817808,0.00044462705,0.0009346633,0.00015679204,0.00008254294,0.000073360956,0.0003056288,0.0009875059,0.00021006537],"category_scores_gemma":[0.0026029958,0.0003583613,0.00006546213,0.0034465874,0.00014074353,0.00012929869,0.00010306651,0.0012290776,8.905747e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026063662,0.00034861165,0.0036169202,0.006847052,0.00037791277,0.000004238345,0.0011468504,0.000009278259,0.9853401,0.0000464111,0.000769859,0.0012321348],"study_design_scores_gemma":[0.0016352179,0.00015722984,0.016201125,0.00042905402,0.0007029259,0.000033671127,0.00045343125,0.00030279753,0.9790451,0.000094824376,0.000539275,0.0004053514],"about_ca_topic_score_codex":0.00011403001,"about_ca_topic_score_gemma":0.00003561494,"teacher_disagreement_score":0.012584206,"about_ca_system_score_codex":0.00022492357,"about_ca_system_score_gemma":0.00026813627,"threshold_uncertainty_score":0.9998868},"labels":[],"label_agreement":null},{"id":"W3040131329","doi":"10.1017/s1431927600036576","title":"Cell Boundary Detection and Volume Approximation of Confocal Microscope Images for Bioinformatics","year":2000,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Plasmolysis; Osmotic dehydration; Protoplast; Tonicity; Cytoplasm; Dehydration; Confocal microscopy; Confocal; Biological system; Microscope; Computer science; Biophysics; Microscopy; Chemistry; Process (computing); Materials science; Cell wall; Cell biology; Biology; Biochemistry; Optics; Physics","score_opus":0.006095288698876063,"score_gpt":0.24083422457438705,"score_spread":0.23473893587551098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040131329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97000647,0.00606643,0.021137366,0.000050564748,0.000019896548,0.0001387025,0.00025218172,0.00004710813,0.0022812944],"genre_scores_gemma":[0.9721247,0.0015281306,0.018517453,0.00008832889,0.00004830624,0.000025839749,0.00013347997,0.000029805637,0.0075039654],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870384,0.0000128317815,0.0005389327,0.00033504565,0.000109961125,0.00029939547],"domain_scores_gemma":[0.9992447,0.00007206948,0.00023294077,0.00025477004,0.00010134583,0.00009420372],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016598991,0.0002549916,0.0005111101,0.00023077238,0.000281,0.00013971653,0.00013069587,0.00018515253,0.00092793227],"category_scores_gemma":[0.00002435821,0.00024960592,0.00017203353,0.00041635908,0.00031546748,0.0002229444,0.00003577465,0.00013529904,0.000010586301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011873462,0.000081595645,0.0010021206,0.00058767054,0.00018898558,3.6237074e-7,0.0002808806,0.000005462413,0.98337835,0.0000028572656,0.00059743144,0.013755551],"study_design_scores_gemma":[0.00081907853,0.00006457035,0.00014577285,0.000019829364,0.0010351705,0.0000134677,0.0005021599,0.0021789193,0.9854255,0.00006773347,0.009469851,0.00025799783],"about_ca_topic_score_codex":0.00012418612,"about_ca_topic_score_gemma":0.000009203319,"teacher_disagreement_score":0.013497553,"about_ca_system_score_codex":0.000043539738,"about_ca_system_score_gemma":0.00003546646,"threshold_uncertainty_score":0.9999956},"labels":[],"label_agreement":null},{"id":"W3041029875","doi":"","title":"Machine Learning based Models for Fresh Produce Yield and Price Forecasting for Strawberry Fruit","year":2020,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; Loblaw Companies Limited","keywords":"Yield (engineering); Horticulture; Machine learning; Agricultural engineering; Mathematics; Computer science; Engineering; Biology; Materials science","score_opus":0.03230730450230037,"score_gpt":0.2287089144398017,"score_spread":0.19640160993750133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041029875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892293,0.000992497,0.0052017746,0.00085507217,0.00007697221,0.00059661455,0.00042106913,0.00015629448,0.0024703762],"genre_scores_gemma":[0.6604692,0.00017877926,0.015035835,0.00003279353,0.00016018514,0.000008769633,0.0033279948,0.000102458696,0.32068402],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879736,0.0000070605483,0.00015049419,0.00055639836,0.00018507436,0.0003036078],"domain_scores_gemma":[0.9989022,0.00025004157,0.0003688171,0.00017659471,0.00019216661,0.00011016724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000097581426,0.00028197098,0.0005214057,0.00018518929,0.00031832836,0.000031435764,0.00027052616,0.0003199483,0.00025784725],"category_scores_gemma":[0.00018606558,0.0003379836,0.00022881493,0.00021108917,0.000041348147,0.00016268308,0.000038838425,0.00032907093,9.308359e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009954052,0.00086162786,0.0049213334,0.05866883,0.004352417,0.0000862579,0.17885822,0.012616196,0.70819986,0.0013335097,0.00920812,0.010939602],"study_design_scores_gemma":[0.0033719155,0.00055042416,0.00007294492,0.00070507854,0.0030008063,0.000003816283,0.14786659,0.38413522,0.45565897,0.0011984757,0.0020679722,0.0013677775],"about_ca_topic_score_codex":0.0033919949,"about_ca_topic_score_gemma":0.0035354667,"teacher_disagreement_score":0.37151903,"about_ca_system_score_codex":0.00005918691,"about_ca_system_score_gemma":0.000089799454,"threshold_uncertainty_score":0.9999072},"labels":[],"label_agreement":null},{"id":"W3044566111","doi":"10.1016/j.compag.2020.105643","title":"Estimation of different ripening stages of Fuji apples using image processing and spectroscopy based on the majority voting method","year":2020,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"European Commission","keywords":"Artificial intelligence; Hyperparameter optimization; Pattern recognition (psychology); Hue; Support vector machine; Ripening; Computer science; Classifier (UML); Multispectral image; Artificial neural network; Mathematics; Chemistry","score_opus":0.01487436650844921,"score_gpt":0.2754951922433126,"score_spread":0.2606208257348634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044566111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85076773,0.0015439156,0.14702912,0.0004318125,0.0000072936523,0.00007718514,0.000005752729,0.000019545652,0.000117675096],"genre_scores_gemma":[0.9694103,0.000060338607,0.03040615,0.00006239548,0.000038584545,0.0000023061693,0.000009602423,0.0000071828235,0.0000031606894],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914145,0.000031151743,0.00023372148,0.0002424261,0.00015427677,0.00019697413],"domain_scores_gemma":[0.99945897,0.00018014947,0.00021277569,0.00007383544,0.00003323277,0.00004106118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115774565,0.00016454945,0.00031129632,0.000045147073,0.00009524614,0.00004914338,0.00012152209,0.00007485333,0.0000068183654],"category_scores_gemma":[0.000050660343,0.00010101458,0.000045079574,0.000360612,0.00006164475,0.00005657855,0.000051027386,0.00031091628,3.16428e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037379185,0.000068724556,0.0046708197,0.0006007067,0.000038872913,0.0000013461733,0.0005254643,0.00440056,0.9862462,0.0011205702,0.00005214318,0.0022371956],"study_design_scores_gemma":[0.00024625138,0.000057596062,0.00084661867,0.000110672394,0.00006739251,0.000002296693,0.00027246756,0.45693293,0.5411809,0.00017210555,0.000005984196,0.0001047556],"about_ca_topic_score_codex":0.000016300219,"about_ca_topic_score_gemma":0.000004498439,"teacher_disagreement_score":0.45253238,"about_ca_system_score_codex":0.000052856594,"about_ca_system_score_gemma":0.000025406862,"threshold_uncertainty_score":0.4119255},"labels":[],"label_agreement":null},{"id":"W3045195721","doi":"10.1016/j.foodchem.2020.127626","title":"Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Western University; Colorado State University","keywords":"Canopy; Absorbance; Near-infrared spectroscopy; Calibration; Crop; Partial least squares regression; Environmental science; Horticulture; Chemistry; Mathematics; Agronomy; Botany; Biology; Statistics; Chromatography","score_opus":0.06495526845292364,"score_gpt":0.29766677416124043,"score_spread":0.23271150570831678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045195721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99350125,0.00018857833,0.00058962055,0.000047898066,0.000011788986,0.000049859613,0.00022399535,0.0000784817,0.0053085173],"genre_scores_gemma":[0.9973911,0.000010585421,0.0022466877,0.000041869902,0.00017784795,0.0000055552723,0.000058536814,0.0000218626,0.00004594612],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856484,0.000014937532,0.00037276084,0.0005006388,0.0002676113,0.00027922957],"domain_scores_gemma":[0.99909765,0.00004508906,0.00034734927,0.0002561746,0.00009501813,0.00015874543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005290765,0.00026849422,0.0004748954,0.000016956325,0.00010124993,0.00006318473,0.00022600398,0.00020756424,0.00046436457],"category_scores_gemma":[0.00013223349,0.00023625165,0.000096421965,0.00033105697,0.00031761755,0.00013011642,0.00013465583,0.00044275,9.88163e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037884223,0.00012345491,0.0050993506,0.00049061526,0.00022304355,0.0000041092994,0.00030912532,0.000023339442,0.993267,0.0000029367145,0.000058319074,0.000019858802],"study_design_scores_gemma":[0.00080048235,0.00024171949,0.00310557,0.00006519483,0.00015674626,0.000025739746,0.0008683731,0.0012367103,0.99308676,0.00017110587,0.000022402955,0.000219199],"about_ca_topic_score_codex":0.000039111226,"about_ca_topic_score_gemma":6.506093e-7,"teacher_disagreement_score":0.005262571,"about_ca_system_score_codex":0.000118029,"about_ca_system_score_gemma":0.000086018554,"threshold_uncertainty_score":0.96340626},"labels":[],"label_agreement":null},{"id":"W3046090113","doi":"10.1002/cjce.23849","title":"Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Partial least squares regression; Calibration; Analyte; Fourier transform infrared spectroscopy; Diethylene glycol; Chemistry; Analytical Chemistry (journal); Absorption (acoustics); Infrared spectroscopy; Materials science; Chromatography; Mathematics; Organic chemistry; Statistics; Chemical engineering; Ethylene glycol; Engineering","score_opus":0.013612475779772027,"score_gpt":0.24356681170024297,"score_spread":0.22995433592047093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046090113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99837655,0.00029765756,0.00019309955,0.00046093654,0.000049452057,0.00005980027,0.000010395211,0.000012555743,0.00053958065],"genre_scores_gemma":[0.9977088,0.000011823936,0.0017413589,0.000024979296,0.00041394783,0.000005841818,0.0000024108683,0.00002457711,0.00006624515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900854,0.0000042822753,0.00042025305,0.000106175496,0.000167242,0.0002935235],"domain_scores_gemma":[0.9991969,0.00016516278,0.00016665056,0.00012071845,0.000081344,0.0002692189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013622027,0.0001351392,0.00033059265,0.00013235636,0.000023968283,0.000028667731,0.00033140846,0.00011877608,0.00021083449],"category_scores_gemma":[0.00066232897,0.000119832344,0.00015121092,0.00038653228,0.00003361676,0.00007765797,0.0000114391305,0.00035525046,0.0000030107458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057074132,0.0000065392883,0.00057464646,0.00009611155,0.00006304863,0.000013110907,0.0003484399,0.0012114813,0.9974484,0.000030111394,0.00010872035,0.00004235451],"study_design_scores_gemma":[0.00050673087,0.000038078673,0.000059987015,0.00010081522,0.00006823991,0.0000193259,0.000045965724,0.0012322074,0.99751836,0.00014028778,0.00015365817,0.00011635191],"about_ca_topic_score_codex":0.0003565463,"about_ca_topic_score_gemma":0.0000047563412,"teacher_disagreement_score":0.0015482594,"about_ca_system_score_codex":0.00029517655,"about_ca_system_score_gemma":0.00028005347,"threshold_uncertainty_score":0.48866212},"labels":[],"label_agreement":null},{"id":"W3048395070","doi":"10.1088/1361-6501/abae8f","title":"Robust image segmentation for feature extraction from internal combustion engine in-cylinder images","year":2020,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Combustion; Segmentation; Computer science; Feature (linguistics); Artificial intelligence; Thresholding; Ignition system; Cylinder; Pattern recognition (psychology); Computer vision; Physics; Mathematics; Image (mathematics); Chemistry; Geometry","score_opus":0.04351247727801705,"score_gpt":0.28303634144502465,"score_spread":0.2395238641670076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048395070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86852586,0.001191965,0.11276816,0.015577616,0.000119300785,0.00024834328,0.00003353197,0.00024172013,0.0012935292],"genre_scores_gemma":[0.994898,0.0000412706,0.004879026,0.00007292207,0.00005024109,0.000023789411,0.0000094217485,0.0000055527717,0.000019731071],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989257,0.0000031986472,0.00013265974,0.00033933544,0.00041194525,0.00018716863],"domain_scores_gemma":[0.9994525,0.000016626958,0.0000769298,0.000097671305,0.00031148526,0.00004480164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028720777,0.00009709383,0.00013169699,0.000246128,0.00010807871,0.00005177362,0.00017844164,0.00009990919,0.000066131746],"category_scores_gemma":[0.0005422899,0.00009051854,0.000018029103,0.00089366315,0.00019964419,0.00024939032,0.00003884479,0.00018800236,0.0000027799283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002144538,0.000035087098,0.0068886364,0.000017932154,0.000011535924,0.0000017027368,0.00003457946,0.000018858007,0.98582584,0.00003812814,0.00032375182,0.006782521],"study_design_scores_gemma":[0.00056518475,0.00005321651,0.0017689917,0.0000129216405,0.00004001978,0.0000022655688,0.0008756865,0.0021527242,0.9939708,0.0003098428,0.00015260142,0.000095733754],"about_ca_topic_score_codex":0.000030234285,"about_ca_topic_score_gemma":0.000016786915,"teacher_disagreement_score":0.1263722,"about_ca_system_score_codex":0.00021587801,"about_ca_system_score_gemma":0.00005889033,"threshold_uncertainty_score":0.36912388},"labels":[],"label_agreement":null},{"id":"W3058615435","doi":"10.1021/acs.analchem.0c02320","title":"Simultaneous Double-Pass Absorption and Fluorescence Excitation–Emission Matrix Spectroscopy for Measurements of Reaction Kinetics","year":2020,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Pheophytin; Analytical Chemistry (journal); Fluorescence; Reaction rate constant; Absorption (acoustics); Absorption spectroscopy; Emission spectrum; Fluorescence spectroscopy; Spectral line; Kinetics; Optics","score_opus":0.044240063524885416,"score_gpt":0.32191799271189453,"score_spread":0.2776779291870091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3058615435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862499,0.00037013166,0.003961305,0.00086030096,0.000021578766,0.00013116271,0.000038893628,0.000109704706,0.008257019],"genre_scores_gemma":[0.9961571,0.000053252927,0.002578205,0.000043993205,0.0002129636,0.000009838376,0.00008276039,0.00002392212,0.00083798065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99852484,0.0000048174197,0.0004180886,0.00042406833,0.00037002447,0.00025816783],"domain_scores_gemma":[0.99895203,0.00019896463,0.00019975334,0.00020562523,0.00020329289,0.00024034349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007657122,0.00021056786,0.0003373031,0.000030477182,0.00006178205,0.000032745138,0.00016478982,0.00020229588,0.00036895482],"category_scores_gemma":[0.00059529225,0.00020966365,0.00012158081,0.00033197465,0.000104340725,0.000071693146,0.000040169136,0.00018862389,0.000004678013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039331062,0.00010382488,0.0013389909,0.00069444155,0.000090627065,0.0000024035612,0.00004060776,0.00003192153,0.9968139,0.000024993831,0.00034322773,0.000121775854],"study_design_scores_gemma":[0.0010353297,0.00005848072,0.000053192623,0.000035370296,0.00037495032,0.0000049295404,0.0001432926,0.012327907,0.9852884,0.00016087876,0.00031300858,0.00020425399],"about_ca_topic_score_codex":0.000008899112,"about_ca_topic_score_gemma":3.0946754e-7,"teacher_disagreement_score":0.012295986,"about_ca_system_score_codex":0.00009871155,"about_ca_system_score_gemma":0.000036667036,"threshold_uncertainty_score":0.8549835},"labels":[],"label_agreement":null},{"id":"W3083820015","doi":"10.1016/j.foodchem.2020.128058","title":"Useful tissues in cabbage head for freshness evaluation with visible and near infrared spectroscopy","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Bio-oriented Technology Research Advancement Institution; Cabinet Office, Government of Japan; Swine Innovation Porc","keywords":"Near-infrared spectroscopy; Infrared; Chemistry; Spectroscopy; Infrared spectroscopy; Spectral line; Horticulture; Head (geology); Core (optical fiber); Analytical Chemistry (journal); Materials science; Biology; Environmental chemistry; Optics; Physics; Composite material","score_opus":0.034101576961622394,"score_gpt":0.3002210838499047,"score_spread":0.2661195068882823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083820015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9823499,0.0016456557,0.00022286763,0.0003816308,0.000009880578,0.00015400197,0.000081756785,0.000091091235,0.015063265],"genre_scores_gemma":[0.99685293,0.00003253363,0.001975887,0.00010618182,0.00020017991,0.00010483184,0.00014766745,0.000039147195,0.0005406414],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985871,0.0000073721158,0.00025511102,0.0005052586,0.0003093957,0.00033576167],"domain_scores_gemma":[0.999298,0.00007477567,0.000113645474,0.0002642374,0.0000900687,0.00015929685],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009527261,0.00024331278,0.00034865027,0.000028058514,0.00009439838,0.00010353862,0.00019628127,0.00017205795,0.0011170723],"category_scores_gemma":[0.00018353983,0.0002373518,0.00005201616,0.0004527644,0.00009212286,0.000119165656,0.000050144175,0.00022361855,0.000004647422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004822367,0.00015322756,0.01854344,0.0013186107,0.00019980394,0.000010706965,0.0008999772,0.000105009174,0.97648513,0.000015706364,0.0014384717,0.0003476536],"study_design_scores_gemma":[0.0020101648,0.00015191152,0.00018592225,0.00005006543,0.00016035579,0.000006813767,0.00067557994,0.002202716,0.99067557,0.00026369744,0.0033183184,0.0002988867],"about_ca_topic_score_codex":0.00002352786,"about_ca_topic_score_gemma":0.000022286662,"teacher_disagreement_score":0.018357519,"about_ca_system_score_codex":0.00010645749,"about_ca_system_score_gemma":0.00013943904,"threshold_uncertainty_score":0.99979603},"labels":[],"label_agreement":null},{"id":"W3088734280","doi":"","title":"Colorimétrie, analyse fractale et analyse de Fourier d’images pour l’étude de l’évolution temporelle du brunissement enzymatique des fruits et légumes minimalement transformés","year":2017,"lang":"fr","type":"article","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Art","score_opus":0.015715524757150254,"score_gpt":0.27853072575702237,"score_spread":0.2628152009998721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088734280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85992736,0.016095458,0.014190364,0.041524783,0.00009153322,0.0004571864,0.00087281043,0.00013852792,0.06670197],"genre_scores_gemma":[0.94521827,0.013210801,0.010084814,0.0018308039,0.00048309463,0.00027925285,0.00011405399,0.0001312378,0.028647652],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9941706,0.00053047395,0.0013522416,0.001125305,0.0016025812,0.0012187645],"domain_scores_gemma":[0.99018985,0.0063044024,0.0014723863,0.0011192036,0.00024977693,0.00066438207],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017744593,0.0010057769,0.0010118161,0.0012010358,0.0014971839,0.0013690065,0.0017462306,0.00034363903,0.0076949345],"category_scores_gemma":[0.006553446,0.0010694045,0.0008927246,0.0010442194,0.0011169934,0.001906613,0.0005770142,0.0010031566,0.0001374556],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00060068385,0.0031217935,0.8485393,0.00083156204,0.0033975057,0.00017515868,0.010025907,0.0012121496,0.059166417,0.007400296,0.06474894,0.000780313],"study_design_scores_gemma":[0.0040211063,0.00041126253,0.6757265,0.0006020174,0.0024176158,0.00017379234,0.004326532,0.007137693,0.25494373,0.0022020151,0.04633814,0.0016996547],"about_ca_topic_score_codex":0.2222213,"about_ca_topic_score_gemma":0.7999576,"teacher_disagreement_score":0.57773626,"about_ca_system_score_codex":0.010545146,"about_ca_system_score_gemma":0.03549424,"threshold_uncertainty_score":0.9998027},"labels":[],"label_agreement":null},{"id":"W3088776951","doi":"10.1007/s11004-020-09894-x","title":"Linking Gy’s Formula to QA/QC Duplicates Statistics","year":2020,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Mathematics; Computer science","score_opus":0.03722410271528681,"score_gpt":0.30017763911397194,"score_spread":0.26295353639868513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088776951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26825294,0.00021230617,0.6247646,0.0064132847,0.00008278059,0.00020465262,0.00014209052,0.00047720576,0.099450134],"genre_scores_gemma":[0.9272944,0.000010087483,0.06914133,0.001435117,0.00016009179,0.000022649914,0.000008211566,0.000015735173,0.0019124036],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99841183,0.000005545925,0.00034891663,0.00037980804,0.00045659524,0.00039727293],"domain_scores_gemma":[0.9990333,0.00028169763,0.00008287101,0.00020755593,0.00005072192,0.0003438615],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014761269,0.00016384084,0.00029181162,0.000057599565,0.0001680344,0.0001540147,0.00055871235,0.000065964276,0.0037299863],"category_scores_gemma":[0.000961591,0.00012600518,0.000063354804,0.0008280532,0.00014455293,0.000089060166,0.00015812923,0.00014514705,0.00068574975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017471911,0.0015034874,0.022848578,0.00434669,0.00031695695,0.00013595141,0.019655399,0.00021543451,0.4888725,0.39797753,0.028937139,0.035015617],"study_design_scores_gemma":[0.00077325985,0.0007242847,0.0004224542,0.00021608279,0.0004979175,0.000041495896,0.007020342,0.081699476,0.6138084,0.2610456,0.031804416,0.0019462425],"about_ca_topic_score_codex":0.000009871784,"about_ca_topic_score_gemma":0.0000012370456,"teacher_disagreement_score":0.6590414,"about_ca_system_score_codex":0.000024672025,"about_ca_system_score_gemma":0.000037350994,"threshold_uncertainty_score":0.99718076},"labels":[],"label_agreement":null},{"id":"W3088783789","doi":"10.3390/s20195546","title":"Non-Destructive Assessment of Chicken Egg Fertility","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Fertility; Hatching; Hyperspectral imaging; Biology; Agriculture; Biotechnology; Ecology; Artificial intelligence; Computer science; Population; Demography","score_opus":0.03675032404445038,"score_gpt":0.37129876012129176,"score_spread":0.33454843607684137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088783789","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015468851,0.9427609,0.000009041775,0.000013610878,0.00006533878,0.00015105943,0.00019190578,0.00006140623,0.0551999],"genre_scores_gemma":[0.0023155701,0.9959323,0.0004299174,0.000007736129,0.00016265683,0.000020293768,0.00012513106,0.000057433805,0.00094899524],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998064,0.000035982517,0.0006914854,0.00058099034,0.00034748888,0.00028009113],"domain_scores_gemma":[0.9984209,0.00017546523,0.00059617806,0.00059740484,0.00007043754,0.00013959233],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008147278,0.00046432164,0.0023445487,0.0001431616,0.000049184313,0.000019568959,0.00042441644,0.00039915624,0.0014697234],"category_scores_gemma":[0.00011164223,0.00038665038,0.00081815803,0.0008231255,0.00012591775,0.000027684022,0.00012638823,0.000713446,0.000043708893],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047277153,0.00063074764,0.0014392184,0.15868245,0.008375173,0.00018987774,0.00036494143,0.000010234995,0.00046741104,0.0004541261,0.0008254337,0.8285131],"study_design_scores_gemma":[0.00034539535,0.00006619931,0.0002945675,0.0035256266,0.0075519583,0.000032996995,0.00035354026,0.00009103211,0.0037697982,0.00019012016,0.9827218,0.0010569675],"about_ca_topic_score_codex":0.00004744332,"about_ca_topic_score_gemma":0.0000015343907,"teacher_disagreement_score":0.98189634,"about_ca_system_score_codex":0.00027130623,"about_ca_system_score_gemma":0.00036893677,"threshold_uncertainty_score":0.99985856},"labels":[],"label_agreement":null},{"id":"W3093020770","doi":"10.1016/j.meatsci.2020.108340","title":"Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review","year":2020,"lang":"en","type":"review","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Universidad de Extremadura","keywords":"Hyperspectral imaging; Magnetic resonance imaging; Computer science; Nuclear magnetic resonance; Medicine; Artificial intelligence; Physics; Radiology","score_opus":0.045706150654292066,"score_gpt":0.3623463330551519,"score_spread":0.31664018240085984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093020770","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009783732,0.9898479,0.000009335736,0.00037776493,0.000060327566,0.0004889712,0.00013998659,0.000085132015,0.008892734],"genre_scores_gemma":[0.00047842134,0.9977131,0.00092460716,0.00023687106,0.000093192575,0.00005436098,0.000018687586,0.00006453969,0.00041626007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930854,0.0003104582,0.0012706899,0.0017519795,0.0027884447,0.00079302065],"domain_scores_gemma":[0.99704564,0.00025080793,0.00068497105,0.0011545816,0.00055883464,0.00030517622],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030684758,0.0006864495,0.0021536762,0.00027258223,0.00034328003,0.00018886529,0.0015372834,0.00012508764,0.001920629],"category_scores_gemma":[0.0023884997,0.0006204114,0.0003923301,0.0033861997,0.0014458679,0.00038310353,0.0003860516,0.0006490957,0.000041368246],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005171861,0.000054171316,0.00013226553,0.011720271,0.000011261947,0.000010830579,0.0000982343,1.1917014e-7,0.002658123,0.00013040297,0.0030574186,0.9821217],"study_design_scores_gemma":[0.00047155606,0.000044503482,0.00007082015,0.020353433,0.0038751874,0.00012378328,0.00019320224,0.0009403006,0.0016079991,0.00014167669,0.9711977,0.0009798025],"about_ca_topic_score_codex":0.00015200362,"about_ca_topic_score_gemma":0.000007872313,"teacher_disagreement_score":0.9811419,"about_ca_system_score_codex":0.0005354771,"about_ca_system_score_gemma":0.0008352065,"threshold_uncertainty_score":0.9996247},"labels":[],"label_agreement":null},{"id":"W3093277599","doi":"10.1016/j.meatsci.2020.108342","title":"Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches","year":2020,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Ministry of Agriculture - Saskatchewan","keywords":"Fatty acid; Chemistry; Food science; Linear discriminant analysis; Subcutaneous fat; Beef cattle; Animal science; Near infrared reflectance spectroscopy; Mathematics; Biology; Near-infrared spectroscopy; Biochemistry","score_opus":0.060883754597398004,"score_gpt":0.2843959950311893,"score_spread":0.22351224043379128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093277599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99307984,0.00027623383,0.0034050702,0.00023887507,0.00001550878,0.00005338369,0.0000055459295,0.000027292406,0.0028982214],"genre_scores_gemma":[0.9960011,0.000041995,0.0038212352,0.000028216326,0.00002922518,0.0000027328651,0.000004529618,0.0000073191763,0.00006362916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988796,0.000009096633,0.00019341554,0.00038855633,0.00033183527,0.00019746108],"domain_scores_gemma":[0.9994969,0.00004090632,0.00011067537,0.00017570204,0.00004466627,0.00013116145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011010523,0.00012227565,0.00020044419,0.00008997918,0.00021850987,0.00014369033,0.00019735655,0.00004361636,0.00009273369],"category_scores_gemma":[0.00022167376,0.00010473821,0.000026729898,0.0006830071,0.0005827363,0.00033502225,0.0001221744,0.00008575387,0.000001008792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001393342,0.00004185141,0.021612905,0.0001175328,0.000009582221,3.371398e-7,0.0015552819,0.000009489542,0.9754631,0.0009369549,0.0000067264427,0.00023231457],"study_design_scores_gemma":[0.00021556925,0.000038224567,0.015314406,0.000017482847,0.00008830168,0.0000021704825,0.0009381515,0.051700324,0.93120474,0.0003516015,0.000012865893,0.00011615194],"about_ca_topic_score_codex":0.00002103237,"about_ca_topic_score_gemma":0.0000013564452,"teacher_disagreement_score":0.051690836,"about_ca_system_score_codex":0.000040163883,"about_ca_system_score_gemma":0.000050246206,"threshold_uncertainty_score":0.42711002},"labels":[],"label_agreement":null},{"id":"W3093335132","doi":"10.1016/j.microc.2020.105640","title":"Bio-inspired algorithm for variable selection in i-PLSR to determine physical properties, thorium and rare earth elements in soils from Brazilian semiarid region","year":2020,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Partial least squares regression; Mathematics; Algorithm; Robustness (evolution); Soil water; Chemometrics; Feature selection; Soil science; Statistics; Computer science; Environmental science; Chemistry; Artificial intelligence; Machine learning","score_opus":0.020139145900177943,"score_gpt":0.25203666824438953,"score_spread":0.23189752234421157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093335132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914131,0.00046635017,0.007244095,0.0005807921,0.000024611703,0.00012912326,0.000033941706,0.00003125182,0.00007674037],"genre_scores_gemma":[0.96820515,0.00007461184,0.029690994,0.00066615845,0.0010810123,0.00003001573,0.00005305592,0.00004073922,0.00015824869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853176,0.000018998573,0.0004290075,0.0004215302,0.0001747614,0.0004239266],"domain_scores_gemma":[0.9994047,0.000056323694,0.00011690402,0.000082190876,0.0000681666,0.0002716601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007582765,0.00022410021,0.0003967504,0.00009965839,0.00008015851,0.000098170014,0.00020071775,0.00016461534,0.0000899512],"category_scores_gemma":[0.00015698436,0.00020675485,0.00007894517,0.00059521716,0.00003178646,0.00014356899,0.000076530574,0.0005287921,0.0000037221268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020165797,0.0001439137,0.004507349,0.00004868879,0.000044432436,0.000017855113,0.00030323444,0.000007441676,0.98625183,9.037619e-7,0.0012351566,0.0072375177],"study_design_scores_gemma":[0.0016353077,0.00009459928,0.00015188883,0.000107734064,0.000062121006,0.000055151257,0.00018750837,0.013656355,0.98168194,0.00015563726,0.0019587222,0.00025305137],"about_ca_topic_score_codex":0.000073250914,"about_ca_topic_score_gemma":0.00001322741,"teacher_disagreement_score":0.023207929,"about_ca_system_score_codex":0.00017416681,"about_ca_system_score_gemma":0.00008415949,"threshold_uncertainty_score":0.84312177},"labels":[],"label_agreement":null},{"id":"W3093570743","doi":"10.1016/j.foodchem.2020.128452","title":"Chemometric analysis of amino acid profiles for detection of fruit juice adulterations – Application to verify authenticity of blood orange juice","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Baden-Württemberg Stiftung","keywords":"Orange juice; Orange (colour); Food science; Chemistry; Adulterant; Fruit juice; Partial least squares regression; Principal component analysis; Chromatography; High-performance liquid chromatography; Valine; Amino acid; Mathematics; Biochemistry; Statistics","score_opus":0.018206735788761075,"score_gpt":0.2629598467173277,"score_spread":0.2447531109285666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093570743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9802926,0.00033234543,0.017426023,0.000074842894,0.000009177409,0.00017816451,0.00080440607,0.000048854767,0.00083359284],"genre_scores_gemma":[0.9981443,0.000012343135,0.0012052753,0.00002397065,0.00009010834,0.00013509713,0.00025644066,0.000021509706,0.00011090861],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838096,0.0000067722285,0.0006518149,0.0004302221,0.00032576345,0.00020444521],"domain_scores_gemma":[0.99828786,0.0001279898,0.0005827733,0.00045068623,0.00041775173,0.00013296127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008188713,0.00020725354,0.0006392331,0.00024931692,0.00005377164,0.000011630003,0.00035306922,0.00021601417,0.00032234113],"category_scores_gemma":[0.0005638898,0.00022713373,0.00036897045,0.004637393,0.00006325925,0.000061932784,0.000065052154,0.00013575221,0.000001758601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010977708,0.00030878105,0.0014392545,0.0015944822,0.0017932841,1.1531706e-7,0.00031254502,0.000083627434,0.9938445,0.000010832866,0.000024913796,0.00047788923],"study_design_scores_gemma":[0.00051640143,0.00012730192,0.00043319914,0.00001712177,0.0052446974,5.940593e-7,0.00038026145,0.0014383311,0.9915388,0.000011945568,0.000107965585,0.0001833663],"about_ca_topic_score_codex":0.0000363988,"about_ca_topic_score_gemma":0.0000072291455,"teacher_disagreement_score":0.017851753,"about_ca_system_score_codex":0.00004150473,"about_ca_system_score_gemma":0.000047535457,"threshold_uncertainty_score":0.92622447},"labels":[],"label_agreement":null},{"id":"W3093700517","doi":"10.1109/iscc50000.2020.9219589","title":"Food Grading System Using Support Vector Machine and YOLOv3 Methods","year":2020,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Grading (engineering); Computer science; Support vector machine; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.06727007901185349,"score_gpt":0.345185934201525,"score_spread":0.2779158551896715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093700517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76707476,0.0027318217,0.1448532,0.00071679504,0.00010347118,0.00009165892,0.00004457623,0.0007076384,0.08367608],"genre_scores_gemma":[0.963153,0.000009769889,0.03615003,0.00022305023,0.0001342283,0.0000019843358,0.000006590238,0.000019896228,0.00030142104],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991852,0.000018159937,0.00020236896,0.00027921048,0.00011105132,0.00020399758],"domain_scores_gemma":[0.99952227,0.00008825746,0.00006830177,0.00013459973,0.000019959809,0.0001666191],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010371885,0.0001453299,0.00028993026,0.000051347608,0.0000850355,0.000044794248,0.00011431524,0.000066155655,0.0015918893],"category_scores_gemma":[0.0000852899,0.00012497546,0.00006934945,0.00036361357,0.000027969014,0.000063852414,0.0000735831,0.00013672886,0.000008418583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042498035,0.00003211423,0.014965257,0.0009102799,0.00045260615,0.00002597646,0.00039052995,0.000009318126,0.9787725,0.0028705522,0.00035984511,0.0011685158],"study_design_scores_gemma":[0.0003487186,0.00008640515,0.000059074588,0.000012492239,0.00032846967,0.00006570342,0.0014069342,0.017467657,0.97885805,0.000029002444,0.0010977887,0.00023967029],"about_ca_topic_score_codex":0.000046111392,"about_ca_topic_score_gemma":0.0000013728799,"teacher_disagreement_score":0.19607827,"about_ca_system_score_codex":0.00005886869,"about_ca_system_score_gemma":0.000021106047,"threshold_uncertainty_score":0.9993208},"labels":[],"label_agreement":null},{"id":"W3093804531","doi":"","title":"Determination of main constituents in wheat using near infrared hyperspectral imaging","year":2007,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Infrared; Environmental science; Environmental chemistry; Chemistry; Optics; Geology; Physics","score_opus":0.015189840062701082,"score_gpt":0.26088433923065485,"score_spread":0.24569449916795377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093804531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9732963,0.00033310987,0.0005174605,0.00001756332,0.00007354847,0.00006614253,0.000031930744,0.000022405677,0.025641542],"genre_scores_gemma":[0.9876547,0.000093536924,0.010308989,0.000005543229,0.00003252169,1.2240618e-7,0.00023727269,0.000025155983,0.001642186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897444,0.000015625017,0.00018253343,0.00027754527,0.00030701043,0.00024283346],"domain_scores_gemma":[0.99916226,0.00005371537,0.0003899474,0.00021694455,0.00012446266,0.000052701627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000105167805,0.0001979396,0.0004317282,0.00059585925,0.00010431528,0.000014955629,0.0002806504,0.00025308985,0.00012873586],"category_scores_gemma":[0.000043265292,0.0002907419,0.0001536226,0.0005629972,0.00014731067,0.00018470307,0.00003664006,0.0002828986,0.0000039699157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090760546,0.00059349387,0.43080363,0.0027313067,0.00033035773,0.0005687247,0.003335797,0.00017142414,0.5561076,0.0002585033,0.00041091134,0.0037806677],"study_design_scores_gemma":[0.004370393,0.00006974249,0.13925897,0.0016250634,0.001974858,0.000043452023,0.35541487,0.014992835,0.47986785,0.00046087074,0.00041178343,0.0015093199],"about_ca_topic_score_codex":0.0034536372,"about_ca_topic_score_gemma":0.05361493,"teacher_disagreement_score":0.35207906,"about_ca_system_score_codex":0.00044844812,"about_ca_system_score_gemma":0.00017689611,"threshold_uncertainty_score":0.99995446},"labels":[],"label_agreement":null},{"id":"W3096293983","doi":"10.1007/s00449-020-02456-9","title":"Development of new media formulations for cell culture operations based on regression models","year":2020,"lang":"en","type":"article","venue":"Bioprocess and Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Partial least squares regression; Principal component analysis; Multivariate statistics; Principal component regression; Lasso (programming language); Optimization problem; Set (abstract data type); Mathematical optimization; Mathematics; Computer science; Linear regression; Elastic net regularization; Regression; Statistics","score_opus":0.0359630829455761,"score_gpt":0.2495623491037133,"score_spread":0.2135992661581372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096293983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4670431,0.0024121308,0.52862775,0.0002546442,0.0000740195,0.00026516727,0.00011850359,0.00019903577,0.001005653],"genre_scores_gemma":[0.97452414,0.000010851136,0.025157303,0.000017697228,0.00010448538,0.000016910135,0.000050470735,0.000011669989,0.0001064924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943924,5.8933097e-7,0.00019975298,0.00015339375,0.000105435276,0.00010157255],"domain_scores_gemma":[0.99973005,0.000018718107,0.000041588562,0.00006355634,0.000046311827,0.000099794925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002363102,0.00011447571,0.00016252707,0.000051218685,0.00006497532,0.000021516556,0.00007107234,0.00008103097,0.000014378021],"category_scores_gemma":[0.00002817489,0.00008618809,0.000034376913,0.00020629712,0.0000036790555,0.00005808162,0.000011143773,0.000047609734,4.230022e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026198442,0.000045460965,0.00007549805,0.002226184,0.000033012784,3.3839646e-7,0.0021984426,0.0293515,0.9649535,0.000442082,0.00016657455,0.00048122095],"study_design_scores_gemma":[0.00030021192,0.000012828215,0.000005204765,0.00009512541,0.00002423756,2.0979559e-7,0.00022686798,0.3608867,0.63729745,0.000003296204,0.0010505661,0.000097299184],"about_ca_topic_score_codex":0.000004528431,"about_ca_topic_score_gemma":0.000002091925,"teacher_disagreement_score":0.50748104,"about_ca_system_score_codex":0.000017423152,"about_ca_system_score_gemma":0.000049961258,"threshold_uncertainty_score":0.3514648},"labels":[],"label_agreement":null},{"id":"W3096313907","doi":"","title":"Long wavelength near-infrared hyperspectral imaging for classification and quality assessment of bulk samples of wheat from different growing locations and crop years","year":2011,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Crop; Environmental science; Quality assessment; Agricultural engineering; Geography; Engineering; Forestry; Evaluation methods","score_opus":0.04309134608111371,"score_gpt":0.27891203541647336,"score_spread":0.23582068933535966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096313907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963843,0.0005010583,0.0015994146,0.000054472555,0.00003558102,0.00011310422,0.00024102004,0.00001801747,0.0010530278],"genre_scores_gemma":[0.99361056,0.00037472654,0.0048158416,0.0000022678344,0.000020885536,0.0000010345286,0.0007115481,0.000016737988,0.000446411],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99910545,0.000023294975,0.00020370472,0.00032536965,0.0002024072,0.00013974721],"domain_scores_gemma":[0.9988245,0.00018235765,0.0005104548,0.00025933463,0.0001661164,0.000057230198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008154371,0.00017401442,0.00048808305,0.00015983015,0.00013592458,0.00001905512,0.00018132987,0.00015588925,0.00004184537],"category_scores_gemma":[0.00003561808,0.00022088991,0.00012728953,0.000114302566,0.00016792883,0.00015311163,0.000041837873,0.00015044067,1.7856982e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003196676,0.00038249965,0.67419875,0.0023644834,0.00089563854,0.0000023895464,0.0029763614,0.000004098003,0.31276035,0.0022566954,0.0001319434,0.0037071195],"study_design_scores_gemma":[0.0006127817,0.000026252677,0.9106872,0.00014252185,0.0007460554,3.544093e-7,0.054386888,0.0007585978,0.032075416,0.0003528577,0.000020064845,0.00019100943],"about_ca_topic_score_codex":0.006401423,"about_ca_topic_score_gemma":0.0143105835,"teacher_disagreement_score":0.28068495,"about_ca_system_score_codex":0.00010303569,"about_ca_system_score_gemma":0.000075621225,"threshold_uncertainty_score":0.96770793},"labels":[],"label_agreement":null},{"id":"W3103807753","doi":"10.1007/s40831-020-00310-6","title":"Estimation of Net Carbon Consumption in Aluminum Electrolysis Using Multivariate Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Sustainable Metallurgy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alcoa (Canada); Université Laval; Centre de Recherche en Sciences Animales de Deschambault","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anode; Carbon footprint; Process engineering; Carbon fibers; Multivariate statistics; Computer science; Electrolytic process; Volume (thermodynamics); Environmental science; Electrolysis; Raw material; Greenhouse gas; Engineering; Chemistry; Algorithm; Electrode","score_opus":0.021602130829857694,"score_gpt":0.2897462136644664,"score_spread":0.2681440828346087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103807753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98136085,0.0010410273,0.017016763,0.00013352539,0.00001063101,0.000036805133,0.0000012377361,0.00000880238,0.0003903367],"genre_scores_gemma":[0.99759597,0.00007837304,0.002027349,0.00002848742,0.000060827762,0.0000013606751,0.0000055897153,0.000013975599,0.0001880684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981639,0.000058071746,0.00087396393,0.00017806835,0.0003823406,0.00034369793],"domain_scores_gemma":[0.9983231,0.00010970445,0.0010197143,0.00014665035,0.00027340424,0.00012743264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004643727,0.00016508398,0.0008269121,0.000961474,0.000035891186,0.000032068274,0.00020874626,0.00011311108,0.00059608807],"category_scores_gemma":[0.0005882295,0.00015443968,0.00036205913,0.0028139744,0.00004679289,0.00026363708,0.000043224623,0.00028817696,6.4270813e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006091765,0.00035142,0.03213968,0.00065836264,0.005783828,0.00039080688,0.001142271,0.50342566,0.4544651,0.00044780716,0.000012465116,0.00057344796],"study_design_scores_gemma":[0.0014789377,0.00012702077,0.0023448719,0.00002352168,0.008114004,0.000025942594,0.0040765074,0.6346012,0.34859636,0.00028257957,0.00007621931,0.000252859],"about_ca_topic_score_codex":0.0006212653,"about_ca_topic_score_gemma":0.00000737529,"teacher_disagreement_score":0.13117552,"about_ca_system_score_codex":0.00033752454,"about_ca_system_score_gemma":0.00015503388,"threshold_uncertainty_score":0.6526747},"labels":[],"label_agreement":null},{"id":"W3107686501","doi":"10.7451/cbe.2019.61.7.1","title":"Assessment of seed germinability of mechanically-damaged soybeans using near-infrared hyperspectral imaging","year":2019,"lang":"en","type":"article","venue":"Canadian Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Infrared; Horticulture; Environmental science; Remote sensing; Materials science; Biology; Optics; Geology; Physics","score_opus":0.007548438483480367,"score_gpt":0.23578255604108617,"score_spread":0.2282341175576058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107686501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932032,0.00028718775,0.00075852213,0.000019182124,0.00014279132,0.000120978904,0.00007377938,0.00005032627,0.0053440174],"genre_scores_gemma":[0.99656606,0.0000018784006,0.0031910846,0.000008319875,0.00004906615,0.0000037500904,0.000010863813,0.000036062946,0.00013293709],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856365,0.000013269365,0.0004823411,0.00029780171,0.00021774606,0.0004252137],"domain_scores_gemma":[0.9988517,0.00007223249,0.0001656206,0.0005159179,0.00012276793,0.00027176715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022935098,0.00020995353,0.00052421493,0.00028105994,0.000044868877,0.000029750401,0.00027418518,0.00012304913,0.00037471383],"category_scores_gemma":[0.0000922609,0.00023438461,0.00016830735,0.0005680107,0.0000341403,0.00009527375,0.000029789971,0.00021859453,0.0000023230673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019861664,0.000015661493,0.070170656,0.0007162183,0.000103563274,0.000008652801,0.000088156674,0.0014419068,0.92708313,0.00035155728,0.000007480118,0.000011046143],"study_design_scores_gemma":[0.0007125588,0.000029968849,0.020046689,0.00031243468,0.00018172145,0.00003540177,0.0015677847,0.2137265,0.7625203,0.00001619259,0.0002653933,0.00058502925],"about_ca_topic_score_codex":0.021589337,"about_ca_topic_score_gemma":0.0005476826,"teacher_disagreement_score":0.21228461,"about_ca_system_score_codex":0.0007851967,"about_ca_system_score_gemma":0.0006462938,"threshold_uncertainty_score":0.984926},"labels":[],"label_agreement":null},{"id":"W3107856855","doi":"10.1109/ccece47787.2020.9255814","title":"Customized Shape Detection Algorithms for Radiometric Calibration of Multispectral Imagers for Precision Agriculture Applications","year":2020,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Multispectral image; Radiometric calibration; Computer science; Calibration; Remote sensing; Radiometry; Precision agriculture; Computer vision; Artificial intelligence; Agriculture; Geography; Mathematics","score_opus":0.025577677746852132,"score_gpt":0.29234940396985126,"score_spread":0.2667717262229991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107856855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00917804,0.0003073064,0.98850274,0.00030029376,0.00002507594,0.0007009464,0.00012790678,0.00014569872,0.00071200496],"genre_scores_gemma":[0.9395321,0.000051625848,0.05801995,0.00008941013,0.00042272417,0.0007663768,0.00026032902,0.000027131822,0.0008303287],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989458,0.000006343486,0.00035112206,0.00034337482,0.00016427405,0.00018910803],"domain_scores_gemma":[0.9990608,0.00033203457,0.00019563262,0.00013704848,0.00017022372,0.00010423648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073775365,0.00015176817,0.00030082528,0.00018115342,0.00011276667,0.0000327867,0.00017005768,0.00015524015,0.00036439465],"category_scores_gemma":[0.0003090489,0.00012325801,0.00025563114,0.0016146518,0.000028135448,0.00014661402,0.000016911248,0.00008780668,0.0000022862855],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023339766,0.00009452693,0.000068565845,0.00017991869,0.00010465002,7.264322e-8,0.000058393252,0.00010418169,0.9764573,0.000099012585,0.0013220547,0.021277942],"study_design_scores_gemma":[0.0016599614,0.00007503486,0.000022562766,0.000002639672,0.00021134959,0.00000134216,0.00023245648,0.13087623,0.8648345,0.00011375406,0.0018307357,0.00013945527],"about_ca_topic_score_codex":0.000021346257,"about_ca_topic_score_gemma":0.000004501559,"teacher_disagreement_score":0.9304828,"about_ca_system_score_codex":0.000072782204,"about_ca_system_score_gemma":0.000024006604,"threshold_uncertainty_score":0.5026316},"labels":[],"label_agreement":null},{"id":"W3111555727","doi":"10.3390/min10121137","title":"Hyperspectral Characteristics of Oil Sand, Part 2: Prediction of Froth Characteristics from Measurements of Froth","year":2020,"lang":"en","type":"article","venue":"Minerals","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; University of Alberta","funders":"University of Alberta","keywords":"Asphalt; Oil sands; Hyperspectral imaging; Illite; Environmental science; Mineralogy; Geology; Materials science; Clay minerals; Remote sensing; Composite material","score_opus":0.05629492070394736,"score_gpt":0.2566421787201047,"score_spread":0.20034725801615733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111555727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99117106,0.0003582139,0.00010466327,0.000117739255,0.0000917523,0.00003884167,0.0027138307,0.000033237586,0.005370642],"genre_scores_gemma":[0.99725884,0.00018728798,0.0008585294,0.0000737737,0.0004743136,0.000007845514,0.00044697322,0.000025811454,0.00066665147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981633,0.000023761695,0.0008545724,0.00028917115,0.00045876027,0.00021047462],"domain_scores_gemma":[0.9985836,0.00007428273,0.00069948274,0.0003172502,0.00020712128,0.00011827475],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009355637,0.00021447445,0.00078904425,0.00007367209,0.000025446598,0.000010256573,0.00025370944,0.00014898732,0.003112757],"category_scores_gemma":[0.00037258273,0.00020744103,0.00018287647,0.00030736282,0.00010159503,0.00007076537,0.000044025044,0.0001532036,0.0000073415345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016246007,0.00020466428,0.08691708,0.00024805777,0.00033223466,0.000001884444,0.00034778364,0.0000012516648,0.91017735,0.000008734098,0.0011781221,0.0004203681],"study_design_scores_gemma":[0.0008388921,0.00009057388,0.007245606,0.000091939066,0.00043478186,6.0185596e-7,0.00012919969,0.00011442784,0.9901829,0.000016283057,0.00070886523,0.00014597186],"about_ca_topic_score_codex":0.00018728636,"about_ca_topic_score_gemma":0.0000016790356,"teacher_disagreement_score":0.0800055,"about_ca_system_score_codex":0.00004022326,"about_ca_system_score_gemma":0.00005585262,"threshold_uncertainty_score":0.99779856},"labels":[],"label_agreement":null},{"id":"W3112092578","doi":"10.1002/cem.3315","title":"A pilot study on parallel factor analysis as a diagnostic tool for oral cancer diagnosis: A statistical modeling approach","year":2020,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Flavin adenine dinucleotide; Nicotinamide adenine dinucleotide; Linear discriminant analysis; Cancer; Chemistry; Pathology; Medicine; Biochemistry; Internal medicine; Mathematics; Statistics; Cofactor; Enzyme; NAD+ kinase","score_opus":0.11917148416493098,"score_gpt":0.36523496594126303,"score_spread":0.24606348177633205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112092578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75767213,0.0013840023,0.23959963,0.0004932723,0.00006230914,0.0002448747,0.0002535231,0.000037508595,0.00025277655],"genre_scores_gemma":[0.991765,0.00038165646,0.006611697,0.0004513675,0.0004507406,0.00019940663,0.000019213294,0.000048203932,0.00007271811],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968865,0.000039774404,0.0010547448,0.0004650257,0.0010884366,0.0004655107],"domain_scores_gemma":[0.9956973,0.0024814995,0.00063614926,0.0002651442,0.0004950901,0.0004247771],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032682397,0.00037260502,0.0012389438,0.0012805224,0.00011486661,0.00015911758,0.0005680769,0.000094883806,0.0009036009],"category_scores_gemma":[0.011935026,0.00030867054,0.0004780495,0.006298823,0.00003693276,0.0001702886,0.00007167315,0.0006433885,0.000007266901],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005207605,0.02142022,0.7926169,0.0010405829,0.027336465,0.00035298223,0.0026678853,0.13126618,0.0026566992,0.0002903563,0.00935794,0.005786178],"study_design_scores_gemma":[0.048115153,0.04761804,0.03869849,0.00033112857,0.10884336,0.000113572256,0.019355353,0.68006724,0.04562544,0.0019780828,0.002768745,0.006485423],"about_ca_topic_score_codex":0.000066134235,"about_ca_topic_score_gemma":0.0000037161112,"teacher_disagreement_score":0.7539184,"about_ca_system_score_codex":0.000288061,"about_ca_system_score_gemma":0.00019933966,"threshold_uncertainty_score":0.9999365},"labels":[],"label_agreement":null},{"id":"W3119648466","doi":"10.47893/ijipvs.2013.1038","title":"BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (BPHE) TECHNIQUE FOR CLASSIFICATION OF DATES","year":2013,"lang":"en","type":"article","venue":"International Journal of Image Processing and Vision Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Kurtosis; Histogram; Pattern recognition (psychology); Brightness; Artificial intelligence; Artificial neural network; Histogram equalization; Classifier (UML); Entropy (arrow of time); Gradation; Computer science; Mathematics; Computer vision; Statistics; Image (mathematics)","score_opus":0.02746553336755307,"score_gpt":0.4083819172009583,"score_spread":0.3809163838334052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119648466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25938687,0.00064106693,0.7375663,0.00073137775,0.000093265655,0.00007360559,0.0000049722903,0.000014472789,0.0014880645],"genre_scores_gemma":[0.97322536,0.000036006586,0.02650397,0.000020248837,0.00006627783,0.000007558924,0.0000028400175,0.000005200157,0.00013254586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988544,0.000006350422,0.00039789005,0.00014715968,0.0004952632,0.00009893899],"domain_scores_gemma":[0.99686396,0.00008819916,0.00063733413,0.000077916615,0.0022782267,0.00005433724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050866604,0.000068422785,0.00012777206,0.00032308657,0.000111379806,0.0002624608,0.0005116987,0.000036322013,0.00011228711],"category_scores_gemma":[0.0006764441,0.000052834614,0.00004041948,0.00035128664,0.00026838822,0.0013228481,0.00006003372,0.00007478317,4.9400563e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017425611,0.00005634553,0.00089136726,0.000069419024,0.000008119966,3.149633e-7,0.00010242393,0.0000043297,0.9794771,0.000111955495,0.00012112889,0.019140063],"study_design_scores_gemma":[0.00024830128,0.00003782604,0.0015015784,0.00019479863,0.000023275956,0.000029131772,0.00032288086,0.012172322,0.9826836,0.002447279,0.00026656972,0.00007242448],"about_ca_topic_score_codex":0.0000145385175,"about_ca_topic_score_gemma":3.2573007e-7,"teacher_disagreement_score":0.71383846,"about_ca_system_score_codex":0.00006063753,"about_ca_system_score_gemma":0.00012094729,"threshold_uncertainty_score":0.25309154},"labels":[],"label_agreement":null},{"id":"W3120192914","doi":"10.3389/fpls.2020.624273","title":"Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean","year":2021,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":231,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Grain Farmers of Ontario; University of Guelph","keywords":"Hyperspectral imaging; Random forest; Support vector machine; Reflectivity; Artificial intelligence; Dimensionality reduction; Machine learning; Feature selection; Computer science; Multilayer perceptron; Robustness (evolution); Algorithm; Remote sensing; Mathematics; Pattern recognition (psychology); Artificial neural network; Biology","score_opus":0.016617205542226756,"score_gpt":0.2573629176460509,"score_spread":0.24074571210382414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120192914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98823506,0.0016868124,0.0056972825,0.000088934416,0.00015260915,0.000051195177,0.00011248348,0.000033469038,0.003942139],"genre_scores_gemma":[0.988721,0.0001800165,0.01085312,0.000018857187,0.000050199666,0.000009219712,0.00006941915,0.00000779934,0.00009037504],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982864,0.00001482336,0.00037549785,0.0005419364,0.0003880517,0.00039327567],"domain_scores_gemma":[0.99940914,0.00013192263,0.00015716164,0.00020547448,0.000035650282,0.000060625563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039465446,0.00013289905,0.00031362218,0.00041413557,0.00007082746,0.000030431826,0.00041797044,0.0000939025,0.00003742428],"category_scores_gemma":[0.0005724722,0.00014120531,0.000030334437,0.0025829903,0.00016229742,0.00018990635,0.000078032,0.0005029081,8.567154e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028616389,0.00006485064,0.6403677,0.000017945753,0.000006257321,0.000039531176,0.00065627595,0.0003963179,0.35778242,0.000041786003,0.000038254424,0.0005600302],"study_design_scores_gemma":[0.0004780332,0.000023008111,0.0150229335,0.00016615773,0.000015297423,0.00001811957,0.004433236,0.2600493,0.71908575,0.00042995752,0.000058175992,0.00022000445],"about_ca_topic_score_codex":0.0011995346,"about_ca_topic_score_gemma":0.00050434895,"teacher_disagreement_score":0.62534475,"about_ca_system_score_codex":0.00030768962,"about_ca_system_score_gemma":0.00014810124,"threshold_uncertainty_score":0.57581854},"labels":[],"label_agreement":null},{"id":"W3120314896","doi":"10.1364/ais.2020.aw1i.2","title":"Potential of spectroscopy for fruits and vegetables processing and quality assessment","year":2020,"lang":"en","type":"article","venue":"Optical Sensors and Sensing Congress","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Quality (philosophy); Quality assessment; Spectroscopy; Food science; Computer science; Environmental science; Agricultural engineering; Biology; Engineering; Physics; Reliability engineering; Evaluation methods","score_opus":0.034352436562780524,"score_gpt":0.3218020020036255,"score_spread":0.287449565440845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120314896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931359,0.00095099816,0.0037576961,0.0007309125,0.000022837887,0.00006713382,0.000019051584,0.000028640146,0.0012868065],"genre_scores_gemma":[0.9822309,0.0001269311,0.017339732,0.00008857296,0.00010251552,6.804277e-7,0.000006046214,0.000014916418,0.00008967544],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989663,0.000015453077,0.00027527788,0.00035332626,0.00015720772,0.00023248367],"domain_scores_gemma":[0.9993383,0.00019062885,0.00011312817,0.000082870356,0.000092004484,0.00018301915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010422082,0.00016247804,0.0004310389,0.00003052747,0.0001429707,0.000113521775,0.0000371654,0.00010536159,0.000019515956],"category_scores_gemma":[0.00017113864,0.00014309293,0.00004319928,0.00010291718,0.00026670372,0.00006804204,0.000056912188,0.00013468733,1.3571983e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015632219,0.000041974028,0.0048753303,0.0010935062,0.00010287448,0.000011448497,0.0001847998,0.000013486749,0.9857037,0.0012475773,0.000024277482,0.006544678],"study_design_scores_gemma":[0.0015519463,0.00015124887,0.004555917,0.000108324035,0.00049800664,0.000035212444,0.0012780182,0.053067878,0.9376084,0.00047344263,0.00026230462,0.00040925705],"about_ca_topic_score_codex":0.00002119483,"about_ca_topic_score_gemma":0.0000025038037,"teacher_disagreement_score":0.05305439,"about_ca_system_score_codex":0.000011561361,"about_ca_system_score_gemma":0.000023635874,"threshold_uncertainty_score":0.583516},"labels":[],"label_agreement":null},{"id":"W3120408525","doi":"10.1016/j.jchromb.2021.122526","title":"Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra","year":2021,"lang":"en","type":"article","venue":"Journal of Chromatography B","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Chemistry; Absorbance; Chromatography; Fluorescence; Elution; Fractionation; Globulin; Fluorescence spectroscopy; Analytical Chemistry (journal)","score_opus":0.016437308102028356,"score_gpt":0.2825734724412907,"score_spread":0.26613616433926235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120408525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99204487,0.00613219,0.0014847814,0.000055830333,0.000026290507,0.00003105897,0.00011409056,0.000017415183,0.00009347989],"genre_scores_gemma":[0.9884697,0.0018684044,0.00940868,0.000013744756,0.000052378215,0.000001270845,0.00016493816,0.000015701991,0.0000051615802],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982236,0.000055260567,0.0007904626,0.00030223883,0.00044412925,0.00018430033],"domain_scores_gemma":[0.9980403,0.00016418062,0.0011182051,0.00021009322,0.00035608065,0.000111095316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014305407,0.00021683544,0.00065903337,0.0011546657,0.00020832557,0.000093600975,0.00013249258,0.00013291772,0.00031410792],"category_scores_gemma":[0.000036978912,0.00021421566,0.0003624611,0.0022340107,0.00019112008,0.00030251368,0.000028805152,0.0002231471,1.6955944e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003835786,0.00022689786,0.23187928,0.000047336125,0.0039670705,0.000008330474,0.00010038392,0.000011640054,0.7634713,0.00003878352,0.000088020744,0.00012260993],"study_design_scores_gemma":[0.00054742565,0.000031105035,0.43065774,0.000055322966,0.0034686383,0.00004594803,0.00038707626,0.0010550618,0.5633716,0.00020914857,0.00002232425,0.00014862264],"about_ca_topic_score_codex":0.00035214302,"about_ca_topic_score_gemma":0.000023980067,"teacher_disagreement_score":0.20009972,"about_ca_system_score_codex":0.00005758755,"about_ca_system_score_gemma":0.000043146087,"threshold_uncertainty_score":0.8735461},"labels":[],"label_agreement":null},{"id":"W3122177595","doi":"","title":"Infinite Order Cross-Validated Local Polynomial Regression","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Polynomial; Polynomial regression; Estimator; Applied mathematics; Nonparametric regression; Mathematics; Mathematical optimization; Regression; Local regression; Sample size determination; Computer science; Statistics; Mathematical analysis","score_opus":0.034919120628332787,"score_gpt":0.36147857869436956,"score_spread":0.32655945806603676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122177595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81779873,0.00033214546,0.00001009012,0.0001940722,0.00025588754,0.00024258703,0.00010888233,0.0001175845,0.18093999],"genre_scores_gemma":[0.9699346,0.004648306,0.0003797317,0.000075129705,0.0005847009,0.00022592265,0.00040277283,0.00013126744,0.023617612],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956919,0.00009086287,0.0010224973,0.0014430033,0.00046991435,0.0012818306],"domain_scores_gemma":[0.99660677,0.00058370933,0.0003753877,0.0017215977,0.00033717012,0.00037537297],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00081414887,0.0005977016,0.0009575142,0.000867147,0.0002550443,0.0006059745,0.0013652057,0.0015037354,0.007854974],"category_scores_gemma":[0.00088795286,0.00058378367,0.00029849456,0.00050208345,0.0008678675,0.00017155043,0.0021376556,0.0038196798,0.00013675618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032072144,0.0035879093,0.21104214,0.00530058,0.0041015325,0.00065197947,0.0019237208,0.05232054,0.1206253,0.0004937703,0.017707998,0.5790373],"study_design_scores_gemma":[0.0110443495,0.00039247578,0.011334391,0.0024465467,0.00041800403,0.00013563552,0.0053834673,0.10807164,0.52405345,0.0038549753,0.3249143,0.007950791],"about_ca_topic_score_codex":0.0006088105,"about_ca_topic_score_gemma":0.000073043724,"teacher_disagreement_score":0.5710865,"about_ca_system_score_codex":0.0015984642,"about_ca_system_score_gemma":0.0009053497,"threshold_uncertainty_score":0.9997925},"labels":[],"label_agreement":null},{"id":"W3123728485","doi":"10.18280/ria.340607","title":"Ensemble Machine Learning Methods to Estimate the Sugarcane Yield Based on Remote Sensing Information","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Ensemble learning; Machine learning; Computer science; Artificial intelligence; Remote sensing; Geography; Materials science","score_opus":0.05636255579063887,"score_gpt":0.35573069938626567,"score_spread":0.2993681435956268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123728485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008254628,0.00008999817,0.96086293,0.008200718,0.000055210166,0.00010483837,0.0000042974007,0.00016134886,0.022266021],"genre_scores_gemma":[0.9444704,0.000016366677,0.05157513,0.002716323,0.00011371523,0.0000019774725,0.000023800083,0.000023401231,0.0010588581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873966,0.000049707633,0.0004118297,0.00027398043,0.00020428478,0.0003205478],"domain_scores_gemma":[0.9985216,0.00071435876,0.00014538568,0.00037379158,0.00008691301,0.00015794563],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039820824,0.0001940105,0.00023862065,0.00010277426,0.0002876435,0.00011639522,0.00029124235,0.00008395931,0.00115081],"category_scores_gemma":[0.0023168363,0.00015647023,0.00011488087,0.0009793559,0.000041017887,0.00009923924,0.00007408787,0.00047061266,0.0005410598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012476223,0.00002333829,0.000096012955,0.00015133613,0.00003355164,0.000009432508,0.0020537793,0.5598814,0.15921776,0.0001881877,0.00044178942,0.27777866],"study_design_scores_gemma":[0.000014773393,0.00004767647,0.000001282476,0.000029466402,0.000025825304,0.0000034555585,0.00045071193,0.52128625,0.46965975,0.00006923173,0.008310023,0.00010154181],"about_ca_topic_score_codex":0.00014832613,"about_ca_topic_score_gemma":0.000009208867,"teacher_disagreement_score":0.9362158,"about_ca_system_score_codex":0.000061228886,"about_ca_system_score_gemma":0.000031335072,"threshold_uncertainty_score":0.9997623},"labels":[],"label_agreement":null},{"id":"W3125129736","doi":"","title":"A Generalized Portmanteau Test for Independence of Two Infinite Order Vector Autoregressive Series","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Mitacs; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Mathematics; Autoregressive model; Test statistic; Series (stratigraphy); Applied mathematics; Asymptotic distribution; Autocorrelation; Univariate; Independence (probability theory); Statistics; Statistical hypothesis testing; Multivariate statistics; Estimator","score_opus":0.03338439883169903,"score_gpt":0.3494717531872219,"score_spread":0.3160873543555229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125129736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9366832,0.0009556063,0.000055907563,0.00034457582,0.00027241107,0.0008734281,0.0010905962,0.000116177886,0.059608117],"genre_scores_gemma":[0.98083025,0.0050335545,0.0043817903,0.000037487578,0.00037293395,0.00062190037,0.00029778946,0.0001169023,0.008307387],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966187,0.00004563092,0.0009826962,0.0010710643,0.00041758272,0.0008643576],"domain_scores_gemma":[0.99651986,0.00093569275,0.00062852225,0.001189562,0.00053804944,0.0001883059],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072128983,0.000468802,0.0010816804,0.00067761296,0.00013919968,0.00013239255,0.0010414646,0.0007213166,0.0008211812],"category_scores_gemma":[0.0026038366,0.00049138995,0.00033376372,0.00033907752,0.0005466989,0.00013126442,0.0008852885,0.0016944045,0.000004102121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005996935,0.007048423,0.3999333,0.024075886,0.0088015795,0.000872869,0.007253115,0.16056685,0.2908139,0.025062757,0.000874149,0.06870025],"study_design_scores_gemma":[0.014845524,0.0008536748,0.006223734,0.0029891706,0.00067621405,0.00015027566,0.003499762,0.013244217,0.89566076,0.04329059,0.014089554,0.004476521],"about_ca_topic_score_codex":0.000580611,"about_ca_topic_score_gemma":0.00054785796,"teacher_disagreement_score":0.6048469,"about_ca_system_score_codex":0.0011506692,"about_ca_system_score_gemma":0.002167003,"threshold_uncertainty_score":0.9997538},"labels":[],"label_agreement":null},{"id":"W3128013506","doi":"10.1016/j.crfs.2021.01.002","title":"Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review","year":2021,"lang":"en","type":"review","venue":"Current Research in Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":478,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Council of Agricultural Research","keywords":"Hyperspectral imaging; Artificial intelligence; Machine learning; Computer science; Quality (philosophy); Field (mathematics); Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.44624173948109974,"score_gpt":0.5678783264917849,"score_spread":0.1216365870106852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128013506","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022552285,0.9980995,0.000088646964,0.00006527723,0.00002501341,0.0007581714,0.00034763018,0.000024093026,0.00036615797],"genre_scores_gemma":[0.0018727178,0.99571353,0.0017176965,0.0000026642226,0.000054941007,0.0004213632,0.000120275254,0.000021258256,0.000075573385],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945014,0.00032528496,0.0014593808,0.0012330833,0.0016642993,0.00081654586],"domain_scores_gemma":[0.9954705,0.0010181307,0.0006683577,0.0010998268,0.0015486181,0.00019452779],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007820106,0.00034803743,0.0032277768,0.0033386052,0.00013912492,0.000060823633,0.0018265598,0.00011283862,0.00012103624],"category_scores_gemma":[0.01397768,0.00028330507,0.00081714435,0.028711816,0.00073455274,0.00013348077,0.0006359878,0.0010181485,9.328806e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011355158,0.00054799457,0.00014615609,0.22169288,0.00064392353,0.0000014889406,0.00006344401,9.702383e-7,0.0066072107,0.00040320383,0.0000942062,0.7697872],"study_design_scores_gemma":[0.0002805116,0.0013570859,0.00001295604,0.107587144,0.005234704,0.000009160523,0.00022016173,0.000040169616,0.40149137,0.00014084215,0.4824139,0.0012120141],"about_ca_topic_score_codex":0.000041253683,"about_ca_topic_score_gemma":0.000019985258,"teacher_disagreement_score":0.76857513,"about_ca_system_score_codex":0.00047976442,"about_ca_system_score_gemma":0.0014520723,"threshold_uncertainty_score":0.9999619},"labels":[],"label_agreement":null},{"id":"W3129273738","doi":"10.1016/j.meatsci.2021.108458","title":"Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics","year":2021,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Swine Innovation Porc","keywords":"Hyperspectral imaging; Partial least squares regression; Intramuscular fat; Predictability; Anisotropy; Orientation (vector space); Chemometrics; Wavelength; Backpropagation; Artificial neural network; Pattern recognition (psychology); Computer science; Mathematics; Biological system; Artificial intelligence; Physics; Statistics; Optics; Chemistry; Machine learning; Food science","score_opus":0.015242570581022094,"score_gpt":0.25302292277807376,"score_spread":0.23778035219705165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129273738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941481,0.0020499018,0.00006478283,0.0004980759,0.00003119183,0.000049286624,0.0000066010216,0.00001539008,0.0031367003],"genre_scores_gemma":[0.99958843,0.00006857909,0.00010039221,0.00009032785,0.00001745272,0.00000488508,0.0000015855443,0.0000046380683,0.00012370141],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986613,0.000026583102,0.00017989702,0.0003986815,0.00045481793,0.00027871536],"domain_scores_gemma":[0.99910676,0.0003202358,0.000057250254,0.00036353775,0.00008191741,0.00007029038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004287945,0.00011580934,0.00021798818,0.00012586874,0.00010754921,0.00005682153,0.00030268903,0.000033549444,0.00020156594],"category_scores_gemma":[0.0017395484,0.000079196754,0.000054151016,0.0022507703,0.00061096647,0.000076632095,0.0000960573,0.00018751051,0.0000013837238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007596967,0.000067053035,0.22254354,0.000032112057,0.0000071530026,0.00000583378,0.00008388686,0.0000019177223,0.77641994,0.000278392,0.00004485217,0.0005077076],"study_design_scores_gemma":[0.0002660863,0.000036224043,0.01915378,0.000018540828,0.000025438027,0.0000068812915,0.0006874238,0.00022388517,0.979382,0.00008310487,0.000038269634,0.000078371726],"about_ca_topic_score_codex":0.00010548146,"about_ca_topic_score_gemma":0.000004449016,"teacher_disagreement_score":0.20338975,"about_ca_system_score_codex":0.0001462482,"about_ca_system_score_gemma":0.00005282444,"threshold_uncertainty_score":0.32295498},"labels":[],"label_agreement":null},{"id":"W3133467947","doi":"","title":"Clustering of Time Series Cytotoxicity Data","year":2020,"lang":"en","type":"article","venue":"MacEwan University Student Research Proceedings","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Cluster analysis; Series (stratigraphy); Wavelet; Data mining; Statistics; Computer science; Artificial intelligence; Mathematics; Geology","score_opus":0.15966140866840403,"score_gpt":0.3760735916397969,"score_spread":0.2164121829713929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133467947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8506152,0.00008021666,0.00021952434,0.0021490548,0.000007652712,0.00017813797,0.00013543972,0.00016536523,0.14644943],"genre_scores_gemma":[0.9899793,0.00014409242,0.000651151,0.000030920448,0.000105482584,6.0335964e-7,0.000027073325,0.000015600363,0.009045776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808115,0.00001227915,0.00015270081,0.00050568715,0.00084657565,0.00040157983],"domain_scores_gemma":[0.9990391,0.00006151284,0.000083353065,0.00026204836,0.00033158215,0.00022238669],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035415086,0.0001345717,0.00027642923,0.00018778222,0.00026689563,0.00006862867,0.0018608731,0.00008839674,0.0017556299],"category_scores_gemma":[0.0002612449,0.00014715276,0.000053984637,0.0012480161,0.0002745432,0.00047248745,0.002862361,0.0004486237,0.000058306152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007207975,0.00035747123,0.094476946,0.0011822538,0.00056024495,0.00008393253,0.005113965,0.000015617681,0.86727005,0.00054882444,0.029446576,0.00022330742],"study_design_scores_gemma":[0.0023717848,0.00060397317,0.005771763,0.00013876031,0.00034136273,0.000016081665,0.09242557,0.00687189,0.8273987,0.000062073705,0.06316623,0.0008317959],"about_ca_topic_score_codex":0.000096590775,"about_ca_topic_score_gemma":0.000009148851,"teacher_disagreement_score":0.13936412,"about_ca_system_score_codex":0.0001445934,"about_ca_system_score_gemma":0.00007195864,"threshold_uncertainty_score":0.9991569},"labels":[],"label_agreement":null},{"id":"W3136097615","doi":"10.3390/agronomy11030575","title":"Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm","year":2021,"lang":"en","type":"article","venue":"Agronomy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Ministerio de Ciencia e Innovación","keywords":"Hyperspectral imaging; Artificial neural network; Nitrogen; Classifier (UML); Pattern recognition (psychology); Artificial intelligence; Computer science; Mathematics; Environmental science; Chemistry","score_opus":0.011513946919728539,"score_gpt":0.24699698520132912,"score_spread":0.23548303828160058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136097615","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99432784,0.000996766,0.0038667042,0.000032878117,0.00005039974,0.000051628376,0.000015242303,0.000014447096,0.0006441036],"genre_scores_gemma":[0.99945414,0.000026714863,0.00030608976,0.000051764146,0.00010675591,0.000008393608,0.000024721061,0.000011112293,0.000010310018],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992362,0.000055207463,0.00019574503,0.00022610147,0.000101146536,0.00018554684],"domain_scores_gemma":[0.99949086,0.00019071205,0.00010751153,0.00014189273,0.000034682533,0.000034330475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009550411,0.00012591055,0.00022930985,0.00006436928,0.00008581721,0.000054633307,0.000067442204,0.000036709218,0.00017038285],"category_scores_gemma":[0.000019472402,0.00010853375,0.000060401046,0.00014614864,0.00016791138,0.00008901662,0.000028715203,0.00017840954,7.519425e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014490429,0.00041307518,0.7638568,0.0001524685,0.00047525202,0.00024895885,0.0007485756,0.006829739,0.20243253,0.00033411445,0.000022026392,0.023037411],"study_design_scores_gemma":[0.0029315243,0.000012270572,0.006028799,0.00004939679,0.00014862767,0.00006273559,0.00095608155,0.37386838,0.61560607,0.00012383595,0.000010765064,0.00020147537],"about_ca_topic_score_codex":0.0008717561,"about_ca_topic_score_gemma":0.00003261851,"teacher_disagreement_score":0.757828,"about_ca_system_score_codex":0.000075231044,"about_ca_system_score_gemma":0.000022567994,"threshold_uncertainty_score":0.4425878},"labels":[],"label_agreement":null},{"id":"W3145050603","doi":"10.1016/j.compag.2021.106106","title":"Mining data from milk mid-infrared spectroscopy and animal characteristics to improve the prediction of dairy cow's liveweight using feature selection algorithms based on partial least squares and Elastic Net regressions","year":2021,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; European Commission","keywords":"Feature selection; Partial least squares regression; Statistics; Mathematics; Robustness (evolution); Dairy cattle; Mean squared error; Regression; Cross-validation; Elastic net regularization; Regression analysis; Computer science; Animal science; Artificial intelligence; Biology","score_opus":0.01710082805569742,"score_gpt":0.25643540979630614,"score_spread":0.2393345817406087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145050603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902129,0.0032771793,0.005455904,0.0004100409,0.00012183738,0.00010264747,0.0003469895,0.000029646651,0.000042836476],"genre_scores_gemma":[0.9924303,0.0005595878,0.00564805,0.00012412798,0.0004771889,0.0000067457854,0.00065921363,0.000016855023,0.000077922865],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986303,0.00004280098,0.0002391952,0.0005848863,0.00019411216,0.000308706],"domain_scores_gemma":[0.9991839,0.00021508755,0.00015094614,0.00027440363,0.00007352813,0.00010212764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070813876,0.00024328196,0.00033148768,0.00006877659,0.00022836201,0.00013028523,0.00017438647,0.00020655163,0.000017433755],"category_scores_gemma":[0.000079126934,0.00016878634,0.00003270907,0.00047305573,0.00006050911,0.000099473364,0.0001707736,0.00048048544,1.3672539e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037210892,0.0001846061,0.024154235,0.0001224511,0.00017898511,0.000019694811,0.00058013207,0.000324334,0.9683427,0.000037644433,0.00463017,0.0010529024],"study_design_scores_gemma":[0.0018407033,0.0006549722,0.046239093,0.0006236749,0.0006554005,0.00008500343,0.0013233623,0.4905368,0.4550541,0.000083627696,0.002353402,0.00054983556],"about_ca_topic_score_codex":0.00003593513,"about_ca_topic_score_gemma":0.00005216346,"teacher_disagreement_score":0.5132886,"about_ca_system_score_codex":0.000096218464,"about_ca_system_score_gemma":0.000099650504,"threshold_uncertainty_score":0.6882907},"labels":[],"label_agreement":null},{"id":"W3147725913","doi":"10.1007/s11694-021-00898-7","title":"Characterising corn grain using infrared imaging and spectroscopic techniques: a review","year":2021,"lang":"en","type":"review","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperspectral imaging; Ranging; Grain quality; Environmental science; Materials science; Remote sensing; Agronomy; Computer science; Biology","score_opus":0.08088087618874153,"score_gpt":0.3377580070613545,"score_spread":0.256877130872613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147725913","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022039308,0.9962122,0.0027718511,0.000042272488,0.00021775792,0.00027650708,0.00004451454,0.000040130893,0.00017437604],"genre_scores_gemma":[0.00011761816,0.9979387,0.00066159025,0.00016588841,0.0007321686,0.000015818601,0.00021506417,0.0000884403,0.000064697466],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99609035,0.00016197345,0.0019911323,0.00042352366,0.00096729875,0.00036574373],"domain_scores_gemma":[0.99464303,0.000039340564,0.0039121797,0.00038757978,0.00084431196,0.00017356673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010275004,0.00060862047,0.0028464196,0.0005716271,0.00013625316,0.00022765693,0.00032456152,0.00023805832,0.00032843015],"category_scores_gemma":[0.00052047055,0.00053884543,0.0005868577,0.0010046152,0.000047405265,0.00046537034,0.00009024276,0.00077191566,0.0000014487434],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010297938,0.00019635145,0.0000813381,0.08361641,0.0011584547,0.00007019519,0.000046750396,9.156564e-9,0.43751305,0.000010318757,0.000108171844,0.47718865],"study_design_scores_gemma":[0.00040723404,0.00012384553,0.00007122824,0.28083128,0.012168173,0.0014224398,0.0000427899,0.000005316508,0.026562754,0.000021027638,0.6773768,0.0009670995],"about_ca_topic_score_codex":0.0000011167477,"about_ca_topic_score_gemma":2.0309238e-7,"teacher_disagreement_score":0.6772686,"about_ca_system_score_codex":0.0008693909,"about_ca_system_score_gemma":0.0007623755,"threshold_uncertainty_score":0.9997063},"labels":[],"label_agreement":null},{"id":"W3150079252","doi":"10.1016/j.aiia.2021.03.001","title":"An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Agriculture and Agri-Food Canada; Department of Agriculture, Australian Government; Oxford Frozen Foods; Department of Agriculture, Nova Scotia","keywords":"Linear discriminant analysis; Gabor wavelet; Artificial intelligence; Pattern recognition (psychology); Wavelet; Gabor filter; Computer science; Mathematics; Computer vision; Wavelet transform; Discrete wavelet transform; Feature extraction","score_opus":0.027517571521411186,"score_gpt":0.3065690043956111,"score_spread":0.27905143287419987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150079252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940743,0.00049249176,0.0023000257,0.0026175324,0.00008816015,0.00012058638,0.00001169637,0.000020920048,0.00027429045],"genre_scores_gemma":[0.9976562,0.00011317481,0.0014354886,0.00012964981,0.00021886028,0.0000463672,0.00015084824,0.0000073837805,0.00024201623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883276,0.000040353505,0.00043169296,0.00031648873,0.00018472018,0.00019399951],"domain_scores_gemma":[0.9992333,0.00013114093,0.00014295737,0.00025212992,0.00019583768,0.000044603566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016711254,0.00013730524,0.0001956063,0.00007999794,0.00008159343,0.000055728688,0.00026148956,0.000261149,0.00013238666],"category_scores_gemma":[0.00021250085,0.00009398007,0.000082547944,0.0010735949,0.0000961254,0.00013811447,0.000023872495,0.00030494947,0.0000028787056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052039948,0.00024676125,0.0020376174,0.00006446057,0.000022553404,0.0000055602895,0.0032802313,0.0014074905,0.96808445,0.006710323,0.00037386673,0.017714646],"study_design_scores_gemma":[0.000038247374,0.00002389758,0.0060112127,0.00003590176,0.00003766978,0.000005027878,0.015273193,0.0034622273,0.95571864,0.019219529,0.00004243799,0.00013200367],"about_ca_topic_score_codex":0.0006643483,"about_ca_topic_score_gemma":0.0058249496,"teacher_disagreement_score":0.017582642,"about_ca_system_score_codex":0.00006849971,"about_ca_system_score_gemma":0.00006723183,"threshold_uncertainty_score":0.3832396},"labels":[],"label_agreement":null},{"id":"W3154204034","doi":"10.1016/j.cherd.2021.04.013","title":"Deep learning-based oriented object detection for in situ image monitoring and analysis: A process analytical technology (PAT) application for taurine crystallization","year":2021,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"In situ; Crystallization; Process analytical technology; Process (computing); Computer science; Artificial intelligence; Taurine; Materials science; Computer vision; Chemistry; Chemical engineering; Engineering","score_opus":0.0061431800560865215,"score_gpt":0.2492168686108865,"score_spread":0.24307368855479997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154204034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30623016,0.00030879793,0.6929553,0.000059990467,0.000006615519,0.00034127428,0.000008242073,0.00005902145,0.00003055008],"genre_scores_gemma":[0.99810207,0.00013095624,0.0005144983,0.0000042876654,0.0000436792,0.00093863206,0.00020307415,0.000020325504,0.000042507367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988401,0.0000129810505,0.00028640055,0.0005213815,0.00012901523,0.00021013834],"domain_scores_gemma":[0.9996056,0.00003812599,0.00015822625,0.00010419427,0.00004595521,0.000047917172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012330194,0.00017063177,0.00025672562,0.0003027362,0.00027675382,0.000029308754,0.000044107197,0.0002003564,0.000018059818],"category_scores_gemma":[0.00014101854,0.00018782215,0.00006161419,0.0010561931,0.00007762149,0.00017165145,0.000020765709,0.0001955829,3.20643e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013490126,0.0004667576,0.044660524,0.0016849532,0.00033720248,0.000001832432,0.00024813256,0.040881522,0.88913906,0.000018076327,4.1468414e-8,0.02121286],"study_design_scores_gemma":[0.0010329417,0.00009937346,0.0021042088,0.000018299519,0.0004600075,0.00000640135,0.0015963633,0.34429726,0.6498294,0.00033651164,0.000043603897,0.00017567088],"about_ca_topic_score_codex":0.000008302655,"about_ca_topic_score_gemma":0.00003694367,"teacher_disagreement_score":0.69244087,"about_ca_system_score_codex":0.00018595847,"about_ca_system_score_gemma":0.00001664317,"threshold_uncertainty_score":0.7659165},"labels":[],"label_agreement":null},{"id":"W3154817101","doi":"10.1016/j.foodcont.2021.108145","title":"Hyperspectral imaging and chemometrics as a non-invasive tool to discriminate and analyze iodine value of pork fat","year":2021,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Swine Innovation Porc","keywords":"Hyperspectral imaging; Partial least squares regression; Chemometrics; Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Mathematics; Chemistry; Computer science; Statistics; Chromatography","score_opus":0.007084777541657475,"score_gpt":0.2433151164546949,"score_spread":0.2362303389130374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154817101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98631036,0.005738443,0.00090850703,0.001204999,0.00003325293,0.00008733585,0.00008398569,0.000031571442,0.005601544],"genre_scores_gemma":[0.9975717,0.00019801837,0.0010831378,0.00038567476,0.00012386209,0.000015378464,0.000012599539,0.000022682183,0.0005869295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860746,0.0000129741375,0.00030516204,0.00046323048,0.0002554365,0.00035572413],"domain_scores_gemma":[0.99896306,0.00024620013,0.00012306176,0.00031538666,0.00017125286,0.00018106351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088929155,0.00023264134,0.00054539525,0.0002884476,0.00008346624,0.00007979646,0.00014242536,0.00007913628,0.00022005143],"category_scores_gemma":[0.0008422631,0.00022507613,0.00012552683,0.0011317316,0.00008051458,0.000096820855,0.00010163249,0.00017084644,0.000004958272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001696416,0.00016350088,0.028869135,0.00025091993,0.00067821774,0.00010975147,0.0004916379,0.000016519845,0.9643275,0.001620691,0.00026346045,0.0030390238],"study_design_scores_gemma":[0.0025800352,0.00016765275,0.0037215832,0.000048891365,0.0008258345,0.00008082912,0.0023086395,0.00046581574,0.9883885,0.0008814027,0.00018236678,0.00034848048],"about_ca_topic_score_codex":0.000076381075,"about_ca_topic_score_gemma":0.000012130937,"teacher_disagreement_score":0.025147552,"about_ca_system_score_codex":0.00006755887,"about_ca_system_score_gemma":0.0000852904,"threshold_uncertainty_score":0.9178338},"labels":[],"label_agreement":null},{"id":"W3154868116","doi":"10.1002/cjce.24138","title":"Multi‐objective <scp>Deng's</scp> grey incidence analysis, orthogonal optimization, and artificial neural network modelling in hot‐maceration‐assisted extraction of <scp>African</scp> cucumber leaves ( <scp> <i>Momordica balsamina</i> </scp> )","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Mean squared error; Maceration (sewage); Backpropagation; Grey relational analysis; Taguchi methods; Artificial intelligence; Approximation error; Mathematics; Pattern recognition (psychology); Statistics; Computer science; Biological system; Engineering; Biology","score_opus":0.015598672883919177,"score_gpt":0.23464989468265873,"score_spread":0.21905122179873956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154868116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9506787,0.0035006932,0.04522618,0.00006358961,0.00014799724,0.00007385653,0.000032294334,0.0000253135,0.00025137665],"genre_scores_gemma":[0.99301493,0.00011214234,0.0060361754,0.000046517565,0.00051120826,0.000008812515,0.00004217268,0.000049092087,0.00017895302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721277,0.00007157238,0.0010954675,0.00040668796,0.00051834085,0.00069514883],"domain_scores_gemma":[0.9963853,0.0014972747,0.00064116984,0.00030254797,0.00060362276,0.0005701284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052692875,0.00038473436,0.0008046946,0.0005431881,0.00020555512,0.00020526678,0.00037439362,0.00031363577,0.000037707203],"category_scores_gemma":[0.0027620667,0.00037490853,0.00029669717,0.0028550827,0.00017401675,0.00038525977,0.00005412977,0.0011153825,0.000001003229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047225353,0.00004346217,0.0062855687,0.000051923198,0.00050284545,0.00009368954,0.0006147346,0.86385775,0.12831995,0.000038108123,0.0001390007,0.00004826858],"study_design_scores_gemma":[0.00042375384,0.000019848694,0.002185092,0.000108163884,0.00094142486,0.0002732529,0.00089399086,0.71822417,0.27666926,0.00006350086,0.00011417627,0.00008339288],"about_ca_topic_score_codex":0.001120747,"about_ca_topic_score_gemma":0.0018807673,"teacher_disagreement_score":0.14834931,"about_ca_system_score_codex":0.00041711063,"about_ca_system_score_gemma":0.0006094658,"threshold_uncertainty_score":0.9998703},"labels":[],"label_agreement":null},{"id":"W3155149667","doi":"10.1002/cem.3346","title":"An efficient and accurate numerical determination of the cluster resolution metric in two dimensions","year":2021,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Genome Canada","keywords":"Subspace topology; Metric (unit); Feature selection; Computation; Resolution (logic); Cluster (spacecraft); Feature (linguistics); Ellipse; Computer science; Algorithm; Variable (mathematics); Selection (genetic algorithm); Mathematics; Data mining; Artificial intelligence","score_opus":0.01763376920538726,"score_gpt":0.32409065719223235,"score_spread":0.30645688798684506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155149667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911956,0.0026803233,0.005287282,0.00023973509,0.00009315155,0.000023989987,0.000004996957,0.000004303789,0.00047062643],"genre_scores_gemma":[0.997129,0.00019731195,0.0024467285,0.00005766465,0.00006529645,6.4162816e-7,0.0000017961535,0.000009870069,0.000091667396],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985318,0.000058412035,0.0005845278,0.00014693006,0.0005082205,0.0001701059],"domain_scores_gemma":[0.9983683,0.00040524147,0.00057754945,0.00023424713,0.00032220903,0.00009242608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045492648,0.000110678986,0.00032491056,0.0010764886,0.000055159388,0.000033001954,0.00020817091,0.00008234215,0.00007890217],"category_scores_gemma":[0.002205688,0.0000802498,0.00012996577,0.006603835,0.000051620547,0.00012178294,0.00008120966,0.00035171685,5.208777e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029873324,0.003505414,0.21695387,0.00034748326,0.00023041853,0.000154489,0.0010158737,0.0116142025,0.741676,0.0002984954,0.0003711651,0.023533901],"study_design_scores_gemma":[0.0020539009,0.000087968554,0.02936555,0.00007188457,0.0003493578,0.00034958206,0.00072269817,0.046491817,0.91973555,0.000294887,0.00026607537,0.00021072497],"about_ca_topic_score_codex":0.000006642899,"about_ca_topic_score_gemma":0.0000021942494,"teacher_disagreement_score":0.18758832,"about_ca_system_score_codex":0.00016930926,"about_ca_system_score_gemma":0.00010979412,"threshold_uncertainty_score":0.3272492},"labels":[],"label_agreement":null},{"id":"W3155907534","doi":"10.1016/j.crfs.2021.03.009","title":"Deep learning and machine vision for food processing: A survey","year":2021,"lang":"en","type":"review","venue":"Current Research in Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":366,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine vision; Artificial intelligence; Computer science; Machine learning; Food processing; Image processing; Food safety; Deep learning; Quality (philosophy); Image (mathematics); Food science","score_opus":0.39839693863594644,"score_gpt":0.5505797874622125,"score_spread":0.15218284882626604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155907534","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015329907,0.9986423,0.00023298546,0.000011896218,0.00006073119,0.00028583637,0.000048955375,0.000024975245,0.0005389873],"genre_scores_gemma":[0.0091841165,0.9901018,0.00012241512,7.6838717e-7,0.0000863914,0.00019395577,0.00013849446,0.000030443829,0.0001415846],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99571073,0.00023539722,0.00050931337,0.0012352831,0.0012774119,0.0010318926],"domain_scores_gemma":[0.9973213,0.0012820656,0.00019917877,0.00035646735,0.000555987,0.00028499373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005432013,0.00032106033,0.0010524583,0.001251543,0.0006446532,0.00057714357,0.0010256704,0.00018109163,0.00006739962],"category_scores_gemma":[0.0048366953,0.00026046138,0.00015945725,0.0072687874,0.0008223319,0.0002178655,0.0007530886,0.0017746107,0.0000041680637],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004413648,0.00012889026,0.00021987582,0.016324097,0.000016686003,0.0000016126477,0.00003568712,3.51205e-7,0.000026028767,0.00006158658,0.00002629428,0.9831545],"study_design_scores_gemma":[0.00038052717,0.0004973351,0.000025418649,0.013927637,0.000099676545,0.000022689173,0.00015579053,0.0011728771,0.0005030265,0.00016741289,0.9824465,0.0006010942],"about_ca_topic_score_codex":0.000010737643,"about_ca_topic_score_gemma":0.00008329434,"teacher_disagreement_score":0.98255336,"about_ca_system_score_codex":0.0004097945,"about_ca_system_score_gemma":0.0014841184,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W3156296248","doi":"10.24908/iqurcp.10452","title":"All-Fiber Photoacoustic Absorption Spectroscopy: Detecting Small Amounts of Dissolved Water in Jet Fuel","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Filtration (mathematics); Environmental science; Dissolved organic carbon; Ignition system; Absorption (acoustics); Materials science; Process engineering; Chemistry; Environmental chemistry; Engineering; Composite material; Aerospace engineering","score_opus":0.09720388428227529,"score_gpt":0.3687743594599134,"score_spread":0.2715704751776381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156296248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9781792,0.00006204059,0.00058439304,0.0014004331,0.000067127854,0.00027834537,0.000009376151,0.00013147583,0.019287577],"genre_scores_gemma":[0.9963012,0.00028448677,0.00087462063,0.000038856215,0.0003951825,0.0001066027,0.000023983726,0.00007000069,0.0019051211],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954326,0.00005025744,0.00080627913,0.0009722541,0.0011400854,0.0015985166],"domain_scores_gemma":[0.9972615,0.00025517304,0.00023305956,0.00037847258,0.001589911,0.00028184676],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015102254,0.00043875826,0.0006621673,0.001037161,0.00021959352,0.00029878487,0.00085762487,0.00034940723,0.0016835218],"category_scores_gemma":[0.0007717447,0.00036193567,0.00013913283,0.0014401531,0.001206931,0.00044691275,0.0004079068,0.0010294068,0.00020784911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031658908,0.00025101023,0.013421925,0.0007063877,0.0001491176,0.000011423188,0.0035885978,0.0000013957945,0.9801875,0.0006624428,0.00038156664,0.00032205912],"study_design_scores_gemma":[0.00087365706,0.00025625087,0.0006477981,0.00027564043,0.00008318936,0.000011333108,0.003957492,0.0013236536,0.97024035,0.021454664,0.00043553676,0.00044040318],"about_ca_topic_score_codex":0.0014596895,"about_ca_topic_score_gemma":0.00023851702,"teacher_disagreement_score":0.020792222,"about_ca_system_score_codex":0.0005623943,"about_ca_system_score_gemma":0.00021649664,"threshold_uncertainty_score":0.99988323},"labels":[],"label_agreement":null},{"id":"W3158171307","doi":"10.1007/s41742-021-00338-w","title":"Chemometric Optimization of a Demineralization Method for Analysing Sediment Organic Matter by Fourier Transform Infrared Spectroscopy","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"Universidad Nacional de Cuyo; H2020 Marie Skłodowska-Curie Actions; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Demineralization; Fourier transform infrared spectroscopy; Principal component analysis; Analytical Chemistry (journal); Chemistry; Organic matter; Spectroscopy; Mineralogy; Mathematics; Materials science; Environmental chemistry; Statistics; Chemical engineering; Engineering; Physics","score_opus":0.021920991149011988,"score_gpt":0.36552805442729563,"score_spread":0.34360706327828366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158171307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17416635,0.002283204,0.81556964,0.0015905116,0.00013923606,0.00012597603,0.00021081939,0.0000072029925,0.0059070773],"genre_scores_gemma":[0.8445647,0.0017798055,0.14407666,0.00022715112,0.00038656488,0.000014604957,0.00037977422,0.000059701248,0.008511035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99752283,0.00005406943,0.00064371084,0.00021246802,0.001326453,0.00024047148],"domain_scores_gemma":[0.998727,0.000344204,0.0003366496,0.00014797287,0.00034223768,0.00010194006],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006689616,0.00012893305,0.00028975174,0.00063978805,0.00006590922,0.000065469685,0.00037558336,0.00013556321,0.013393866],"category_scores_gemma":[0.00030740091,0.00012489862,0.00021522399,0.0007362642,0.000072509734,0.000237277,0.00007132123,0.0003587749,0.000005320888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000209545,0.00052198226,0.0045528472,0.000036924313,0.0007198815,0.0000127507665,0.00011414032,0.0018306802,0.9868066,0.000014530119,0.003672358,0.0015077867],"study_design_scores_gemma":[0.0012417515,0.00008247814,0.00020735839,0.000033222506,0.00014974563,0.00005800428,0.00058617955,0.007364513,0.98765546,0.00041823936,0.0020938262,0.0001092002],"about_ca_topic_score_codex":0.00000731899,"about_ca_topic_score_gemma":8.967963e-7,"teacher_disagreement_score":0.67149293,"about_ca_system_score_codex":0.0006911616,"about_ca_system_score_gemma":0.000102591584,"threshold_uncertainty_score":0.987508},"labels":[],"label_agreement":null},{"id":"W3158751907","doi":"","title":"Rumen sensors: data and interpretation for key rumen metabolic processes : Biosensors and Big data management","year":2019,"lang":"en","type":"article","venue":"Socio-Environmental Systems Modeling","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cargill (Canada)","funders":"","keywords":"Rumen; Key (lock); Big data; Interpretation (philosophy); Computer science; Biology; Data mining; Food science; Computer security; Fermentation","score_opus":0.04326872577630238,"score_gpt":0.26720433091590123,"score_spread":0.22393560513959887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158751907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9824413,0.008139819,0.0070636743,0.00008328154,0.00010194023,0.00046145578,0.0010554557,0.000060796992,0.00059229514],"genre_scores_gemma":[0.99418694,0.0023312496,0.0008695454,0.00002673427,0.00016834305,0.00003888402,0.0011493899,0.00004818581,0.0011807081],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980657,0.000023102017,0.00039526357,0.00091454765,0.00027955006,0.00032185132],"domain_scores_gemma":[0.9986397,0.00008373393,0.00017411346,0.0010003528,0.000008673149,0.0000933953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031498284,0.0002692079,0.0003931829,0.00009404414,0.00017957216,0.0001564236,0.00052987464,0.00012549845,0.000037885093],"category_scores_gemma":[0.00002579137,0.0002562354,0.00003041821,0.00010359008,0.00006791152,0.0004720077,0.00086696004,0.000119296754,0.000014968211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011539691,0.0018190817,0.58686674,0.036469903,0.01376088,0.000036125257,0.017459229,0.025187787,0.2590065,0.0009851812,0.0017230539,0.055531558],"study_design_scores_gemma":[0.0016274953,0.000045690966,0.00009449886,0.00023858102,0.0012381403,0.00002519742,0.12658529,0.8640846,0.0010481104,0.00018123022,0.0040795677,0.00075162464],"about_ca_topic_score_codex":0.000075095035,"about_ca_topic_score_gemma":7.777581e-7,"teacher_disagreement_score":0.8388968,"about_ca_system_score_codex":0.00008695806,"about_ca_system_score_gemma":0.000012876656,"threshold_uncertainty_score":0.999989},"labels":[],"label_agreement":null},{"id":"W3165875446","doi":"10.18280/ria.350208","title":"A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Cluster analysis; Standardization; Medicinal plants; Component (thermodynamics); Computer science; Feature (linguistics); Data mining; Artificial intelligence; Traditional medicine; Medicine; Biology; Botany","score_opus":0.07112992877300807,"score_gpt":0.33603069513218253,"score_spread":0.2649007663591745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165875446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48625877,0.00032650252,0.51095045,0.00042275057,0.000087155735,0.00015843386,0.000023573513,0.000084842206,0.0016875378],"genre_scores_gemma":[0.99491745,0.00019889789,0.00063414837,0.000055623037,0.000087283464,0.00015317556,0.00015660907,0.00001845102,0.0037783585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984491,0.000028057117,0.00054014573,0.00047123438,0.00023447713,0.0002770116],"domain_scores_gemma":[0.99901104,0.00029609754,0.0001471266,0.00034456688,0.00010941716,0.00009176918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034059212,0.0001503326,0.00024499887,0.00011943414,0.00012371546,0.00005369888,0.00019297184,0.00018093229,0.000722606],"category_scores_gemma":[0.0005742858,0.00015872928,0.000109022956,0.00065713824,0.000041704654,0.00013981485,0.000037051133,0.00021653797,0.000048052767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002566178,0.000715177,0.00060540624,0.00031163712,0.00006136289,0.00001952251,0.0022993768,0.031014523,0.95306206,0.004615476,0.00037380977,0.006665055],"study_design_scores_gemma":[0.00007600075,0.000009050994,0.000005998167,0.000028279212,0.000022521124,0.0000073849505,0.00078683614,0.54065925,0.45729935,0.0007606962,0.00024761105,0.00009705697],"about_ca_topic_score_codex":0.0000392124,"about_ca_topic_score_gemma":0.000933336,"teacher_disagreement_score":0.5103163,"about_ca_system_score_codex":0.00014712046,"about_ca_system_score_gemma":0.000108885164,"threshold_uncertainty_score":0.7912029},"labels":[],"label_agreement":null},{"id":"W3167523213","doi":"10.1016/j.aca.2021.338716","title":"Combinatorial projection pursuit analysis for exploring multivariate chemical data","year":2021,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fundação Araucária; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Projection pursuit; Principal component analysis; Maxima and minima; Projection (relational algebra); Chemometrics; Cluster analysis; Pattern recognition (psychology); Kurtosis; Independent component analysis; Multivariate statistics; Artificial intelligence; Class (philosophy); Computer science; Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.11624620761018069,"score_gpt":0.33688174499835405,"score_spread":0.22063553738817338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167523213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.971888,0.00009234031,0.0020233088,0.0022185275,0.0005169118,0.00026258334,0.00071938906,0.0004840077,0.021794945],"genre_scores_gemma":[0.99443126,0.000075197284,0.0018771144,0.00007306732,0.0006742956,0.00005958178,0.0019320135,0.00003936398,0.0008381143],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99757665,0.000021546952,0.0004765524,0.0010104023,0.00040991,0.0005049398],"domain_scores_gemma":[0.9975414,0.00037782924,0.0001829163,0.001506752,0.00021772076,0.00017334532],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022602997,0.0002859502,0.0006588169,0.00028081174,0.00017407729,0.00018745223,0.0008779141,0.00019411076,0.0014173817],"category_scores_gemma":[0.0012163894,0.0002889167,0.00040439283,0.0023657333,0.00006698051,0.0003722254,0.00044340309,0.00030595058,0.000013856095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017107186,0.00074710255,0.0037978913,0.00013275794,0.0072654737,0.000012159474,0.000106935055,0.0000025219958,0.98297584,0.0019932077,0.0024885142,0.00030654823],"study_design_scores_gemma":[0.0015406747,0.00002382834,0.00045449304,0.000017634646,0.010738073,0.000010204073,0.00043610064,0.025273696,0.95048237,0.0012114994,0.009228686,0.00058273395],"about_ca_topic_score_codex":0.000044794033,"about_ca_topic_score_gemma":0.000014739618,"teacher_disagreement_score":0.03249344,"about_ca_system_score_codex":0.00015620513,"about_ca_system_score_gemma":0.00015674281,"threshold_uncertainty_score":0.9999563},"labels":[],"label_agreement":null},{"id":"W3172471931","doi":"10.2316/j.2021.206-0616","title":"A PROPRIETARILY DEVELOPED BIONIC OLFACTORY SYSTEM USED FOR RAPID DETECTION OF DETERIORATED REFRIGERATED-STORED APPLES","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Horticulture; Biology","score_opus":0.02807072326213364,"score_gpt":0.2753467540966839,"score_spread":0.24727603083455027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172471931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89483863,0.0006971561,0.10369255,0.0001605654,0.00043075258,0.000053597607,0.000025091,0.00002944888,0.00007221265],"genre_scores_gemma":[0.99265426,0.000092008726,0.006972243,0.000010403035,0.00014809784,0.000003206865,0.00003678253,0.000011169934,0.0000718002],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890405,0.000019257057,0.00057068403,0.000113076596,0.00030464094,0.000088271954],"domain_scores_gemma":[0.99777263,0.00008272566,0.00070445205,0.00006962747,0.0013272953,0.000043239914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017098372,0.00010218949,0.00024413632,0.00019568861,0.000053734886,0.00007326796,0.00011799419,0.00009813216,0.000035088222],"category_scores_gemma":[0.00015680808,0.00009070713,0.00009863663,0.00019509721,0.000028979004,0.00016227584,0.000020994006,0.0000962981,6.478981e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007586361,0.00006708709,0.0008102274,0.00016503055,0.00042485513,0.000011326699,0.00013556377,0.0008663635,0.99354875,0.0002618035,0.000018050116,0.0036150822],"study_design_scores_gemma":[0.0009696573,0.00006508215,0.001533276,0.00015709171,0.00017579786,0.0001607786,0.0005256128,0.016363088,0.9795532,0.000093715884,0.00029269254,0.000109994005],"about_ca_topic_score_codex":0.0000067622705,"about_ca_topic_score_gemma":0.000007513877,"teacher_disagreement_score":0.09781566,"about_ca_system_score_codex":0.00018029237,"about_ca_system_score_gemma":0.0002068822,"threshold_uncertainty_score":0.36989295},"labels":[],"label_agreement":null},{"id":"W3172800932","doi":"10.1515/hf-2021-0027","title":"On-line characterization of wood chip brightness and chemical composition by means of visible and near-infrared spectroscopy","year":2021,"lang":"en","type":"article","venue":"Holzforschung","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations","funders":"","keywords":"Softwood; Near-infrared spectroscopy; Pulp (tooth); Spectrometer; Spectroscopy; Materials science; Brightness; Chemometrics; Calibration; Chip; Lignin; Analytical Chemistry (journal); Chemistry; Optics; Composite material; Computer science; Mathematics; Environmental chemistry; Chromatography","score_opus":0.009442884600612036,"score_gpt":0.2530485936511032,"score_spread":0.24360570905049117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172800932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99514884,0.0004272986,0.0016050488,0.000078418074,0.000017497687,0.000037625123,0.000118062744,0.000024073788,0.0025431311],"genre_scores_gemma":[0.99785864,0.00022188654,0.0010019002,0.00004583383,0.000040254523,0.0000036904232,0.0004555725,0.0000165527,0.00035567625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990868,0.000012625179,0.00028723705,0.00026673966,0.00018653435,0.00016006157],"domain_scores_gemma":[0.99941385,0.000069051675,0.00016795043,0.00019787841,0.00007931638,0.000071972594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052218067,0.00014493805,0.00034283017,0.00005340805,0.00005499392,0.000031293446,0.00007114352,0.00011741808,0.0004107641],"category_scores_gemma":[0.000038531016,0.00014539706,0.00005044922,0.00033430223,0.000117782896,0.00010118977,0.000045373417,0.00013123905,0.0000010600779],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007585773,0.00016984432,0.0014935009,0.00020231752,0.00006558185,0.000002736015,0.00009933974,0.0000020731618,0.9973671,0.0003600178,0.00004766954,0.00011396439],"study_design_scores_gemma":[0.000703826,0.00004285631,0.00094307103,0.00007153761,0.00010906857,0.000014081955,0.00005482256,0.0005664818,0.9970529,0.00025699122,0.00005120337,0.00013314505],"about_ca_topic_score_codex":0.000009387404,"about_ca_topic_score_gemma":6.3448397e-7,"teacher_disagreement_score":0.002709789,"about_ca_system_score_codex":0.000023701456,"about_ca_system_score_gemma":0.000030911644,"threshold_uncertainty_score":0.59291196},"labels":[],"label_agreement":null},{"id":"W3173546966","doi":"10.1093/mnras/stab3183","title":"PHANGS-HST: new methods for star cluster identification in nearby galaxies","year":2021,"lang":"en","type":"preprint","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Space Agency; Space Telescope Science Institute; National Aeronautics and Space Administration","keywords":"Physics; Astrophysics; Stars; Star cluster; Cluster (spacecraft); Luminosity; Galaxy; Pipeline (software); Open cluster; Selection (genetic algorithm); Astronomy; Computer science; Artificial intelligence","score_opus":0.02109616207627486,"score_gpt":0.3084858802577194,"score_spread":0.28738971818144454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173546966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99231786,0.0020255751,0.00388739,0.0008155985,0.0003024718,0.00028458014,0.000100873,0.00003477691,0.00023085918],"genre_scores_gemma":[0.88925487,0.0000025070026,0.10743377,0.00002755717,0.00036737518,0.00007738512,0.00015869905,0.000042472493,0.0026353465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978491,0.00008107832,0.00077709055,0.00067693,0.0002104017,0.00040540073],"domain_scores_gemma":[0.99785745,0.00047202007,0.0006904631,0.00078386575,0.000092877424,0.000103346545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005120204,0.0003578581,0.000752838,0.00003648843,0.00009812247,0.00016384675,0.001014142,0.0005089644,0.00042066045],"category_scores_gemma":[0.00026735428,0.00030463422,0.0012659692,0.00017874157,0.00017058538,0.000076647666,0.0010142762,0.00074118626,0.0000020821738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002241625,0.0006216698,0.054155238,0.0016387117,0.0017405029,1.2925919e-7,0.0024221833,0.9258897,0.0052878642,0.000020537473,0.004366684,0.0036326582],"study_design_scores_gemma":[0.0013951149,0.000026519758,0.07568171,0.00022690368,0.0013649887,1.577125e-8,0.0016657186,0.6719945,0.24563585,0.00025882476,0.0010797663,0.0006700604],"about_ca_topic_score_codex":0.0013198825,"about_ca_topic_score_gemma":0.00009409657,"teacher_disagreement_score":0.25389513,"about_ca_system_score_codex":0.00041563512,"about_ca_system_score_gemma":0.00023767957,"threshold_uncertainty_score":0.9999406},"labels":[],"label_agreement":null},{"id":"W3174919757","doi":"10.1038/s41598-021-92858-w","title":"Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Biotechnology and Biological Sciences Research Council; Cranfield University; Fondation OCP; Université Mohammed VI Polytechnique","keywords":"Partial least squares regression; Fourier transform infrared spectroscopy; Spectroscopy; Correlation coefficient; Coefficient of determination; Mathematics; Analytical Chemistry (journal); Soil test; Metric (unit); Moment (physics); Linear regression; Calibration; Sample (material); Chemistry; Statistics; Environmental science; Soil science; Soil water; Physics; Chromatography; Optics","score_opus":0.013893996262859951,"score_gpt":0.30307608156754573,"score_spread":0.28918208530468575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174919757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9817696,0.00074555894,0.011552503,0.00024337425,0.0003787421,0.00013871904,0.000006113681,0.000029558041,0.0051358095],"genre_scores_gemma":[0.9912325,0.000032253127,0.0064559793,0.000032193322,0.000075200274,0.000027812192,0.000025969917,0.000019876363,0.002098227],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99746513,0.000025161138,0.00062551233,0.0008992249,0.0005944453,0.0003905364],"domain_scores_gemma":[0.99818414,0.00004647603,0.0004178164,0.0010826335,0.0001399973,0.00012892454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005650194,0.00020487422,0.00035595035,0.00013688383,0.00027796757,0.00020953143,0.00018434472,0.00008629095,0.0005284109],"category_scores_gemma":[0.000097778924,0.0001623705,0.0000822285,0.0019014073,0.00024374138,0.00012763834,0.00017590051,0.0002088609,0.0000030054496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019708064,0.000094741816,0.045582596,0.000068595764,0.00002407627,0.00008207596,0.00048510978,0.000518052,0.9521417,0.00009673054,0.0007853535,0.000101259386],"study_design_scores_gemma":[0.00013871344,0.0000051588127,0.0006815182,0.00011707301,0.000044527864,0.00012209188,0.0010103887,0.0008975363,0.99309313,0.0017284715,0.0019816794,0.00017969904],"about_ca_topic_score_codex":0.00015253181,"about_ca_topic_score_gemma":0.00014604413,"teacher_disagreement_score":0.044901077,"about_ca_system_score_codex":0.0002771297,"about_ca_system_score_gemma":0.00021649535,"threshold_uncertainty_score":0.6621277},"labels":[],"label_agreement":null},{"id":"W3176994354","doi":"10.1371/journal.pone.0253390","title":"Fourier Transform Infrared Spectroscopy vibrational bands study of Spinacia oleracea and Trigonella corniculata under biochar amendment in naturally contaminated soil","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Higher Education Commision, Pakistan; Taif University","keywords":"Fourier transform infrared spectroscopy; Tetrapyrrole; Spinach; Spinacia; Chemistry; Infrared spectroscopy; Chlorophyll; Nuclear chemistry; Analytical Chemistry (journal); Photochemistry; Organic chemistry; Biochemistry","score_opus":0.027758675891388244,"score_gpt":0.251625379112885,"score_spread":0.22386670322149677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176994354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99526566,0.0013877159,0.00007849312,0.00021264142,0.0000137691495,0.0001618202,0.000044192282,0.00003055099,0.0028051548],"genre_scores_gemma":[0.99662334,0.00022035204,0.00075654814,0.000065348795,0.00004240172,0.00002600786,0.00013130376,0.000021286667,0.0021133972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983468,0.000027210803,0.0004691273,0.00040541802,0.00048587853,0.00026553616],"domain_scores_gemma":[0.99926126,0.000087210254,0.00013511229,0.0002873628,0.00014256978,0.00008649393],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009689612,0.0002198054,0.0005359754,0.00017089602,0.000067167326,0.000043543416,0.00013478902,0.00013825731,0.0017847312],"category_scores_gemma":[0.00005626015,0.00021887933,0.000060209717,0.00066261826,0.00006078047,0.00011453671,0.000050761082,0.0002867641,0.0000042937913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023079495,0.0054205344,0.02527517,0.00018471431,0.0008482052,0.00003454033,0.000569507,0.000022201466,0.9672284,0.00011208207,0.000042530464,0.000031305248],"study_design_scores_gemma":[0.0034793827,0.00023512721,0.00891371,0.000097277676,0.0004062628,0.000003310088,0.0024341443,0.00064482656,0.98316467,0.00037792735,0.000005323636,0.0002380348],"about_ca_topic_score_codex":0.000037267535,"about_ca_topic_score_gemma":0.00011683817,"teacher_disagreement_score":0.016361462,"about_ca_system_score_codex":0.00013358404,"about_ca_system_score_gemma":0.000100611345,"threshold_uncertainty_score":0.99912775},"labels":[],"label_agreement":null},{"id":"W3177162038","doi":"10.1016/j.foodchem.2021.130406","title":"An evaluation of IR spectroscopy for authentication of adulterated turmeric powder using pattern recognition","year":2021,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Ferdowsi University of Mashhad","keywords":"Starch; Food science; Chemistry; Curcuma; Chromatography; Materials science; Mathematics; Botany; Biology","score_opus":0.06033084172934251,"score_gpt":0.33844054664884626,"score_spread":0.27810970491950376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177162038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99491173,0.0003156658,0.0035503225,0.000020786876,0.000030002302,0.000070138834,0.00021968414,0.00002778779,0.0008538575],"genre_scores_gemma":[0.99528867,0.000013250982,0.0032744622,0.00001143137,0.00012511619,0.000026707785,0.0011922192,0.000024022342,0.000044119726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871165,0.000021172673,0.00040321818,0.0003245258,0.0003574904,0.00018194577],"domain_scores_gemma":[0.9981784,0.00004762558,0.0003671594,0.0003752311,0.00097582926,0.000055720124],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016765702,0.00015135412,0.0002685171,0.000041666462,0.000045023036,0.000018292385,0.00010770849,0.00015328855,0.0024540653],"category_scores_gemma":[0.00020369055,0.00017108569,0.00012346952,0.0004088816,0.000046999518,0.00009550301,0.000017135508,0.00009273478,0.0000012910913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003105809,0.00029214186,0.00052173523,0.0005265364,0.00015212223,3.2235036e-7,0.00012611263,0.000033528322,0.99709815,8.29652e-7,0.00002397013,0.0011935119],"study_design_scores_gemma":[0.0008331874,0.0000477218,0.00008508725,0.00007640247,0.00077045197,0.000009811532,0.00078820845,0.010880936,0.9859385,0.0003993708,0.0000056884546,0.00016462666],"about_ca_topic_score_codex":0.000011797047,"about_ca_topic_score_gemma":0.0000020985683,"teacher_disagreement_score":0.011159624,"about_ca_system_score_codex":0.00011826204,"about_ca_system_score_gemma":0.00022530311,"threshold_uncertainty_score":0.99845785},"labels":[],"label_agreement":null},{"id":"W3178350705","doi":"10.18280/acsm.450307","title":"Investigating the Effect of Addition of Olive Husk Ash on Asphalt Binder Properties","year":2021,"lang":"en","type":"article","venue":"Annales de Chimie Science des Matériaux","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jordan University of Science and Technology","keywords":"Husk; Asphalt; Softening point; Cement; Lime; Environmental science; Waste management; Asphalt concrete; Fly ash; Specific gravity; Materials science; Pulp and paper industry; Composite material; Engineering; Metallurgy","score_opus":0.04143850440354934,"score_gpt":0.2909899976298051,"score_spread":0.24955149322625575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178350705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907709,0.0008366046,0.00011132235,0.00012756037,0.000029106757,0.000046178422,0.000018034676,0.00002373708,0.00803658],"genre_scores_gemma":[0.9987626,0.00011629274,0.0006240421,0.000116182135,0.000056199726,0.0000134854745,0.000007781114,0.000009409483,0.00029397017],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865294,0.00005394188,0.0002490587,0.00026762192,0.00047259746,0.00030381617],"domain_scores_gemma":[0.9988657,0.00029521677,0.00020797912,0.00033867353,0.00021026324,0.00008216376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052919076,0.00013977004,0.000237941,0.00011810927,0.00026383062,0.000052883825,0.00041451084,0.000056984,0.0004795409],"category_scores_gemma":[0.001843578,0.000089384404,0.00011103247,0.0011409213,0.0015017982,0.00016529136,0.000098981174,0.00017235418,0.000011669006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021750844,0.00004562466,0.0087940525,0.0006057005,0.00005934992,0.000003357359,0.0009914306,0.0005281617,0.98639655,0.0003811167,0.000310069,0.0018628343],"study_design_scores_gemma":[0.0001725717,0.0001075857,0.002853894,0.00027631377,0.00007064429,0.000016299893,0.0011547493,0.0007054297,0.99395317,0.0005522671,0.000040777297,0.00009630151],"about_ca_topic_score_codex":0.000039931616,"about_ca_topic_score_gemma":0.0000067823453,"teacher_disagreement_score":0.007991761,"about_ca_system_score_codex":0.000057184214,"about_ca_system_score_gemma":0.00024436996,"threshold_uncertainty_score":0.5533438},"labels":[],"label_agreement":null},{"id":"W3180416354","doi":"10.1002/cjs.11632","title":"On the spectral coherence between two periodically correlated processes","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coherence (philosophical gambling strategy); Estimator; Mathematics; Measure (data warehouse); Fourier transform; Series (stratigraphy); Spectral analysis; Statistical physics; Spectral method; Spectral density; Mathematical analysis; Physics; Statistics; Computer science; Spectroscopy; Quantum mechanics; Data mining","score_opus":0.022810602239942652,"score_gpt":0.2549405536647652,"score_spread":0.23212995142482254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180416354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9418646,0.002488617,0.015805313,0.0026259192,0.00029206686,0.000056234974,0.0016143208,0.000018766286,0.03523414],"genre_scores_gemma":[0.99625534,0.000038867314,0.0017789232,0.00028578506,0.00019332577,6.695686e-7,0.000024497142,0.000014400039,0.0014081656],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990194,0.000018701376,0.00032839534,0.00011191729,0.00023937528,0.0002822104],"domain_scores_gemma":[0.9980935,0.0005861696,0.00021975618,0.00015321479,0.0005096771,0.000437691],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000099421246,0.0001223929,0.00023528065,0.000086635846,0.00020337495,0.0001373139,0.00029052843,0.00006192011,0.008765945],"category_scores_gemma":[0.0023324853,0.00009189166,0.000049608458,0.0004993362,0.000169551,0.000039506795,0.0000074869326,0.000516808,0.00002857556],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001577429,0.00034834116,0.44507813,0.0009073834,0.0036544402,0.019603789,0.0045225522,0.00083110237,0.013327096,0.11323211,0.38404068,0.01429663],"study_design_scores_gemma":[0.0070486846,0.0014566488,0.06069984,0.0019159219,0.005954635,0.0040175244,0.012069351,0.00063896505,0.6474411,0.15383126,0.10150004,0.003426076],"about_ca_topic_score_codex":0.0005572037,"about_ca_topic_score_gemma":0.0047266614,"teacher_disagreement_score":0.63411397,"about_ca_system_score_codex":0.00016354285,"about_ca_system_score_gemma":0.0036369844,"threshold_uncertainty_score":0.9921402},"labels":[],"label_agreement":null},{"id":"W3183932977","doi":"10.1080/00273171.2021.1941729","title":"Copula-Based Redundancy Analysis","year":2021,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Copula (linguistics); Computer science; Redundancy (engineering); Regression; Regression analysis; Econometrics; Bivariate analysis; Multivariate statistics; Statistics; Data mining; Machine learning; Mathematics","score_opus":0.2776338551734261,"score_gpt":0.5132476847527523,"score_spread":0.2356138295793262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183932977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9808566,0.000639124,0.001090459,0.0003774743,0.000052509833,0.000074454925,0.00010477429,0.0001608277,0.016643772],"genre_scores_gemma":[0.9815495,0.000019255367,0.0025605243,0.000033220753,0.000095031515,0.000055179884,0.00032099712,0.000036231377,0.015330054],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966883,0.00016264382,0.0003797505,0.0007540543,0.0011559064,0.00085930905],"domain_scores_gemma":[0.9975749,0.00028988713,0.000072733164,0.0009617504,0.0008069477,0.00029374973],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056160765,0.00022112206,0.00047968712,0.00073981175,0.000398125,0.00021437921,0.0004605722,0.00024860018,0.023817694],"category_scores_gemma":[0.0004508025,0.00021763984,0.00039630348,0.0063329237,0.0001880094,0.00010231126,0.0002061268,0.0008383268,0.00022664154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012811342,0.0021462515,0.15822022,0.00007936569,0.00069533853,0.0006754447,0.0001992355,0.0001663815,0.8330694,0.0003755912,0.0009174339,0.0033272528],"study_design_scores_gemma":[0.0010671825,0.000049993967,0.018117422,0.000019943185,0.0014535554,0.0000040748637,0.00073991745,0.002255368,0.97206944,0.00015047807,0.0036798643,0.00039275433],"about_ca_topic_score_codex":0.0023680397,"about_ca_topic_score_gemma":0.00021247752,"teacher_disagreement_score":0.14010279,"about_ca_system_score_codex":0.00035791245,"about_ca_system_score_gemma":0.00041661668,"threshold_uncertainty_score":0.9770747},"labels":[],"label_agreement":null},{"id":"W3184593782","doi":"10.82308/18391","title":"Non-destructive assessment of chicken egg fertility using hyperspectral imaging technique","year":2019,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Waikato; McGill University","keywords":"Hyperspectral imaging; Environmental science; Remote sensing; Biology; Artificial intelligence; Computer science; Geography","score_opus":0.01450551685316083,"score_gpt":0.28209754805511716,"score_spread":0.26759203120195635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184593782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8786239,0.000079259495,0.000013819009,0.0000099333565,0.000107171196,0.0002776207,0.00024708203,0.00015884887,0.12048234],"genre_scores_gemma":[0.9894222,0.000016799728,0.009931115,0.000058171823,0.000028750437,0.000036758054,0.00002634323,0.00009125338,0.00038858486],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99679285,0.00007252725,0.0007477726,0.0009715542,0.0006722389,0.00074307196],"domain_scores_gemma":[0.9978085,0.00015595544,0.00048651826,0.0010237196,0.00029113903,0.00023416567],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005691489,0.00052796304,0.0008215026,0.0003552233,0.00039894605,0.000049460687,0.0006869379,0.0003026763,0.0019277114],"category_scores_gemma":[0.00017742292,0.000547517,0.00040561045,0.0010301758,0.00017013116,0.0006832146,0.00030624072,0.0011459437,0.0000356113],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055906097,0.000223494,0.028782306,0.00020358333,0.00020375286,0.000014826886,0.000003289664,0.00004128342,0.9635854,0.0057055824,2.8512e-7,0.0011802971],"study_design_scores_gemma":[0.00075232965,0.00005063436,0.011539985,0.00011238087,0.00027232303,0.00005361071,0.00037546153,0.00050873496,0.9779958,0.0075820764,0.00016426123,0.0005923987],"about_ca_topic_score_codex":0.0005473804,"about_ca_topic_score_gemma":0.00001288509,"teacher_disagreement_score":0.120093755,"about_ca_system_score_codex":0.0013639438,"about_ca_system_score_gemma":0.000096879376,"threshold_uncertainty_score":0.9996976},"labels":[],"label_agreement":null},{"id":"W3186700072","doi":"10.1016/j.ijpharm.2021.120953","title":"In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis","year":2021,"lang":"en","type":"article","venue":"International Journal of Pharmaceutics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coating; Computer science; Process (computing); Image processing; Artificial intelligence; Computer vision; Process engineering; Engineering; Materials science; Image (mathematics); Nanotechnology","score_opus":0.05545690213516631,"score_gpt":0.4152520109742452,"score_spread":0.3597951088390789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186700072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88316244,0.0008986803,0.11507774,0.00030684585,0.00026663666,0.000040363477,0.00008685374,0.0000066761795,0.00015377694],"genre_scores_gemma":[0.98816377,0.0002477242,0.011130183,0.00008442934,0.00025081282,0.0000026091698,0.000016270878,0.000014154686,0.000090024354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998483,0.000025986108,0.0007810144,0.00014719204,0.00040028823,0.00016251157],"domain_scores_gemma":[0.9963366,0.00046890083,0.00086791476,0.00007629574,0.0021880018,0.00006231088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033047353,0.00012611796,0.00038421032,0.0005037175,0.000036288315,0.000048892867,0.00024791353,0.00006611218,0.0006222261],"category_scores_gemma":[0.001216398,0.00012658845,0.00022254263,0.001075622,0.00004508047,0.00025583763,0.000053279182,0.00022852927,4.8708984e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022932036,0.00028813933,0.0037235178,0.0001440546,0.0018879508,0.00005958415,0.00016672941,0.00637581,0.98634726,0.000008700974,0.0000048202946,0.0007641121],"study_design_scores_gemma":[0.0015753106,0.000022351054,0.00017124921,0.000056500314,0.0016174032,0.000092229435,0.0004974016,0.046154957,0.94929254,0.0002115603,0.00020472471,0.000103798244],"about_ca_topic_score_codex":0.000037715967,"about_ca_topic_score_gemma":0.000024938676,"teacher_disagreement_score":0.10500137,"about_ca_system_score_codex":0.0002656317,"about_ca_system_score_gemma":0.00025379608,"threshold_uncertainty_score":0.68129396},"labels":[],"label_agreement":null},{"id":"W3190750345","doi":"10.1111/1541-4337.12801","title":"Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts","year":2021,"lang":"en","type":"review","venue":"Comprehensive Reviews in Food Science and Food Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Mycotoxin; Hyperspectral imaging; Mold; Multispectral image; Aflatoxin; Environmental science; Contamination; Computer science; Materials science; Biotechnology; Biology; Artificial intelligence","score_opus":0.10687494146093063,"score_gpt":0.42964076104788773,"score_spread":0.3227658195869571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190750345","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028535495,0.9697506,0.00020295485,0.000013362795,0.000029759887,0.001098711,0.000022358807,0.000009569591,0.0003371866],"genre_scores_gemma":[0.077475354,0.9214213,0.0008107696,0.000008592871,0.000029891324,0.00022622623,0.00000829577,0.00001596548,0.000003614468],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99722815,0.00015387042,0.0008787441,0.0009408883,0.0003194132,0.00047895376],"domain_scores_gemma":[0.9986104,0.00046789108,0.00028229426,0.00027924293,0.0002487111,0.00011149813],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011846256,0.00034906925,0.0017204704,0.0011525579,0.00017138688,0.00007031802,0.0001653738,0.00018787851,0.0000037794591],"category_scores_gemma":[0.0006129773,0.00031210957,0.000088373105,0.0026173734,0.0009242342,0.00029235904,0.0003504649,0.0006806282,1.207821e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023313884,0.00010099443,0.0021401355,0.01742573,0.00003577301,0.0000038910916,0.00017670084,1.6379252e-7,0.005553591,0.00062882423,0.0000014895888,0.9739094],"study_design_scores_gemma":[0.01057998,0.007407609,0.031382717,0.21905264,0.0022457466,0.0012679488,0.008195209,0.00085842295,0.13166964,0.014167524,0.5665544,0.0066181365],"about_ca_topic_score_codex":0.00004708824,"about_ca_topic_score_gemma":0.00018246187,"teacher_disagreement_score":0.96729124,"about_ca_system_score_codex":0.00040030378,"about_ca_system_score_gemma":0.00020592789,"threshold_uncertainty_score":0.9999331},"labels":[],"label_agreement":null},{"id":"W3191783915","doi":"10.1111/ijfs.15291","title":"Establishment of novel standardised operating procedures for LF‐NMR: used in rapid detection of typical fruit and vegetable","year":2021,"lang":"en","type":"article","venue":"International Journal of Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"T2 relaxation; Chemistry; Relaxation (psychology); Mushroom; Analytical Chemistry (journal); Proton NMR; Food science; Nuclear magnetic resonance; Chromatography; Physics; Stereochemistry; Medicine","score_opus":0.019595848021957425,"score_gpt":0.3108065136951233,"score_spread":0.2912106656731659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191783915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875777,0.0007858018,0.010979196,0.0003904997,0.00007457513,0.00003101584,0.000015659336,0.000005913194,0.00013966112],"genre_scores_gemma":[0.99742293,0.00005264494,0.0024663447,0.0000094783845,0.000028850567,0.0000038110961,6.0458905e-7,0.0000037172822,0.0000116160445],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889416,0.0000035286366,0.00042734592,0.00014944571,0.00039519917,0.0001303389],"domain_scores_gemma":[0.9985748,0.000072308765,0.00033327925,0.00008395393,0.0009059457,0.000029735851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030397336,0.00006586193,0.00022590162,0.0007619717,0.00003827958,0.000029994464,0.00036602057,0.00007994678,0.000026494456],"category_scores_gemma":[0.0015292884,0.00005923274,0.000052237825,0.00085901766,0.00028403712,0.00020038687,0.000084080915,0.00015151686,3.4743916e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070608214,0.00015265921,0.008516914,0.000030271674,0.00005982118,0.00000285999,0.00007514375,0.00019948851,0.98813957,0.0008095614,0.0000016137516,0.0019415056],"study_design_scores_gemma":[0.00108007,0.00025818183,0.0008101899,0.0000815698,0.00002481071,0.00010593667,0.00089842087,0.00058614224,0.99532264,0.00073254365,0.000049500075,0.000050004586],"about_ca_topic_score_codex":0.0000033203369,"about_ca_topic_score_gemma":0.000022997909,"teacher_disagreement_score":0.009845254,"about_ca_system_score_codex":0.00013190042,"about_ca_system_score_gemma":0.00042534774,"threshold_uncertainty_score":0.24154411},"labels":[],"label_agreement":null},{"id":"W3193659382","doi":"10.1016/j.foodchem.2021.130951","title":"Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq “Crimsin Glory” H.T.) petals during infrared drying","year":2021,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research","keywords":"Petal; Chemistry; DPPH; Rose (mathematics); Partial least squares regression; ABTS; Antioxidant; Chemometrics; Near-infrared spectroscopy; Food science; Botany; Analytical Chemistry (journal); Chromatography; Mathematics; Biochemistry; Horticulture; Biology; Statistics","score_opus":0.0176985925128862,"score_gpt":0.24857804303885622,"score_spread":0.23087945052597003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193659382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902524,0.0011892444,0.000083453626,0.00010696557,0.000102137106,0.00013551241,0.000500769,0.0001282054,0.0075013465],"genre_scores_gemma":[0.9935653,0.00013575245,0.002067182,0.000059782888,0.00022522447,0.000041026473,0.000115756804,0.00007520323,0.0037147694],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99672014,0.000039911312,0.0009806067,0.00075959816,0.0007997306,0.0007000414],"domain_scores_gemma":[0.9970747,0.0003245331,0.00064506265,0.0013512032,0.00032796373,0.00027651098],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017284458,0.0005044028,0.000978275,0.000100829595,0.00025377804,0.00015526942,0.0005830066,0.00032996756,0.0016269074],"category_scores_gemma":[0.0011278535,0.00046522878,0.00045857267,0.0014708787,0.0002779937,0.00023001806,0.00044539722,0.00061729876,0.000006835298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019837958,0.0005353245,0.007742013,0.0009315686,0.0006215638,0.000028897006,0.0006509747,0.000116395364,0.9882902,0.000005959741,0.00083854026,0.00004021754],"study_design_scores_gemma":[0.0009598256,0.000085724074,0.0020849877,0.00026751077,0.00038147616,0.00005435827,0.0010113224,0.000088761975,0.9939191,0.0000683674,0.0006483277,0.0004302486],"about_ca_topic_score_codex":0.000046456935,"about_ca_topic_score_gemma":0.000005579174,"teacher_disagreement_score":0.005657025,"about_ca_system_score_codex":0.00021493879,"about_ca_system_score_gemma":0.00037220973,"threshold_uncertainty_score":0.99977994},"labels":[],"label_agreement":null},{"id":"W3193706685","doi":"10.3390/rs13163185","title":"Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Smut; Hyperspectral imaging; Remote sensing; Biology; Agronomy; Environmental science; Geography","score_opus":0.06746465980432459,"score_gpt":0.34510185826070366,"score_spread":0.2776371984563791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193706685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9790851,0.000732368,0.014631902,0.000027962478,0.00008095045,0.000016223572,0.000010773413,0.000034919693,0.0053798016],"genre_scores_gemma":[0.9831429,0.00015133012,0.016292484,0.00000998372,0.00016248427,9.849365e-10,0.00005031764,0.00001734803,0.000173138],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898726,0.000025284573,0.00030358334,0.0002990088,0.00021487882,0.00016999732],"domain_scores_gemma":[0.9987828,0.00010774047,0.0002674552,0.0006140566,0.00018557548,0.00004237209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011880267,0.0001179472,0.000280001,0.00011914225,0.00006179662,0.000017351063,0.00008159021,0.00011496566,0.00010248382],"category_scores_gemma":[0.00041273137,0.0001317167,0.00008883086,0.00069089915,0.000048394613,0.00016333998,0.000080659585,0.00017633701,0.0000020461875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015052774,0.00003914808,0.0002853335,0.00016194375,0.00010277397,0.00002081398,0.000050274455,0.00018316248,0.99442405,0.0000064658793,0.000016894157,0.0046940893],"study_design_scores_gemma":[0.00017526765,0.00000587239,0.000276904,0.00008738722,0.00032930332,0.00025871734,0.00038282634,0.059149224,0.93898404,0.00012091959,0.00011560644,0.000113939495],"about_ca_topic_score_codex":0.0013231741,"about_ca_topic_score_gemma":0.0000242561,"teacher_disagreement_score":0.05896606,"about_ca_system_score_codex":0.0001124774,"about_ca_system_score_gemma":0.0000998927,"threshold_uncertainty_score":0.5371252},"labels":[],"label_agreement":null},{"id":"W3194263458","doi":"10.1007/s12161-021-02113-1","title":"Improving Intramuscular Fat Assessment in Pork by Synergy Between Spectral and Spatial Features in Hyperspectral Image","year":2021,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Swine Innovation Porc","keywords":"Hyperspectral imaging; Intramuscular fat; Image fusion; Pattern recognition (psychology); Sensor fusion; Artificial intelligence; Computer science; Fusion; Filter (signal processing); Support vector machine; Fuse (electrical); Principal component analysis; Computer vision; Image (mathematics); Food science; Chemistry; Engineering","score_opus":0.016638483771324857,"score_gpt":0.3561505676517951,"score_spread":0.3395120838804702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194263458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90194434,0.0029734878,0.07870707,0.001264759,0.000059328337,0.00009091281,0.00006233757,0.000087127475,0.014810632],"genre_scores_gemma":[0.8889736,0.0000633299,0.11028532,0.00005740813,0.00025521868,0.000009920921,0.000052429376,0.000030219875,0.0002725353],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975761,0.00021413247,0.0005049098,0.0007415327,0.00030839583,0.0006549026],"domain_scores_gemma":[0.99873847,0.00054549763,0.000092063696,0.000349479,0.000046153902,0.00022834545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049125135,0.00031911506,0.0007724773,0.00023708724,0.000057321875,0.00010618071,0.00021962843,0.00029343675,0.0006034087],"category_scores_gemma":[0.0007420662,0.00031028833,0.00018787096,0.0010140798,0.00013823289,0.00011502877,0.00015346326,0.0009840713,0.0000012557739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006265183,0.00062054815,0.071520425,0.00029312138,0.00067555043,0.00040690537,0.00016563239,0.00002176188,0.88184834,0.0051650126,0.00015458615,0.039065465],"study_design_scores_gemma":[0.0014360596,0.0001827225,0.031402893,0.000051974584,0.00047327662,0.000031385654,0.001529946,0.0050639007,0.95593536,0.00303344,0.00018225618,0.000676768],"about_ca_topic_score_codex":0.0007988016,"about_ca_topic_score_gemma":0.00023553937,"teacher_disagreement_score":0.07408703,"about_ca_system_score_codex":0.00030859135,"about_ca_system_score_gemma":0.0001088835,"threshold_uncertainty_score":0.9999349},"labels":[],"label_agreement":null},{"id":"W3195420108","doi":"","title":"An Improved Method for Generalized Constrained Canonical Correlation Analysis","year":2002,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Canonical correlation; Redundancy (engineering); Mathematics; Row and column spaces; Row; Block matrix; Algorithm; Orthogonal transformation; Matrix (chemical analysis); Canonical form; Applied mathematics; Computer science; Pure mathematics; Statistics; Eigenvalues and eigenvectors","score_opus":0.01432413156269867,"score_gpt":0.30743529764410243,"score_spread":0.29311116608140375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195420108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07982109,0.0028686605,0.913988,0.00041897674,0.000063658474,0.00009482113,0.000026819183,0.00009662155,0.0026213708],"genre_scores_gemma":[0.9790534,0.0008579809,0.01275771,0.00011183837,0.0004209723,0.00001994522,0.00006909117,0.00003370139,0.0066754036],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708444,0.00006223365,0.00048308168,0.00036341156,0.00022713398,0.0017796931],"domain_scores_gemma":[0.9988724,0.00017228271,0.00031396575,0.00029938534,0.00015689661,0.00018509511],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00093643763,0.0002317069,0.0004828272,0.00033786715,0.00030322184,0.0000954238,0.00033283615,0.0002104293,0.0029207326],"category_scores_gemma":[0.00016260735,0.00021023076,0.0005236853,0.00094979035,0.000050575723,0.00017820745,0.000012525495,0.001134242,0.0000068736817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009852593,0.0018714872,0.013654983,0.00008545202,0.0341913,0.000015580741,0.0007222713,0.008525786,0.699587,0.14415185,0.0009698908,0.09523915],"study_design_scores_gemma":[0.0054513584,0.00063547085,0.000112506896,0.0000055500714,0.013568301,0.00043142834,0.0019052995,0.90488887,0.032052886,0.038641933,0.0014178978,0.00088849117],"about_ca_topic_score_codex":0.00014493706,"about_ca_topic_score_gemma":0.0006106158,"teacher_disagreement_score":0.9012303,"about_ca_system_score_codex":0.00086527405,"about_ca_system_score_gemma":0.00052326504,"threshold_uncertainty_score":0.9979907},"labels":[],"label_agreement":null},{"id":"W3196403701","doi":"10.1515/npprj-2021-0048","title":"Visualization of multiscale ring formation in a rotary kiln","year":2021,"lang":"en","type":"article","venue":"Nordic Pulp & Paper Research Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canfor Pulp Products (Canada); University of British Columbia","funders":"","keywords":"Kiln; Visualization; Rotary kiln; Computer science; Process engineering; Manufacturing engineering; Engineering; Waste management; Data mining","score_opus":0.056822869612985924,"score_gpt":0.4020503057202971,"score_spread":0.34522743610731116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196403701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96654606,0.0026860514,0.0013842292,0.0003504697,0.00003583811,0.00004667233,0.000004964984,0.000014676328,0.028931042],"genre_scores_gemma":[0.997168,0.0009921548,0.00042766173,0.0000307821,0.00016584138,0.000008565487,0.000021943139,0.0000148332265,0.0011702431],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979488,0.00013512146,0.0004630494,0.00017006653,0.00086593686,0.00041700242],"domain_scores_gemma":[0.99899065,0.00014137996,0.000115465635,0.00019529404,0.00042007293,0.00013716225],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00084737735,0.0000959055,0.00022094256,0.00044542277,0.00018113005,0.000085293264,0.00022081076,0.00010577142,0.0047162855],"category_scores_gemma":[0.00083584536,0.00009058073,0.00009811342,0.0014140191,0.00006923836,0.00048674675,0.00010544892,0.0007351042,0.000017550956],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073881696,0.00033779978,0.030682482,0.00021616362,0.00006943187,0.0002593072,0.0006827462,0.00010450672,0.95470154,0.00019638755,0.0005756138,0.012100146],"study_design_scores_gemma":[0.0013629799,0.000047872243,0.009669588,0.00024406097,0.00003134373,0.00042359444,0.0030412038,0.0020447972,0.9794761,0.0012509037,0.0022450779,0.00016248903],"about_ca_topic_score_codex":0.00011921451,"about_ca_topic_score_gemma":0.00013215416,"teacher_disagreement_score":0.03062192,"about_ca_system_score_codex":0.00026145412,"about_ca_system_score_gemma":0.0001932367,"threshold_uncertainty_score":0.9961935},"labels":[],"label_agreement":null},{"id":"W3197583449","doi":"10.1016/j.aca.2021.339001","title":"Advanced data fusion: Random forest proximities and pseudo-sample principle towards increased prediction accuracy and variable interpretation","year":2021,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Random forest; Principal component analysis; Sensor fusion; Data mining; Data set; Fusion; Sample (material); Pattern recognition (psychology); Artificial intelligence; Computer science; Chemistry","score_opus":0.018806362044160963,"score_gpt":0.2853355387915155,"score_spread":0.26652917674735455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197583449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98946494,0.0002810463,0.0015093759,0.00067175896,0.000046349825,0.00014866702,0.0006532666,0.0001387523,0.007085828],"genre_scores_gemma":[0.99058557,0.0010695474,0.005791368,0.00015732784,0.00012071585,0.0000213856,0.001675197,0.000024839137,0.00055402453],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837446,0.00003540384,0.0003838281,0.00066361396,0.00025725324,0.00028546492],"domain_scores_gemma":[0.99803334,0.0006981703,0.00016679411,0.00080698705,0.00013448195,0.00016020857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019548656,0.00023410138,0.00041314354,0.000108684064,0.00020569075,0.00018673191,0.0002865924,0.00013517843,0.00089804106],"category_scores_gemma":[0.0031670032,0.00021830785,0.000051252304,0.00046050924,0.00011689461,0.00059526204,0.00049508613,0.00023357652,0.0000018834694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025701239,0.0010774527,0.075948715,0.0017840489,0.0025205691,0.00004378212,0.0012288273,0.00002502697,0.8996129,0.0034849357,0.0040812064,0.007622406],"study_design_scores_gemma":[0.013119045,0.00024097963,0.021029318,0.00055701414,0.0051046223,0.00033527747,0.0038589928,0.557975,0.35905358,0.009568001,0.027447533,0.0017106375],"about_ca_topic_score_codex":0.00009074102,"about_ca_topic_score_gemma":0.00005118096,"teacher_disagreement_score":0.55794996,"about_ca_system_score_codex":0.000057457437,"about_ca_system_score_gemma":0.00018550725,"threshold_uncertainty_score":0.98329204},"labels":[],"label_agreement":null},{"id":"W3198190733","doi":"10.1039/d1ay01124c","title":"Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry","year":2021,"lang":"en","type":"review","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Advanced Studies in Basic Sciences","keywords":"Principal component analysis; Sparse PCA; Heteroscedasticity; Curse of dimensionality; Computer science; Factor analysis; Covariance matrix; Covariance; Homoscedasticity; Preprocessor; Rank (graph theory); Chemometrics; Multivariate statistics; Subspace topology; Data mining; Mathematics; Statistics; Artificial intelligence; Machine learning; Algorithm","score_opus":0.26480634392286806,"score_gpt":0.5757641307773685,"score_spread":0.3109577868545005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198190733","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019920906,0.53328484,0.4652499,0.000016245718,0.00003426841,0.0001091801,0.000114632865,0.000027852388,0.0011431997],"genre_scores_gemma":[0.00011775919,0.41826493,0.5804016,0.000008808031,0.00012957242,0.0001018126,0.00036298402,0.000080736754,0.00053180614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99347085,0.00084034615,0.002801502,0.00146837,0.00048791364,0.00093101757],"domain_scores_gemma":[0.9828796,0.014298526,0.00080420234,0.0012267074,0.00037651978,0.00041447065],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019414794,0.000939487,0.009280006,0.0008350251,0.00009433061,0.00009722437,0.0009096506,0.0013635579,0.0023881902],"category_scores_gemma":[0.0054096538,0.0008607862,0.003939728,0.005149667,0.00033832967,0.000083207255,0.0003167761,0.0014893208,0.0000027173608],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010113409,0.0032463253,0.0006128431,0.099605076,0.029217662,0.000042127245,0.0003077188,0.0018061037,0.0016266012,0.0059999074,0.00007404906,0.8573605],"study_design_scores_gemma":[0.0007093391,0.000059115126,0.0000031478512,0.0038472824,0.10355207,0.00001652026,0.00049847603,0.58200324,0.020615784,0.0010664706,0.28540623,0.002222358],"about_ca_topic_score_codex":0.00006316472,"about_ca_topic_score_gemma":0.0000035938563,"teacher_disagreement_score":0.8551381,"about_ca_system_score_codex":0.00045147675,"about_ca_system_score_gemma":0.00033904376,"threshold_uncertainty_score":0.9999329},"labels":[],"label_agreement":null},{"id":"W3199037011","doi":"10.22175/mmb.12951","title":"Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review","year":2021,"lang":"en","type":"review","venue":"Meat and Muscle Biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Software portability; Computer science; Quality (philosophy); Emerging technologies; Grading (engineering); Engineering; Artificial intelligence","score_opus":0.1339482195411001,"score_gpt":0.3854173803358649,"score_spread":0.25146916079476483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199037011","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041919593,0.99505705,0.00016033993,0.000025625923,0.000038129143,0.00012943315,0.00012354787,0.00005549647,0.00021843798],"genre_scores_gemma":[0.0037896943,0.99548155,0.0004051534,0.000016428223,0.00005301731,0.000004209336,0.00021427151,0.0000140666525,0.000021628483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868,0.00011061373,0.00050684216,0.0004578365,0.00006555812,0.00017918184],"domain_scores_gemma":[0.99912184,0.00019169456,0.0003273126,0.00025265993,0.000067657995,0.000038828122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016023344,0.00026820332,0.001691318,0.00013201746,0.00009157796,0.000015689131,0.000063969026,0.00052158296,0.00002354771],"category_scores_gemma":[0.0001433877,0.00021521511,0.00013823126,0.00029877207,0.00027301966,0.000032588585,0.0001567176,0.0002471867,1.634945e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034117659,0.000026472679,0.00032642347,0.07047754,0.00033909103,0.000006194097,0.000028565139,1.6532978e-8,0.021012172,0.00015050021,0.000040179344,0.90758944],"study_design_scores_gemma":[0.0010276617,0.00017124217,0.00008176847,0.09581257,0.016023979,0.0010920605,0.0011627709,0.0000654943,0.020075923,0.00027008264,0.8627411,0.0014753236],"about_ca_topic_score_codex":0.000098138975,"about_ca_topic_score_gemma":0.000006303913,"teacher_disagreement_score":0.9061141,"about_ca_system_score_codex":0.000045802608,"about_ca_system_score_gemma":0.00003374334,"threshold_uncertainty_score":0.87762177},"labels":[],"label_agreement":null},{"id":"W3199777484","doi":"10.18280/mmep.080407","title":"Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Sultan Ageng Tirtayasa","keywords":"Multicollinearity; Outlier; Statistics; Variance inflation factor; Regression analysis; Elastic net regularization; Regression; Lasso (programming language); Mathematics; Linear regression; Robust regression; Regression diagnostic; Mean squared error; Computer science; Econometrics; Polynomial regression","score_opus":0.15807138763617712,"score_gpt":0.2920989461802843,"score_spread":0.1340275585441072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199777484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22102378,0.0011431241,0.7776359,0.00003690923,0.0000147827495,0.00006463492,0.000019341182,0.000036488622,0.000025040974],"genre_scores_gemma":[0.7697438,0.00037915935,0.22951734,0.000006997383,0.00006327262,0.000012516389,0.00005781882,0.0000338619,0.00018522727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989581,0.0000050208937,0.0002990758,0.0004058408,0.00010283333,0.00022914795],"domain_scores_gemma":[0.9994034,0.00015652279,0.00005308682,0.00025814338,0.000043491054,0.000085326355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023058635,0.00017367696,0.00031327992,0.00007923383,0.00009378694,0.00012380553,0.000076207514,0.00010516095,0.000003298864],"category_scores_gemma":[0.00020542095,0.00015860188,0.000020288358,0.00013883442,0.000028275186,0.00013626587,0.0001377383,0.00016865852,1.1179819e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007609593,0.00004614614,0.00004328879,0.0014851283,0.000025667194,0.0000025037032,0.00016379272,0.98396057,0.013749804,0.00031264016,0.0000027402264,0.00020010695],"study_design_scores_gemma":[0.0004315796,0.000004309498,9.431437e-7,0.00044283798,0.00010113299,0.000020923011,0.00004343341,0.9825459,0.011033647,0.0051759053,0.000013818062,0.00018553344],"about_ca_topic_score_codex":0.000009474864,"about_ca_topic_score_gemma":0.0000011631856,"teacher_disagreement_score":0.54872,"about_ca_system_score_codex":0.00002260501,"about_ca_system_score_gemma":0.000021214584,"threshold_uncertainty_score":0.6467597},"labels":[],"label_agreement":null},{"id":"W3200099300","doi":"10.82308/43098","title":"Novel approaches to automated quality control analyses of edible oils by Fourier transform infrared spectroscopy : determination of free fatty acid and moisture content","year":2005,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Sultan Qaboos University","keywords":"Water content; Fourier transform infrared spectroscopy; Moisture; Food science; Chemistry; Infrared spectroscopy; Fourier transform; Fatty acid; Analytical Chemistry (journal); Quality (philosophy); Infrared; Content (measure theory); Spectroscopy; Environmental science; Chromatography; Mathematics; Biochemistry; Engineering; Chemical engineering; Organic chemistry; Optics; Physics","score_opus":0.08116416978863486,"score_gpt":0.29955008789797377,"score_spread":0.2183859181093389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200099300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97415125,0.0007380748,0.001243275,0.00022377212,0.000047294485,0.00038028747,0.0041159615,0.00028418453,0.01881587],"genre_scores_gemma":[0.9686081,0.00006014533,0.030507173,0.00016427547,0.00003318813,0.00006454276,0.000096361524,0.00005570407,0.00041050417],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965612,0.000087700486,0.0012500577,0.0007386597,0.0007856751,0.00057665043],"domain_scores_gemma":[0.9975777,0.0003176512,0.00064269186,0.00080823526,0.0003061708,0.00034750003],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00068588514,0.0005293467,0.001138113,0.00036469707,0.00028260725,0.00004611136,0.00060203136,0.00046669287,0.0002534373],"category_scores_gemma":[0.0010650798,0.00050312787,0.00032993345,0.0009392782,0.00019049253,0.0005853938,0.0001123019,0.0005421776,0.000004726347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032014545,0.0005743035,0.0004772468,0.0003170467,0.00040425893,0.0000016707683,0.00001965093,0.000024500283,0.98895687,0.0014102167,0.000019861473,0.0074742273],"study_design_scores_gemma":[0.0033343306,0.00015390267,0.000992101,0.000068139634,0.0005846253,0.000010232193,0.00026002337,0.0007802349,0.9918184,0.00089992257,0.00059523276,0.00050285476],"about_ca_topic_score_codex":0.00048859016,"about_ca_topic_score_gemma":0.0002402647,"teacher_disagreement_score":0.029263899,"about_ca_system_score_codex":0.0003804044,"about_ca_system_score_gemma":0.000035119596,"threshold_uncertainty_score":0.99974203},"labels":[],"label_agreement":null},{"id":"W3201947131","doi":"10.3847/1538-4357/ac2c66","title":"A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. III. Disentangling Multiple Components in H ii Regions","year":2021,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Fonds de recherche du Québec – Nature et technologies; Université de Montréal; Institut national des sciences de l'Univers; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Centre National de la Recherche Scientifique; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Artificial intelligence; Artificial neural network; Convolutional neural network; Computer science; Spectral line; Filter (signal processing); Pattern recognition (psychology); Component (thermodynamics); Line (geometry); Kinematics; Telescope; Inference; Physics; Segmentation; Galaxy; Astrophysics; Computer vision; Mathematics; Astronomy; Geometry","score_opus":0.04800305053096856,"score_gpt":0.2774129259861079,"score_spread":0.22940987545513933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201947131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96911365,0.00014228534,0.02700411,0.0016875425,0.000056307217,0.000041990974,0.0000059654108,0.00003666363,0.001911479],"genre_scores_gemma":[0.98616755,0.00004047594,0.011812313,0.0002761324,0.00042525638,0.00000924008,0.000038761067,0.000021744481,0.0012085302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986223,0.0000738908,0.00033276464,0.00024631387,0.00032397616,0.00040077616],"domain_scores_gemma":[0.9991056,0.00023611244,0.00012700018,0.00027062563,0.00008573657,0.00017493936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009829517,0.00019355639,0.00031445362,0.000098705175,0.00054332335,0.00012010871,0.00041820086,0.00005990583,0.0002000682],"category_scores_gemma":[0.0003006975,0.00014459823,0.00019870368,0.00091624283,0.000047769605,0.00010956618,0.00021792867,0.0014210584,0.000011359925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027554212,0.001165518,0.049958143,0.000018934446,0.00022474243,0.00009055071,0.0011359105,0.0058072032,0.937168,0.003055282,0.0005311887,0.00056896004],"study_design_scores_gemma":[0.006051748,0.00047282834,0.03299197,0.00039565563,0.0006818755,0.00060109067,0.013508322,0.12885971,0.8053166,0.0053906203,0.0043883673,0.0013411964],"about_ca_topic_score_codex":0.00012289968,"about_ca_topic_score_gemma":0.000016505466,"teacher_disagreement_score":0.13185142,"about_ca_system_score_codex":0.00012025772,"about_ca_system_score_gemma":0.000058862704,"threshold_uncertainty_score":0.6173871},"labels":[],"label_agreement":null},{"id":"W3203056790","doi":"10.3390/e23101293","title":"An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities","keywords":"Residual; Computer science; Preprocessor; Artificial intelligence; Pattern recognition (psychology); Data pre-processing; Process (computing); Residual neural network; Data mining; Algorithm","score_opus":0.016400307870356057,"score_gpt":0.2918339197376935,"score_spread":0.27543361186733745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203056790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9317899,0.0011413697,0.06489871,0.00006766516,0.00038412606,0.00011335138,0.000057127763,0.00021886302,0.0013289441],"genre_scores_gemma":[0.9266043,0.000033226144,0.068211935,0.00016507076,0.0028990633,0.000036280595,0.0001903055,0.000078491794,0.001781355],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835205,0.000029661598,0.00031189303,0.0005168179,0.00019783308,0.0005917386],"domain_scores_gemma":[0.9989068,0.000099868754,0.00016275173,0.0005382429,0.00014064906,0.00015170332],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011252928,0.00023593298,0.0003391126,0.000046436664,0.00043380322,0.00021572961,0.00020497966,0.0001992629,0.0015803842],"category_scores_gemma":[0.00015095751,0.00025262113,0.000165395,0.00059673213,0.00005968688,0.0001662113,0.0000426037,0.00023429339,0.000008618496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019750281,0.0001109492,0.0026529306,0.000049801794,0.00014391076,0.000016082,0.000059628626,0.00047707977,0.995044,0.0001564349,0.0008434201,0.00024829534],"study_design_scores_gemma":[0.00087210076,0.00007620404,0.00026920432,0.000014253059,0.00026471625,0.000019560215,0.00030000208,0.026006073,0.9673406,0.0017675739,0.0027733354,0.00029640744],"about_ca_topic_score_codex":0.00008239502,"about_ca_topic_score_gemma":0.000047970054,"teacher_disagreement_score":0.027703393,"about_ca_system_score_codex":0.00020188905,"about_ca_system_score_gemma":0.00016840477,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W3203608049","doi":"","title":"CHALLENGES AND BENEFITS OF PROCESS ANALYTICAL TECHNOLOGIES (PAT) FOR FOOD INDUSTRIES","year":2020,"lang":"en","type":"article","venue":"Advanced Food and Nutritional Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Traceability; Food processing; Food industry; Process (computing); Quality assurance; Quality (philosophy); Process analytical technology; Food safety; Hazard analysis and critical control points; Critical control point; Computer science; Risk analysis (engineering); Identification (biology); Manufacturing engineering; Engineering; Work in process; Food packaging; Business; Reliability engineering; Operations management; Hazard analysis","score_opus":0.08987904443119588,"score_gpt":0.3145956232156411,"score_spread":0.22471657878444523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203608049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9348772,0.056743752,0.000038231105,0.0067085302,0.0000065257896,0.000069150374,0.00019588794,0.000069035945,0.0012916527],"genre_scores_gemma":[0.9946299,0.004201305,0.0010556268,0.000037563634,0.000035338697,0.00002734232,0.000005694844,0.0000032974986,0.0000038898734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922967,0.0000020700334,0.00013971441,0.0002994144,0.00017228255,0.0001568534],"domain_scores_gemma":[0.9995786,0.00018657342,0.000066936875,0.000043340784,0.00007235,0.000052219337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042138035,0.000097079064,0.00019378119,0.00006506201,0.00013254322,0.00001714448,0.00013748577,0.00008453005,0.000009997836],"category_scores_gemma":[0.0002888529,0.000079916266,0.000026734408,0.0003771339,0.00054381293,0.0001686241,0.00004082061,0.0000754079,9.5280924e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077861344,0.000837491,0.013982687,0.004916059,0.0006193209,0.0000020982527,0.0015211338,0.0004630688,0.032787506,0.6076227,0.0002743109,0.33619505],"study_design_scores_gemma":[0.0014712572,0.003060025,0.0012618883,0.00015067568,0.00011858604,0.00001263798,0.019177975,0.00028219464,0.8769656,0.09550169,0.0016208497,0.00037666314],"about_ca_topic_score_codex":2.4187287e-7,"about_ca_topic_score_gemma":0.000004205318,"teacher_disagreement_score":0.8441781,"about_ca_system_score_codex":0.0000048106544,"about_ca_system_score_gemma":0.000026127329,"threshold_uncertainty_score":0.32588908},"labels":[],"label_agreement":null},{"id":"W3204164233","doi":"10.1016/j.tifs.2021.09.014","title":"A concise review on food quality assessment using digital image processing","year":2021,"lang":"en","type":"review","venue":"Trends in Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":159,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Image processing; Food quality; Artificial intelligence; Machine learning; Feature extraction; Quality (philosophy); Digital image processing; Food processing; Machine vision; Image (mathematics); Food science","score_opus":0.15760327961407863,"score_gpt":0.4730843709484331,"score_spread":0.3154810913343544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204164233","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002554126,0.9844039,0.00008927725,0.000120683835,0.000053676158,0.00012203218,0.000092278715,0.00023432873,0.014628417],"genre_scores_gemma":[0.0086603025,0.98980415,0.0010151024,0.000050756888,0.00004242105,0.00011185959,0.00006581284,0.00004793929,0.00020163831],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99552345,0.000041567455,0.0012404756,0.0015146954,0.00079471443,0.00088509714],"domain_scores_gemma":[0.9973653,0.00011218406,0.0009325248,0.0012875998,0.00018230992,0.000120068406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006861791,0.0006508099,0.002863362,0.0030811224,0.00024951668,0.00024956142,0.0017835993,0.0005860113,0.0003534031],"category_scores_gemma":[0.0008093631,0.0005539619,0.00047089154,0.01893852,0.0013213855,0.00035249678,0.0005947565,0.0013945365,0.0000089904115],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.096667e-7,0.00021132833,0.00002426457,0.011639542,0.0000593582,0.000028032613,0.000007353473,2.3896024e-7,0.00012968127,0.00077063224,0.000028308938,0.9871004],"study_design_scores_gemma":[0.0010843205,0.0010237342,0.0000074885256,0.18009534,0.0030363759,0.000804833,0.0008285876,0.00007277209,0.0070615485,0.0009641683,0.8010212,0.0039996277],"about_ca_topic_score_codex":0.0000028926725,"about_ca_topic_score_gemma":0.0000051558745,"teacher_disagreement_score":0.98310083,"about_ca_system_score_codex":0.0013870026,"about_ca_system_score_gemma":0.001474263,"threshold_uncertainty_score":0.9996912},"labels":[],"label_agreement":null},{"id":"W3204190980","doi":"10.18280/ts.380433","title":"Polycystic Ovarian Syndrome Detection by Using Two-Stage Image Denoising","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Polycystic ovary; Median filter; Speckle noise; Salt-and-pepper noise; Computer science; Artificial intelligence; Noise (video); Noise reduction; Computer vision; Gaussian noise; Pattern recognition (psychology); Medicine; Mathematics; Image processing; Speckle pattern; Pathology; Image (mathematics)","score_opus":0.01669079987286921,"score_gpt":0.2695060050541849,"score_spread":0.2528152051813157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204190980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9009036,0.00055951776,0.09463604,0.00004913069,0.00006399062,0.000045170615,0.000053072636,0.000101053374,0.0035884636],"genre_scores_gemma":[0.99731076,0.000007069318,0.00124021,0.00010435732,0.00014301689,0.0000072356715,0.000060373255,0.000036458645,0.0010905294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984366,0.000030400364,0.00034998436,0.00041775507,0.00035480276,0.00041040144],"domain_scores_gemma":[0.9993738,0.00006920082,0.00013364312,0.00022883262,0.000063228224,0.00013131522],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012300085,0.00023219365,0.00027726247,0.00011179759,0.0002471833,0.00015817466,0.00014871749,0.00009136936,0.011500436],"category_scores_gemma":[0.000030276611,0.00025530282,0.00013949104,0.00056679046,0.00005471393,0.00019725463,0.00005287591,0.00021888856,0.000033816646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003493198,0.00014117923,0.00031114486,0.00007157334,0.0001656219,0.00023191242,0.000048322298,0.000097593875,0.9980888,0.000021301872,0.00005389241,0.0007337626],"study_design_scores_gemma":[0.00090441224,0.000027727754,0.00012716572,0.000026759722,0.00032465725,0.0002520561,0.000413692,0.005192765,0.991863,0.000051721552,0.00051894534,0.00029708695],"about_ca_topic_score_codex":0.00017228482,"about_ca_topic_score_gemma":0.000014259723,"teacher_disagreement_score":0.09640718,"about_ca_system_score_codex":0.00026877123,"about_ca_system_score_gemma":0.00006271277,"threshold_uncertainty_score":0.9999899},"labels":[],"label_agreement":null},{"id":"W3205957043","doi":"10.1007/s11336-021-09809-7","title":"Bayesian Mixture Model of Extended Redundancy Analysis","year":2021,"lang":"en","type":"article","venue":"Psychometrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Ministry of Education","keywords":"Multivariate statistics; Redundancy (engineering); Bayesian probability; Covariance; Mathematics; Extant taxon; Statistics; Probabilistic logic; Statistical model; Regression analysis; Computer science; Econometrics; Data mining","score_opus":0.019898917473956584,"score_gpt":0.2966759414036694,"score_spread":0.2767770239297128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205957043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5147271,0.020718971,0.1608193,0.00078030373,0.00019550126,0.00008117929,0.00034318623,0.00027622303,0.30205822],"genre_scores_gemma":[0.97786707,0.00046424562,0.007839368,0.00009140725,0.000077356,0.0000073892156,0.000083595274,0.000024012526,0.013545536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982784,0.000015515605,0.00044361426,0.0005120438,0.00042727415,0.0003231504],"domain_scores_gemma":[0.9984157,0.00014659826,0.00020543137,0.0008586408,0.00022489362,0.00014870754],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010725057,0.00020990161,0.0006189681,0.0010725188,0.00006663606,0.000035333964,0.00031917507,0.00019586357,0.008312871],"category_scores_gemma":[0.00041718315,0.00020838721,0.00056328624,0.011363005,0.000063113366,0.00007690474,0.00004906145,0.00024143422,0.0000141683595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016005868,0.0023452826,0.0803121,0.00051592424,0.0115073295,0.000046846162,0.0005625174,0.0024915729,0.8807631,0.0022954463,0.007347579,0.011652237],"study_design_scores_gemma":[0.0008094813,0.000021616572,0.0023464689,0.000018902698,0.003784761,0.000008155559,0.000460534,0.011710533,0.97620076,0.0029764466,0.0012097192,0.0004526171],"about_ca_topic_score_codex":0.00001615045,"about_ca_topic_score_gemma":0.000012924539,"teacher_disagreement_score":0.46313998,"about_ca_system_score_codex":0.00005874333,"about_ca_system_score_gemma":0.000082357314,"threshold_uncertainty_score":0.99259365},"labels":[],"label_agreement":null},{"id":"W3206006423","doi":"10.1109/access.2021.3119544","title":"Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing Municipal Education Commission","keywords":"Curing (chemistry); Autoencoder; Convolutional neural network; Computer science; Deep learning; Mean squared error; Artificial intelligence; Artificial neural network; Curing of tobacco; Feature extraction; Pattern recognition (psychology); Materials science; Mathematics; Composite material; Statistics","score_opus":0.048000405462839846,"score_gpt":0.36995337828490915,"score_spread":0.3219529728220693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206006423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8747616,0.00059544883,0.122569025,0.000050272345,0.000078995516,0.000033042885,0.0000037985496,0.00005461072,0.0018532235],"genre_scores_gemma":[0.9976314,0.000010926555,0.0019620666,0.000093808616,0.0001269275,0.000008704575,0.0000035342262,0.000030444673,0.00013224171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986705,0.000040786992,0.00031895027,0.00034479055,0.00034237877,0.0002825877],"domain_scores_gemma":[0.99896806,0.00012895593,0.00017805383,0.00040338386,0.00026688722,0.00005462983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015099026,0.0001737791,0.00030686404,0.00008304021,0.0001993785,0.00010752306,0.0006333603,0.000121025594,0.00051025796],"category_scores_gemma":[0.0003196902,0.00014300492,0.00017874765,0.0011645303,0.000035518686,0.00019549686,0.0001112225,0.0003583487,0.0000034094162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014804022,0.00006960207,0.014364441,0.00023269176,0.00008228872,0.0000064258525,0.00012022479,0.86026216,0.12434293,0.000005433296,0.0000026329624,0.0004963663],"study_design_scores_gemma":[0.00017619986,0.0000019681454,0.00002047582,0.000054368003,0.00013569003,0.000005205967,0.00023420456,0.39895627,0.6001911,0.00010422922,0.000012393411,0.000107882996],"about_ca_topic_score_codex":0.00014263045,"about_ca_topic_score_gemma":0.000030219524,"teacher_disagreement_score":0.4758482,"about_ca_system_score_codex":0.00007516605,"about_ca_system_score_gemma":0.00018596019,"threshold_uncertainty_score":0.5831571},"labels":[],"label_agreement":null},{"id":"W3206162810","doi":"10.3390/s21206745","title":"Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Preprocessor; Algorithm; Spectroscopy; Computer science; Data mining; Artificial intelligence; Physics","score_opus":0.126389807894402,"score_gpt":0.34428437905299875,"score_spread":0.21789457115859676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206162810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97712594,0.0019634452,0.020142278,0.000013281594,0.000029878463,0.000084993044,0.000032170235,0.000024945566,0.0005830524],"genre_scores_gemma":[0.97687846,0.00008522638,0.022830477,0.0000029324851,0.000074306285,0.000009165084,0.000018280045,0.000017567534,0.00008358948],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988352,0.000028396687,0.0003352761,0.000265278,0.0003918052,0.0001440269],"domain_scores_gemma":[0.9988265,0.000036100442,0.00017968503,0.00016896533,0.0007585924,0.000030129431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004121531,0.0001115909,0.00027981566,0.00008372777,0.00007706242,0.000019851315,0.00004488004,0.00008741162,0.00006256181],"category_scores_gemma":[0.0004445772,0.00010612185,0.000075174015,0.00023971013,0.000054548807,0.00009012003,0.00002454951,0.00006614508,9.268519e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056526296,0.000051622537,0.00019143471,0.00033521198,0.00012478995,2.0721313e-7,0.00037720858,0.20972526,0.78873175,0.0000045627485,0.0000010804218,0.00040035124],"study_design_scores_gemma":[0.00049084687,0.000009620703,0.0000046937635,0.00006467971,0.00046628693,0.0000037535551,0.0010886147,0.49955633,0.49813554,0.0001355798,3.607137e-7,0.000043684817],"about_ca_topic_score_codex":0.0000480336,"about_ca_topic_score_gemma":0.000002135334,"teacher_disagreement_score":0.2905962,"about_ca_system_score_codex":0.00008432308,"about_ca_system_score_gemma":0.00019059567,"threshold_uncertainty_score":0.43275234},"labels":[],"label_agreement":null},{"id":"W3207128008","doi":"10.1177/00037028211047898","title":"Calculation of Spectral Optical Constants Using Combined Ellipsometric and Reflectance Methods for Smooth and Rough Bulk Samples","year":2021,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Specular reflection; Optics; Spectral line; Ellipsometry; Materials science; Scattering; Analytical Chemistry (journal); Specularity; Diffuse reflection; Chemistry; Reflection (computer programming); Thin film; Physics","score_opus":0.04383827387992017,"score_gpt":0.37091821974220635,"score_spread":0.32707994586228617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207128008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8027283,0.0042082225,0.18462884,0.00009446407,0.000047845246,0.00019269608,0.000042503885,0.000068616115,0.007988513],"genre_scores_gemma":[0.6245114,0.00039497166,0.37477985,0.0000324918,0.000069759226,0.000013999103,0.000021858417,0.00002850127,0.00014713689],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982698,0.000021300244,0.0004643139,0.0005999187,0.00020010461,0.0004445766],"domain_scores_gemma":[0.9985333,0.0006967825,0.00021266066,0.00031952816,0.000103435785,0.00013429942],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023670027,0.00027610885,0.00067649153,0.00021025343,0.00015512505,0.0000664229,0.000113553244,0.00018544662,0.00026098551],"category_scores_gemma":[0.00024906534,0.00028785225,0.00009135334,0.001153367,0.00025143917,0.00007325573,0.00007215234,0.0002066085,0.000001061863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019297295,0.000104341634,0.0012384529,0.00018665714,0.0001405869,0.0000025614374,0.00008167969,0.000015602336,0.95803803,0.03913451,0.000027795832,0.00083679974],"study_design_scores_gemma":[0.0013080422,0.000068152905,0.00041134132,0.000022813121,0.00035432214,0.00001947319,0.00016444939,0.0021159097,0.9800279,0.014992986,0.0002306672,0.0002839504],"about_ca_topic_score_codex":0.000020217367,"about_ca_topic_score_gemma":0.000006376734,"teacher_disagreement_score":0.19015099,"about_ca_system_score_codex":0.0001251854,"about_ca_system_score_gemma":0.00011636373,"threshold_uncertainty_score":0.9999574},"labels":[],"label_agreement":null},{"id":"W3209887881","doi":"10.1007/s12393-021-09298-5","title":"Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries","year":2021,"lang":"en","type":"article","venue":"Food Engineering Reviews","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China","keywords":"Berry; Hyperspectral imaging; Artificial intelligence; Image processing; Computer science; Horticulture; Biology","score_opus":0.03845202940998268,"score_gpt":0.3065903640199113,"score_spread":0.26813833460992864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209887881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52841324,0.10027485,0.36990756,0.000017725786,0.00008573151,0.00014955057,0.0000051127745,0.00020092893,0.0009453],"genre_scores_gemma":[0.9694935,0.00120512,0.029201824,0.0000040422374,0.000048597554,0.000018836976,0.0000024629132,0.00001429898,0.000011337599],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992188,0.0000058586215,0.0003990433,0.00016245985,0.00007642156,0.00013742091],"domain_scores_gemma":[0.9996635,0.000021522063,0.00010241063,0.0001492957,0.000040889892,0.000022418426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009994142,0.000112571666,0.00031246635,0.00010777429,0.00001958243,0.000013997485,0.000074763695,0.00006277492,0.00006566838],"category_scores_gemma":[0.0003101833,0.00011545903,0.00007132716,0.0008434808,0.000013548257,0.00008996562,0.000024981082,0.00013891014,9.876427e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002535178,0.000025361578,0.00003739583,0.0008443494,0.000007671423,0.0000015850395,0.00004900086,0.00090148655,0.83764124,0.000077518314,2.7225062e-7,0.16041157],"study_design_scores_gemma":[0.000011985252,0.000014857816,0.000007639286,0.00040733573,0.000030777723,0.000007592822,0.00007835122,0.0032634318,0.99485534,0.00012759784,0.0010849572,0.00011010154],"about_ca_topic_score_codex":0.000004705307,"about_ca_topic_score_gemma":0.000013536816,"teacher_disagreement_score":0.44108024,"about_ca_system_score_codex":0.0000705352,"about_ca_system_score_gemma":0.000024032195,"threshold_uncertainty_score":0.47082824},"labels":[],"label_agreement":null},{"id":"W3210096009","doi":"10.1080/07060661.2021.1932163","title":"Canadian Plant Disease Survey 2021 Volume 101: Disease Highlights 2020","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Plant Pathology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medical physics; Medicine","score_opus":0.012565600541551923,"score_gpt":0.2188745946168784,"score_spread":0.20630899407532646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210096009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93598896,0.019158911,0.000065448294,0.007834965,0.001340313,0.00006766862,0.03225496,0.000015474116,0.0032732866],"genre_scores_gemma":[0.9891221,0.00056487333,0.00007172693,0.0007698472,0.0006773644,0.0000033113517,0.0021205647,0.000035778125,0.0066344286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99778235,0.00015477161,0.0005640956,0.00038616368,0.00023656622,0.0008760579],"domain_scores_gemma":[0.9930941,0.00015567796,0.00026872772,0.00043824644,0.00031944754,0.005723798],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029488525,0.00028573172,0.00055433373,0.00051161513,0.00027083198,0.00010596663,0.0004905251,0.00019599921,0.014281521],"category_scores_gemma":[0.001022163,0.00028157514,0.00022735755,0.0005575848,0.0001282967,0.00012032073,0.000023325716,0.0005211533,0.00013466926],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016060856,0.00007045892,0.51819044,0.00008186166,0.00030791867,0.27345794,0.0001428727,0.00007074126,0.0020033102,0.00030578952,0.20504795,0.00016010905],"study_design_scores_gemma":[0.0014030191,0.00008209375,0.652047,0.00018650411,0.0011668283,0.0063377423,0.00036374494,0.00018198822,0.004434391,0.00053959363,0.3320768,0.0011803171],"about_ca_topic_score_codex":0.1164119,"about_ca_topic_score_gemma":0.8735843,"teacher_disagreement_score":0.7571724,"about_ca_system_score_codex":0.00067284674,"about_ca_system_score_gemma":0.012015981,"threshold_uncertainty_score":0.99996364},"labels":[],"label_agreement":null},{"id":"W3210910652","doi":"10.1007/s11203-022-09281-9","title":"On the integrated mean squared error of wavelet density estimation for linear processes","year":2022,"lang":"en","type":"article","venue":"Statistical Inference for Stochastic Processes","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada; Simons Foundation","keywords":"Mean squared error; Mathematics; Statistics; Wavelet; Estimation; Mean absolute error; Computer science; Artificial intelligence; Engineering","score_opus":0.042296495720806314,"score_gpt":0.3420768136747442,"score_spread":0.2997803179539379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210910652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023072364,0.00006789143,0.97235256,0.00021787519,0.000041650328,0.00044016223,0.0035838364,0.000075233234,0.00014842671],"genre_scores_gemma":[0.9836135,0.0000018983922,0.014320137,0.00009078068,0.00003427874,0.0010283574,0.0006847726,0.000027333026,0.00019894597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984453,0.00001725764,0.00043008357,0.00038245803,0.00038763724,0.00033726977],"domain_scores_gemma":[0.9913952,0.007163073,0.00028080837,0.00023334012,0.0008507242,0.000076887365],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002101147,0.00023971677,0.00038757664,0.00010410288,0.00046639156,0.000037426835,0.00039304773,0.00006351081,0.000999697],"category_scores_gemma":[0.02390442,0.00017649028,0.000058098256,0.00079677405,0.00021878343,0.0000669185,0.00007184876,0.00024515355,0.000003084256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0096943835,0.005501568,0.00024577184,0.03396293,0.0015780756,0.000012528837,0.006594762,0.053104725,0.020629264,0.843432,0.010887684,0.014356299],"study_design_scores_gemma":[0.00225248,0.0026775007,0.00003319198,0.0003073519,0.0012241849,0.000015419891,0.006537658,0.39612654,0.21124455,0.3781528,0.0003783476,0.0010499571],"about_ca_topic_score_codex":0.000041914434,"about_ca_topic_score_gemma":0.00003794594,"teacher_disagreement_score":0.9605411,"about_ca_system_score_codex":0.00009517916,"about_ca_system_score_gemma":0.0007275012,"threshold_uncertainty_score":0.9999135},"labels":[],"label_agreement":null},{"id":"W3211857593","doi":"10.1016/j.lwt.2021.112760","title":"Authentication of cinnamon spice samples using FT-IR spectroscopy and chemometric classification","year":2021,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Directorate for Biological Sciences; Queen's University; Queen's University Belfast","keywords":"Adulterant; Cassia; Spice; Mathematics; Chemometrics; Chromatography; Chemistry; Food science; Engineering; Medicine","score_opus":0.055166858181388084,"score_gpt":0.31385173384430415,"score_spread":0.2586848756629161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211857593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98801214,0.001243366,0.0059506837,0.00018421326,0.000032531498,0.00002766372,0.000013038449,0.000038046735,0.0044983122],"genre_scores_gemma":[0.98683083,0.00027258325,0.012086525,0.000028264285,0.00008350753,0.0000032158493,0.00004699551,0.000014869258,0.0006332058],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990437,0.000012048963,0.00027011544,0.00029864395,0.00020015381,0.00017534518],"domain_scores_gemma":[0.9991952,0.00009856085,0.00018853278,0.00033381968,0.00012525333,0.000058639893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051337764,0.000120137054,0.00023117232,0.0002010222,0.00007027185,0.000035763358,0.00008729466,0.00010368009,0.0007724352],"category_scores_gemma":[0.000256682,0.00012635531,0.000060463706,0.0013565795,0.0000673811,0.00009073902,0.00004480128,0.00010926848,0.000005874265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069191,0.00008529868,0.018117527,0.0001267425,0.000048316717,0.0000016653948,0.000108389795,0.0000016238569,0.9800735,0.000995094,0.00006332282,0.00037161086],"study_design_scores_gemma":[0.00021458422,0.000009482672,0.017090682,0.000019665638,0.00018871637,0.000014413878,0.00071021676,0.0013085376,0.9795248,0.0005560972,0.00024151112,0.000121301295],"about_ca_topic_score_codex":0.000059790214,"about_ca_topic_score_gemma":0.0000051087995,"teacher_disagreement_score":0.0061358404,"about_ca_system_score_codex":0.000082104896,"about_ca_system_score_gemma":0.00002878196,"threshold_uncertainty_score":0.8457624},"labels":[],"label_agreement":null},{"id":"W3212535423","doi":"10.1016/j.aca.2021.339255","title":"Evaluating the impact of NIR pre-processing methods via multiblock partial least-squares","year":2021,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Preprocessor; Data processing; Property (philosophy); Least-squares function approximation; Noise (video); Data pre-processing; Artificial intelligence; Data mining; Computer science; Pattern recognition (psychology); Chemistry; Machine learning; Statistics; Mathematics","score_opus":0.06532855896785628,"score_gpt":0.4570709860493717,"score_spread":0.3917424270815154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212535423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906664,0.00037058213,0.001206893,0.000401743,0.000024508507,0.00005721469,0.000019008405,0.000069244365,0.007184441],"genre_scores_gemma":[0.9907078,0.00004138381,0.007856723,0.000054988537,0.00021126846,0.000009619407,0.000027197188,0.000034740835,0.0010562659],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978791,0.00014832309,0.0005747631,0.0004935188,0.00042891977,0.00047539893],"domain_scores_gemma":[0.9977623,0.00068646803,0.0003652854,0.00077039655,0.00027949273,0.00013604607],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005039161,0.0002946264,0.0005643146,0.0001147934,0.00023697666,0.00010188504,0.00048649719,0.00015286038,0.0035430673],"category_scores_gemma":[0.0014304166,0.00020049134,0.00054654584,0.0012317973,0.0001798503,0.00013531432,0.00017997356,0.00041621752,0.000008110408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007698297,0.00021720745,0.0034055267,0.00008492796,0.00065029546,0.0000032088506,0.00034166587,0.00010014327,0.987782,0.000013289837,0.00022953894,0.0070951926],"study_design_scores_gemma":[0.000385459,0.000079677564,0.002272327,0.000043286716,0.0009416914,0.000027961909,0.0004055816,0.1626263,0.83253616,0.00037947163,0.00005822814,0.00024383872],"about_ca_topic_score_codex":0.00013315899,"about_ca_topic_score_gemma":0.0000071904665,"teacher_disagreement_score":0.16252616,"about_ca_system_score_codex":0.000116430754,"about_ca_system_score_gemma":0.00034633445,"threshold_uncertainty_score":0.99736786},"labels":[],"label_agreement":null},{"id":"W3212638034","doi":"10.3389/frans.2021.729732","title":"Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion","year":2021,"lang":"en","type":"article","venue":"Frontiers in Analytical Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Collinearity; Raw data; Computer science; Multivariate statistics; Data mining; Quality (philosophy); Latent variable; Product (mathematics); Canonical correlation; Projection (relational algebra); Statistics; Artificial intelligence; Machine learning; Mathematics; Algorithm","score_opus":0.09953150331406756,"score_gpt":0.3389609227201223,"score_spread":0.23942941940605472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212638034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46798116,0.00007043247,0.5313616,0.00015475525,0.000078235695,0.000089283276,0.000022607457,0.000021752889,0.00022018807],"genre_scores_gemma":[0.8753821,0.000015245754,0.124363065,0.000028765604,0.00006809561,0.0000042431047,0.000022101523,0.000009276532,0.00010710009],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834275,0.000021149819,0.00038159086,0.00059372524,0.00030183734,0.00035896956],"domain_scores_gemma":[0.9993015,0.00007892606,0.00012687675,0.00021534937,0.00012663759,0.00015069619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004184989,0.00014339914,0.00037592283,0.00039644272,0.00031831922,0.00026176576,0.00021728694,0.00010928608,0.000010853754],"category_scores_gemma":[0.00054765836,0.00014007677,0.00004021077,0.0017184942,0.00013919565,0.00052190427,0.00015326557,0.00014548887,1.9337412e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065771186,0.000074059644,0.02887342,0.00013842131,0.00006054716,0.0000027965023,0.0015308671,0.089996174,0.87504166,0.0014714468,0.00041080048,0.0023340518],"study_design_scores_gemma":[0.00019212966,0.000005438716,0.00048278747,0.000061479215,0.00007180222,9.755306e-7,0.0010075154,0.8095138,0.18562266,0.0028642605,0.000024557685,0.00015257136],"about_ca_topic_score_codex":0.00025301104,"about_ca_topic_score_gemma":0.000018113351,"teacher_disagreement_score":0.71951765,"about_ca_system_score_codex":0.0005422814,"about_ca_system_score_gemma":0.00011751945,"threshold_uncertainty_score":0.57121646},"labels":[],"label_agreement":null},{"id":"W3214847588","doi":"10.1139/cjps-2021-0148","title":"Cucumber powdery mildew detection using hyperspectral data","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"A&L Canada Laboratories (Canada); Western University; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Powdery mildew; Principal component analysis; Hyperspectral imaging; Mathematics; Support vector machine; Pattern recognition (psychology); Spectral line; Artificial intelligence; Horticulture; Biology; Statistics; Computer science; Physics","score_opus":0.06203475429201802,"score_gpt":0.2934352105997783,"score_spread":0.23140045630776027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214847588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944213,0.0016248988,0.0004576992,0.00015020187,0.0003132929,0.000007773287,0.00015796711,0.000004593583,0.0028622772],"genre_scores_gemma":[0.9980247,0.000035483048,0.0013839001,0.00009554975,0.00023350512,6.223683e-8,0.0000076399265,0.000005741441,0.00021343131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988908,0.000008145124,0.00021709154,0.00022284477,0.00029775608,0.00036337145],"domain_scores_gemma":[0.99874544,0.00003717691,0.0001444637,0.00033933966,0.00018979127,0.0005438055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030878707,0.00008423885,0.00015669037,0.0003103,0.00030876626,0.00015676513,0.00070227403,0.000047372916,0.0008717537],"category_scores_gemma":[0.00048416143,0.00008013025,0.000039763618,0.0010909277,0.00023543464,0.00049551297,0.00003671922,0.00024525897,0.000005676297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004775226,0.000011861414,0.008294131,0.000012574675,0.000036058722,0.0008921006,0.000107504726,0.000067974615,0.9896459,0.000054103737,0.00049158535,0.00038146114],"study_design_scores_gemma":[0.00021590282,0.000015848238,0.00075413415,0.000055783945,0.00015449974,0.0066766962,0.0013512714,0.0025290456,0.9824666,0.00012473376,0.0054572043,0.00019828959],"about_ca_topic_score_codex":0.0041683326,"about_ca_topic_score_gemma":0.013650432,"teacher_disagreement_score":0.0094821,"about_ca_system_score_codex":0.00035091964,"about_ca_system_score_gemma":0.0038686015,"threshold_uncertainty_score":0.9545092},"labels":[],"label_agreement":null},{"id":"W36398867","doi":"10.1007/s00107-010-0490-2","title":"Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range","year":2010,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Near-infrared spectroscopy; Moisture; Linear discriminant analysis; Spectroscopy; Abies lasiocarpa; Water content; Analytical Chemistry (journal); Materials science; Mathematics; Chemistry; Botany; Composite material; Chromatography; Optics; Physics; Pinus contorta; Statistics; Geology","score_opus":0.025645033306421924,"score_gpt":0.266738146524905,"score_spread":0.2410931132184831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36398867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9884185,0.0028143255,0.000007737835,0.001020437,0.000083521394,0.000050181472,0.0000026191683,0.000007649455,0.0075950045],"genre_scores_gemma":[0.9969008,0.00032989235,0.002105757,0.00012220202,0.00041322925,5.2589405e-7,0.000001719812,0.000019948458,0.00010594419],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99882805,0.00012392833,0.00041258096,0.00021904599,0.00020417469,0.00021223412],"domain_scores_gemma":[0.99927646,0.00007037732,0.00026900557,0.00024366847,0.000076839824,0.00006365265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010108575,0.00015285768,0.00024992906,0.0002098166,0.000103932354,0.00017013674,0.00024412826,0.000039449587,0.0000937975],"category_scores_gemma":[0.00036267162,0.00010924666,0.00003470065,0.0005870537,0.00012686088,0.00022758539,0.000043236447,0.00067420333,0.0000027728004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011906551,0.0002043136,0.026236909,0.00011046301,0.00004570956,0.00014581074,0.0028749532,0.000004600637,0.9680528,0.0001007552,0.00073391874,0.0013707147],"study_design_scores_gemma":[0.005345489,0.0005357625,0.20122613,0.00031683396,0.00035360685,0.0014452059,0.0077050063,0.00026784005,0.76640636,0.0011159637,0.01450325,0.000778582],"about_ca_topic_score_codex":0.000007814187,"about_ca_topic_score_gemma":0.0000062926374,"teacher_disagreement_score":0.20164645,"about_ca_system_score_codex":0.000018923361,"about_ca_system_score_gemma":0.000062401654,"threshold_uncertainty_score":0.44549495},"labels":[],"label_agreement":null},{"id":"W390810642","doi":"","title":"Use of near Infrared Reflectance Spectroscopy for the Characterization of Wheat and Barley Grain Entering Feedlots in Western Canada","year":2015,"lang":"en","type":"article","venue":"SHAREOK (University of Oklahoma)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Near infrared reflectance spectroscopy; Reflectivity; Environmental science; Near-infrared spectroscopy; Characterization (materials science); Agronomy; Spectroscopy; Remote sensing; Materials science; Geography; Optics; Biology","score_opus":0.039266407226372424,"score_gpt":0.23774654933528133,"score_spread":0.1984801421089089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W390810642","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985782,0.00011228797,0.00045406548,0.00015327435,0.000024430665,0.0000794855,0.00023216952,0.000007396229,0.00035867683],"genre_scores_gemma":[0.99839556,0.000068437716,0.00060980086,0.000014534055,0.000011125772,4.803931e-7,0.000049733244,0.000007729934,0.00084258243],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993989,0.000010620555,0.00014027825,0.00015147842,0.00016292461,0.00013580947],"domain_scores_gemma":[0.99937075,0.00007599602,0.00019899737,0.00019517167,0.00010600004,0.00005306644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068946545,0.00009064616,0.00025147808,0.00007449559,0.000054863936,0.000012262401,0.0001963195,0.000052552445,0.0000800144],"category_scores_gemma":[0.00006654148,0.0000959387,0.000041969597,0.00027627574,0.00009373679,0.00020093037,0.000074094634,0.00006985294,1.6016017e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003764455,0.00005731124,0.14689666,0.0002877099,0.00010351547,0.00000605583,0.0015929852,0.000021956503,0.84987235,0.000042700667,0.00050169724,0.00024058472],"study_design_scores_gemma":[0.0026995782,0.0001808216,0.22004698,0.0003088353,0.00025084036,0.0000075172934,0.0049820784,0.0027775075,0.76450783,0.00014093795,0.003736653,0.0003604275],"about_ca_topic_score_codex":0.038040742,"about_ca_topic_score_gemma":0.06399747,"teacher_disagreement_score":0.08536456,"about_ca_system_score_codex":0.000119222655,"about_ca_system_score_gemma":0.00017709992,"threshold_uncertainty_score":0.968365},"labels":[],"label_agreement":null},{"id":"W4200004931","doi":"10.1177/00037028211061174","title":"Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra","year":2021,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Deconvolution; Resolution (logic); Raman spectroscopy; Hyperspectral imaging; Spectral resolution; Principal component analysis; Spectral line; Chemistry; Analytical Chemistry (journal); Biological system; Optics; Computer science; Artificial intelligence; Algorithm; Physics; Chromatography","score_opus":0.04932808879162943,"score_gpt":0.4243672786569498,"score_spread":0.37503918986532037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200004931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17661552,0.005547014,0.5911362,0.0009097195,0.0002725638,0.00052439835,0.000040223575,0.00018103592,0.22477333],"genre_scores_gemma":[0.67575496,0.00010325646,0.32306385,0.00008192753,0.00029195327,0.00021967842,0.00009834139,0.000037902904,0.00034813574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99742275,0.0000700019,0.000569678,0.0007069029,0.0006234005,0.000607274],"domain_scores_gemma":[0.9980543,0.0004898351,0.00018039945,0.0006270566,0.0005195151,0.00012884864],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010638995,0.00028992564,0.0005630347,0.00014197546,0.00017435712,0.00005789239,0.00024390445,0.00019850473,0.006358459],"category_scores_gemma":[0.00077206956,0.00031272965,0.00025815138,0.000651673,0.00016994082,0.00008641262,0.00005708005,0.0002594011,0.000019804533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012492215,0.00035803812,0.000029829964,0.0001229323,0.00016647615,9.489033e-7,0.00011310653,0.000019127927,0.92421806,0.07233272,0.00048954145,0.0020242883],"study_design_scores_gemma":[0.0012292807,0.00007613313,0.00006785213,0.000015848469,0.0010483018,0.000007163819,0.0006486101,0.0023437487,0.95457983,0.038920756,0.0007722685,0.00029021048],"about_ca_topic_score_codex":0.000013161371,"about_ca_topic_score_gemma":0.000006596333,"teacher_disagreement_score":0.49913946,"about_ca_system_score_codex":0.00052977743,"about_ca_system_score_gemma":0.00033400665,"threshold_uncertainty_score":0.99993247},"labels":[],"label_agreement":null},{"id":"W4200037699","doi":"10.18280/i2m.200502","title":"Evaluation of Chemical Quality on Juices and Wine Produced from Mamao Fruit (Antidesma Puncticulatum Miq.) Within Near-Infrared Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kasetsart University","keywords":"Wine; Brix; Chemistry; Near-infrared spectroscopy; Analytical Chemistry (journal); Grape wine; Fourier transform infrared spectroscopy; Food science; Chromatography; Physics; Optics","score_opus":0.06324494767898554,"score_gpt":0.35917054690488576,"score_spread":0.2959255992259002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200037699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994616,0.00044853357,0.00004089617,0.0008581769,0.0001691516,0.00014230922,0.00007623522,0.00008633939,0.0035624104],"genre_scores_gemma":[0.9944675,0.000025455969,0.004525273,0.00018483047,0.0001692153,0.000034334906,0.00040081202,0.000020540767,0.00017204155],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970775,0.00023358974,0.0006706504,0.00068917684,0.0010573241,0.00027175577],"domain_scores_gemma":[0.998183,0.00029345593,0.00049357716,0.0005083236,0.00043348415,0.0000881661],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010379596,0.00027652585,0.00050250324,0.000079447265,0.00009838106,0.00009964316,0.00017047153,0.00023413588,0.0018432902],"category_scores_gemma":[0.001829883,0.00025569924,0.00011650022,0.00048349722,0.00017660031,0.0002014683,0.00007058131,0.00030002266,0.00001214273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024450888,0.00021705715,0.07704246,0.00006115664,0.0003928929,0.0000048466454,0.00026310017,0.00008959997,0.9202636,0.00012798807,0.0002550185,0.0010377602],"study_design_scores_gemma":[0.0020734784,0.00006635018,0.032566793,0.000028866814,0.00081288785,0.0000049812606,0.000614353,0.0011287676,0.9586066,0.003839766,0.000030476465,0.00022669313],"about_ca_topic_score_codex":0.00013131615,"about_ca_topic_score_gemma":0.000013938666,"teacher_disagreement_score":0.044475667,"about_ca_system_score_codex":0.00020522089,"about_ca_system_score_gemma":0.00031532327,"threshold_uncertainty_score":0.9999895},"labels":[],"label_agreement":null},{"id":"W4205873495","doi":"10.18280/ts.380622","title":"Detection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Support vector machine; Artificial intelligence; Convolution (computer science); Pattern recognition (psychology); Artificial neural network; Computer science; Contextual image classification; Image (mathematics); Machine learning","score_opus":0.0244025280814863,"score_gpt":0.2629781710836513,"score_spread":0.238575643002165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205873495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96633065,0.002000126,0.031204564,0.0001103432,0.000036588015,0.000050453113,0.000021295124,0.000036915695,0.00020906367],"genre_scores_gemma":[0.9987742,0.000029154196,0.000653744,0.00005308582,0.00030931272,0.0000047734115,0.000050690116,0.000013009338,0.00011207979],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904865,0.000028240303,0.00026134192,0.00025900817,0.00020267507,0.00020005477],"domain_scores_gemma":[0.9994812,0.00006508864,0.00014928757,0.00012649005,0.00006521351,0.00011273617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043364787,0.0001389078,0.00020012168,0.00006313427,0.00010333192,0.00003336974,0.000059998263,0.00006600931,0.0007521146],"category_scores_gemma":[0.000029576948,0.00013982924,0.00008633367,0.0003951738,0.000056351968,0.00007328551,0.000026959538,0.00011188763,0.0000010639701],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116794516,0.0000983743,0.015109303,0.00011230687,0.00013179447,0.000006907407,0.00012534764,0.0036560593,0.9725223,0.00014030244,0.00012550408,0.00785496],"study_design_scores_gemma":[0.002333613,0.00013663649,0.09345007,0.00011670167,0.0015239886,0.00012049568,0.001032037,0.08734283,0.8102918,0.0022968748,0.000770929,0.00058398565],"about_ca_topic_score_codex":0.000057560675,"about_ca_topic_score_gemma":0.000030507477,"teacher_disagreement_score":0.1622305,"about_ca_system_score_codex":0.00006488503,"about_ca_system_score_gemma":0.000073927644,"threshold_uncertainty_score":0.8235127},"labels":[],"label_agreement":null},{"id":"W4206246081","doi":"10.17918/d8xt02","title":"Monitoring the adaptation to experimental pain using functional near infrared spectroscopy","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Drexel University","keywords":"Adaptation (eye); Functional near-infrared spectroscopy; Infrared; Infrared spectroscopy; Spectroscopy; Psychology; Computer science; Physics; Neuroscience; Optics; Cognition","score_opus":0.05131212770081027,"score_gpt":0.3393709910188229,"score_spread":0.2880588633180126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206246081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86216486,0.001755711,0.0017547644,0.00004696689,0.0010282072,0.00018124991,0.0000144926835,0.00015871806,0.13289504],"genre_scores_gemma":[0.8693087,0.000028779643,0.004330744,0.00006586483,0.0014611458,0.00008665338,0.0003175918,0.000080828555,0.12431969],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838686,0.000023847673,0.00031215983,0.0004471526,0.0005040711,0.00032591316],"domain_scores_gemma":[0.99882543,0.00011118967,0.0002937597,0.00055525807,0.00010766969,0.00010671527],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018849107,0.00035101885,0.0003119026,0.00012295505,0.0009187843,0.0004345187,0.0004123339,0.0002781504,0.005342948],"category_scores_gemma":[0.00022410476,0.00028590896,0.00018965556,0.00021293439,0.00003875062,0.00015496982,0.000029325154,0.00037565225,0.000064247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025985867,0.0001157571,0.0010879027,0.000091827795,0.00033271822,0.0000062012464,0.0021395322,0.0005193314,0.9911792,0.000045982655,0.002822302,0.001399339],"study_design_scores_gemma":[0.00018954242,0.000030990555,0.0004916916,0.000089109184,0.00017695379,0.0000023002865,0.020457694,0.0017340248,0.97476256,0.00006152709,0.0016528047,0.0003507953],"about_ca_topic_score_codex":0.0005196415,"about_ca_topic_score_gemma":0.000035510428,"teacher_disagreement_score":0.018318163,"about_ca_system_score_codex":0.0003704561,"about_ca_system_score_gemma":0.0002138202,"threshold_uncertainty_score":0.9999593},"labels":[],"label_agreement":null},{"id":"W4206988213","doi":"10.1016/j.chemosphere.2022.133746","title":"Rapid detection of ionic contents in water through sensor fusion and convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Chemosphere","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Convolutional neural network; Ionic bonding; Ionic strength; Chemistry; Salinity; Contamination; Environmental chemistry; Environmental science; Aqueous solution; Analytical Chemistry (journal); Ion; Computer science; Artificial intelligence; Machine learning; Geology","score_opus":0.01750835724945032,"score_gpt":0.23343341171850213,"score_spread":0.2159250544690518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206988213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949766,0.0025441924,0.000032304397,0.0001076446,0.00007921834,0.000045708803,0.0000071619916,0.00003005195,0.002177147],"genre_scores_gemma":[0.9982777,0.000079777936,0.000099013785,0.00010364273,0.00007641451,0.000019207693,0.000033504828,0.00001226786,0.0012984647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991098,0.000018795825,0.00022629078,0.00022800884,0.00018291028,0.00023417539],"domain_scores_gemma":[0.9996848,0.00004074293,0.0000735999,0.00014132664,0.000029700699,0.000029800043],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007171082,0.000113528695,0.00020953303,0.000012096987,0.00014618294,0.000008173021,0.00009281025,0.000064207554,0.005169396],"category_scores_gemma":[0.000016409063,0.00009990563,0.00006167578,0.0002557795,0.000066627865,0.00006171376,0.00013421758,0.00023523893,0.0000035393466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031220657,0.00017442113,0.028451568,0.00012390509,0.00008455678,0.000013564986,0.0003714285,0.0020550245,0.9655516,0.00003730577,0.00067787856,0.0021465379],"study_design_scores_gemma":[0.0012603477,0.00005556669,0.0024733583,0.000010001664,0.00005545214,0.000037921665,0.0014033748,0.0026723747,0.9882438,0.00038096786,0.0032287762,0.00017806549],"about_ca_topic_score_codex":0.00011284967,"about_ca_topic_score_gemma":0.000011873899,"teacher_disagreement_score":0.02597821,"about_ca_system_score_codex":0.000108120745,"about_ca_system_score_gemma":0.000010972326,"threshold_uncertainty_score":0.99574},"labels":[],"label_agreement":null},{"id":"W4210561346","doi":"10.1111/jfpe.13975","title":"Chickpea varietal classification using deep convolutional neural networks with transfer learning","year":2022,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Council of Agricultural Research","keywords":"Transfer of learning; Convolutional neural network; Artificial intelligence; Deep learning; RGB color model; Computer science; Pattern recognition (psychology); Machine learning","score_opus":0.015245546433527326,"score_gpt":0.23154576947196132,"score_spread":0.216300223038434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210561346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7791171,0.0018033347,0.21876922,0.000027158963,0.00006880031,0.000018782583,0.0000019296078,0.000032961725,0.00016072392],"genre_scores_gemma":[0.9990736,0.0000098534,0.00059395185,0.000011418834,0.00025134152,0.0000050663994,0.0000060214675,0.000026408834,0.000022304488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896616,0.000009607451,0.00030468585,0.00012205601,0.0003852749,0.00021221899],"domain_scores_gemma":[0.99954855,0.00004773352,0.00015186453,0.000061633924,0.000113341106,0.000076879696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013918581,0.00013667661,0.00021718722,0.00016857806,0.00019202333,0.000036122816,0.00018273783,0.000046325426,0.00033180814],"category_scores_gemma":[0.000030713178,0.00012746056,0.00009170392,0.00052751426,0.000018585064,0.00019355761,0.000016127276,0.0008834399,8.2271455e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081379556,0.000043728203,0.0015785809,0.000059055004,0.00021937583,0.000010808243,0.00016922546,0.9745565,0.023077358,0.0000682437,0.0000013328756,0.0001343853],"study_design_scores_gemma":[0.00059627334,0.00024444208,0.0003002999,0.00001671353,0.0003042413,0.00076534605,0.000816059,0.9855963,0.011050392,0.000010371325,0.0001049928,0.00019457584],"about_ca_topic_score_codex":0.0000010376596,"about_ca_topic_score_gemma":2.9940972e-7,"teacher_disagreement_score":0.21995655,"about_ca_system_score_codex":0.00013172952,"about_ca_system_score_gemma":0.000067362176,"threshold_uncertainty_score":0.5197691},"labels":[],"label_agreement":null},{"id":"W4212948524","doi":"10.1016/j.meatsci.2022.108774","title":"Application of Vis-NIR and SWIR spectroscopy for the segregation of bison muscles based on their color stability","year":2022,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Principal component analysis; Near-infrared spectroscopy; Linear discriminant analysis; Calibration; Spectroscopy; Analytical Chemistry (journal); Stability (learning theory); Materials science; Environmental science; Chemistry; Mathematics; Optics; Chromatography; Statistics; Physics; Computer science","score_opus":0.020889233997762796,"score_gpt":0.27930017266845,"score_spread":0.2584109386706872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212948524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98530704,0.00011427669,0.013272563,0.0002674168,0.000019410414,0.00018544213,0.00006499389,0.000015447302,0.00075338845],"genre_scores_gemma":[0.9990095,0.0000061566866,0.00082082633,0.00004044656,0.000010464812,0.00008311613,0.0000051557554,0.000004183687,0.000020181697],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991053,0.000012424569,0.00015892832,0.00025617014,0.00033661854,0.00013060267],"domain_scores_gemma":[0.9989826,0.0004198262,0.00015740753,0.00033162243,0.000082516824,0.000026039688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055646914,0.00007258311,0.00012921839,0.00008684213,0.00032454744,0.000013194437,0.000323153,0.00001798681,0.00015209384],"category_scores_gemma":[0.0001758345,0.00005049748,0.000043912438,0.00090369623,0.00040446228,0.000053280786,0.000056288587,0.00007048829,2.1971215e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006532708,0.000093297735,0.006982311,0.000044600907,0.000004303869,1.6358458e-8,0.00013910333,0.0003780541,0.99037296,0.00071477646,0.000014733382,0.001190539],"study_design_scores_gemma":[0.00017886734,0.000105060455,0.004104653,0.0000026604407,0.000023751718,2.508583e-7,0.0009100306,0.056696568,0.9373549,0.00042409208,0.00015129766,0.00004786271],"about_ca_topic_score_codex":0.00006707781,"about_ca_topic_score_gemma":0.000009722134,"teacher_disagreement_score":0.056318514,"about_ca_system_score_codex":0.00009373988,"about_ca_system_score_gemma":0.000091056536,"threshold_uncertainty_score":0.24961893},"labels":[],"label_agreement":null},{"id":"W4214939969","doi":"10.3390/molecules27051642","title":"Development of a Nuclear Magnetic Resonance Method and a Near Infrared Calibration Model for the Rapid Determination of Lipid Content in the Field Pea (Pisum sativum)","year":2022,"lang":"en","type":"article","venue":"Molecules","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sativum; Pisum; Field pea; Extraction (chemistry); Rapeseed; Forage; Chemistry; Partial least squares regression; Food science; Chromatography; Agronomy; Botany; Biology; Mathematics","score_opus":0.034450477976195375,"score_gpt":0.2862988980298597,"score_spread":0.2518484200536643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214939969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78817415,0.004738585,0.20303456,0.001791602,0.000023962799,0.0004993987,0.0000570649,0.000020603387,0.0016600674],"genre_scores_gemma":[0.95287174,0.000034542532,0.046432536,0.0003518422,0.000007596141,0.00012194338,0.000007197674,0.0000084333715,0.0001641598],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994168,0.000034626926,0.00021126507,0.000108414984,0.00015085515,0.0000779934],"domain_scores_gemma":[0.99938625,0.00035263537,0.00009945532,0.00012714892,0.000025865373,0.000008658996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022047327,0.00006207014,0.00011257472,0.00003827294,0.00011778083,0.000013689908,0.00015136845,0.000026511947,0.00012601237],"category_scores_gemma":[0.00014634828,0.000043895172,0.00003576883,0.00015423945,0.000031931126,0.000020792555,0.000057825557,0.000076852375,4.548848e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008318075,0.0004973326,0.0009310039,0.0005483643,0.00008928305,0.0000035963653,0.04902286,0.0016374012,0.6857087,0.0035263582,0.0020646222,0.25513864],"study_design_scores_gemma":[0.0006606948,0.00011840003,0.00083663774,0.00001635726,0.000064219654,0.0000027501928,0.0042942944,0.7412535,0.24774918,0.0005237688,0.0043702796,0.00010991891],"about_ca_topic_score_codex":0.000026824839,"about_ca_topic_score_gemma":0.000010243014,"teacher_disagreement_score":0.7396161,"about_ca_system_score_codex":0.000020964822,"about_ca_system_score_gemma":0.00003889544,"threshold_uncertainty_score":0.1789993},"labels":[],"label_agreement":null},{"id":"W4220692670","doi":"10.1007/s11540-021-09528-7","title":"Development of a Novel Image Analysis and Classification Algorithms to Separate Tubers from Clods and Stones","year":2022,"lang":"en","type":"article","venue":"Potato Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Ilam University","keywords":"Preprocessor; Artificial intelligence; Histogram; Pattern recognition (psychology); Image processing; Linear discriminant analysis; Artificial neural network; Computer science; Digital image; Image (mathematics); Quadratic classifier; Mathematics; Computer vision; Support vector machine","score_opus":0.10943984718351307,"score_gpt":0.4192005031105162,"score_spread":0.30976065592700314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220692670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958098,0.00037645755,0.0019970317,0.00018457985,0.00000587346,0.000060890347,0.000089514404,0.0000144403275,0.00146141],"genre_scores_gemma":[0.9785296,0.000041985168,0.02015577,0.000016472917,0.000016117534,0.000084661275,0.00008919314,0.000011478243,0.0010547262],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984903,0.000038062855,0.00023674827,0.0003801054,0.0005979798,0.0002568083],"domain_scores_gemma":[0.99924743,0.00020411938,0.000053967557,0.00023109006,0.00012588549,0.00013748166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005588793,0.0000942167,0.00023370534,0.000596613,0.00030331613,0.000048999857,0.00018606438,0.000034434008,0.0009042837],"category_scores_gemma":[0.000090803165,0.000095759606,0.00003916798,0.002234673,0.00010588726,0.000047932048,0.00033651007,0.00024414275,0.0000054004777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077619436,0.00018618301,0.025906663,0.000039430037,0.0006928772,0.000004402657,0.002805818,0.000015361944,0.96524787,0.00005584302,0.00016616043,0.004801759],"study_design_scores_gemma":[0.000975236,0.00005865763,0.1641892,0.000009803322,0.00045870786,0.0000057473003,0.01599008,0.0105722435,0.8042143,0.00009967829,0.0030682818,0.00035805386],"about_ca_topic_score_codex":0.000421477,"about_ca_topic_score_gemma":0.000045928336,"teacher_disagreement_score":0.16103359,"about_ca_system_score_codex":0.00012952801,"about_ca_system_score_gemma":0.00009926773,"threshold_uncertainty_score":0.99012727},"labels":[],"label_agreement":null},{"id":"W4220709370","doi":"10.1177/00037028221094598","title":"Temperature-Dependent Infrared Refractive Index of Polymers from a Calibrated Attenuated Total Reflection Infrared Measurement","year":2022,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Absorbance; Attenuated total reflection; Total internal reflection; Infrared; Refractive index; Infrared spectroscopy; Optics; Calibration; Chemistry; Analytical Chemistry (journal); Polarization (electrochemistry); Angle of incidence (optics); Materials science; Reflection (computer programming); Absorption (acoustics); Optoelectronics; Chromatography; Physics","score_opus":0.01445999304892479,"score_gpt":0.24817666674113023,"score_spread":0.23371667369220545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220709370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9126431,0.0006839053,0.00040501414,0.00012006638,0.00019575506,0.00031992982,0.00036021345,0.00036173998,0.0849103],"genre_scores_gemma":[0.9961627,0.000027203114,0.0003672593,0.00020112837,0.00017197496,0.00026703856,0.00035835907,0.00008427744,0.0023600224],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962457,0.00007340374,0.0007296354,0.00085406326,0.001497429,0.00059980305],"domain_scores_gemma":[0.99825567,0.00009110842,0.00057986093,0.00074713264,0.00014857762,0.00017764226],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024716996,0.00046992832,0.0007170695,0.00039452524,0.0004387068,0.00007153987,0.00047855702,0.00024189377,0.01251697],"category_scores_gemma":[0.00006432086,0.00050967844,0.00019142739,0.0018074262,0.00011233137,0.00014836149,0.00026075338,0.0010821023,0.000022220353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014161469,0.0004441175,0.0012484977,0.000024950632,0.0007184942,0.000013736804,0.0004502643,0.0002185708,0.9932246,0.00015758183,0.0020032914,0.000079731486],"study_design_scores_gemma":[0.002018412,0.00014802397,0.00096643186,0.000011774131,0.00028900403,0.000009145072,0.0031688008,0.0001264101,0.99086076,0.0014017473,0.0005089278,0.0004905437],"about_ca_topic_score_codex":0.0011410468,"about_ca_topic_score_gemma":0.000033206892,"teacher_disagreement_score":0.08351966,"about_ca_system_score_codex":0.0013740554,"about_ca_system_score_gemma":0.00034937006,"threshold_uncertainty_score":0.9997355},"labels":[],"label_agreement":null},{"id":"W4221031187","doi":"10.1002/cche.10546","title":"Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning","year":2022,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture; Dalhousie University","funders":"Mitacs; Killam Trusts","keywords":"Principal component analysis; Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Preprocessor; Mathematics; Partial least squares regression; Computer science; Biological system; Statistics; Biology","score_opus":0.017305981080536624,"score_gpt":0.2868273581916766,"score_spread":0.26952137711114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221031187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988867,0.0003162442,0.006280041,0.00004724576,0.000008147189,0.000079310856,0.00003489196,0.00005735936,0.004309774],"genre_scores_gemma":[0.9782022,0.000015466394,0.02065608,0.000021217136,0.000024284203,0.000084360094,0.00022417968,0.00002801273,0.0007441791],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861103,0.000024982466,0.00036644156,0.000411684,0.0002786779,0.00030720435],"domain_scores_gemma":[0.99905103,0.00029150667,0.00031132813,0.00023686103,0.000036261263,0.000072995404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025759687,0.00019755747,0.00038543806,0.00004551551,0.00023146199,0.000037949463,0.00017955452,0.00009059813,0.00031648937],"category_scores_gemma":[0.00023610839,0.00021142297,0.00006571899,0.00047083973,0.0001367385,0.00008385516,0.000095370546,0.0004184484,2.4019357e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022348473,0.000543044,0.0652349,0.0036823296,0.0005086457,0.000026174484,0.0021777502,0.04860612,0.87033564,0.00021097937,0.00024720546,0.0061923913],"study_design_scores_gemma":[0.0019165275,0.00009964877,0.0023787946,0.00003121886,0.00013068173,0.000018280049,0.0032354565,0.011668808,0.97939646,0.0006729651,0.00010982139,0.00034132745],"about_ca_topic_score_codex":0.00040038905,"about_ca_topic_score_gemma":0.0000065227155,"teacher_disagreement_score":0.10906086,"about_ca_system_score_codex":0.00016833971,"about_ca_system_score_gemma":0.00005609663,"threshold_uncertainty_score":0.8621578},"labels":[],"label_agreement":null},{"id":"W4221124979","doi":"10.1007/s11222-021-10066-y","title":"Sparse functional partial least squares regression with a locally sparse slope function","year":2022,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; FPInnovations; Brock University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Functional data analysis; Mathematics; Chemometrics; Regularization (linguistics); Applied mathematics; Least-squares function approximation; Algorithm; Generalized least squares; Function (biology); Mathematical optimization; Computer science; Statistics; Artificial intelligence; Machine learning","score_opus":0.0193744584787349,"score_gpt":0.24888229606848034,"score_spread":0.22950783758974544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221124979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35951978,0.0005549391,0.6355882,0.00009311186,0.00021692723,0.00006643677,0.0001775577,0.00011316654,0.0036698366],"genre_scores_gemma":[0.9951899,0.000008263539,0.0034779292,0.0001238769,0.00021403174,0.000008358574,0.00015907898,0.000019688541,0.000798843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883574,0.000024117571,0.00020919807,0.00032546528,0.00035688834,0.0002485888],"domain_scores_gemma":[0.9994361,0.000119982804,0.0001536476,0.00014024052,0.000062772924,0.00008728416],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011015084,0.00016056518,0.0001865507,0.00007405073,0.00068912597,0.00006620451,0.00007890855,0.0000327358,0.002243042],"category_scores_gemma":[0.000031521762,0.00013954475,0.000027032142,0.00027005438,0.00007237008,0.000035929956,0.00014780267,0.00028407888,0.000004076502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0094854925,0.0028123779,0.19281071,0.0014853319,0.0024933529,0.0011518642,0.002947794,0.12571827,0.06621615,0.11926953,0.082058065,0.39355105],"study_design_scores_gemma":[0.008177479,0.0024831113,0.024451334,0.00027812453,0.0018978111,0.0007277691,0.0136926565,0.87490875,0.022838658,0.008270283,0.039585903,0.002688125],"about_ca_topic_score_codex":0.00005361427,"about_ca_topic_score_gemma":0.000010381852,"teacher_disagreement_score":0.74919045,"about_ca_system_score_codex":0.0000582423,"about_ca_system_score_gemma":0.00007121877,"threshold_uncertainty_score":0.998669},"labels":[],"label_agreement":null},{"id":"W4223955323","doi":"10.1109/icacta54488.2022.9753597","title":"Enhanced Framework for Semantic Segmentation of Agricultural Products","year":2022,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Segmentation; Agriculture; Artificial intelligence; Natural language processing; Geography","score_opus":0.016012490940367816,"score_gpt":0.2863231195266046,"score_spread":0.2703106285862368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223955323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9748133,0.00011267269,0.017732238,0.00029292388,0.00006063669,0.000100694786,0.000018015795,0.00004966865,0.006819886],"genre_scores_gemma":[0.9847126,0.000004979749,0.009866479,0.000041002433,0.000065972235,0.0000895593,0.000060290076,0.000005754548,0.005153388],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994534,0.000004274209,0.00013717511,0.00015128442,0.0001445413,0.0001092729],"domain_scores_gemma":[0.9996501,0.00007390456,0.00009243855,0.00011390967,0.000053853266,0.000015745163],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000039785824,0.00006281414,0.00011716689,0.00003430089,0.0000928042,0.0000064874052,0.0001015828,0.00002314138,0.004874668],"category_scores_gemma":[0.00009395786,0.00005085556,0.000052847652,0.00040578865,0.000012614828,0.000033451164,0.00003431328,0.00007563285,0.0000022122615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000165094,0.00008241367,0.00016895834,0.00010988454,0.000056583885,8.002465e-8,0.00018698101,0.00006718953,0.9959779,0.002483458,0.00067408796,0.00017599543],"study_design_scores_gemma":[0.00015678421,0.00003480651,0.00025374693,0.000002270599,0.00007321401,0.000001282491,0.0034297218,0.00001392043,0.9949947,0.0008804202,0.00008586246,0.000073249146],"about_ca_topic_score_codex":0.000010081547,"about_ca_topic_score_gemma":6.482583e-7,"teacher_disagreement_score":0.009899313,"about_ca_system_score_codex":0.000048641876,"about_ca_system_score_gemma":0.000015361406,"threshold_uncertainty_score":0.99603504},"labels":[],"label_agreement":null},{"id":"W4224273854","doi":"10.1080/07038992.2022.2059755","title":"Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Dimensionality reduction; Curse of dimensionality; Artificial neural network; Inversion (geology); Artificial intelligence; Computer science; Pattern recognition (psychology); Data set; Imaging spectrometer; Algorithm; Remote sensing; Data mining; Spectrometer; Geology; Optics","score_opus":0.06187705001994575,"score_gpt":0.31794575428325356,"score_spread":0.2560687042633078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224273854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53049433,0.0015316192,0.46508136,0.0010491961,0.00019549638,0.00008311134,0.00010479494,0.000016170528,0.0014438949],"genre_scores_gemma":[0.5587783,0.00000815126,0.44073105,0.00030997864,0.00006275342,2.6845589e-8,0.000025707657,0.000024268737,0.00005975211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988246,0.000053250205,0.00028472158,0.0002666255,0.0002332833,0.00033750208],"domain_scores_gemma":[0.9987333,0.00027587314,0.00020724436,0.00027021387,0.00013589648,0.00037750663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048595804,0.00015079616,0.00030063358,0.00039454643,0.0006883829,0.00012258932,0.00026073292,0.000058155834,0.00005138264],"category_scores_gemma":[0.0004552121,0.00016095919,0.000097345044,0.00038445916,0.00003886226,0.00014755,0.000037961592,0.00042257563,4.111917e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002516988,0.00010098205,0.0015971165,0.00064746506,0.0018887155,0.0025247817,0.007718614,0.37238458,0.37059897,0.0004868551,0.015836319,0.22369862],"study_design_scores_gemma":[0.00063628395,0.0001365702,0.0000039037022,0.0000707997,0.0002259823,0.00022406234,0.003107825,0.97572666,0.018224813,0.00033916178,0.0011302293,0.00017373187],"about_ca_topic_score_codex":0.003389105,"about_ca_topic_score_gemma":0.001576773,"teacher_disagreement_score":0.60334206,"about_ca_system_score_codex":0.00047366697,"about_ca_system_score_gemma":0.00077227794,"threshold_uncertainty_score":0.6563725},"labels":[],"label_agreement":null},{"id":"W4225266676","doi":"10.1016/j.polymdegradstab.2022.109963","title":"Quantifying stabilizing additive hydrolysis and kinetics through principal component analysis of infrared spectra of cross-linked polyethylene pipe","year":2022,"lang":"en","type":"article","venue":"Polymer Degradation and Stability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinetics; Cross-linked polyethylene; Principal component analysis; Polyethylene; Hydrolysis; Infrared; Infrared spectroscopy; Chemistry; Materials science; Component (thermodynamics); Chemical engineering; Polymer chemistry; Organic chemistry; Biological system; Thermodynamics; Computer science; Optics; Physics; Engineering; Biology","score_opus":0.05289758669806226,"score_gpt":0.3201836208260656,"score_spread":0.26728603412800334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225266676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994685,0.0027038888,0.00047545187,0.00006283201,0.000022252934,0.000081744016,0.00074118166,0.000031016454,0.0011966425],"genre_scores_gemma":[0.99899465,0.00016443693,0.00035664055,0.000019539115,0.000012135527,0.0000180515,0.00031852603,0.000012648672,0.00010339887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99798673,0.00010266859,0.000738784,0.00047515216,0.0004646531,0.00023198532],"domain_scores_gemma":[0.9984619,0.00043752114,0.0004737636,0.00042198287,0.00012244319,0.00008238463],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031359302,0.00020492332,0.00067601545,0.00028105822,0.0001973226,0.000032629374,0.00015406597,0.00007124434,0.0031232946],"category_scores_gemma":[0.00017092066,0.00021063049,0.00020298902,0.0015269674,0.00038488093,0.00015216485,0.00019349428,0.00022179715,1.3784869e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019081112,0.0004686432,0.36314917,0.00014509553,0.0010427875,8.953067e-7,0.0019937379,0.000041609383,0.63174725,0.00058751553,0.0000017322102,0.00063074636],"study_design_scores_gemma":[0.00054372667,0.00007136331,0.101775564,0.000004470603,0.0014705823,0.0000015978057,0.003427864,0.004391057,0.88793075,0.00012910766,0.000053139338,0.00020079548],"about_ca_topic_score_codex":0.0008194124,"about_ca_topic_score_gemma":0.00011271331,"teacher_disagreement_score":0.2613736,"about_ca_system_score_codex":0.000110877256,"about_ca_system_score_gemma":0.000044729222,"threshold_uncertainty_score":0.997788},"labels":[],"label_agreement":null},{"id":"W4231662553","doi":"10.1142/9789812835925_0002","title":"PASSIVE STANDOFF DETECTION OF SURFACE CONTAMINANTS: A NOVEL APPROACH BY DIFFERENTIAL POLARIZATION FTIR SPECTROMETRY","year":2009,"lang":"en","type":"book-chapter","venue":"Selected topics in electornics and systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Fourier transform infrared spectroscopy; Polarization (electrochemistry); Mass spectrometry; Materials science; Analytical Chemistry (journal); Chemistry; Environmental chemistry; Optics; Physics; Chromatography; Physical chemistry","score_opus":0.010332661486368305,"score_gpt":0.21995413824528057,"score_spread":0.20962147675891227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231662553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6647693,0.049218647,0.053808242,0.000060354483,0.0009651821,0.0019676501,0.0018989193,0.00044642054,0.22686528],"genre_scores_gemma":[0.89849156,0.0010088189,0.000076268734,0.0000053570616,0.00023649936,0.0000052885985,0.0004517149,0.00006288884,0.09966158],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997692,0.000023775867,0.00080340245,0.0006209838,0.00045795433,0.0004018845],"domain_scores_gemma":[0.99849725,0.000088002125,0.0006881034,0.00033235564,0.000302446,0.00009187162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009563899,0.000488398,0.0010489128,0.0003415307,0.00009060449,0.000078235484,0.00021819149,0.0009318229,0.00006859074],"category_scores_gemma":[0.00008468119,0.0004885032,0.000116218136,0.00047917158,0.00006541105,0.0000617896,0.000035707595,0.00084498624,8.545278e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109864675,0.0002576742,0.00071258197,0.0007777148,0.00054775196,0.0000044426756,0.00009237221,0.00001368094,0.98860717,0.0076029897,0.00012203773,0.0011517331],"study_design_scores_gemma":[0.009414979,0.0016104742,0.0021358884,0.0013906372,0.0027192081,0.00021875338,0.0003941278,0.018993935,0.9310858,0.002651503,0.024650622,0.0047340333],"about_ca_topic_score_codex":0.00022570504,"about_ca_topic_score_gemma":0.000060240218,"teacher_disagreement_score":0.23372227,"about_ca_system_score_codex":0.0004197983,"about_ca_system_score_gemma":0.000118266405,"threshold_uncertainty_score":0.99975663},"labels":[],"label_agreement":null},{"id":"W4232393710","doi":"10.3847/1538-4357/abe627","title":"A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. II. H ii Region Line Ratios","year":2021,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Université de Montréal; Mitacs; Institut national des sciences de l'Univers; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Centre National de la Recherche Scientifique; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Artificial neural network; Algorithm; Computer science; Artificial intelligence; Line (geometry); Spectral line; Residual; Noise (video); Machine learning; Mathematics; Physics; Geometry","score_opus":0.036496903909694886,"score_gpt":0.27825992618858386,"score_spread":0.24176302227888896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232393710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8531947,0.0002487759,0.13227293,0.0074562035,0.00008422861,0.000051740495,0.0000048104534,0.00007622005,0.006610382],"genre_scores_gemma":[0.96894,0.00006591529,0.019092064,0.0007964478,0.0013798714,0.000009380006,0.000033745073,0.000027693368,0.0096548535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986072,0.00007110954,0.0003241554,0.00026022122,0.00036416302,0.00037310805],"domain_scores_gemma":[0.9989896,0.00013216876,0.00016408695,0.00033698388,0.000187654,0.00018951826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010277506,0.00021733898,0.0003117009,0.000072469236,0.001014583,0.00015139762,0.0004430325,0.000079295685,0.0005112686],"category_scores_gemma":[0.0003887078,0.00015217437,0.00023834371,0.0008784727,0.00005016899,0.00013261678,0.00025132962,0.0014962951,0.000019418218],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023820171,0.0008272754,0.001695972,0.000021719356,0.00033212945,0.00006342937,0.0008348526,0.0030227264,0.97879153,0.007556298,0.0044033993,0.002212443],"study_design_scores_gemma":[0.0011104194,0.0007769828,0.00088942697,0.0000793444,0.00046988347,0.0005984154,0.0019322769,0.012219462,0.9686221,0.0031432707,0.009649535,0.00050891185],"about_ca_topic_score_codex":0.000027142984,"about_ca_topic_score_gemma":0.0000025202537,"teacher_disagreement_score":0.11574532,"about_ca_system_score_codex":0.00008415064,"about_ca_system_score_gemma":0.00011157726,"threshold_uncertainty_score":0.7803455},"labels":[],"label_agreement":null},{"id":"W4233426296","doi":"10.1007/978-1-4614-6170-8_100263","title":"Spectral Technique","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Materials science","score_opus":0.014800131268981321,"score_gpt":0.2511580318411717,"score_spread":0.23635790057219036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233426296","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000045555726,0.00034083624,0.0045467163,0.000059911,0.000027043929,0.000042855987,0.000013101736,0.0003075065,0.99465746],"genre_scores_gemma":[0.0038978478,0.00013196631,0.0013525323,0.00014393394,0.00055491034,0.000011595657,0.000052575473,0.00007390914,0.99378073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989205,0.0000010974592,0.0002498866,0.00038254028,0.00021527891,0.0002307133],"domain_scores_gemma":[0.9991603,0.00004701412,0.00013465167,0.00052892015,0.000036899484,0.00009220056],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000048577906,0.00034122998,0.00044963916,0.00016604764,0.000043846965,0.000027446444,0.00030683953,0.0006079069,0.13159911],"category_scores_gemma":[0.00001554124,0.00030492977,0.0002752497,0.000033261276,0.00007613173,0.00001529435,0.00006144094,0.00051545224,0.0008845306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013330865,0.000020935848,0.000028203453,0.00024677478,0.00042544372,0.0000372783,0.000006253896,4.381297e-7,0.028488321,0.90392715,0.06502682,0.0017790352],"study_design_scores_gemma":[0.0000822767,0.000016726512,4.7116967e-7,0.000036498008,0.00025701994,0.000028786124,0.000003179665,0.000002789837,0.32747984,0.028845847,0.6428009,0.00044563017],"about_ca_topic_score_codex":0.000013933498,"about_ca_topic_score_gemma":0.0000057022835,"teacher_disagreement_score":0.8750813,"about_ca_system_score_codex":0.000108027074,"about_ca_system_score_gemma":0.000036745456,"threshold_uncertainty_score":0.9999403},"labels":[],"label_agreement":null},{"id":"W4233630243","doi":"10.1002/9781119516651.ch15","title":"Simple Linear Regression Analysis","year":2020,"lang":"en","type":"other","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Simple linear regression; Proper linear model; Regression diagnostic; Simple (philosophy); Regression analysis; Linear regression; Statistics; Mathematics; Segmented regression; Regression; Residual; Polynomial regression; Linear model; Set (abstract data type); Computer science; Algorithm","score_opus":0.017722908904895016,"score_gpt":0.30797597035446483,"score_spread":0.2902530614495698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233630243","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014475619,0.0012605418,0.0013197968,0.00007411267,0.000015688485,0.000017753156,0.00006987333,0.00048608254,0.99674165],"genre_scores_gemma":[0.0027245122,0.00035994564,0.0011752966,0.00018571476,0.00057043706,0.000004375655,0.00052966195,0.00024157051,0.9942085],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898386,0.0000053029908,0.00017271582,0.00041873037,0.00023244116,0.00018697366],"domain_scores_gemma":[0.99921614,0.000027055532,0.00017041781,0.00045289617,0.000010930034,0.00012254635],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000016211177,0.00026112163,0.00059653446,0.00040112474,0.000030458044,0.00001879709,0.00027348704,0.0003734825,0.44065392],"category_scores_gemma":[0.000045321918,0.00019521062,0.00038063727,0.0015809489,0.000028538156,0.000010744488,0.00007736384,0.00023193016,0.00056441914],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041659137,0.000027469187,0.0013012761,0.0000844421,0.002490799,0.000013313147,0.000008030527,0.0000017247838,0.0012958511,0.000039529434,0.9945382,0.00019521984],"study_design_scores_gemma":[0.00010860132,0.000004866656,0.000005562485,0.000011698088,0.0036678936,3.6280113e-7,0.000070550166,0.00031798996,0.030340387,0.00001962268,0.96515244,0.00029999967],"about_ca_topic_score_codex":0.0004648873,"about_ca_topic_score_gemma":0.0000925748,"teacher_disagreement_score":0.4400895,"about_ca_system_score_codex":0.00002779539,"about_ca_system_score_gemma":0.000022061857,"threshold_uncertainty_score":0.79604584},"labels":[],"label_agreement":null},{"id":"W4233975524","doi":"10.21611/qirt.2008.02_01_14","title":"Chosen aspects of thermographic studies on detection of physiological disorders and mechanical defects in apples","year":2008,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université Laval","keywords":"Thermography; Computer science; Biomedical engineering; Medicine; Optics; Infrared; Physics","score_opus":0.0324178143200024,"score_gpt":0.28046363714786876,"score_spread":0.24804582282786636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233975524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960905,0.0009088309,0.000026367454,0.000012767771,0.000008254046,0.000033606128,0.0000012785372,0.000021617867,0.0028967995],"genre_scores_gemma":[0.99816304,0.0017418052,0.000034341232,0.000010788755,0.0000118684675,0.0000075954063,8.505113e-7,0.000005156393,0.000024526074],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993945,0.000014122544,0.00018961546,0.0001807556,0.00011361467,0.00010743086],"domain_scores_gemma":[0.9995947,0.00016875452,0.00008333241,0.000111076835,0.000019903522,0.000022246708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048345722,0.0001003183,0.00029475382,0.00012832563,0.000030885334,0.0000010834864,0.000061702085,0.00008234619,0.0000525858],"category_scores_gemma":[0.0001349225,0.0000715965,0.000073113,0.00041784157,0.00017256204,0.00002002342,0.000031725252,0.00010271176,5.155921e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005306709,0.00021163355,0.00935732,0.00007540852,0.000098957855,0.00000142681,0.00010724674,0.000013338648,0.9883472,0.001455445,0.0000041374337,0.0002748392],"study_design_scores_gemma":[0.00034196375,0.00015231085,0.027371995,0.000014465556,0.000030690324,0.0000017439095,0.0008040971,0.000041326795,0.96772456,0.0034366443,0.0000022901322,0.00007790241],"about_ca_topic_score_codex":0.000051034425,"about_ca_topic_score_gemma":0.00011703465,"teacher_disagreement_score":0.02062261,"about_ca_system_score_codex":0.000012929236,"about_ca_system_score_gemma":0.0000062821446,"threshold_uncertainty_score":0.29196206},"labels":[],"label_agreement":null},{"id":"W4234169617","doi":"10.1117/12.833478","title":"Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada)","funders":"","keywords":"Hyperspectral imaging; Chemical imaging; Remote sensing; Radiometric calibration; Spectrometer; Computer science; Calibration; Data acquisition; Imaging spectrometer; Imaging spectroscopy; Infrared; Image sensor; Optics; Artificial intelligence; Physics","score_opus":0.007640972655028323,"score_gpt":0.2260876276612109,"score_spread":0.21844665500618257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234169617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99585015,0.00017128664,0.00009471029,0.0010404976,0.000033793673,0.00016348339,0.000019895208,0.00009232023,0.0025338752],"genre_scores_gemma":[0.9740694,0.00007625949,0.025249813,0.00005694388,0.00023135699,0.000029797893,0.000006185297,0.000030424137,0.0002497928],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835616,6.129093e-9,0.000459556,0.00037545172,0.00050529477,0.00030352146],"domain_scores_gemma":[0.99865973,0.00006379286,0.00030703723,0.000053299165,0.000810532,0.00010562364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021611624,0.00025540215,0.00031629734,0.00010125674,0.00008007567,0.00013827797,0.00037519375,0.00012263798,0.0000191451],"category_scores_gemma":[0.00029485385,0.0002090979,0.00027718558,0.00036095854,0.00016948333,0.0003925875,0.0000475821,0.00026931724,4.885998e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015847756,0.00009623355,0.000391778,0.00020171504,0.00026274193,1.3831553e-7,0.000174471,0.000010741798,0.96989876,0.028163154,0.00020143129,0.00044033068],"study_design_scores_gemma":[0.00091315224,0.00009064819,0.0007277053,0.00008991434,0.00030061998,0.00003981069,0.0022156504,0.0070678764,0.98728806,0.00078194455,0.00023240299,0.00025222506],"about_ca_topic_score_codex":0.00000484159,"about_ca_topic_score_gemma":4.7943043e-8,"teacher_disagreement_score":0.02738121,"about_ca_system_score_codex":0.00020853292,"about_ca_system_score_gemma":0.000018240567,"threshold_uncertainty_score":0.85267645},"labels":[],"label_agreement":null},{"id":"W4234450132","doi":"10.1007/978-94-007-0893-8_7","title":"Deriving Biomedical Diagnostics from Spectroscopic Data","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Principal component analysis; Computer science; Thyroid cancer; Biliary tract cancer; Cancer; Pattern recognition (psychology); Data mining; Mathematics; Medicine; Artificial intelligence; Internal medicine","score_opus":0.06830500683343574,"score_gpt":0.2853871301125784,"score_spread":0.21708212327914264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234450132","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015580449,0.0069409804,0.0024739096,0.000086541404,0.00021767618,0.00004648322,0.0009295863,0.00026011295,0.9888889],"genre_scores_gemma":[0.0049215895,0.008239091,0.0038635717,0.00027127142,0.002025923,0.0000048244,0.005709337,0.00016385298,0.9748005],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975114,0.0000034210652,0.0005292466,0.0010372798,0.0004935744,0.0004250995],"domain_scores_gemma":[0.9966257,0.0004708933,0.00026574935,0.002322386,0.000044408716,0.00027084007],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006460357,0.00055265625,0.000753826,0.00019559379,0.000089713714,0.00006754828,0.0016959843,0.00084385223,0.25945663],"category_scores_gemma":[0.00027811187,0.00050267164,0.00015153391,0.000069170215,0.00026062355,0.000100238154,0.0010036855,0.0008058255,0.0017455213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007601191,0.00076918624,0.0040724957,0.000746257,0.007340044,0.0012371258,0.00023956067,9.084782e-8,0.018814227,0.111333184,0.84042865,0.0149431545],"study_design_scores_gemma":[0.00047971067,0.000047909056,0.00005678051,0.00030246575,0.0026056522,0.000012211847,0.00004102129,0.000035559107,0.036928628,0.042532526,0.9156327,0.001324817],"about_ca_topic_score_codex":0.00039301987,"about_ca_topic_score_gemma":0.00006717083,"teacher_disagreement_score":0.2577111,"about_ca_system_score_codex":0.00011987773,"about_ca_system_score_gemma":0.00015104884,"threshold_uncertainty_score":0.9997425},"labels":[],"label_agreement":null},{"id":"W4234753471","doi":"10.1002/9781119274780.ch160","title":"Empirical Modeling of The Baking Furnace To Predict Baked Anode Properties","year":2016,"lang":"en","type":"other","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alcoa (Canada); Université Laval","funders":"Alcoa","keywords":"Anode; Categorical variable; Work (physics); Process engineering; Soft sensor; Position (finance); Materials science; Environmental science; Metallurgy; Computer science; Mechanical engineering; Engineering; Statistics; Mathematics; Electrode; Chemistry","score_opus":0.046615655552971,"score_gpt":0.3056578763199722,"score_spread":0.25904222076700123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234753471","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024854722,0.0010321537,0.0037085891,0.0004051082,0.000079977166,0.00009570419,0.000029487754,0.00017651464,0.991987],"genre_scores_gemma":[0.18007454,0.000059673075,0.00039784648,0.00015764064,0.00033385208,0.00001365706,0.000001663064,0.00018507172,0.8187761],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998767,0.000013361204,0.0002716778,0.00031990255,0.00036593896,0.00026208142],"domain_scores_gemma":[0.99913293,0.000027062939,0.00014715866,0.0005762019,0.00004385125,0.000072772236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005778861,0.00025033733,0.0003955811,0.00014814576,0.000042599746,0.000017949795,0.00055099843,0.00028622762,0.020347524],"category_scores_gemma":[0.00010662707,0.0001235048,0.00018127887,0.00025608315,0.00006182821,0.00002065687,0.00019237935,0.00018703428,0.00007238696],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006375942,0.00019947976,0.0056359232,0.0008653764,0.000975373,0.0000033014287,0.00032624186,0.0003526573,0.11283537,0.00011057211,0.878011,0.0006209632],"study_design_scores_gemma":[0.00079584063,0.00003470816,0.000015300395,0.0020987594,0.00077018375,0.000011538017,0.00067779695,0.0070324424,0.6267663,0.00011930031,0.36062577,0.0010520689],"about_ca_topic_score_codex":0.00018002419,"about_ca_topic_score_gemma":0.000047239206,"teacher_disagreement_score":0.51738524,"about_ca_system_score_codex":0.00007259635,"about_ca_system_score_gemma":0.00008696733,"threshold_uncertainty_score":0.980548},"labels":[],"label_agreement":null},{"id":"W4235705222","doi":"10.1063/1.3086111","title":"Focus on spectroscopy","year":2009,"lang":"en","type":"article","venue":"Physics Today","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Spectroscopy; Materials science; Engineering physics; Physics; Optics; Astronomy","score_opus":0.015151139585799908,"score_gpt":0.2809321596236575,"score_spread":0.2657810200378576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235705222","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18630248,0.00025077234,0.0022387123,0.001344156,0.00006447696,0.000038498678,0.000013391726,0.00026854224,0.809479],"genre_scores_gemma":[0.9959275,0.000028986655,0.0004439995,0.00045115667,0.0006538732,0.0000033592214,0.000010333845,0.000014655881,0.0024660928],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991057,0.000004454412,0.00012507434,0.00026321807,0.0002115972,0.00028997884],"domain_scores_gemma":[0.9994255,0.000047734982,0.000060463255,0.00036674354,0.000022860115,0.00007675336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000028215534,0.0001677274,0.00020291352,0.000037150945,0.000080190686,0.000036011377,0.00020003723,0.000058956633,0.0011425496],"category_scores_gemma":[0.00002080002,0.00015335041,0.00011934441,0.00035031527,0.000025337047,0.00006601687,0.000013562841,0.00020954252,0.00035694035],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005370592,0.0005934283,0.0006763173,0.000013459381,0.00007972957,0.000012692553,0.000107646076,0.000034340497,0.924823,0.052407704,0.008981487,0.012216486],"study_design_scores_gemma":[0.00021649271,0.000069601105,0.00019747062,0.000007371991,0.00004831839,9.872081e-7,0.000019924806,0.000045866556,0.928661,0.0693635,0.0011972463,0.00017223197],"about_ca_topic_score_codex":0.0000104383025,"about_ca_topic_score_gemma":4.4873804e-7,"teacher_disagreement_score":0.8096251,"about_ca_system_score_codex":0.00007601084,"about_ca_system_score_gemma":0.000018698105,"threshold_uncertainty_score":0.9997705},"labels":[],"label_agreement":null},{"id":"W4235840963","doi":"10.22215/etd/2007-07648","title":"Aspects of nonlinear dielectric spectroscopy in the noninvasive determination of glucose","year":2007,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage","funders":"Ontario Centres of Excellence","keywords":"Dielectric spectroscopy; Nonlinear system; Dielectric; Polarization (electrochemistry); Biological system; Computer science; Biosensor; Spectroscopy; Biomedical engineering; Materials science; Electronic engineering; Chemistry; Physics; Electrode; Medicine; Nanotechnology; Optoelectronics; Engineering; Biology; Electrochemistry","score_opus":0.011739861363815874,"score_gpt":0.3147258695958383,"score_spread":0.30298600823202243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235840963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5834947,0.0014529964,0.00019239091,0.000010837918,0.000051232186,0.00012165152,0.000016119877,0.000018419778,0.41464162],"genre_scores_gemma":[0.9924869,0.00064291677,0.0018000884,0.000027888293,0.00013241447,0.000016940929,0.0006025412,0.000037913127,0.004252377],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980149,0.000021443213,0.0007347074,0.0003362229,0.00057865627,0.00031401555],"domain_scores_gemma":[0.99815136,0.0004492939,0.0007165977,0.0004322661,0.00021649481,0.000033985874],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025291654,0.00030716034,0.00062358147,0.0007779496,0.00003620278,0.00001547377,0.0005286264,0.0004228512,0.0010922892],"category_scores_gemma":[0.0002936183,0.00023596932,0.00024002428,0.001831532,0.000051457726,0.000061592,0.000013507141,0.00050575874,0.0000072746197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032084028,0.0009887775,0.0010342365,0.0015328822,0.00016714918,0.00006126075,0.0017595614,0.0000024563938,0.9884329,0.0024764016,0.000111885514,0.0031116612],"study_design_scores_gemma":[0.0003490949,0.000105371306,0.0007556047,0.00008867238,0.00028929455,0.0000065303207,0.0029046491,0.00012889698,0.9944486,0.00066209043,0.000052966454,0.00020823783],"about_ca_topic_score_codex":0.0003347959,"about_ca_topic_score_gemma":0.0012161089,"teacher_disagreement_score":0.41038924,"about_ca_system_score_codex":0.00012516633,"about_ca_system_score_gemma":0.0002114655,"threshold_uncertainty_score":0.9998208},"labels":[],"label_agreement":null},{"id":"W4237287462","doi":"10.21611/qirt.2010.149","title":"Image analysis in computer vision: A high level means for Non-Destructive evaluation of the marbling in the beef meat","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Marbled meat; Computer vision; Computer science; Artificial intelligence","score_opus":0.0563973930933514,"score_gpt":0.3460772393015124,"score_spread":0.289679846208161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237287462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886039,0.000012863327,0.00044596908,0.00079319096,0.00017238696,0.0003351067,0.00016279044,0.0000062489685,0.009467558],"genre_scores_gemma":[0.9954995,0.000009008997,0.0042361245,0.000058442154,0.000043125197,0.00008349453,0.000011985601,0.000011398758,0.00004692658],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99816304,0.000029817593,0.00046676397,0.00030784542,0.00086460717,0.00016793594],"domain_scores_gemma":[0.9971147,0.0003820219,0.0006421974,0.00022012623,0.0016211249,0.000019811949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097090966,0.00019442175,0.00031722657,0.000657255,0.00008670457,0.00007533339,0.0013138894,0.00011493922,0.0002985149],"category_scores_gemma":[0.00050898205,0.00011134454,0.00038363374,0.0018928781,0.00037616582,0.00023571738,0.000107834494,0.00046791745,6.2801377e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000479554,0.00046209045,0.09503947,0.000078735626,0.0013827899,1.8481214e-7,0.004556099,0.0001306382,0.69972754,0.1971769,0.00014237236,0.00082362146],"study_design_scores_gemma":[0.0019191733,0.00014952988,0.5261525,0.00024434592,0.0010035897,0.0000020574473,0.0055513466,0.09598184,0.23100339,0.13767074,0.000012339312,0.00030918422],"about_ca_topic_score_codex":0.00016651265,"about_ca_topic_score_gemma":0.0001759035,"teacher_disagreement_score":0.46872416,"about_ca_system_score_codex":0.00004196161,"about_ca_system_score_gemma":0.00006677792,"threshold_uncertainty_score":0.45404986},"labels":[],"label_agreement":null},{"id":"W4239125148","doi":"10.5539/jmr.v7n2","title":"A Rational Approximation for Efficient Computation of the Voigt Function in Quantitative Spectroscopy","year":2015,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"York University","funders":"National Research Council Canada; York University","keywords":"Computation; Mathematics; HITRAN; Rational function; Voigt profile; Function (biology); Applied mathematics; Discrete mathematics; Algorithm; Pure mathematics; Spectroscopy; Quantum mechanics; Spectral line; Physics","score_opus":0.18981876308282702,"score_gpt":0.4478901841709403,"score_spread":0.25807142108811326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239125148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8028298,0.00025259113,0.19432703,0.00033528628,0.00005787218,0.00016155331,0.000005577818,0.0000033982544,0.002026895],"genre_scores_gemma":[0.96655726,0.000006582495,0.03321785,0.0000049728296,0.00006749441,0.00000929989,0.0000025041845,0.000009480933,0.00012454533],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817234,0.00006285561,0.00055751024,0.00008055827,0.0009714898,0.00015524171],"domain_scores_gemma":[0.99752456,0.0006880134,0.000466388,0.00010562392,0.0011659574,0.000049440776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024691192,0.00006865801,0.00022496417,0.0003584086,0.000059110003,0.000030925457,0.00020067632,0.000058749392,0.000032622647],"category_scores_gemma":[0.0016427778,0.000046371148,0.00010151434,0.00071942824,0.000091073474,0.00007514998,0.0000338707,0.0002893256,0.000002664834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016915366,0.0048445673,0.004066678,0.0026431025,0.0004996926,0.000004755824,0.01528753,0.079191856,0.80572027,0.079257414,0.006231781,0.000560818],"study_design_scores_gemma":[0.0027043573,0.00061759213,0.0006052681,0.00033592738,0.000118496915,0.000027411215,0.019086566,0.29403394,0.5753614,0.10687402,0.000101248515,0.00013381259],"about_ca_topic_score_codex":0.000006784397,"about_ca_topic_score_gemma":0.000005084864,"teacher_disagreement_score":0.23035891,"about_ca_system_score_codex":0.00025876038,"about_ca_system_score_gemma":0.00027524985,"threshold_uncertainty_score":0.19666772},"labels":[],"label_agreement":null},{"id":"W4239414934","doi":"10.1093/jicru_1.3.77","title":"Appendix 3. Literature Review of Response Calculations for Multi-Sphere Spectrometers","year":2001,"lang":"en","type":"article","venue":"Journal of the ICRU","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Spectrometer; Computer science; Physics; Optics","score_opus":0.03130415193246047,"score_gpt":0.3352346937889072,"score_spread":0.30393054185644675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239414934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59967643,0.3771432,0.0073589473,0.0110708745,0.0005358473,0.00040116976,0.00021071272,0.00003366133,0.0035691487],"genre_scores_gemma":[0.80590713,0.09485092,0.051579196,0.0026193394,0.0013503861,0.0000184203,0.00003943544,0.00011242348,0.04352277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905056,0.00004030768,0.00046102292,0.00008184167,0.00022472467,0.00014157],"domain_scores_gemma":[0.9986502,0.00019092651,0.0006016761,0.00027014047,0.00023020829,0.000056844598],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039653358,0.00010545675,0.000324656,0.00008563443,0.00006713288,0.000018513472,0.00036445,0.00006858625,0.001076535],"category_scores_gemma":[0.0008303974,0.000065054635,0.0004569669,0.0006220768,0.000036599413,0.000084588086,0.000028085871,0.00024405665,0.0000040723476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024687413,0.00079911517,0.0060889046,0.0066825277,0.0015000742,0.00010341939,0.00048183795,0.00011711736,0.884845,0.00024028981,0.09426489,0.0024080626],"study_design_scores_gemma":[0.0041056024,0.00035337935,0.0036196744,0.020244243,0.0027478116,0.0024558362,0.0005665916,0.0003956534,0.6376476,0.0005378312,0.326759,0.0005667442],"about_ca_topic_score_codex":0.0000019946867,"about_ca_topic_score_gemma":0.0000020134783,"teacher_disagreement_score":0.28229228,"about_ca_system_score_codex":0.00008539572,"about_ca_system_score_gemma":0.000071746,"threshold_uncertainty_score":0.9998366},"labels":[],"label_agreement":null},{"id":"W4239742656","doi":"10.1533/9780857095770.400","title":"Grain quality evaluation by computer vision","year":2012,"lang":"en","type":"book-chapter","venue":"Woodhead Publishing Limited eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Kernel (algebra); Starch; Water content; Agricultural engineering; Quality (philosophy); Grain quality; Computer vision; Artificial intelligence; Environmental science; Computer science; Food science; Mathematics; Horticulture; Biology; Engineering; Physics","score_opus":0.0549531098317387,"score_gpt":0.3159228940930353,"score_spread":0.26096978426129663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239742656","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013160479,0.0036191556,0.0005876098,0.00035532095,0.00049967377,0.00025865776,0.00035048948,0.0005062845,0.99250674],"genre_scores_gemma":[0.10624675,0.00007263284,0.0012699771,0.0012605396,0.0031962914,0.00006683169,0.006383919,0.000329239,0.88117385],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950871,0.000087690394,0.0010890627,0.0010609622,0.0018812444,0.0007939453],"domain_scores_gemma":[0.9963112,0.00044406348,0.0009893447,0.0012514031,0.0005759355,0.00042809217],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019274139,0.00083343114,0.00097550236,0.000574501,0.00029640942,0.0015428807,0.00091862644,0.0017779444,0.0068064625],"category_scores_gemma":[0.00029766245,0.0008451096,0.0004982254,0.00011805352,0.00018979642,0.00090312073,0.00032910015,0.0019250929,0.00017899374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015035633,0.00025805627,0.00023561201,0.0007385431,0.0013829826,0.000011043575,0.00070047355,0.000005739316,0.01677892,0.033481333,0.5738756,0.37238136],"study_design_scores_gemma":[0.002028976,0.00012591231,0.00006688842,0.0005341803,0.0021964835,0.00002720022,0.0000965509,0.00074644806,0.017774863,0.015632086,0.95840174,0.0023686436],"about_ca_topic_score_codex":0.00015480498,"about_ca_topic_score_gemma":0.000028809007,"teacher_disagreement_score":0.38452616,"about_ca_system_score_codex":0.0006676576,"about_ca_system_score_gemma":0.00019498444,"threshold_uncertainty_score":0.999518},"labels":[],"label_agreement":null},{"id":"W4243214206","doi":"10.26434/chemrxiv.14477691","title":"Machine Learning as a Tool to Engineer Microstructures: Morphological Prediction of Tannin-Based Colloids Using Bayesian Surrogate Models","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Australian Institute for Musculoskeletal Science; Academy of Finland; Finnish Center for Artificial Intelligence; Aalto-Yliopisto; European Commission","keywords":"Adhesion; Colloid; Tannic acid; Biological system; Surrogate model; Suspension (topology); Artificial intelligence; Nanotechnology; Particle (ecology); Materials science; Computer science; Colloidal particle; Machine learning; Mathematics; Chemistry; Chemical engineering; Engineering; Biology","score_opus":0.02448849709255734,"score_gpt":0.27051668377848404,"score_spread":0.2460281866859267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243214206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9546341,0.0014194135,0.041925255,0.00006237821,0.00013993555,0.00015438681,0.0001083321,0.00019420403,0.0013619956],"genre_scores_gemma":[0.9847425,0.000059228023,0.013041214,0.000121100544,0.00018086557,0.00003906576,0.0008639883,0.00007974584,0.0008722997],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974311,0.000035932455,0.00067812664,0.0009241898,0.00046422955,0.0004664248],"domain_scores_gemma":[0.9983491,0.00011148345,0.00035552366,0.00070503086,0.00028232933,0.00019655084],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019497414,0.00055773876,0.00093996513,0.00030011084,0.00011643274,0.00011003814,0.0004638041,0.00085396966,0.0022951704],"category_scores_gemma":[0.00045810686,0.0005671796,0.0004589685,0.0007452695,0.000096999356,0.000061684594,0.00040266576,0.0012773986,0.0000035942976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007631802,0.00009622003,0.0031317594,0.0004897453,0.0002466655,0.0000609634,0.00015946393,0.21892299,0.7767244,0.000011771327,0.000038061637,0.000041668605],"study_design_scores_gemma":[0.0005190478,0.000035532375,0.00010309068,0.00023682707,0.00044024317,0.000026078706,0.00017258344,0.16243415,0.8352377,0.00025560136,0.00009965057,0.00043953024],"about_ca_topic_score_codex":0.00023204568,"about_ca_topic_score_gemma":0.000002061023,"teacher_disagreement_score":0.058513306,"about_ca_system_score_codex":0.00034393414,"about_ca_system_score_gemma":0.00034270276,"threshold_uncertainty_score":0.99967796},"labels":[],"label_agreement":null},{"id":"W4243356458","doi":"10.1211/jpp.61.08.0001","title":"Analysis of solid-state transformations of pharmaceutical compounds using vibrational spectroscopy","year":2009,"lang":"en","type":"article","venue":"Journal of Pharmacy and Pharmacology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":212,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Drug Research and Development","funders":"","keywords":"Raman spectroscopy; Amorphous solid; Solid-state; Context (archaeology); Spectroscopy; Infrared spectroscopy; Biopharmaceutical; Infrared; Chemistry; Dissolution; Active ingredient; Materials science; Physical chemistry; Crystallography; Organic chemistry; Physics; Optics; Bioinformatics","score_opus":0.04057305995270358,"score_gpt":0.4091646617791534,"score_spread":0.3685916018264498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243356458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9795803,0.0017806592,0.016949981,0.0004979191,0.00011693144,0.000042347365,0.0001417096,0.000008503718,0.0008816545],"genre_scores_gemma":[0.99557257,0.00137942,0.0025265198,0.00033384512,0.00015671855,6.003245e-7,0.000011071041,0.0000071999702,0.000012072046],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998222,0.000060666855,0.0010468044,0.00013120518,0.00029763326,0.00024170075],"domain_scores_gemma":[0.99851745,0.000219763,0.000749672,0.00006920037,0.00026913368,0.00017480046],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003088768,0.00017493965,0.0007574871,0.0007917069,0.000083103354,0.0000107561045,0.00021810205,0.00005489077,0.003337961],"category_scores_gemma":[0.0000162665,0.00015997764,0.00035343607,0.0010947456,0.00018124105,0.000294786,0.000016159876,0.0003766162,4.588679e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006342932,0.00048054033,0.005512962,0.00008593375,0.0028023156,0.00001292976,0.00035289789,0.0033818004,0.9859599,0.000056314013,0.0002838753,0.0004362595],"study_design_scores_gemma":[0.0023583877,0.00010165729,0.00064648635,0.0000084810035,0.008208386,0.0000774754,0.00007052253,0.042399418,0.94538575,0.0002334083,0.00038603164,0.00012399859],"about_ca_topic_score_codex":0.000007792517,"about_ca_topic_score_gemma":7.798093e-7,"teacher_disagreement_score":0.04057413,"about_ca_system_score_codex":0.000057267673,"about_ca_system_score_gemma":0.00015051271,"threshold_uncertainty_score":0.99757314},"labels":[],"label_agreement":null},{"id":"W4245487586","doi":"10.2307/3315858","title":"Nonparametric estimation of renewal processes from count data","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Boulevard; Humanities; Art; Citation; Library science; Art history; Geography; Archaeology; Computer science","score_opus":0.0476254757573944,"score_gpt":0.2782889601620581,"score_spread":0.23066348440466372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245487586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38328922,0.016839834,0.5609432,0.00016341184,0.00059861556,0.00009818931,0.020646075,0.00001699133,0.01740448],"genre_scores_gemma":[0.9206172,0.0001345827,0.07883788,0.000027048447,0.00005802172,2.7862092e-7,0.00017551973,0.000012329569,0.00013713371],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901474,0.00001153246,0.00044477382,0.00011745382,0.00023355696,0.00017794261],"domain_scores_gemma":[0.9981784,0.0003673131,0.00046069146,0.00029223345,0.00040373765,0.00029760998],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014554057,0.00010008371,0.00026224594,0.00029363154,0.00005635504,0.000042927244,0.00039147362,0.0000676241,0.001827997],"category_scores_gemma":[0.0047086007,0.00009700697,0.000021192944,0.00068431674,0.00008717419,0.00012555587,0.0000079473475,0.00015813392,0.000004710761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033493032,0.0012132452,0.4161182,0.005492841,0.006027081,0.003130813,0.004357834,0.018611668,0.020875698,0.040580284,0.42569917,0.057558224],"study_design_scores_gemma":[0.0077749128,0.0009436856,0.009781739,0.0013141374,0.009224264,0.0010820302,0.0071849213,0.021575447,0.65356344,0.16203347,0.12236427,0.0031576997],"about_ca_topic_score_codex":0.0050830524,"about_ca_topic_score_gemma":0.005666646,"teacher_disagreement_score":0.63268775,"about_ca_system_score_codex":0.00013566313,"about_ca_system_score_gemma":0.002555473,"threshold_uncertainty_score":0.9990845},"labels":[],"label_agreement":null},{"id":"W4247460708","doi":"10.1016/b978-0-08-100596-5.00173-6","title":"Barley Grading and Marketing","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Marketing; Business; Grading (engineering); Production (economics); Value (mathematics); Quality (philosophy); Agricultural economics; Mathematics; Agricultural science; Agricultural engineering; Engineering; Economics; Statistics; Environmental science; Physics; Microeconomics","score_opus":0.02661811824556263,"score_gpt":0.26302232940349163,"score_spread":0.236404211157929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247460708","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029935673,0.012753598,0.0000012936251,0.000023493145,0.00006586238,0.00005683563,0.000025188961,0.000106849184,0.9866675],"genre_scores_gemma":[0.004091007,0.00031654353,0.00025289177,0.00007133145,0.0005003763,0.00000855907,0.00002475749,0.00010035786,0.99463415],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984702,0.000009326317,0.00035643717,0.0005139486,0.0003364669,0.00031361953],"domain_scores_gemma":[0.9988687,0.00015355991,0.00024151341,0.00042722112,0.00008683334,0.00022217011],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000343221,0.0004338556,0.00060542487,0.00018159559,0.0001152363,0.00007565765,0.0002341564,0.00042887568,0.0026803978],"category_scores_gemma":[0.000114346716,0.00042289335,0.00016395362,0.000019386753,0.00013511608,0.00003298685,0.00017534154,0.00058945635,0.00007279055],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031106465,0.000005522854,0.00012370822,0.00041737192,0.00037398262,0.000058733054,0.000089937865,8.933909e-8,0.0010017083,0.0021105204,0.0014863726,0.99430096],"study_design_scores_gemma":[0.0002449711,0.000010640561,0.000004516213,0.0003068809,0.0005525588,0.000047297817,0.000037119407,0.0000059222093,0.0012731921,0.007342852,0.98963815,0.0005359054],"about_ca_topic_score_codex":6.5499097e-7,"about_ca_topic_score_gemma":0.0000033417848,"teacher_disagreement_score":0.99376506,"about_ca_system_score_codex":0.00014469653,"about_ca_system_score_gemma":0.0000762021,"threshold_uncertainty_score":0.99982226},"labels":[],"label_agreement":null},{"id":"W4248321556","doi":"10.46427/gold2020.1904","title":"Before and after Spectra: Enhanced Chemical Speciation Below Former Detection Limits","year":2020,"lang":"en","type":"article","venue":"Goldschmidt Abstracts","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Genetic algorithm; Spectral line; Computer science; Physics; Biology; Evolutionary biology; Astronomy","score_opus":0.0100393719863421,"score_gpt":0.2328839853296943,"score_spread":0.22284461334335218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248321556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98204035,0.00021411647,0.00030466763,0.0005736595,0.00005118452,0.00006801724,0.000012160896,0.00014245824,0.016593408],"genre_scores_gemma":[0.99770564,0.00004945332,0.00043882965,0.0003499725,0.0008389256,0.000020446429,0.000026271657,0.00003062086,0.0005398611],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985668,0.0000046580008,0.0003403682,0.00045294914,0.0002692375,0.00036597945],"domain_scores_gemma":[0.9993188,0.000039064285,0.00015673474,0.00017645967,0.000054672568,0.00025427318],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000047801605,0.0002461679,0.00027503315,0.00006551258,0.0000678715,0.000076323944,0.00012419622,0.00024433772,0.0015399117],"category_scores_gemma":[0.00016971117,0.00024299526,0.00010349149,0.0003471899,0.000054886616,0.00026404497,0.000037143913,0.00035828238,0.00018309045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014854394,0.000057509085,0.0019085979,0.00011232085,0.000077520985,0.000015732328,0.0005303917,0.0000054662387,0.98865837,0.000016051174,0.00012699964,0.0083425185],"study_design_scores_gemma":[0.00044766834,0.000049237296,0.025535025,0.000013912973,0.00012547808,0.00000937396,0.0001203104,0.0000627785,0.9721386,0.00019430314,0.0010601876,0.00024312202],"about_ca_topic_score_codex":0.0000128948195,"about_ca_topic_score_gemma":0.00005038793,"teacher_disagreement_score":0.023626426,"about_ca_system_score_codex":0.00009321161,"about_ca_system_score_gemma":0.000024209075,"threshold_uncertainty_score":0.99937284},"labels":[],"label_agreement":null},{"id":"W4251664639","doi":"10.1007/978-1-4939-7131-2_101255","title":"Spectral Technique","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Environmental science; Computer science","score_opus":0.01873191029564432,"score_gpt":0.2648984438787206,"score_spread":0.24616653358307627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251664639","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001829142,0.00036940942,0.00067512866,0.000038151975,0.000034486427,0.000046453326,0.000024116356,0.0003065558,0.9984874],"genre_scores_gemma":[0.0011195063,0.00014561124,0.0018214486,0.00010236628,0.00089144346,0.000010416276,0.0000500774,0.000072336465,0.9957868],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890023,7.921933e-7,0.00024091471,0.00040185635,0.00021655012,0.000239632],"domain_scores_gemma":[0.9991672,0.000029350489,0.00012652946,0.00053016975,0.000057710316,0.00008901133],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000040377974,0.00033644994,0.00038517697,0.00016969357,0.00005066805,0.000029530554,0.00031910968,0.00062111695,0.4384424],"category_scores_gemma":[0.000013236213,0.0002995279,0.00025000938,0.000042112766,0.00014194983,0.000028826202,0.00007595822,0.0004192716,0.0019712085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052180985,0.0000916137,0.000056830155,0.00044805114,0.0014209421,0.00015470915,0.00005060442,1.2964834e-7,0.08684597,0.40170655,0.5079981,0.0011743254],"study_design_scores_gemma":[0.00006579012,0.000023035627,3.4219872e-7,0.00003475058,0.00023194234,0.000030447807,0.000008629379,9.521264e-7,0.47503442,0.04432956,0.47984946,0.0003906681],"about_ca_topic_score_codex":0.000011349957,"about_ca_topic_score_gemma":0.000007746495,"teacher_disagreement_score":0.4364712,"about_ca_system_score_codex":0.00012789713,"about_ca_system_score_gemma":0.000052532985,"threshold_uncertainty_score":0.9999457},"labels":[],"label_agreement":null},{"id":"W4252282010","doi":"10.3989/gya.2003.v54.i1.278","title":"Application of the Hazard Analysis and Critical Control Point (HACCP) system in a bottling line of virgin olive oil","year":2003,"lang":"en","type":"article","venue":"Grasas y Aceites","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cameco (Canada); Adidas (Canada)","funders":"","keywords":"Bottling line; Hazard analysis and critical control points; Critical control point; Hazard analysis; Pulp and paper industry; Olive oil; Hazard; Business; Environmental science; Food science; Chemistry; Food safety; Engineering; Reliability engineering","score_opus":0.0065039327190835276,"score_gpt":0.2562906665297792,"score_spread":0.2497867338106957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252282010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98559153,0.0030169808,0.0087124845,0.00009961866,0.000013664594,0.000043906457,0.000029376746,0.000022053333,0.0024703841],"genre_scores_gemma":[0.9993074,0.00006122964,0.00047940653,0.000021085787,0.000017430795,0.000019998042,0.0000037550465,0.000008188231,0.000081487684],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990189,0.000045045228,0.00038670562,0.00021145897,0.00017873278,0.00015915188],"domain_scores_gemma":[0.99894327,0.00044164681,0.00016813514,0.00030699687,0.00009862083,0.000041335214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023165083,0.00011677758,0.00042509436,0.00020216592,0.000043128606,0.000012319573,0.00013708315,0.00009679216,0.00005710492],"category_scores_gemma":[0.000589747,0.00008872068,0.00015002074,0.0012282971,0.00012851553,0.00004159365,0.00002551489,0.00012949227,0.0000010776956],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004669705,0.00018716855,0.69911885,0.001012613,0.0006109119,0.0000017064132,0.00024959995,0.000343287,0.28890195,0.009164411,0.000011602199,0.0003511775],"study_design_scores_gemma":[0.0009175576,0.00002365733,0.0069321985,0.00011628542,0.0020326318,0.000007087644,0.002537147,0.011305799,0.9754524,0.00044263905,0.000052625743,0.00017992841],"about_ca_topic_score_codex":0.00011015944,"about_ca_topic_score_gemma":0.000058801932,"teacher_disagreement_score":0.69218665,"about_ca_system_score_codex":0.000046210087,"about_ca_system_score_gemma":0.000022039047,"threshold_uncertainty_score":0.36179242},"labels":[],"label_agreement":null},{"id":"W4252636014","doi":"10.1504/ijpti.2016.083690","title":"Development of an objective freshness index for a variety of Mediterranean eggplant","year":2016,"lang":"en","type":"article","venue":"International Journal of Postharvest Technology and Innovation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Solanum; Horticulture; Mediterranean climate; Mathematics; Agriculture; Botany; Biology; Ecology","score_opus":0.01958909260846098,"score_gpt":0.3097978358699167,"score_spread":0.29020874326145574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252636014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97526085,0.000055770404,0.02379651,0.00053415034,0.00010874785,0.0000280813,0.000041490086,0.000009730542,0.0001646521],"genre_scores_gemma":[0.9931203,0.000017149785,0.006697481,0.000021484784,0.00006912006,0.0000038644866,0.000015492737,0.0000059658614,0.00004910108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989725,0.0000045304296,0.0006344549,0.00009835736,0.00021253806,0.000077629],"domain_scores_gemma":[0.99772215,0.00007320193,0.0008376748,0.000074968724,0.0012758642,0.000016125512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024355591,0.00008067161,0.00020669379,0.0013211551,0.00002633526,0.0000065671784,0.00026909445,0.000158892,0.00007776469],"category_scores_gemma":[0.00035789981,0.000057699664,0.000030823834,0.00057714834,0.00014094597,0.00025905838,0.000036223737,0.000102610466,1.7178603e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004154163,0.00019744513,0.01930486,0.000035517463,0.00045617486,0.0000039436327,0.0002554207,7.735857e-7,0.897105,0.032960914,0.000034985977,0.049229592],"study_design_scores_gemma":[0.0011586369,0.000136063,0.0077714166,0.00011251201,0.00003221944,0.00010023477,0.00043843652,0.000024287108,0.98149353,0.0079584755,0.00070096226,0.00007324311],"about_ca_topic_score_codex":0.0000026786388,"about_ca_topic_score_gemma":0.00000876919,"teacher_disagreement_score":0.084388554,"about_ca_system_score_codex":0.0000576232,"about_ca_system_score_gemma":0.0001163126,"threshold_uncertainty_score":0.2352924},"labels":[],"label_agreement":null},{"id":"W4253191119","doi":"10.22215/etd/2018-13404","title":"Signal Processing for Raman Spectroscopy","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Health Canada; University of Ottawa","keywords":"Raman spectroscopy; Preprocessor; Noise (video); Computer science; SIGNAL (programming language); Signal processing; Artifact (error); Artificial intelligence; Biological system; Algorithm; Pattern recognition (psychology); Physics; Digital signal processing; Optics","score_opus":0.016768556888121337,"score_gpt":0.3256813345025409,"score_spread":0.30891277761441954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253191119","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088106945,0.0022530402,0.007847046,0.00004823405,0.00021563176,0.00024402454,0.000063645726,0.000469954,0.9007515],"genre_scores_gemma":[0.27684933,0.00008349969,0.026236286,0.00015518609,0.003188558,0.0002583999,0.0051697684,0.00022586837,0.68783313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984078,0.0000025930453,0.0003471495,0.00055080827,0.0002665209,0.0004251211],"domain_scores_gemma":[0.99910307,0.000050818824,0.0002828331,0.00024178726,0.00022874723,0.00009276268],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000067771005,0.00037210103,0.0004688603,0.00019136949,0.0002209196,0.00014154722,0.00033630317,0.00048086242,0.019458313],"category_scores_gemma":[0.000039332048,0.00034006638,0.00025203757,0.00031924658,0.000038722268,0.000081878425,0.000011575904,0.00025274826,0.0000816234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071105594,0.00033364282,0.00023302839,0.004535153,0.00060670415,0.0000051465527,0.0006265796,5.8212544e-7,0.90397465,0.00057642243,0.072457865,0.015939182],"study_design_scores_gemma":[0.00032995292,0.000057143803,0.0000134587035,0.00008255594,0.0005537234,0.0000017218317,0.0013890524,0.00021110286,0.98035187,0.0012407498,0.0153365135,0.00043213443],"about_ca_topic_score_codex":0.000022201662,"about_ca_topic_score_gemma":0.00006940366,"teacher_disagreement_score":0.21291837,"about_ca_system_score_codex":0.000117060095,"about_ca_system_score_gemma":0.00020873551,"threshold_uncertainty_score":0.9999051},"labels":[],"label_agreement":null},{"id":"W4253288028","doi":"10.2118/2005-111","title":"Chemically Assisted Ignition Technologies for a Light Oil Air Injection Process","year":2005,"lang":"en","type":"article","venue":"Canadian International Petroleum Conference","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ignition system; Process (computing); Process engineering; Secondary air injection; Materials science; Environmental science; Automotive engineering; Petroleum engineering; Computer science; Engineering; Aerospace engineering; Operating system","score_opus":0.017035371357664298,"score_gpt":0.26749215809496024,"score_spread":0.25045678673729593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253288028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7175219,0.00018978729,0.0010515697,0.016806778,0.00019442049,0.000050336566,0.00031763315,0.00044201873,0.26342553],"genre_scores_gemma":[0.9896695,0.000020681398,0.00040374332,0.00024245697,0.00024627458,0.00016103058,0.00022506983,0.000017411061,0.009013842],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988872,0.00000313888,0.00024115593,0.0003429045,0.00021100388,0.00031462792],"domain_scores_gemma":[0.9990993,0.00004278221,0.00012236495,0.00017441154,0.00042674885,0.00013437674],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000052182906,0.00017166416,0.00016639223,0.00039706036,0.00013568078,0.000080831494,0.0004698846,0.00020573704,0.0013849037],"category_scores_gemma":[0.00032941537,0.00018365015,0.00008737007,0.00022028056,0.00006583826,0.00021149557,0.000020544243,0.00021166506,0.000044152974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006502052,0.0006942643,0.02914986,0.0008769678,0.002106592,0.00005849471,0.00097505737,0.0021421262,0.75052226,0.048629873,0.032420576,0.13177375],"study_design_scores_gemma":[0.0015864156,0.00007775872,0.0008984945,0.00015764713,0.00018882306,0.00011829722,0.0017294927,0.018858705,0.78664124,0.0035979862,0.18524358,0.0009015514],"about_ca_topic_score_codex":0.0020300345,"about_ca_topic_score_gemma":0.020571696,"teacher_disagreement_score":0.27214757,"about_ca_system_score_codex":0.00071511127,"about_ca_system_score_gemma":0.0004273564,"threshold_uncertainty_score":0.999528},"labels":[],"label_agreement":null},{"id":"W4253422797","doi":"10.1111/insr.12065","title":"Discussion","year":2014,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics","score_opus":0.014633659135080745,"score_gpt":0.3445559530567201,"score_spread":0.32992229392163935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253422797","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019477295,0.012985713,0.2845101,0.0124582285,0.00026078816,0.00007018004,0.00016237346,0.00013810521,0.6892197],"genre_scores_gemma":[0.88375765,0.04226175,0.020419711,0.010813034,0.0011651423,0.00009388872,0.00086469867,0.0000601394,0.040563997],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992507,0.000011191953,0.00020788221,0.00016082662,0.00026205304,0.00010732152],"domain_scores_gemma":[0.9995147,0.00018423554,0.000053793043,0.00012412004,0.00004809531,0.00007508548],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000100435216,0.00007957181,0.00016840333,0.000021981625,0.000027822849,0.000019328543,0.0002034618,0.00002532856,0.037389852],"category_scores_gemma":[0.0013827387,0.00004749778,0.000052609328,0.00008485939,0.000034602042,0.000036390313,0.00003993792,0.000094533585,0.0004956853],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015536982,0.00020326803,0.0021614502,0.0016599998,0.00016332905,0.000012789894,0.000007782785,0.0000012659102,0.0024201134,0.42713928,0.10668293,0.45953226],"study_design_scores_gemma":[0.00013223647,0.0000103609445,0.0004104879,0.00048318555,0.00011570475,0.000009669871,0.0000030795895,0.00043679733,0.0013589598,0.009419409,0.9874867,0.00013341176],"about_ca_topic_score_codex":0.00000771724,"about_ca_topic_score_gemma":8.978451e-7,"teacher_disagreement_score":0.88356286,"about_ca_system_score_codex":0.00004171501,"about_ca_system_score_gemma":0.000008592893,"threshold_uncertainty_score":0.9634901},"labels":[],"label_agreement":null},{"id":"W4254047381","doi":"10.1007/978-1-4939-7131-2_101078","title":"Singular Value Decomposition = Principal Component Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Principal component analysis; Singular value decomposition; Value (mathematics); Decomposition; Component (thermodynamics); Mathematics; Statistics; Physics; Algorithm; Chemistry; Thermodynamics","score_opus":0.01778786902560732,"score_gpt":0.28722962010170544,"score_spread":0.26944175107609813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254047381","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044809454,0.0005621473,0.0043426524,0.000044530287,0.000055968034,0.00004830293,0.00005758836,0.00020901562,0.99019885],"genre_scores_gemma":[0.053375002,0.00012548998,0.0028080947,0.00016711571,0.00062493887,0.0000046890036,0.0012343562,0.00007746143,0.94158286],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979892,0.0000056824642,0.000519241,0.00067300827,0.00050644757,0.000306461],"domain_scores_gemma":[0.99853283,0.00007161265,0.0003351121,0.0007521058,0.00014457983,0.00016378085],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009526538,0.00047411848,0.00086897815,0.0006291928,0.00014325997,0.00007777662,0.0003524126,0.0005550416,0.1145514],"category_scores_gemma":[0.000012449036,0.00044855502,0.0008307628,0.00022074462,0.00014783141,0.000046673496,0.00013445319,0.0003514637,0.0007363932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006868216,0.0017806644,0.004035085,0.0017306537,0.1723071,0.00076745293,0.00082155503,0.0008594663,0.14086476,0.61857337,0.054087564,0.0034854875],"study_design_scores_gemma":[0.001353792,0.0002062979,0.00023992662,0.00023087369,0.078131706,0.000062055224,0.00008691301,0.005357054,0.41968614,0.032189567,0.4581991,0.004256577],"about_ca_topic_score_codex":0.00007214478,"about_ca_topic_score_gemma":0.000023439088,"teacher_disagreement_score":0.5863838,"about_ca_system_score_codex":0.0003066121,"about_ca_system_score_gemma":0.000045920056,"threshold_uncertainty_score":0.9997966},"labels":[],"label_agreement":null},{"id":"W4256067699","doi":"10.1515/iupac.85.0180","title":"Fluorescence Correlation Spectroscopy","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"EaglePicher (Canada)","funders":"","keywords":"Fluorescence correlation spectroscopy; Fluorescence; Spectroscopy; Diffusion; Focus (optics); Fluorescence spectroscopy; Chemistry; Physics; Optics; Materials science","score_opus":0.012068721593813931,"score_gpt":0.38202318694942927,"score_spread":0.3699544653556153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256067699","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012336373,0.0013486386,0.00059221825,0.00023176832,0.0003892348,0.00008321115,0.9961919,0.00015916012,0.0008805093],"genre_scores_gemma":[0.00008600473,0.0020826044,0.00019830745,0.000103056125,0.0015919169,0.000013541067,0.9930427,0.00005761124,0.0028242222],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965932,0.000024984007,0.0006171914,0.0008033782,0.0013148233,0.0006464594],"domain_scores_gemma":[0.9975515,0.00017008912,0.0004931803,0.0011953827,0.0003633377,0.00022653268],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022581767,0.00063008105,0.0008414253,0.00034243264,0.00017472968,0.000094583134,0.00074663333,0.0007145336,0.051848046],"category_scores_gemma":[0.0007776487,0.00050354097,0.00028856783,0.000526449,0.00021522274,0.00012862947,0.00016686419,0.00086673687,0.000020422058],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008990589,0.00019967108,0.00008321958,0.00019931569,0.0001919933,0.000043296946,0.0000043856408,0.0000010563432,0.003652697,0.000009015453,0.9951168,0.00040865145],"study_design_scores_gemma":[0.00070383056,0.000073665884,0.000012869099,0.00026313955,0.00057039823,0.000018046736,0.000026280899,0.000014941616,0.018798921,0.00019454086,0.9787132,0.00061015476],"about_ca_topic_score_codex":0.0001253425,"about_ca_topic_score_gemma":0.000094930634,"teacher_disagreement_score":0.051827624,"about_ca_system_score_codex":0.0010102617,"about_ca_system_score_gemma":0.0006401423,"threshold_uncertainty_score":0.9997416},"labels":[],"label_agreement":null},{"id":"W4280512262","doi":"10.3389/fpls.2022.836488","title":"A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy","year":2022,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"European Research Council; Centre National de la Recherche Scientifique; Agence Nationale de la Recherche; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Région Occitanie Pyrénées-Méditerranée; Deutsche Forschungsgemeinschaft","keywords":"Phenomics; Trait; Artificial intelligence; Computer science; Deep learning; Perspective (graphical); Machine learning; Ecology; Diversity (politics); Data science; Biochemical engineering; Biology; Genomics; Engineering; Sociology","score_opus":0.007976268451476148,"score_gpt":0.24771299297761398,"score_spread":0.23973672452613784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280512262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9629392,0.002203113,0.0014300405,0.00011028712,0.00041668335,0.00010003676,0.00012514308,0.00010524317,0.032570224],"genre_scores_gemma":[0.99296576,0.00021175762,0.0055647944,0.00009484547,0.00005949075,0.000025750756,0.000025525602,0.000017227276,0.0010348521],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981219,0.00001307288,0.00019666119,0.00064666633,0.0005011735,0.00052049954],"domain_scores_gemma":[0.99945664,0.00009313809,0.00012220959,0.00018626447,0.000023620245,0.000118113654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041481497,0.00017855126,0.00027630574,0.00039400443,0.0009914335,0.0001431033,0.00046342504,0.000039876973,0.0003181361],"category_scores_gemma":[0.00018768206,0.0001870039,0.000039319195,0.0010972328,0.00042302525,0.00013922264,0.00021218273,0.0006702556,0.0000034919726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021685923,0.00084300723,0.6442494,0.0001408313,0.00028055624,0.000690676,0.025950437,0.047761872,0.25760993,0.007991443,0.011388755,0.0009244969],"study_design_scores_gemma":[0.0023607318,0.0005300299,0.003115744,0.00007109824,0.00013055126,0.00020807634,0.13458484,0.6714272,0.16995476,0.005552338,0.010685893,0.0013787637],"about_ca_topic_score_codex":0.00008950954,"about_ca_topic_score_gemma":0.0000056148424,"teacher_disagreement_score":0.64113367,"about_ca_system_score_codex":0.00125003,"about_ca_system_score_gemma":0.00015844587,"threshold_uncertainty_score":0.7625798},"labels":[],"label_agreement":null},{"id":"W4280517196","doi":"10.3389/frans.2022.867938","title":"Review of Variable Selection Methods for Discriminant-Type Problems in Chemometrics","year":2022,"lang":"en","type":"article","venue":"Frontiers in Analytical Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"","keywords":"Partial least squares regression; Feature selection; Chemometrics; Linear discriminant analysis; Mathematics; Pattern recognition (psychology); Discriminant; Rank (graph theory); Artificial intelligence; Statistics; Block (permutation group theory); Dimensionality reduction; Variable (mathematics); Selection (genetic algorithm); Random variate; Curse of dimensionality; Computer science; Machine learning; Random variable; Combinatorics","score_opus":0.03466902877218367,"score_gpt":0.37949252130050615,"score_spread":0.34482349252832245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280517196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005706449,0.098250546,0.86564654,0.000979773,0.00080432947,0.0007534977,0.000031270905,0.00006994246,0.027757663],"genre_scores_gemma":[0.14807005,0.01127033,0.83534974,0.00082639704,0.00006744844,0.00024824246,0.000031387164,0.00003811767,0.0040982934],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983537,0.000037356185,0.0004325475,0.00041352265,0.00036022888,0.00040261738],"domain_scores_gemma":[0.99931103,0.0001536836,0.00013475114,0.00020617574,0.0001166567,0.00007771687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00260598,0.00010756954,0.00042416868,0.0007890211,0.00012151955,0.000015212051,0.00053888286,0.000041455103,0.00053949066],"category_scores_gemma":[0.0028117427,0.00010112175,0.00007145794,0.016123796,0.00022073665,0.00012326663,0.00015219575,0.00026358277,3.7153868e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041436174,0.004765462,0.4549442,0.03615906,0.00030504266,0.000008666144,0.0007051314,0.008471769,0.24973685,0.035068713,0.07417121,0.13524954],"study_design_scores_gemma":[0.0022441586,0.0007055976,0.00232333,0.001962754,0.0008665471,0.000030991894,0.0022778942,0.65143657,0.17466596,0.046131223,0.11588655,0.0014684419],"about_ca_topic_score_codex":0.00015716549,"about_ca_topic_score_gemma":0.0000024105725,"teacher_disagreement_score":0.6429648,"about_ca_system_score_codex":0.0006414142,"about_ca_system_score_gemma":0.00024568604,"threshold_uncertainty_score":0.7746949},"labels":[],"label_agreement":null},{"id":"W4280523150","doi":"10.1080/10837450.2022.2078348","title":"Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation","year":2022,"lang":"en","type":"article","venue":"Pharmaceutical Development and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Ascorbic acid; Near-infrared spectroscopy; Process analytical technology; Materials science; Analytical Chemistry (journal); Biological system; Chromatography; Chemistry; Chemical engineering; Optics","score_opus":0.06444672797517939,"score_gpt":0.3297383672982385,"score_spread":0.2652916393230591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280523150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920248,0.0010999887,0.00069084665,0.0044586356,0.000090851,0.00017601643,0.0000108375425,0.00022238912,0.0012256794],"genre_scores_gemma":[0.99609756,0.000049085935,0.0028619224,0.0004806425,0.000054045366,0.00017779929,0.00004733024,0.000016429305,0.00021516657],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856085,0.000030309857,0.00029237766,0.00042887373,0.00030140113,0.00038616362],"domain_scores_gemma":[0.9995336,0.00007159282,0.00006144116,0.00010521188,0.000046056888,0.000182106],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018828112,0.00019641095,0.0002527783,0.00020551188,0.00047834858,0.000051247203,0.00016933864,0.000112472066,0.0010034243],"category_scores_gemma":[0.000050166735,0.00019490832,0.000018862065,0.0005854487,0.00014427713,0.00009356695,0.00039729456,0.0005126525,0.0000067609826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002842021,0.00033193573,0.30650368,0.00024349835,0.00030474158,0.00007337395,0.00039251114,0.000015515647,0.54244024,0.0049810535,0.0011575107,0.14327171],"study_design_scores_gemma":[0.0011348937,0.000027027818,0.00076296355,0.000012909882,0.00009186282,0.0000643203,0.0002457925,0.0021420743,0.89535385,0.00039727372,0.09946014,0.00030690958],"about_ca_topic_score_codex":0.0000026577977,"about_ca_topic_score_gemma":6.2976653e-7,"teacher_disagreement_score":0.35291356,"about_ca_system_score_codex":0.00013205923,"about_ca_system_score_gemma":0.00007245261,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W4280574407","doi":"10.1007/s12247-022-09652-y","title":"Hierarchical Multivariate Curve Resolution Coupled to Raman Imaging for Fast Characterization of Pharmaceutical Tablets","year":2022,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Innovation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Pfizer (Canada); University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Pfizer Canada; Mitacs","keywords":"Principal component analysis; Outlier; Multivariate statistics; Chemical imaging; Raman spectroscopy; Computer science; Biological system; Chemometrics; Pattern recognition (psychology); Characterization (materials science); Data mining; Artificial intelligence; Analytical Chemistry (journal); Materials science; Chemistry; Machine learning; Nanotechnology; Optics; Chromatography; Physics","score_opus":0.05255272825113555,"score_gpt":0.38386768509543046,"score_spread":0.3313149568442949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280574407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81656945,0.00009707794,0.17889357,0.0034249725,0.00038501716,0.00022765128,0.00012875501,0.000031455646,0.00024206183],"genre_scores_gemma":[0.9957987,0.000018850891,0.0023880496,0.00095242064,0.00045140472,0.000034458822,0.00015066956,0.000031430252,0.00017402072],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974024,0.000080214406,0.001227981,0.00024411303,0.0006799837,0.0003652964],"domain_scores_gemma":[0.99786854,0.00024435954,0.0007642651,0.00013877747,0.0008330995,0.00015097058],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012309141,0.0001889675,0.00042129774,0.0007477574,0.0002304312,0.000042932432,0.00030608135,0.00006748429,0.0015290881],"category_scores_gemma":[0.00061680947,0.0001905639,0.00014550857,0.0021536995,0.00007422792,0.0002809208,0.00013406253,0.00074407633,0.0000023109485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014603633,0.00049368956,0.0014337584,0.00011962987,0.00014876988,0.00000824048,0.000120652345,0.0004445299,0.9879851,0.0025790907,0.00033746284,0.0048687304],"study_design_scores_gemma":[0.0036249312,0.00017666465,0.0012775768,0.000038279326,0.00039048685,0.00008465869,0.00017423085,0.14060119,0.8308899,0.000541119,0.02192911,0.00027187017],"about_ca_topic_score_codex":0.0000049242008,"about_ca_topic_score_gemma":1.108957e-7,"teacher_disagreement_score":0.17922924,"about_ca_system_score_codex":0.0003575831,"about_ca_system_score_gemma":0.00014178456,"threshold_uncertainty_score":0.9993836},"labels":[],"label_agreement":null},{"id":"W4281705692","doi":"10.1007/s11694-022-01464-5","title":"Non-Destructive Quality Monitoring of Flaxseed During Storage","year":2022,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Partial least squares regression; Calibration; Principal component analysis; Cross-validation; Linear discriminant analysis; Predictive modelling; Mathematics; Relative humidity; Coefficient of determination; Mean squared prediction error; Chemistry; Statistics; Analytical Chemistry (journal); Chromatography; Physics; Meteorology","score_opus":0.045469357880147526,"score_gpt":0.27816198431594646,"score_spread":0.23269262643579894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281705692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99872035,0.00013917957,0.00055920443,0.00003633263,0.00027168778,0.000044504577,0.000022561288,0.000011259934,0.00019494821],"genre_scores_gemma":[0.9994757,0.000032781212,0.00009322737,0.000004717583,0.0003124475,0.0000061075452,0.00000953426,0.000016915346,0.000048551115],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774736,0.000046338613,0.0007279154,0.00013425617,0.0011864904,0.0001576229],"domain_scores_gemma":[0.99780595,0.000016284854,0.0014604058,0.00016319817,0.00048649963,0.000067678506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000624017,0.00013128346,0.00035653435,0.00024513926,0.00016401909,0.000021125916,0.00022369665,0.000041536783,0.00030848035],"category_scores_gemma":[0.00012062904,0.00013539853,0.0001622446,0.0004433382,0.000018482015,0.0002543534,0.00005377782,0.00030536746,4.0879056e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021451108,0.00018027447,0.040277846,0.00013003725,0.000265241,0.0000032246826,0.00042418268,0.000078665835,0.95827293,0.000008063044,0.0000016626842,0.00014334306],"study_design_scores_gemma":[0.000593189,0.00011946518,0.30280763,0.000035810597,0.00011412113,0.000014314106,0.0007450664,0.0000046171704,0.695437,0.000017543718,0.000024138186,0.000087107685],"about_ca_topic_score_codex":0.0000043292625,"about_ca_topic_score_gemma":2.3427047e-7,"teacher_disagreement_score":0.26283595,"about_ca_system_score_codex":0.00062534475,"about_ca_system_score_gemma":0.00011256379,"threshold_uncertainty_score":0.55213916},"labels":[],"label_agreement":null},{"id":"W4281927579","doi":"10.1016/j.crfs.2022.05.016","title":"Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends","year":2022,"lang":"en","type":"review","venue":"Current Research in Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Scholarship Council; Queen's University; Queen's University Belfast","keywords":"Supply chain; Hyperspectral imaging; Quality (philosophy); Factory (object-oriented programming); Meat packing industry; Scope (computer science); Product (mathematics); Food safety; Computer science; Production (economics); Scale (ratio); Risk analysis (engineering); Biochemical engineering; Environmental science; Business; Engineering; Artificial intelligence; Food science; Marketing; Mathematics","score_opus":0.4319672919952592,"score_gpt":0.5832627653633037,"score_spread":0.1512954733680445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281927579","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068553665,0.99632335,0.00000603122,0.00043839746,0.0005865921,0.0006140542,0.00032921977,0.000028437336,0.0009883984],"genre_scores_gemma":[0.003640295,0.99398047,0.00001841534,0.0000036929528,0.0008821755,0.0011953441,0.00017254849,0.000027724402,0.00007932066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9925679,0.00047167976,0.0006959648,0.0014312711,0.0033230376,0.0015101422],"domain_scores_gemma":[0.9970256,0.0011303714,0.00028907094,0.00094194576,0.000368338,0.00024465303],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0134324385,0.0004173492,0.0008867842,0.000981194,0.0016507587,0.0005553154,0.0021710156,0.00009476278,0.00063303235],"category_scores_gemma":[0.0012160041,0.00028783298,0.00036704575,0.0070991195,0.0014609427,0.00028819995,0.00092242344,0.002272035,0.000003347454],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008738192,0.00013075281,0.000100006546,0.0038503604,0.000018579343,5.569264e-7,0.0004469249,4.5672124e-7,0.00001858343,0.0009816315,0.00030973175,0.99413365],"study_design_scores_gemma":[0.0003715219,0.000053677704,0.000051737967,0.00069770345,0.00013546449,0.000013414951,0.0021942528,0.0003045633,0.00015038374,0.00046827222,0.99518853,0.00037045046],"about_ca_topic_score_codex":0.00002783177,"about_ca_topic_score_gemma":0.000033800457,"teacher_disagreement_score":0.9948788,"about_ca_system_score_codex":0.001627436,"about_ca_system_score_gemma":0.0015800867,"threshold_uncertainty_score":0.9999574},"labels":[],"label_agreement":null},{"id":"W4281941961","doi":"10.36939/ir.202206021141","title":"3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Multispectral image; Computer science; Convolutional neural network; Context (archaeology); Artificial intelligence; Preprocessor; Pattern recognition (psychology); Geography","score_opus":0.011209479452901077,"score_gpt":0.2721233966562288,"score_spread":0.26091391720332774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281941961","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21954025,0.09038248,0.031385306,0.003576196,0.003578933,0.0026550286,0.0027569826,0.0026943772,0.6434304],"genre_scores_gemma":[0.9231596,0.00017040681,0.00024383454,0.00025901923,0.0008857401,0.00020687739,0.032958247,0.00006647091,0.042049795],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829286,0.0000042622714,0.00035477977,0.0004802569,0.0005014856,0.0003663272],"domain_scores_gemma":[0.9992021,0.00022282866,0.00019576629,0.00012140907,0.00010592699,0.0001520119],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000070464346,0.0003113546,0.0003899256,0.00008615169,0.00035361366,0.000194304,0.000257024,0.0002537387,0.012601238],"category_scores_gemma":[0.00014545296,0.0002616565,0.00017406879,0.00023227498,0.000057522855,0.00013731474,0.00007551576,0.00058563263,0.0000011891516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012292378,0.0022939378,0.06519975,0.010957158,0.0062380857,0.00022707196,0.0026133838,0.005310566,0.042630672,0.011284454,0.75205636,0.09995932],"study_design_scores_gemma":[0.003343787,0.00008625491,0.0029191636,0.00024335898,0.0015306143,0.0003528364,0.015206804,0.8991853,0.0019297135,0.0008919164,0.0715532,0.0027570566],"about_ca_topic_score_codex":0.00004807489,"about_ca_topic_score_gemma":0.000059664966,"teacher_disagreement_score":0.8938747,"about_ca_system_score_codex":0.0001008363,"about_ca_system_score_gemma":0.00007342303,"threshold_uncertainty_score":0.99998355},"labels":[],"label_agreement":null},{"id":"W4282024449","doi":"10.1177/09670335221098527","title":"Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants","year":2022,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación","keywords":"Hyperspectral imaging; Discriminative model; Nitrogen; Firefly algorithm; Mathematics; Horticulture; Artificial intelligence; Algorithm; Computer science; Pattern recognition (psychology); Particle swarm optimization; Chemistry; Biology","score_opus":0.025394301318154393,"score_gpt":0.2828456247883549,"score_spread":0.2574513234702005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282024449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990906,0.0030741368,0.00040242984,0.0002309597,0.00017694339,0.00018875238,0.000086070926,0.00004041416,0.004894299],"genre_scores_gemma":[0.9575415,0.00042014575,0.039751653,0.0006390694,0.00029040469,0.00006327447,0.00001841547,0.000108621294,0.0011669154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99645513,0.0001220923,0.0012822682,0.00047857297,0.00089278485,0.0007691528],"domain_scores_gemma":[0.99828875,0.0002514615,0.0005797861,0.00041735393,0.000094817675,0.0003678448],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068090355,0.00040963865,0.001086337,0.0008783661,0.00026912568,0.00026960528,0.00072321034,0.00013450837,0.0029929911],"category_scores_gemma":[0.00032403515,0.00041201795,0.00022153623,0.001489291,0.000107902,0.00026293175,0.0002884292,0.0012906087,0.000017817989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049229194,0.0016686242,0.11326424,0.00038039428,0.0012792293,0.0061855004,0.0063903793,0.0033779854,0.84586906,0.00014552036,0.014686652,0.0018294691],"study_design_scores_gemma":[0.007848351,0.0018387565,0.015004111,0.00021573347,0.00033620762,0.0017301111,0.0070622684,0.002594997,0.94226825,0.009772722,0.010047414,0.0012810798],"about_ca_topic_score_codex":0.0002515259,"about_ca_topic_score_gemma":0.00004538673,"teacher_disagreement_score":0.09826013,"about_ca_system_score_codex":0.0007294195,"about_ca_system_score_gemma":0.00036352646,"threshold_uncertainty_score":0.99983317},"labels":[],"label_agreement":null},{"id":"W4283722390","doi":"10.5713/ab.22.0076","title":"Quantitation of relationship and development of nutrient prediction with vibrational molecular structure spectral profiles of feedstocks and co-products from canola bio-oil processing","year":2022,"lang":"en","type":"article","venue":"Animal Bioscience","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Tianjin Agricultural University; Canola Council of Canada; Western Grains Research Foundation; University of Saskatchewan; Saskatchewan Forage Network; Natural Sciences and Engineering Research Council of Canada; Saskatchewan Canola Development Commission","keywords":"Canola; Carbohydrate; Chemistry; Nutrient; Food science; Biochemistry; Organic chemistry","score_opus":0.01824098785995379,"score_gpt":0.253166170594799,"score_spread":0.23492518273484522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283722390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99836224,0.0005035529,0.00081398856,0.000046513294,0.000006481419,0.000036196157,0.00013915662,0.000007830782,0.000084057756],"genre_scores_gemma":[0.98595047,0.0000050300855,0.013958697,0.000004169793,0.0000070028455,0.0000051886454,0.00005735581,0.0000037592656,0.000008317471],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913704,0.000010150906,0.0002116994,0.00022469077,0.0003355843,0.00008081345],"domain_scores_gemma":[0.9995596,0.000030053709,0.00023121778,0.00006823756,0.00008641057,0.00002450119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007362193,0.00007265834,0.000116349656,0.000109928435,0.00015964186,0.000011760294,0.0000754444,0.00002703879,0.000042708707],"category_scores_gemma":[0.000057890535,0.000064781096,0.000008299015,0.00053436396,0.00020324306,0.00012438955,0.000032974487,0.00007471071,2.8592014e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006816712,0.00003706989,0.13401552,0.000098769684,0.000011011932,3.1514642e-7,0.0007149715,0.000022442513,0.8644333,0.00048440238,0.000002228353,0.00011179798],"study_design_scores_gemma":[0.00016162757,0.000107918924,0.11969729,0.000020660915,0.000027188145,0.0000038902067,0.0012557887,0.00027417546,0.8782938,0.00009460881,0.0000054760317,0.000057545218],"about_ca_topic_score_codex":0.00004492403,"about_ca_topic_score_gemma":0.000006152072,"teacher_disagreement_score":0.014318233,"about_ca_system_score_codex":0.000035705576,"about_ca_system_score_gemma":0.00024602874,"threshold_uncertainty_score":0.26416963},"labels":[],"label_agreement":null},{"id":"W4283804635","doi":"10.1002/aocs.12616","title":"Analysis of cruciferin content in whole seeds of <scp><i>Brassica napus</i> L</scp>. by <scp>near‐infrared</scp> spectroscopy","year":2022,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Canola Council of Canada; Research Manitoba","keywords":"Brassica; Population; Food science; Ingredient; High protein; Near-infrared spectroscopy; Biology; Chemistry; Agronomy","score_opus":0.011411265990701358,"score_gpt":0.24958591856256715,"score_spread":0.2381746525718658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283804635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907563,0.0021503505,0.000040775136,0.00030280123,0.00006423861,0.000032718017,0.0002873852,0.00002007401,0.006345403],"genre_scores_gemma":[0.9864039,0.0005910224,0.0010208075,0.0005138861,0.00010103997,0.000013653218,0.000027967948,0.000052322262,0.011275397],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99606836,0.00010073945,0.0014077649,0.00041380708,0.0014046819,0.0006046199],"domain_scores_gemma":[0.99400544,0.0008618699,0.0038131797,0.00078958034,0.00031328603,0.00021666275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074789225,0.00040128958,0.0017493999,0.00019561608,0.00022450174,0.000050371986,0.0015508758,0.0001214148,0.00022631114],"category_scores_gemma":[0.0007658665,0.00033916623,0.0022210514,0.0049358606,0.00085195655,0.00010353081,0.00044878578,0.0013238309,0.0000010358416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026586553,0.0006418051,0.054161914,0.00012571015,0.003393569,0.000006541113,0.0020854096,0.0006846786,0.89113873,0.0000026011658,0.04760262,0.00012982555],"study_design_scores_gemma":[0.001051557,0.00015859935,0.004009897,0.000043169304,0.0021711255,0.000030377902,0.024158964,0.00050979387,0.9535836,0.00003079856,0.014147504,0.00010460314],"about_ca_topic_score_codex":0.0003468986,"about_ca_topic_score_gemma":0.0000054093994,"teacher_disagreement_score":0.06244487,"about_ca_system_score_codex":0.00085770455,"about_ca_system_score_gemma":0.0003954757,"threshold_uncertainty_score":0.99990606},"labels":[],"label_agreement":null},{"id":"W4284989591","doi":"10.3390/foods11131936","title":"Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network","year":2022,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Identification (biology); Residual; Task (project management); Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Remote sensing; Geography; Engineering; Biology; Ecology; Algorithm","score_opus":0.010787793545323488,"score_gpt":0.264508620829869,"score_spread":0.2537208272845455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284989591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995871,0.0029418536,0.0005818149,0.00013534987,0.00003961594,0.00004226979,0.0001791618,0.000017164568,0.00019174814],"genre_scores_gemma":[0.9988401,0.000034346762,0.0008110946,0.000008431517,0.000024942414,0.000011603274,0.000105371444,0.000008336542,0.0001557737],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900097,0.000030758696,0.00036280084,0.00017632486,0.0002930285,0.00013610475],"domain_scores_gemma":[0.99935085,0.00008680317,0.0002965464,0.0001441483,0.000092018396,0.00002966065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002121781,0.000083735344,0.00020892837,0.00009047544,0.00012321529,0.000010251006,0.00012873475,0.000037701346,0.00026754854],"category_scores_gemma":[0.00005963626,0.000094683506,0.00006696615,0.0005261526,0.0003029211,0.000059062095,0.00006566701,0.0001391605,1.9729255e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017700683,0.0005513268,0.2470527,0.00016505568,0.00023910422,9.2244176e-7,0.00044944082,0.00038859417,0.7416654,0.006828463,0.0022957844,0.000186158],"study_design_scores_gemma":[0.0032343245,0.00030932052,0.050860517,0.000033331064,0.000568775,0.000025623382,0.0038696397,0.021807665,0.91370845,0.004140358,0.0010253446,0.00041666764],"about_ca_topic_score_codex":0.00012680901,"about_ca_topic_score_gemma":0.0000033351432,"teacher_disagreement_score":0.19619218,"about_ca_system_score_codex":0.00003829139,"about_ca_system_score_gemma":0.000052303498,"threshold_uncertainty_score":0.38610813},"labels":[],"label_agreement":null},{"id":"W4285012945","doi":"10.3390/agriculture12070995","title":"Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing","year":2022,"lang":"en","type":"article","venue":"Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Ilam University","keywords":"Linear discriminant analysis; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Image processing; Artificial neural network; MATLAB; Computer science; Kernel (algebra); Image (mathematics); Mathematics; Machine learning","score_opus":0.009252900966838755,"score_gpt":0.2416292306897176,"score_spread":0.23237632972287883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285012945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966758,0.0014789545,0.00007076613,0.00001664125,0.000017816754,0.000042588763,0.000025378868,0.000023658213,0.0016484243],"genre_scores_gemma":[0.99941134,0.000021829175,0.00025903294,0.000018679122,0.00006997211,0.000013104576,0.000020276831,0.000007813525,0.00017792374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993296,0.000017172944,0.00018396803,0.00018431025,0.00014484927,0.00014006418],"domain_scores_gemma":[0.9997123,0.000015382566,0.00014224956,0.00007317092,0.000033440254,0.000023488235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007324526,0.00010822509,0.00019176076,0.00007010403,0.00012489343,0.000030434348,0.000088736306,0.00006775439,0.00041184333],"category_scores_gemma":[0.000028997743,0.0000811303,0.00003080217,0.0004968737,0.000027816108,0.00009339339,0.00006974034,0.00015246683,2.7691468e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001838845,0.000039069037,0.0011531563,0.00013555092,0.000015734578,0.000005266841,0.00023066015,0.000030827487,0.99806905,0.0000145560925,0.00003147597,0.00025626505],"study_design_scores_gemma":[0.00020836682,0.000014349695,0.0016567991,0.000018078854,0.000059295147,0.000049279057,0.0021002472,0.00005006067,0.9953961,0.000268382,0.00006819562,0.00011088666],"about_ca_topic_score_codex":0.00016799642,"about_ca_topic_score_gemma":0.000009861531,"teacher_disagreement_score":0.0027355966,"about_ca_system_score_codex":0.00006457189,"about_ca_system_score_gemma":0.000014254308,"threshold_uncertainty_score":0.45093957},"labels":[],"label_agreement":null},{"id":"W4285590261","doi":"10.1080/15538362.2022.2092580","title":"Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision","year":2022,"lang":"en","type":"article","venue":"International Journal of Fruit Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Iran National Science Foundation; Canada Foundation for Innovation","keywords":"Ripeness; Ripening; Horticulture; Machine vision; Artificial intelligence; Food science; Mathematics; Computer science; Chemistry; Biology","score_opus":0.05674417809326837,"score_gpt":0.33356818737296523,"score_spread":0.27682400927969686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285590261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99029964,0.00014697984,0.009348802,0.000021663638,0.00007990191,0.000017528044,0.000022542707,0.000001331264,0.00006161664],"genre_scores_gemma":[0.9983648,0.000010368688,0.0015798453,0.0000032132136,0.000027549104,7.7332544e-7,8.540848e-7,0.0000030965127,0.0000094638335],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871075,0.0000071813765,0.00035136958,0.00009627054,0.00077279133,0.000061657935],"domain_scores_gemma":[0.99870634,0.000041283074,0.0006927651,0.000060451766,0.00047402762,0.000025121331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025814163,0.00005818478,0.00016905669,0.00028689834,0.00006120981,0.0000130376675,0.00035458457,0.000016218717,0.000050421004],"category_scores_gemma":[0.00014516569,0.000049541817,0.000054625238,0.00037709458,0.0003437386,0.00029765491,0.0001303014,0.00011129925,3.3844533e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010151001,0.00008795215,0.0018591919,0.00003186446,0.000038913236,8.4955633e-7,0.00025411043,0.003876274,0.9911513,0.000052997188,5.138605e-7,0.0025445553],"study_design_scores_gemma":[0.00025745705,0.000057953992,0.0024769185,0.000056624558,0.000036717847,0.0000803478,0.0005056617,0.033550464,0.96248716,0.00044817847,9.0793026e-7,0.000041588046],"about_ca_topic_score_codex":0.00012322131,"about_ca_topic_score_gemma":0.0000012381063,"teacher_disagreement_score":0.029674191,"about_ca_system_score_codex":0.0001304359,"about_ca_system_score_gemma":0.00012183657,"threshold_uncertainty_score":0.20202567},"labels":[],"label_agreement":null},{"id":"W4286518014","doi":"10.18280/ria.360312","title":"Optimal Solution of Classification (Prediction) Problem","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Class (philosophy); Mean squared prediction error; Predictive modelling; Feature (linguistics); Computer science; Process (computing); Linear prediction; Artificial intelligence; Machine learning; Regression; Linear regression; Data mining; Pattern recognition (psychology); Mathematics; Algorithm; Statistics","score_opus":0.04528371614471216,"score_gpt":0.2833378084472199,"score_spread":0.23805409230250774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286518014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7082589,0.0013648153,0.18706451,0.0009919043,0.00028287177,0.00024123276,0.00017220953,0.0002975575,0.10132596],"genre_scores_gemma":[0.9925101,0.000049699564,0.0013775388,0.000016856418,0.00006170138,0.00006994358,0.000062394065,0.000013126975,0.005838668],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885094,0.000019685007,0.00042574923,0.00027871187,0.00022498182,0.0001999338],"domain_scores_gemma":[0.99928033,0.00006466059,0.00020282688,0.00033064449,0.00007618225,0.00004534577],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020768239,0.000100281555,0.0001627157,0.00013696727,0.0002674023,0.000012991689,0.00026962272,0.00004730665,0.009464494],"category_scores_gemma":[0.000036894657,0.00011313414,0.000106009924,0.0007663827,0.000075769225,0.000077372875,0.000095733405,0.00022428347,0.0000613571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101220234,0.0008626012,0.004744697,0.00023086436,0.00010904954,0.0000045625748,0.0013589404,0.1087754,0.8581752,0.009931224,0.0027648483,0.012941378],"study_design_scores_gemma":[0.000050646977,0.00008037995,0.000055668832,0.000010511406,0.00006942996,0.00002128447,0.0044947173,0.25620237,0.73009807,0.0005430905,0.008239753,0.00013408328],"about_ca_topic_score_codex":0.000034388726,"about_ca_topic_score_gemma":0.0000010908617,"teacher_disagreement_score":0.28425112,"about_ca_system_score_codex":0.00014404695,"about_ca_system_score_gemma":0.000041301006,"threshold_uncertainty_score":0.991441},"labels":[],"label_agreement":null},{"id":"W4286655485","doi":"10.1007/978-3-031-10467-1_12","title":"Independent Component Analysis for Spectral Unmixing of Raman Microscopic Images of Single Human Cells","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Hyperspectral imaging; Independent component analysis; Principal component analysis; Raman spectroscopy; Pattern recognition (psychology); Artificial intelligence; Chemical imaging; Biological system; Cluster analysis; Spectral imaging; Computer science; Physics; Biology; Optics","score_opus":0.019540073239401923,"score_gpt":0.25436369087563393,"score_spread":0.234823617636232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286655485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20720534,0.268451,0.4032735,0.0001184616,0.0021690098,0.0031305007,0.0022332617,0.0002541875,0.11316476],"genre_scores_gemma":[0.99491036,0.00032601177,0.00022789044,0.000010395104,0.00029804406,0.000020180476,0.00029756868,0.000056272074,0.0038532824],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978668,0.000024257202,0.0009315872,0.0005325219,0.00031634237,0.0003285077],"domain_scores_gemma":[0.9981309,0.0004336585,0.0008721629,0.00042897067,0.00007190061,0.00006237946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023558481,0.00041028898,0.0015501035,0.0005383978,0.00010886578,0.00004895198,0.0002859784,0.00046847988,0.00058033195],"category_scores_gemma":[0.000015104349,0.0003940575,0.00046500718,0.00027937317,0.00012563374,0.00002608511,0.00011096621,0.000531823,1.3299005e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024206826,0.0004439686,0.01993511,0.0061361277,0.009422037,0.00005109034,0.00062783767,0.38045195,0.5772903,0.0045509418,0.0003179293,0.000530627],"study_design_scores_gemma":[0.0062722634,0.0014690231,0.0009205683,0.0030706488,0.023069425,0.000057514673,0.0005214806,0.034351066,0.9095984,0.0062266323,0.00979253,0.004650414],"about_ca_topic_score_codex":0.00046771907,"about_ca_topic_score_gemma":0.00014546468,"teacher_disagreement_score":0.787705,"about_ca_system_score_codex":0.00018530003,"about_ca_system_score_gemma":0.000023397548,"threshold_uncertainty_score":0.9998511},"labels":[],"label_agreement":null},{"id":"W4287184095","doi":"10.48550/arxiv.2105.03377","title":"Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine\\n learning","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Galaxy; Physics; Astrophysics; Spectral line; Active galactic nucleus; Galaxy merger; Universe; Emission spectrum; Galaxy formation and evolution; Astronomy","score_opus":0.035010717966029815,"score_gpt":0.1792442666658545,"score_spread":0.1442335486998247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287184095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9642788,0.0015879493,0.008552848,0.00020060765,0.00018072456,0.00025200247,0.00005589512,0.00016080606,0.024730328],"genre_scores_gemma":[0.9791764,0.0027930187,0.0001282552,0.000030026853,0.00025798145,0.0000015662768,0.00012159736,0.000101066114,0.01739008],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563575,0.00027914578,0.0007043164,0.0019958685,0.0003882978,0.0009966292],"domain_scores_gemma":[0.9951342,0.00066427153,0.0015816757,0.0019275417,0.00037752104,0.00031483464],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047195732,0.0009897655,0.001353637,0.0004204909,0.00079730665,0.0001977507,0.0019037529,0.0007368517,0.005978363],"category_scores_gemma":[0.00028536518,0.000885487,0.0008433514,0.0031964586,0.0008004681,0.0002872908,0.0017154709,0.0036383355,0.000034288427],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007455096,0.0006374611,0.7295853,0.00161166,0.005414224,0.0011491204,0.0018759723,0.25048044,0.0055431,0.002773204,0.00006503528,0.00011898168],"study_design_scores_gemma":[0.006329211,0.00095508876,0.013036043,0.0025072433,0.019987568,0.00030195882,0.05761866,0.47273582,0.41777325,0.0025415833,0.0015194855,0.0046940655],"about_ca_topic_score_codex":0.0019637488,"about_ca_topic_score_gemma":0.00039776147,"teacher_disagreement_score":0.7165493,"about_ca_system_score_codex":0.00054185756,"about_ca_system_score_gemma":0.00065430877,"threshold_uncertainty_score":0.9993596},"labels":[],"label_agreement":null},{"id":"W4288143301","doi":"10.1051/0004-6361/201935223","title":"Machine learning in APOGEE","year":2019,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Conselho Nacional de Desenvolvimento Científico e Tecnológico; York University; Carnegie Mellon University; Office of Science; Johns Hopkins University; College of Engineering, Michigan State University; Harvard University; Ohio State University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; University of Arizona; Princeton University; Brookhaven National Laboratory; U.S. Department of Energy","keywords":"Cluster analysis; Star cluster; Stars; Metric (unit); Computer science; Cluster (spacecraft); Galaxy; Open cluster; Pattern recognition (psychology); Astrophysics; Artificial intelligence; Physics","score_opus":0.005268641933300821,"score_gpt":0.21284569668395417,"score_spread":0.20757705475065336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288143301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98327667,0.00024062894,0.0038187893,0.000034241824,0.000016359783,0.000025042658,0.000005425558,0.000035603716,0.012547268],"genre_scores_gemma":[0.9936402,0.000014494414,0.004191096,0.000013412568,0.00006664953,0.0000035164032,0.000029377234,0.000012098756,0.0020291605],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993835,0.0000060001976,0.00011811169,0.000207775,0.00006927911,0.00021533208],"domain_scores_gemma":[0.9997405,0.000029897765,0.000052115334,0.00012243408,0.000008862178,0.000046208876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002557107,0.000120644916,0.00018445853,0.00005132022,0.00003835193,0.000025960118,0.00008482352,0.000038461105,0.0009003407],"category_scores_gemma":[0.000004413833,0.00012015496,0.00004393605,0.00016505814,0.000024457026,0.00009957259,0.000052664633,0.00027092765,0.00007359459],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051700074,0.00015578319,0.71892065,0.00006325139,0.00009548242,0.000004296024,0.00012593463,0.0011475977,0.06861665,0.0017307026,0.000017790042,0.20907018],"study_design_scores_gemma":[0.008530259,0.00062983396,0.048811816,0.00017173412,0.00038743275,0.000021091435,0.0051028715,0.007966777,0.5116569,0.0015480674,0.41295817,0.002215023],"about_ca_topic_score_codex":0.00005155427,"about_ca_topic_score_gemma":0.0000016363981,"teacher_disagreement_score":0.6701088,"about_ca_system_score_codex":0.000023919398,"about_ca_system_score_gemma":0.000014752059,"threshold_uncertainty_score":0.9858099},"labels":[],"label_agreement":null},{"id":"W4288459213","doi":"10.1016/j.saa.2022.121684","title":"Exploring the use of infrared absorption spectroscopy and two-trace two-dimensional correlation analysis for the resolution of multi-component drug mixtures","year":2022,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"TRACE (psycholinguistics); Heroin; Harm reduction; Subtraction; Correlation; Computer science; Drug; Mathematics; Medicine; Psychiatry","score_opus":0.04223313977684112,"score_gpt":0.2770671233612065,"score_spread":0.23483398358436536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288459213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95325726,0.0033597145,0.0416701,0.00075258524,0.00011969344,0.0005313086,0.0001848438,0.00005472954,0.000069738875],"genre_scores_gemma":[0.9898572,0.00033427455,0.009025741,0.00012767367,0.0000791126,0.00021299341,0.0002038873,0.000051293362,0.00010785018],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970393,0.00018167426,0.00074090966,0.00075474364,0.000728106,0.0005552649],"domain_scores_gemma":[0.9978949,0.0004404922,0.00063062186,0.000783687,0.00013068964,0.00011961793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045937812,0.00044790944,0.0006796204,0.000477904,0.00074340886,0.00010728734,0.00041847042,0.00006347648,0.00015797107],"category_scores_gemma":[0.0001307291,0.00034203427,0.00053173286,0.0015115234,0.00040641887,0.00019935393,0.00030818625,0.0004974233,5.1780944e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006005898,0.0002572484,0.0007888619,0.000052402604,0.001490756,0.000006334942,0.00030954083,0.0066980347,0.9885327,0.0010620418,0.00016910242,0.000032363987],"study_design_scores_gemma":[0.0015477646,0.00018119699,0.0012113217,0.000018836248,0.0027899432,0.00003480468,0.0003055234,0.04093413,0.95166147,0.00039240677,0.00056903774,0.00035355723],"about_ca_topic_score_codex":0.0003496347,"about_ca_topic_score_gemma":0.00005404707,"teacher_disagreement_score":0.036871243,"about_ca_system_score_codex":0.00016151648,"about_ca_system_score_gemma":0.00007874685,"threshold_uncertainty_score":0.99990314},"labels":[],"label_agreement":null},{"id":"W4292168321","doi":"10.1111/rssc.12583","title":"Statistical Integration of Heterogeneous Omics Data: Probabilistic Two-Way Partial Least Squares (PO2PLS)","year":2022,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Institute of Genetics; Universitair Medisch Centrum Utrecht; E-Rare","keywords":"Partial least squares regression; Probabilistic logic; Data integration; Omics; Computer science; Statistical model; Data mining; Statistics; Computational biology; Data science; Mathematics; Machine learning; Biology; Artificial intelligence; Bioinformatics","score_opus":0.022616889318122474,"score_gpt":0.28201730529210517,"score_spread":0.25940041597398267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292168321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025961028,0.00036329875,0.9333622,0.0003575088,0.0007205817,0.00034739415,0.037715465,0.000057317124,0.0011151943],"genre_scores_gemma":[0.9189353,0.000041276053,0.079314604,0.00014439544,0.0003521343,0.000028067641,0.0006982884,0.00006661218,0.0004193167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956768,0.00016610544,0.00157707,0.0005052203,0.0014882217,0.00058654265],"domain_scores_gemma":[0.9958624,0.0015064926,0.0012564738,0.0007963248,0.00030894033,0.00026931806],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007363626,0.00042242536,0.00093373074,0.000054893033,0.0006875337,0.00012590161,0.0014594318,0.00012296079,0.0046753334],"category_scores_gemma":[0.0011386666,0.00033003397,0.00023994273,0.00042282738,0.0009080119,0.00011667625,0.00087795494,0.0012677928,0.000006170877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0053668637,0.0042772554,0.0018788115,0.0017782396,0.0044219615,0.00028255724,0.0033359383,0.1103645,0.046484414,0.614512,0.18882592,0.018471511],"study_design_scores_gemma":[0.01786147,0.0054635038,0.0060027293,0.000265949,0.017856028,0.0015049786,0.03324339,0.4336263,0.122701064,0.30695957,0.048672847,0.0058421586],"about_ca_topic_score_codex":0.000121108285,"about_ca_topic_score_gemma":0.00003820859,"teacher_disagreement_score":0.89297426,"about_ca_system_score_codex":0.0004982671,"about_ca_system_score_gemma":0.00039498816,"threshold_uncertainty_score":0.9999152},"labels":[],"label_agreement":null},{"id":"W4293298660","doi":"10.21203/rs.3.rs-1925394/v1","title":"Fusion of spectral and textural data of hyperspectral imaging for glycine content prediction in beef using SFCN algorithms","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Variable elimination; Computer science; Residual; Convolutional neural network; Support vector machine; Pattern recognition (psychology); Algorithm; Artificial intelligence; Matrix (chemical analysis); Mathematics; Chemistry; Machine learning","score_opus":0.2324702868301527,"score_gpt":0.44771478086827576,"score_spread":0.21524449403812307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293298660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98764366,0.007234803,0.0005091464,0.00020688768,0.00008295416,0.00052194245,0.0030691247,0.000028253939,0.0007032284],"genre_scores_gemma":[0.99389356,0.00065241766,0.0034092613,0.0000028324155,0.0002575076,0.000046611753,0.0014031184,0.000038084065,0.00029662464],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99707276,0.00008452575,0.0006070082,0.0007998515,0.00090348115,0.0005323709],"domain_scores_gemma":[0.99807113,0.00034825568,0.00025228187,0.0009131148,0.00031586093,0.0000993329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011426675,0.00023422936,0.0006269348,0.0007606062,0.00013701465,0.000042346674,0.00072871725,0.00016326962,0.00081478676],"category_scores_gemma":[0.000565733,0.00023185092,0.00013904653,0.000652997,0.0002490231,0.00013332989,0.0020487008,0.0011783751,2.168776e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011370128,0.00085934723,0.16978636,0.008129508,0.00044078275,0.000052440286,0.0011590719,0.0008827644,0.8126546,0.00027062878,0.0005209649,0.0041065146],"study_design_scores_gemma":[0.005783153,0.00060809194,0.03089569,0.0020767057,0.0006418532,0.00006908776,0.03237709,0.41692683,0.5070347,0.0022702026,0.00039544457,0.00092114205],"about_ca_topic_score_codex":0.0029298272,"about_ca_topic_score_gemma":0.000048676116,"teacher_disagreement_score":0.41604406,"about_ca_system_score_codex":0.00053386716,"about_ca_system_score_gemma":0.00029595074,"threshold_uncertainty_score":0.9454606},"labels":[],"label_agreement":null},{"id":"W4293562815","doi":"10.1016/j.ces.2022.118039","title":"Sequential fixed-fluidized bed foam granulation (SFFBFG) and drying: Multivariate model development for water content monitoring with near–infrared spectroscopy","year":2022,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer Canada; Université de Sherbrooke","keywords":"Granulation; Fluidized bed; Near-infrared spectroscopy; Infrared spectroscopy; Water content; Materials science; Chromatography; Chemistry; Analytical Chemistry (journal); Chemical engineering; Composite material; Engineering; Organic chemistry; Geotechnical engineering; Optics","score_opus":0.030230503169545283,"score_gpt":0.26007955275732547,"score_spread":0.22984904958778019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293562815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9694781,0.00007243109,0.030004345,0.000041600353,0.00007017663,0.00011718891,0.000007795597,0.0001374699,0.00007093953],"genre_scores_gemma":[0.93697774,0.0000015854912,0.06261578,0.000011438152,0.00006192183,0.0001431865,0.000022098371,0.00002432696,0.00014191773],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99828637,0.0000024087583,0.00024501196,0.00048923574,0.00043923466,0.0005377624],"domain_scores_gemma":[0.99947125,0.000049592956,0.00005169232,0.00019959865,0.00007583088,0.00015205574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022473084,0.00020705014,0.0002482388,0.00010784857,0.00045888845,0.00012952958,0.00031443642,0.00004596805,0.000047422483],"category_scores_gemma":[0.00009451645,0.00017320216,0.00004681148,0.00035878178,0.00012643974,0.00017958204,0.00020161082,0.00021553058,7.098346e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007963192,0.00003184777,0.00031150028,0.000055758777,0.000028277887,0.0000014970825,0.00047782256,0.01172225,0.98720676,0.000037208196,0.000005334965,0.000042130236],"study_design_scores_gemma":[0.00087336876,0.000016548689,0.00002582264,0.0000138316855,0.000038122373,0.0000063169937,0.000067647874,0.14207982,0.8565533,0.000035060086,0.000058970712,0.00023119106],"about_ca_topic_score_codex":0.000008601919,"about_ca_topic_score_gemma":4.6740258e-8,"teacher_disagreement_score":0.13065343,"about_ca_system_score_codex":0.0003957095,"about_ca_system_score_gemma":0.000114409064,"threshold_uncertainty_score":0.70629793},"labels":[],"label_agreement":null},{"id":"W4293664241","doi":"10.3389/fpls.2022.963591","title":"Determination of spectral resolutions for multispectral detection of apple bruises using visible/near-infrared hyperspectral reflectance imaging","year":2022,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Rural Development Administration","keywords":"Bruise; Hyperspectral imaging; Multispectral image; Computer science; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Computer vision; Wavelength; Near-infrared spectroscopy; Optics; Physics; Geography","score_opus":0.017959987148056102,"score_gpt":0.2868610357288898,"score_spread":0.26890104858083375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293664241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8678745,0.00081379415,0.12937647,0.000020861553,0.0003360148,0.00018495138,0.00038727754,0.00004106141,0.00096507114],"genre_scores_gemma":[0.9240445,0.000018026305,0.07576148,0.00000734235,0.000031949818,0.000028637442,0.000017138955,0.000010932598,0.00008000215],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982981,0.000018478888,0.0003831781,0.00040780258,0.00045655185,0.00043590376],"domain_scores_gemma":[0.99927163,0.00007070789,0.00028996568,0.00023899505,0.00007557292,0.000053120777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039671527,0.0001337011,0.00028596443,0.00060496945,0.00048545914,0.00002892675,0.00045359993,0.000033594675,0.000049444487],"category_scores_gemma":[0.00019805889,0.00015502583,0.00009856598,0.0019946843,0.00045770206,0.0002826092,0.00007475058,0.00019533935,1.04579115e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014671893,0.00009970656,0.017852234,0.000055497883,0.000009723773,0.0000035321589,0.0004384958,0.0012952184,0.97930694,0.00012592254,0.000070529095,0.0005955135],"study_design_scores_gemma":[0.00043559654,0.000049581893,0.0008978344,0.000016517377,0.000044521403,0.00003140447,0.0016991908,0.24192898,0.75359493,0.001088581,0.00006398147,0.00014884463],"about_ca_topic_score_codex":0.00014036274,"about_ca_topic_score_gemma":0.000020524496,"teacher_disagreement_score":0.24063377,"about_ca_system_score_codex":0.00071036076,"about_ca_system_score_gemma":0.00024392478,"threshold_uncertainty_score":0.632177},"labels":[],"label_agreement":null},{"id":"W4294316065","doi":"10.1364/aio.2022.t1a.2","title":"Multi-Dimensional Hadamard-Transform Fluorescence Imaging","year":2022,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Hadamard transform; Multiplexing; Computer science; Pixel; Fluorescence; Artificial intelligence; Computer vision; Computer graphics (images); Optics; Physics; Telecommunications","score_opus":0.013562892333203734,"score_gpt":0.2621476662550077,"score_spread":0.24858477392180395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294316065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5441537,0.004506385,0.006265381,0.0067074657,0.0005520584,0.00025234686,0.00020892505,0.0016055389,0.4357482],"genre_scores_gemma":[0.9726231,0.000007118485,0.005699982,0.0004818682,0.000044003737,0.000026569398,0.000033948625,0.000017167762,0.021066243],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894756,0.000006697128,0.00016537691,0.0002697021,0.0003432865,0.00026738906],"domain_scores_gemma":[0.99960333,0.000048790604,0.000036557707,0.00021025266,0.000026984933,0.00007406854],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000840469,0.000121622565,0.00013870791,0.0000908233,0.00034267665,0.000021768825,0.00025229045,0.000019135103,0.048143823],"category_scores_gemma":[0.00002061555,0.00011606688,0.0001061011,0.0003753603,0.000043862532,0.00007313654,0.00012465278,0.00025818718,0.00004567944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051204363,0.00043892628,0.026959646,0.00003903522,0.00011759683,0.000077327415,0.0001412619,0.00027382732,0.95122,0.0004284661,0.016610678,0.003642046],"study_design_scores_gemma":[0.0011805508,0.000017653862,0.000973818,0.000004750217,0.00013418833,0.000079284284,0.0016629944,0.01994816,0.94840306,0.0002534472,0.026833292,0.00050878356],"about_ca_topic_score_codex":0.00016602132,"about_ca_topic_score_gemma":0.0000056298195,"teacher_disagreement_score":0.4284694,"about_ca_system_score_codex":0.00012926153,"about_ca_system_score_gemma":0.000048928177,"threshold_uncertainty_score":0.9527263},"labels":[],"label_agreement":null},{"id":"W4295339011","doi":"10.26434/chemrxiv-2022-b97x0","title":"OpenPCA and Raman mapping to decipher complex spectral datasets from multi-component samples: application to cannabinoids and cannabis trichomes","year":2022,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Principal component analysis; Raman spectroscopy; Computer science; Pattern recognition (psychology); Biological system; Artificial intelligence; Pooling; Data mining; Biology; Physics; Optics","score_opus":0.05536077717590449,"score_gpt":0.31502580862592283,"score_spread":0.25966503145001835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295339011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879361,0.0018767248,0.0050669303,0.0017076165,0.00009684773,0.00066139165,0.0019894945,0.0001596534,0.0005052784],"genre_scores_gemma":[0.97663283,0.00020762,0.012766243,0.0008425576,0.0003552526,0.00085763884,0.007130264,0.00009985101,0.0011077261],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99679273,0.000032010517,0.0005881374,0.0016249367,0.00039725215,0.0005649368],"domain_scores_gemma":[0.9978085,0.000077415156,0.0002507661,0.0012305585,0.000060459985,0.0005723104],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018951319,0.00061215286,0.0009009682,0.00036014532,0.00029658797,0.0002726066,0.00086205045,0.00026486444,0.0022162013],"category_scores_gemma":[0.00015260244,0.0006771465,0.00012319729,0.00062292814,0.000092540795,0.00007112323,0.0023229714,0.00070713053,0.000020653404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003162412,0.0008477927,0.08333526,0.0013816232,0.0015356314,0.00005743926,0.0062176967,0.0005586438,0.7921019,0.000085803644,0.106540754,0.0070211925],"study_design_scores_gemma":[0.0034379074,0.00008491136,0.22455728,0.0002487759,0.0011619857,0.000024576524,0.0061424286,0.0030725154,0.32051954,0.00056902744,0.43644086,0.003740215],"about_ca_topic_score_codex":0.01118847,"about_ca_topic_score_gemma":0.0005040548,"teacher_disagreement_score":0.47158238,"about_ca_system_score_codex":0.0005275447,"about_ca_system_score_gemma":0.00011016097,"threshold_uncertainty_score":0.999568},"labels":[],"label_agreement":null},{"id":"W4296347520","doi":"10.5731/pdajpst.2020.012443","title":"An Investigation into the Spatial Distribution of Moisture in Freeze-Dried Products Using NIR Spectroscopy and Chemical Imaging","year":2022,"lang":"en","type":"article","venue":"PDA Journal of Pharmaceutical Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"","keywords":"Moisture; Near-infrared spectroscopy; Water content; Freeze-drying; Materials science; Environmental science; Process analytical technology; Process engineering; Biological system; Chemistry; Chromatography; Composite material; Optics; Chemical engineering; Geology; Physics","score_opus":0.016166593428126607,"score_gpt":0.32487404595269664,"score_spread":0.30870745252457005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296347520","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99475175,0.0011073258,0.00037087896,0.003668505,0.00003581715,0.00003632864,0.000003504678,0.000009401112,0.000016457487],"genre_scores_gemma":[0.999112,0.00005723051,0.00071698264,0.000056494177,0.000049025344,0.0000020185596,0.0000018071828,0.000003534708,9.026454e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998909,0.000023067898,0.00027789088,0.0002008606,0.00038042016,0.00020874637],"domain_scores_gemma":[0.9994033,0.000041085535,0.00019523098,0.000114569986,0.00016708838,0.000078725054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007221067,0.000082747436,0.00017563399,0.00023558825,0.00022115308,0.000027614333,0.00035168946,0.000042685584,0.000034354],"category_scores_gemma":[0.0003961235,0.00006307006,0.000015435931,0.0017707555,0.0014809173,0.00018837622,0.0001787491,0.0005821973,4.722196e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031504907,0.000053920463,0.05613272,0.000015053133,0.0000067156575,0.00000696346,0.00016942418,0.000008865317,0.93906707,0.0005202278,0.000006739845,0.0039808224],"study_design_scores_gemma":[0.00034519198,0.000048238406,0.0014246315,0.000008315953,0.000059248418,0.00018604986,0.0011002009,0.0044467677,0.9888791,0.003311012,0.00012393463,0.00006733307],"about_ca_topic_score_codex":0.000102680264,"about_ca_topic_score_gemma":0.0000042820843,"teacher_disagreement_score":0.05470809,"about_ca_system_score_codex":0.0001897472,"about_ca_system_score_gemma":0.00019029464,"threshold_uncertainty_score":0.5456502},"labels":[],"label_agreement":null},{"id":"W4296556106","doi":"","title":"Modèles d'équations structurelles Partial Least Squares (PLS-SEM)","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Partial least squares regression; Mathematics; Statistics","score_opus":0.02031140757621272,"score_gpt":0.257277311118244,"score_spread":0.23696590354203126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296556106","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36083555,0.028003274,0.20688349,0.034303408,0.001287682,0.0009898939,0.00222477,0.0010071624,0.3644648],"genre_scores_gemma":[0.8607981,0.0024897081,0.020648899,0.000114376824,0.00017489507,0.00023538666,0.0028825793,0.00013299315,0.11252305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99134403,0.0039809314,0.0011590915,0.0015924991,0.0010653582,0.00085809786],"domain_scores_gemma":[0.9907313,0.0027027251,0.0010983971,0.0034006902,0.0016478124,0.0004191055],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027962474,0.00080784573,0.00088719995,0.00039011368,0.0016109062,0.0006967806,0.0025501845,0.00065496267,0.026860718],"category_scores_gemma":[0.0026567166,0.0009511929,0.00072280975,0.0013715115,0.00072634564,0.00021107425,0.0024344244,0.0019563811,0.00017437885],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010693925,0.0039630677,0.02433889,0.0012808158,0.002057531,0.000043196924,0.020422904,0.007780603,0.059432313,0.8414028,0.010103455,0.029067429],"study_design_scores_gemma":[0.0016261247,0.0000017836492,0.0052182386,0.0011981484,0.0015332279,0.0000541963,0.0045150504,0.038352225,0.6899055,0.01814961,0.23714079,0.002305104],"about_ca_topic_score_codex":0.0042855935,"about_ca_topic_score_gemma":0.0016292002,"teacher_disagreement_score":0.8232533,"about_ca_system_score_codex":0.0005985404,"about_ca_system_score_gemma":0.0006731187,"threshold_uncertainty_score":0.99968886},"labels":[],"label_agreement":null},{"id":"W4296619941","doi":"10.1093/jas/skac247.726","title":"PSVIII-19 Predict Indigestible Fiber Fraction of Barley Plant Silage by Using non-Destructive Mid-IR vs Near-IR Spectroscopic Techniques","year":2022,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Silage; Fourier transform infrared spectroscopy; Forage; Near-infrared spectroscopy; Analytical Chemistry (journal); Chemistry; Fiber; Fourier transform; Spectroscopy; Neutral Detergent Fiber; Agronomy; Mathematics; Materials science; Animal science; Chromatography; Physics; Food science; Biology; Optics","score_opus":0.013406111505385319,"score_gpt":0.28848074412226177,"score_spread":0.2750746326168764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296619941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99560934,0.00024194452,0.00013976681,0.000051369247,0.00005216962,0.00005263428,0.00004934599,0.000019036417,0.0037843648],"genre_scores_gemma":[0.99603486,0.00005187138,0.003655284,0.000038329927,0.00012730582,0.0000023217574,0.000002621954,0.000012683723,0.000074715084],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977021,0.000020803487,0.0005098212,0.00028071165,0.0010715484,0.00041497787],"domain_scores_gemma":[0.9984877,0.00007848802,0.00088496605,0.00018277878,0.00016345222,0.00020261525],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055395946,0.00016819726,0.00038632317,0.00034504372,0.00057261385,0.00009467246,0.0006168509,0.00006938899,0.0016423793],"category_scores_gemma":[0.00013962867,0.0001519414,0.00012407932,0.0015648262,0.00042015096,0.0005735299,0.00017537855,0.0006105687,0.000001828514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024321424,0.00012683336,0.023797842,0.000027224993,0.00002937984,0.00002272768,0.00015143496,0.000039380568,0.9751225,0.000012398925,0.00038337402,0.000043678207],"study_design_scores_gemma":[0.0002561913,0.00094582647,0.004495427,0.00003979154,0.000096859134,0.0002651815,0.0013323332,0.00033439242,0.9915463,0.00014874862,0.0003898317,0.00014914648],"about_ca_topic_score_codex":0.00031431628,"about_ca_topic_score_gemma":9.854994e-7,"teacher_disagreement_score":0.019302415,"about_ca_system_score_codex":0.0008960498,"about_ca_system_score_gemma":0.00077768625,"threshold_uncertainty_score":0.99927026},"labels":[],"label_agreement":null},{"id":"W4296629889","doi":"10.1016/j.foodcont.2022.109389","title":"Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach","year":2022,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Agricultural Research Service; National Natural Science Foundation of China; McGill University; China Scholarship Council; U.S. Department of Agriculture","keywords":"Computer science; Sorting; Support vector machine; Artificial intelligence; Task (project management); Genetic algorithm; Quality (philosophy); Genetic programming; Machine learning; Data mining; Pattern recognition (psychology); Algorithm; Engineering","score_opus":0.04338918901573694,"score_gpt":0.2759448093521233,"score_spread":0.23255562033638635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296629889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7094359,0.00064537453,0.28818694,0.000017437482,0.000040158215,0.00041548812,0.0008054847,0.00014817993,0.00030506199],"genre_scores_gemma":[0.8730698,0.0000018295603,0.12632224,0.000036955284,0.000046814755,0.00035468047,0.00005538669,0.000048363072,0.00006393772],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976449,0.00006525187,0.00058104336,0.0005819796,0.00056731724,0.0005595277],"domain_scores_gemma":[0.99847686,0.00019446936,0.00047278675,0.000609121,0.00010011776,0.00014666823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018766003,0.000314502,0.00061494275,0.0001538969,0.00049175706,0.00007231889,0.00039202534,0.00009703548,0.00012561133],"category_scores_gemma":[0.0001471389,0.00030642678,0.00025056538,0.00090746995,0.00018936058,0.00008727027,0.000099290366,0.00045246808,6.0279496e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021430086,0.00647568,0.17591487,0.0006494048,0.0059469556,0.0000090635385,0.0021013133,0.12098677,0.68486744,0.000118950746,0.000051855466,0.0007347128],"study_design_scores_gemma":[0.032900386,0.005153496,0.009039941,0.000055089626,0.0035396053,0.0001648444,0.011153309,0.72549665,0.20944075,0.000082997045,0.0011678237,0.0018051],"about_ca_topic_score_codex":0.00028942182,"about_ca_topic_score_gemma":0.000034572393,"teacher_disagreement_score":0.6045099,"about_ca_system_score_codex":0.00027941016,"about_ca_system_score_gemma":0.00022744577,"threshold_uncertainty_score":0.9999388},"labels":[],"label_agreement":null},{"id":"W4296744480","doi":"10.3390/s22197134","title":"Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Ilam University","keywords":"Ripeness; Linear discriminant analysis; Artificial intelligence; Machine vision; Pattern recognition (psychology); Computer science; Artificial neural network; Image processing; Computer vision; Mathematics; Food science; Image (mathematics); Ripening; Biology","score_opus":0.018964245284748216,"score_gpt":0.2855045582277232,"score_spread":0.266540312942975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296744480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958648,0.00007649964,0.002856977,0.0000069064326,0.000044977114,0.00006427473,0.000030822895,0.000027843967,0.0010268629],"genre_scores_gemma":[0.9926344,8.6422085e-7,0.006972951,0.000004054871,0.000017814365,0.000028798195,0.00003977482,0.000012981335,0.00028835583],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992026,0.000010079274,0.00031594594,0.00015496145,0.0001976705,0.0001187582],"domain_scores_gemma":[0.99948096,0.00005276061,0.00021325935,0.00016113123,0.000062819454,0.000029041008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015852065,0.00008094639,0.00018097824,0.00012508358,0.00010899905,0.000005311866,0.00014757941,0.000018205223,0.00012829795],"category_scores_gemma":[0.000027306629,0.00008580174,0.000060216425,0.00020302432,0.000020712347,0.000028023736,0.00004319455,0.000053878874,4.988128e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020091583,0.00042666355,0.004484819,0.002194072,0.00014082869,0.0000087821045,0.0043112924,0.00190443,0.95738846,0.00079133775,0.000022070764,0.028126333],"study_design_scores_gemma":[0.0001764544,0.000014064716,0.0000749383,0.000016263166,0.00005506248,0.0000069075913,0.011385322,0.012093914,0.9753657,0.000015580925,0.00070410996,0.00009165948],"about_ca_topic_score_codex":0.00002050053,"about_ca_topic_score_gemma":0.0000043902805,"teacher_disagreement_score":0.028034672,"about_ca_system_score_codex":0.00018387481,"about_ca_system_score_gemma":0.000050354305,"threshold_uncertainty_score":0.3498893},"labels":[],"label_agreement":null},{"id":"W4296748320","doi":"10.1016/j.anscip.2022.07.472","title":"81. Predicting pellet quality of pelleted feed using ensemble learning models","year":2022,"lang":"en","type":"article","venue":"Animal - science proceedings","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Pellet; Quality (philosophy); Computer science; Materials science; Physics; Composite material","score_opus":0.08283185158796756,"score_gpt":0.32672674334168467,"score_spread":0.2438948917537171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296748320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9664434,0.00016758374,0.00023010619,0.000058308116,0.000026340596,0.00005733916,0.000006384439,0.00012094195,0.032889575],"genre_scores_gemma":[0.9976813,0.000010307212,0.0015781228,0.00003950827,0.0000632895,0.000009600802,0.0000023434625,0.000020626434,0.00059485313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99707866,0.000009718826,0.0004885237,0.0006361557,0.0011948858,0.0005920314],"domain_scores_gemma":[0.9988284,0.000081855986,0.0004983897,0.00012810194,0.0003282259,0.00013500183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013851537,0.00019166105,0.00035313208,0.00032918082,0.00125424,0.0001097962,0.0007430725,0.00005665,0.00083576323],"category_scores_gemma":[0.0006095311,0.00020504295,0.000118891614,0.0029096392,0.000391026,0.00065588683,0.00060077396,0.00053599733,0.000002956219],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007571603,0.00007247534,0.024604464,0.000089448535,0.000018001692,9.461338e-7,0.0017246525,0.0010377688,0.97122556,0.0010503802,0.0000389963,0.000061569066],"study_design_scores_gemma":[0.00036874262,0.00016711115,0.00079397863,0.000020743244,0.00007816389,0.00002748704,0.022077998,0.20962425,0.7658308,0.00060277653,0.000088420464,0.0003195362],"about_ca_topic_score_codex":0.00037400934,"about_ca_topic_score_gemma":6.552489e-7,"teacher_disagreement_score":0.20858648,"about_ca_system_score_codex":0.0002878281,"about_ca_system_score_gemma":0.00022132233,"threshold_uncertainty_score":0.96467274},"labels":[],"label_agreement":null},{"id":"W4296999117","doi":"10.5194/epsc2022-561","title":"Minimum Noise Fraction analysis of ExoMars/TGO-NOMAD LNO channel nadir data: SNR enhancement and application","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nadir; Occultation; Hyperspectral imaging; Remote sensing; Noise (video); Orbiter; Signal-to-noise ratio (imaging); Physics; Computer science; Geology; Satellite; Optics; Artificial intelligence; Astrophysics","score_opus":0.0322490551319136,"score_gpt":0.3240282596235279,"score_spread":0.29177920449161426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296999117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7087206,0.009961367,0.20236048,0.0006072865,0.00029548677,0.0007430259,0.0027553376,0.00028315536,0.07427322],"genre_scores_gemma":[0.9803691,0.002054755,0.0012862671,0.00007680835,0.00015451206,0.00027020028,0.009663428,0.000030452587,0.0060944594],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970788,0.000045601853,0.0008865791,0.001202612,0.00053249917,0.00025392105],"domain_scores_gemma":[0.99646026,0.0002470685,0.0010263392,0.0020481497,0.000106952946,0.000111233974],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006111717,0.00036401834,0.001219462,0.00076899736,0.00012752331,0.000056462326,0.00077665935,0.0003072502,0.010275078],"category_scores_gemma":[0.00012911213,0.00036651932,0.0003049776,0.0012638132,0.00006923677,0.00011260697,0.0016428995,0.00053683476,0.0000071336135],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006189782,0.0047643003,0.04871452,0.0054017697,0.077029794,0.000023972943,0.0013502993,0.0053172936,0.80386084,0.0009071953,0.017293293,0.03471773],"study_design_scores_gemma":[0.0010445936,0.0000942497,0.009081673,0.00007540166,0.054013334,0.0000052872206,0.005193639,0.20799194,0.69564563,0.0012907174,0.023462834,0.002100671],"about_ca_topic_score_codex":0.001499416,"about_ca_topic_score_gemma":0.00010852824,"teacher_disagreement_score":0.2716485,"about_ca_system_score_codex":0.0002945592,"about_ca_system_score_gemma":0.00009501161,"threshold_uncertainty_score":0.9998787},"labels":[],"label_agreement":null},{"id":"W4297051178","doi":"10.18280/ijdne.170411","title":"Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”","year":2022,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Partial least squares regression; Algorithm; Principal component analysis; Support vector machine; Mangifera; Mean squared error; Second derivative; Mathematics; Principal component regression; Pattern recognition (psychology); Artificial intelligence; Computer science; Statistics; Botany","score_opus":0.021569096767609028,"score_gpt":0.33098927762369035,"score_spread":0.3094201808560813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297051178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98604673,0.0024856217,0.010932285,0.000029115317,0.00011035624,0.00020223774,0.00007679515,0.0000054223256,0.000111413174],"genre_scores_gemma":[0.9898027,0.00022730032,0.009704446,0.000015559168,0.00010555098,0.000021533773,0.000017992774,0.000015363108,0.000089549445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846005,0.000051648418,0.0005260072,0.00017639567,0.0006631199,0.00012274714],"domain_scores_gemma":[0.998277,0.00017773364,0.0007804412,0.00009709716,0.00061932986,0.000048385424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006955809,0.0001559652,0.0003276318,0.00024699606,0.0001471069,0.000053618445,0.00021054642,0.000080961,0.000110035086],"category_scores_gemma":[0.00006253936,0.0001373124,0.000096020165,0.00016830685,0.000045649955,0.00021690386,0.000060972103,0.00028640334,3.725055e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008027649,0.009006423,0.0900435,0.00066132954,0.0115953265,0.00096574845,0.023333179,0.051419802,0.20118293,0.000087670975,0.00037562233,0.6033008],"study_design_scores_gemma":[0.0042725476,0.0026781869,0.0015436932,0.00007527194,0.001512149,0.0024984612,0.0062335343,0.9118246,0.06840795,0.0006584844,0.00006390884,0.00023120275],"about_ca_topic_score_codex":0.00003169706,"about_ca_topic_score_gemma":0.0000088844345,"teacher_disagreement_score":0.8604048,"about_ca_system_score_codex":0.00032817977,"about_ca_system_score_gemma":0.00021716855,"threshold_uncertainty_score":0.5599437},"labels":[],"label_agreement":null},{"id":"W4297224645","doi":"10.1111/1541-4337.13039","title":"Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology","year":2022,"lang":"en","type":"article","venue":"Comprehensive Reviews in Food Science and Food Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Multispectral image; Imaging technology; Computer science; Process engineering; Environmental science; Remote sensing; Artificial intelligence; Engineering","score_opus":0.04668315191208979,"score_gpt":0.3151816873620386,"score_spread":0.26849853544994884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297224645","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41735995,0.570308,0.0070201294,0.0010345018,0.00020976084,0.0010579504,0.000044190994,0.00013941416,0.0028260334],"genre_scores_gemma":[0.99344736,0.0055352864,0.00051650155,0.00020989671,0.000028346902,0.00021178552,0.0000029687926,0.000011348796,0.000036503534],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983439,0.000021318774,0.00040120023,0.00059489405,0.00024728623,0.00039144012],"domain_scores_gemma":[0.9992211,0.000075476324,0.00018477613,0.00026275983,0.00017795301,0.000077934325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050052674,0.0001834122,0.00042996393,0.00044583957,0.0008520831,0.00006207317,0.00035200626,0.00004410148,0.000048991707],"category_scores_gemma":[0.00026041927,0.00017035265,0.00005528496,0.0020878036,0.00046385024,0.0001901949,0.00041025874,0.00024444883,0.0000010423561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093394265,0.00020656022,0.007230157,0.0015669971,0.000054564734,0.000003465893,0.00080390845,0.000047662495,0.3442182,0.0014113779,0.00012039173,0.6442433],"study_design_scores_gemma":[0.0011954738,0.0009272895,0.000633741,0.0002561821,0.00016894464,0.00022609418,0.007899694,0.0042177574,0.47557387,0.0070333,0.50102955,0.00083812664],"about_ca_topic_score_codex":0.0000036043577,"about_ca_topic_score_gemma":0.00001877461,"teacher_disagreement_score":0.6434052,"about_ca_system_score_codex":0.00020382585,"about_ca_system_score_gemma":0.000101177764,"threshold_uncertainty_score":0.69467795},"labels":[],"label_agreement":null},{"id":"W4297235612","doi":"10.3390/land11101638","title":"Innovative Fusion-Based Strategy for Crop Residue Modeling","year":2022,"lang":"en","type":"article","venue":"Land","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Partial least squares regression; Random forest; Support vector machine; Mean squared error; Fusion; Residue (chemistry); Mathematics; Statistics; Artificial intelligence; Computer science; Chemistry","score_opus":0.04239617869585699,"score_gpt":0.30773194622087346,"score_spread":0.2653357675250165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297235612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793003,0.0002946133,0.00923663,0.00019744372,0.00003100664,0.000059544767,0.00014212084,0.000065622815,0.010672723],"genre_scores_gemma":[0.9968661,0.000002289643,0.00038832932,0.00016445522,0.0000824882,0.00006512195,0.00012792001,0.000013539366,0.0022897257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993896,0.000006339655,0.00013111871,0.00017598842,0.00013627131,0.00016067563],"domain_scores_gemma":[0.9996322,0.0000777215,0.000049799677,0.00014443282,0.000069731825,0.000026163694],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007768455,0.00008039627,0.00012492138,0.000074196345,0.00023849623,0.000020782043,0.0001280195,0.00003341949,0.002680204],"category_scores_gemma":[0.0000455018,0.00007530426,0.000040312756,0.0004989842,0.000012173748,0.000021547352,0.000038297036,0.00014321443,0.0000022546933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012700956,0.0006722875,0.10954721,0.00044975875,0.0004202606,0.000049269893,0.00050023204,0.6121167,0.26130936,0.0019972338,0.008986832,0.002680779],"study_design_scores_gemma":[0.0036398154,0.00025911833,0.00021638797,0.000016383367,0.0001706726,0.000007308286,0.0018753608,0.58055884,0.4007198,0.005240384,0.006625055,0.0006708915],"about_ca_topic_score_codex":0.00027760532,"about_ca_topic_score_gemma":0.000038707167,"teacher_disagreement_score":0.13941044,"about_ca_system_score_codex":0.000058730937,"about_ca_system_score_gemma":0.000085580075,"threshold_uncertainty_score":0.9982315},"labels":[],"label_agreement":null},{"id":"W4297319646","doi":"10.26434/chemrxiv-2022-b97x0-v2","title":"OpenPCA and Raman mapping to decipher complex spectral datasets from multi-component samples: application to cannabis trichomes","year":2022,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Principal component analysis; Raman spectroscopy; Computer science; Pattern recognition (psychology); Pooling; Biological system; Deconvolution; Artificial intelligence; Coding (social sciences); Data mining; Biology; Physics; Mathematics; Optics; Algorithm; Statistics","score_opus":0.07024661616267878,"score_gpt":0.3260564148879455,"score_spread":0.2558097987252667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297319646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97521394,0.0014431353,0.016616212,0.0015560753,0.00015724203,0.0008310776,0.0025660822,0.0002669649,0.0013492765],"genre_scores_gemma":[0.9648182,0.00012935228,0.019246627,0.0009373316,0.00047976276,0.00095764326,0.012228494,0.00011045274,0.001092138],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966886,0.000030806135,0.0006335778,0.0016165362,0.0004573059,0.0005731956],"domain_scores_gemma":[0.9975072,0.00010671098,0.00028485345,0.0015409708,0.000058836224,0.00050139765],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018619992,0.0006020314,0.00090484146,0.00036291295,0.00026870443,0.0002455239,0.0012425866,0.0002588269,0.006080534],"category_scores_gemma":[0.00016662222,0.0006708215,0.00018359679,0.00074388424,0.000062621235,0.00006440157,0.0023879244,0.00074857334,0.00007740212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026422457,0.0010413037,0.035924453,0.00090197293,0.0015194772,0.000048783568,0.0039517963,0.0010815583,0.8372675,0.00006570682,0.11341561,0.0045175925],"study_design_scores_gemma":[0.0025231955,0.00005285339,0.08956421,0.00021251047,0.0009256461,0.000012261715,0.004331658,0.002690389,0.39942575,0.00053559407,0.49661517,0.0031107727],"about_ca_topic_score_codex":0.005502559,"about_ca_topic_score_gemma":0.0003385462,"teacher_disagreement_score":0.43784177,"about_ca_system_score_codex":0.00063011,"about_ca_system_score_gemma":0.00010121001,"threshold_uncertainty_score":0.9995743},"labels":[],"label_agreement":null},{"id":"W4297540327","doi":"10.1002/cjce.24684","title":"Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Robustness (evolution); Autoencoder; Noise reduction; Data mining; Artificial neural network","score_opus":0.06041833809179187,"score_gpt":0.3131566120621054,"score_spread":0.2527382739703135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297540327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9358811,0.0017917106,0.0608537,0.000607492,0.00031795056,0.0000787025,0.00003066888,0.000047721936,0.00039099046],"genre_scores_gemma":[0.98026466,6.679602e-7,0.01936407,0.000039264087,0.00024872733,0.000009075259,0.000010304092,0.000030381687,0.00003282308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982503,0.00006679985,0.0004893658,0.00015440834,0.00065667724,0.00038243385],"domain_scores_gemma":[0.99853754,0.00040659963,0.00026720663,0.0002555651,0.00018849969,0.00034457463],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014592054,0.00017073473,0.00030820744,0.00018439419,0.0003548059,0.00011562009,0.00050070067,0.00007389125,0.0013134211],"category_scores_gemma":[0.000863976,0.00015802262,0.00018464334,0.00057326624,0.000047124693,0.00009998859,0.000032284992,0.00081030186,8.663012e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018900324,0.000011604884,0.00061562576,0.000037844213,0.00012586448,0.000012807341,0.0003340799,0.6562413,0.3418816,0.000026158257,0.00008219642,0.0006119928],"study_design_scores_gemma":[0.00062014273,0.000017362612,0.00018182608,0.00003549761,0.00032892785,0.000083120656,0.00034987717,0.2384905,0.75876474,0.00027787374,0.00057881285,0.00027130987],"about_ca_topic_score_codex":0.002244896,"about_ca_topic_score_gemma":0.000039530445,"teacher_disagreement_score":0.4177508,"about_ca_system_score_codex":0.0015614829,"about_ca_system_score_gemma":0.0009929847,"threshold_uncertainty_score":0.9995995},"labels":[],"label_agreement":null},{"id":"W4300106639","doi":"10.12697/acutm.2004.08.21","title":"MANOVA with singular variance matrix","year":2004,"lang":"en","type":"article","venue":"Acta et Commentationes Universitatis Tartuensis de Mathematica","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Multivariate analysis of variance; Estimator; Rank (graph theory); Dimension (graph theory); Multivariate statistics; Variance (accounting); Statistics; Applied mathematics; Data Matrix; Inference; Design matrix; Matrix (chemical analysis); Linear model; Computer science; Combinatorics","score_opus":0.012387240847863567,"score_gpt":0.273852803232217,"score_spread":0.2614655623843534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300106639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6565439,0.00025668982,0.17910732,0.030238748,0.00004276133,0.00032785453,0.000080917605,0.00057342864,0.13282835],"genre_scores_gemma":[0.85874325,0.000033096905,0.13893418,0.0007226245,0.000017788729,0.000006284582,0.000057666508,0.000031840787,0.0014532796],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895555,0.00002271005,0.00020936952,0.00025804297,0.00026905368,0.00028527513],"domain_scores_gemma":[0.99900717,0.00028217523,0.000177707,0.00033825525,0.00008790921,0.00010679368],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000094844116,0.00021440301,0.00027453742,0.00016980615,0.00020474027,0.00008011741,0.0002432125,0.00007402617,0.0027893933],"category_scores_gemma":[0.00005137459,0.00020626163,0.000073796364,0.00045183458,0.00010226263,0.00032679093,0.00005391217,0.00015860575,0.00011910728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045159037,0.0023368278,0.0028828394,0.0014827908,0.0033733377,0.000777132,0.028196722,0.0019856123,0.21378514,0.7284797,0.015458952,0.0007893761],"study_design_scores_gemma":[0.019680558,0.0007758882,0.0022687174,0.0010872433,0.006330839,0.0013171454,0.096534334,0.0014157936,0.66623116,0.16491462,0.03549586,0.0039478256],"about_ca_topic_score_codex":0.00008702136,"about_ca_topic_score_gemma":0.000026154607,"teacher_disagreement_score":0.5635651,"about_ca_system_score_codex":0.00030932558,"about_ca_system_score_gemma":0.00008792773,"threshold_uncertainty_score":0.9981222},"labels":[],"label_agreement":null},{"id":"W4300409770","doi":"10.1016/b978-075067123-1/50023-5","title":"Chemical analysis","year":2003,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada)","funders":"","keywords":"Vapor pressure; Water vapor; Moisture; Vapour pressure of water; Partial pressure; Humidity; Saturation (graph theory); Calibration; Relative humidity; Measure (data warehouse); Ideal gas law; Hygrometer; Environmental science; Petroleum engineering; Materials science; Chemistry; Thermodynamics; Engineering; Computer science; Composite material; Physics; Mathematics; Organic chemistry; Oxygen","score_opus":0.01382830833800223,"score_gpt":0.25089620302276716,"score_spread":0.23706789468476494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300409770","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013778532,0.0024168831,0.0000067874525,0.000029884059,0.000045621397,0.00005035202,0.000058756657,0.000116319854,0.9971376],"genre_scores_gemma":[0.0024381517,0.00012198283,0.00027284247,0.00037901493,0.00028205418,0.000015729074,0.00013478532,0.00009786911,0.99625754],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788135,0.000005274494,0.00053031975,0.0007289726,0.00045302056,0.00040108478],"domain_scores_gemma":[0.9983467,0.00007308523,0.0003132514,0.00096583465,0.00009183935,0.00020931514],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008604754,0.0005833485,0.0011298872,0.0004528856,0.000077563636,0.000054753047,0.00043575617,0.0007666048,0.023097258],"category_scores_gemma":[0.000033289354,0.0005672599,0.0011349369,0.000116313764,0.0001662793,0.000020328209,0.00009200748,0.00075452396,0.00032084045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048744198,0.000090334346,0.00015955308,0.00061716605,0.026881868,0.00031637712,0.00018392867,0.0000026720472,0.013710552,0.010338115,0.0040110457,0.94363964],"study_design_scores_gemma":[0.00017798995,0.000005293326,4.823825e-7,0.000036553392,0.009572189,0.000015364581,0.000009715464,0.0000023583234,0.037979636,0.0034358094,0.9480879,0.0006767536],"about_ca_topic_score_codex":5.269678e-7,"about_ca_topic_score_gemma":0.0000036300444,"teacher_disagreement_score":0.94407684,"about_ca_system_score_codex":0.00020363792,"about_ca_system_score_gemma":0.000062531595,"threshold_uncertainty_score":0.9996779},"labels":[],"label_agreement":null},{"id":"W4300565490","doi":"","title":"Copulas with prescribed correlation matrix","year":2017,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Correlation; Mathematics; Medicine; Geometry","score_opus":0.01570987683505707,"score_gpt":0.2596842076366975,"score_spread":0.2439743308016404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300565490","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1533432,0.015401512,0.25533214,0.021583397,0.0006123654,0.00085180404,0.00037898074,0.0006201129,0.5518765],"genre_scores_gemma":[0.66101056,0.0013792472,0.029344903,0.00003732396,0.0000891232,0.00008142488,0.0012283168,0.00010060305,0.30672848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9943111,0.0019728653,0.00082236534,0.0013769199,0.000805165,0.0007116189],"domain_scores_gemma":[0.9890531,0.0015509676,0.0016831508,0.0045591537,0.002771193,0.00038246418],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028230322,0.0007380692,0.00086808746,0.0002481219,0.0011388682,0.0010369276,0.0023256245,0.0008605685,0.0030637064],"category_scores_gemma":[0.0025087765,0.0007662955,0.00039111922,0.0005231553,0.0007912155,0.00033066646,0.0012023757,0.0014951439,0.00034510077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007010046,0.008511331,0.19744875,0.005074561,0.0053856242,0.00020873972,0.03452535,0.0032645646,0.0624944,0.58866316,0.029766042,0.063956484],"study_design_scores_gemma":[0.0038667996,0.000002676738,0.015539804,0.008322212,0.0029018747,0.00014102238,0.0007201295,0.04809345,0.7511006,0.0076175206,0.15875012,0.0029437977],"about_ca_topic_score_codex":0.004681524,"about_ca_topic_score_gemma":0.0018647914,"teacher_disagreement_score":0.6886062,"about_ca_system_score_codex":0.00045900032,"about_ca_system_score_gemma":0.0005835008,"threshold_uncertainty_score":0.99991167},"labels":[],"label_agreement":null},{"id":"W4300895459","doi":"","title":"Contrôle ultrasonore du processus de la fermentation du yaourt","year":2010,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science","score_opus":0.00599012186632765,"score_gpt":0.23750264837484855,"score_spread":0.2315125265085209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300895459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7629966,0.00278929,0.019596063,0.024128545,0.00028304043,0.00019200094,0.000078453944,0.0002269901,0.18970898],"genre_scores_gemma":[0.93105805,0.0014188406,0.009705432,0.00018543816,0.00016396008,0.00004623867,0.00023642907,0.000053311298,0.0571323],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964639,0.0015405472,0.00047362072,0.00059470354,0.0003718138,0.0005554399],"domain_scores_gemma":[0.9945357,0.0022621618,0.00041929417,0.0010315999,0.0014349265,0.00031632822],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034758674,0.0003405065,0.00035185862,0.00014969743,0.0005688194,0.0005537441,0.0008999293,0.0004725884,0.0050410777],"category_scores_gemma":[0.004260174,0.00039045798,0.00020752048,0.0008286144,0.000678389,0.0003069695,0.00018318246,0.00085718534,0.00016713252],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049585524,0.0030670885,0.1032608,0.0005126626,0.00037530053,0.00003436813,0.014385193,0.000019493435,0.714898,0.112714306,0.01107719,0.03960604],"study_design_scores_gemma":[0.0012846801,6.456624e-7,0.007733644,0.00029132213,0.00031604437,0.00013342906,0.0010302394,0.0036143623,0.9066941,0.0020997087,0.07633943,0.0004623988],"about_ca_topic_score_codex":0.0017165247,"about_ca_topic_score_gemma":0.00094236934,"teacher_disagreement_score":0.19179612,"about_ca_system_score_codex":0.00020343615,"about_ca_system_score_gemma":0.00039189422,"threshold_uncertainty_score":0.99985474},"labels":[],"label_agreement":null},{"id":"W4301395912","doi":"10.1177/09670335221124611","title":"Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy","year":2022,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Infrared; Infrared spectroscopy; Near-infrared spectroscopy; Spectroscopy; Sampling (signal processing); Materials science; Two-dimensional infrared spectroscopy; Analytical Chemistry (journal); Chemistry; Optics; Physics; Detector","score_opus":0.032167616686089646,"score_gpt":0.32641684073973615,"score_spread":0.2942492240536465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301395912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95367706,0.0064372015,0.016049104,0.0040291706,0.0020806799,0.0015395094,0.00074776006,0.00045325252,0.014986271],"genre_scores_gemma":[0.66822284,0.00068945286,0.29061097,0.0047977255,0.0057844585,0.0011779449,0.0008814978,0.0009014308,0.026933674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9906962,0.0002555278,0.0029489656,0.0012368796,0.0025882118,0.0022742366],"domain_scores_gemma":[0.99373436,0.00074165163,0.0020205898,0.0019826642,0.00078747026,0.0007332654],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018454423,0.0010803385,0.0022583345,0.0007162205,0.0037679276,0.001660731,0.002358212,0.0003855241,0.014079776],"category_scores_gemma":[0.0018695246,0.0009259964,0.0020813337,0.0044972966,0.0009361793,0.0009697557,0.0006207025,0.0026259036,0.000057328467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008617791,0.006564709,0.113135695,0.001776079,0.028291814,0.00088278187,0.012424443,0.019635618,0.549266,0.0054917843,0.25360096,0.00031235983],"study_design_scores_gemma":[0.0075255167,0.0053819558,0.002725467,0.00025868494,0.016963078,0.0003236928,0.004908322,0.01732145,0.4989853,0.035231676,0.4078804,0.0024944758],"about_ca_topic_score_codex":0.000066609566,"about_ca_topic_score_gemma":0.000013128204,"teacher_disagreement_score":0.2854542,"about_ca_system_score_codex":0.0016140753,"about_ca_system_score_gemma":0.0015775977,"threshold_uncertainty_score":0.9996751},"labels":[],"label_agreement":null},{"id":"W4302009119","doi":"10.32920/21265788.v1","title":"Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Filter bank; Artificial intelligence; Gabor filter; Filter (signal processing); Segmentation; Pattern recognition (psychology); Computer science; Computer vision; Feature (linguistics); Discriminative model; Image texture; Feature extraction; Frequency domain; Image segmentation","score_opus":0.024728337878558874,"score_gpt":0.30226820129794124,"score_spread":0.27753986341938236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302009119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46954834,0.0033699481,0.44557598,0.0036759898,0.00087771827,0.003009766,0.0032317315,0.0015556433,0.06915489],"genre_scores_gemma":[0.51997405,0.00018036466,0.3053409,0.0009124482,0.0005882264,0.0035936078,0.004604503,0.00026314158,0.16454275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980454,0.000049635913,0.0003835609,0.0007865531,0.00037559323,0.00035929808],"domain_scores_gemma":[0.99826086,0.0004956081,0.000297692,0.0007097963,0.00013461326,0.00010145913],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025474082,0.000438041,0.000535404,0.00024266692,0.00019882689,0.0001029163,0.0004533321,0.00036013365,0.036388025],"category_scores_gemma":[0.00019511547,0.00035916682,0.0002834019,0.00027075526,0.00005076839,0.00007158508,0.00030354338,0.000693862,0.000011833758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.038314316,0.0011690841,0.0026320037,0.004375986,0.00725295,0.00019302698,0.0047650263,0.04289281,0.84555477,0.00041218556,0.05059985,0.0018380138],"study_design_scores_gemma":[0.0074927225,0.00031736944,0.00011361477,0.0001265798,0.002325679,0.000026160129,0.0056188256,0.016431795,0.96141165,0.0016463108,0.0025876446,0.0019016457],"about_ca_topic_score_codex":0.00012468462,"about_ca_topic_score_gemma":0.0000088577735,"teacher_disagreement_score":0.1402351,"about_ca_system_score_codex":0.00039777774,"about_ca_system_score_gemma":0.0002075772,"threshold_uncertainty_score":0.99988604},"labels":[],"label_agreement":null},{"id":"W4302009120","doi":"10.32920/21265788","title":"Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Filter bank; Gabor filter; Filter (signal processing); Segmentation; Pattern recognition (psychology); Computer science; Computer vision; Feature (linguistics); Discriminative model; Frequency domain; Image texture; Feature extraction; Image segmentation","score_opus":0.024728337878558874,"score_gpt":0.30226820129794124,"score_spread":0.27753986341938236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302009120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46954834,0.0033699481,0.44557598,0.0036759898,0.00087771827,0.003009766,0.0032317315,0.0015556433,0.06915489],"genre_scores_gemma":[0.51997405,0.00018036466,0.3053409,0.0009124482,0.0005882264,0.0035936078,0.004604503,0.00026314158,0.16454275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980454,0.000049635913,0.0003835609,0.0007865531,0.00037559323,0.00035929808],"domain_scores_gemma":[0.99826086,0.0004956081,0.000297692,0.0007097963,0.00013461326,0.00010145913],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025474082,0.000438041,0.000535404,0.00024266692,0.00019882689,0.0001029163,0.0004533321,0.00036013365,0.036388025],"category_scores_gemma":[0.00019511547,0.00035916682,0.0002834019,0.00027075526,0.00005076839,0.00007158508,0.00030354338,0.000693862,0.000011833758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.038314316,0.0011690841,0.0026320037,0.004375986,0.00725295,0.00019302698,0.0047650263,0.04289281,0.84555477,0.00041218556,0.05059985,0.0018380138],"study_design_scores_gemma":[0.0074927225,0.00031736944,0.00011361477,0.0001265798,0.002325679,0.000026160129,0.0056188256,0.016431795,0.96141165,0.0016463108,0.0025876446,0.0019016457],"about_ca_topic_score_codex":0.00012468462,"about_ca_topic_score_gemma":0.0000088577735,"teacher_disagreement_score":0.1402351,"about_ca_system_score_codex":0.00039777774,"about_ca_system_score_gemma":0.0002075772,"threshold_uncertainty_score":0.99988604},"labels":[],"label_agreement":null},{"id":"W4304183120","doi":"10.1002/ls.1624","title":"Titrimetric‐comparable <scp>BN</scp> results determined for in‐service lubricants using quantitative <scp>FTIR</scp> spectroscopy","year":2022,"lang":"en","type":"article","venue":"Lubrication Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fourier transform infrared spectroscopy; Titration; Potentiometric titration; Reproducibility; Analytical Chemistry (journal); Chemistry; Lubricant; Chromatography; Materials science; Chemical engineering; Organic chemistry; Engineering","score_opus":0.07599326961628704,"score_gpt":0.35957667611483324,"score_spread":0.2835834064985462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304183120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97031116,0.0006243026,0.0058990866,0.00015563885,0.00021256536,0.00037241925,0.00017979027,0.00014302383,0.022102019],"genre_scores_gemma":[0.9684463,0.00003720018,0.02717643,0.0003568397,0.00008370204,0.0001562347,0.00008050568,0.000039322586,0.0036234255],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99561816,0.00006717392,0.00081921014,0.0012543756,0.0011893286,0.0010517193],"domain_scores_gemma":[0.9955115,0.0020952132,0.0008051481,0.0008537895,0.00048510058,0.00024928048],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015333261,0.00033263274,0.0005250489,0.0014608686,0.0011385888,0.00023568812,0.001637982,0.00008491883,0.00007994294],"category_scores_gemma":[0.0039670113,0.00036487554,0.0001315547,0.015937686,0.00029491127,0.00066509214,0.0003819618,0.0004009842,0.000046098194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003960392,0.000422399,0.008170835,0.00006287432,0.000038597194,0.000007325358,0.0015849221,0.0019026964,0.9822997,0.0008021538,0.004494605,0.0001743071],"study_design_scores_gemma":[0.0015449352,0.0001548767,0.004534651,0.000018910221,0.00011342294,0.000032876997,0.00595389,0.065065,0.90952927,0.0008187998,0.012071362,0.00016198108],"about_ca_topic_score_codex":0.00060333253,"about_ca_topic_score_gemma":0.000026616835,"teacher_disagreement_score":0.07277039,"about_ca_system_score_codex":0.0011076928,"about_ca_system_score_gemma":0.00068895624,"threshold_uncertainty_score":0.9998803},"labels":[],"label_agreement":null},{"id":"W4306160751","doi":"10.1177/00037028221133851","title":"Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra","year":2022,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Raman spectroscopy; Wavenumber; Perturbation (astronomy); Cluster analysis; Chemistry; Spectral line; Correlation coefficient; Statistical physics; Physics; Mathematics; Optics; Statistics; Quantum mechanics","score_opus":0.014002763264440743,"score_gpt":0.2748988051514158,"score_spread":0.26089604188697507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306160751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93305475,0.0023809092,0.02328774,0.0012350621,0.000319273,0.00080789323,0.00028787254,0.00017482946,0.038451657],"genre_scores_gemma":[0.99342835,0.000020763033,0.005675543,0.00012325835,0.00016953754,0.00021847575,0.000045425266,0.00003708773,0.00028158407],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985268,0.00001294479,0.00036486477,0.0003430668,0.00040596825,0.00034635968],"domain_scores_gemma":[0.9987643,0.0003929046,0.00034142294,0.00040865235,0.000048186885,0.000044531687],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024593336,0.0002083241,0.00041062443,0.0001628773,0.00025747874,0.000015186217,0.00046469417,0.000044659624,0.0022786956],"category_scores_gemma":[0.00002822477,0.00017958638,0.00018433151,0.0004850684,0.00014615848,0.000029903542,0.00016458803,0.0002777359,0.000002101257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061838876,0.00014529546,0.00010473425,0.00008925919,0.00023701543,7.3831865e-7,0.0005781381,0.0070571825,0.9815467,0.009006021,0.00032853067,0.0002880154],"study_design_scores_gemma":[0.0010052039,0.0001735514,0.00006465338,0.000007148198,0.00028166443,0.000008971519,0.002562109,0.011802658,0.98176634,0.0019754488,0.00016914314,0.0001831112],"about_ca_topic_score_codex":0.00008203338,"about_ca_topic_score_gemma":0.000044156226,"teacher_disagreement_score":0.06037356,"about_ca_system_score_codex":0.00017370384,"about_ca_system_score_gemma":0.000060966162,"threshold_uncertainty_score":0.9986334},"labels":[],"label_agreement":null},{"id":"W4307042788","doi":"10.1177/09670335221124612","title":"Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1: Generalised theory of sampling","year":2022,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Sampling (signal processing); Infrared; Statistics; Remote sensing; Materials science; Mathematics; Optics; Physics; Geology; Detector","score_opus":0.03242355119621351,"score_gpt":0.3108562403559913,"score_spread":0.2784326891597778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307042788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97425115,0.0045815315,0.009378612,0.0009649052,0.00059143413,0.00023225842,0.00020764706,0.000105666484,0.009686782],"genre_scores_gemma":[0.92050564,0.00027117174,0.07066557,0.00087493553,0.0015216144,0.000065325105,0.000112471505,0.0001772316,0.0058060554],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9948543,0.00022380131,0.0018814369,0.00055192446,0.0015435407,0.0009450162],"domain_scores_gemma":[0.99592465,0.0007304388,0.0014774706,0.0011175938,0.0004501896,0.00029966838],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015443526,0.0005155989,0.0014364509,0.0006266834,0.0013484468,0.00037735785,0.0014974544,0.00019629282,0.017100528],"category_scores_gemma":[0.0010745014,0.00041290242,0.0012306615,0.003389006,0.00060371053,0.00038291153,0.00038132563,0.0019023311,0.0000116964475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038591563,0.0028042963,0.09074944,0.0008888679,0.01619617,0.00027483454,0.011164234,0.033316452,0.8036465,0.015952641,0.02069083,0.00045654908],"study_design_scores_gemma":[0.0046283873,0.0028645203,0.0064142044,0.00023735252,0.017689744,0.00037315616,0.010780716,0.0059376503,0.7823179,0.08003676,0.08687902,0.0018406308],"about_ca_topic_score_codex":0.0000477242,"about_ca_topic_score_gemma":0.0000047506983,"teacher_disagreement_score":0.08433524,"about_ca_system_score_codex":0.00058255775,"about_ca_system_score_gemma":0.00079039874,"threshold_uncertainty_score":0.99995166},"labels":[],"label_agreement":null},{"id":"W4307349181","doi":"10.1007/s10661-022-10652-1","title":"Augmentation of field fluorescence measures for improved in situ contaminant detection","year":2022,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fluoranthene; Pyrene; Environmental science; Contamination; Environmental chemistry; Biological system; Chemistry; Ecology","score_opus":0.011939381064933804,"score_gpt":0.28104697539629897,"score_spread":0.26910759433136516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307349181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99828124,0.00027903492,0.0010288224,0.000031075902,0.00009278868,0.00008287101,0.000013314639,0.0000084345775,0.00018242733],"genre_scores_gemma":[0.9986429,0.000116071096,0.0009002595,0.0000042631773,0.00004204114,0.00013844832,0.000010002634,0.000007002884,0.00013897648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942344,0.000011463796,0.00015818,0.00016594554,0.00012934054,0.00011165322],"domain_scores_gemma":[0.99974614,0.000069438196,0.00008083325,0.0000779023,0.0000019396937,0.000023753824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009651006,0.00007700714,0.00011572394,0.00005316514,0.00011700874,0.000008325919,0.00005606229,0.000024357483,0.00006341624],"category_scores_gemma":[0.0000069390153,0.000082703686,0.000034837296,0.00005618817,0.000017326076,0.000044467488,0.00005378159,0.00012226924,9.7221026e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045491477,0.00011565305,0.049756557,0.00002209499,0.000019192803,5.779581e-7,0.000087314875,0.000024274932,0.9209539,0.0000034163113,0.000001813381,0.028969694],"study_design_scores_gemma":[0.0006295858,0.00017306887,0.0319187,0.000007295881,0.000035882094,0.0000015265418,0.0031954497,0.00029950065,0.9634552,0.000028760674,0.00017261045,0.00008244682],"about_ca_topic_score_codex":0.00007520285,"about_ca_topic_score_gemma":0.0000027601936,"teacher_disagreement_score":0.042501252,"about_ca_system_score_codex":0.000255432,"about_ca_system_score_gemma":0.00000792026,"threshold_uncertainty_score":0.33725584},"labels":[],"label_agreement":null},{"id":"W4307472446","doi":"10.1117/12.2635946","title":"Visually assessing maize leaves: from spectral sampling to high-fidelity color reproduction","year":2022,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reproduction; Sampling (signal processing); Computer vision; Fidelity; High fidelity; Artificial intelligence; Biology; Telecommunications; Ecology; Physics; Acoustics","score_opus":0.0405206629111149,"score_gpt":0.3365961928330013,"score_spread":0.2960755299218864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307472446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9774382,0.00014763577,0.009840196,0.000941322,0.00022694781,0.00006489271,0.000040297713,0.0002992668,0.011001228],"genre_scores_gemma":[0.97647965,0.0000066137695,0.017733006,0.00054133224,0.0006506086,0.00003587573,0.00008171656,0.000029148698,0.004442065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99800855,0.000029906692,0.0003748035,0.0008014825,0.00042748897,0.00035778622],"domain_scores_gemma":[0.99892926,0.00019318322,0.00012854666,0.0005707002,0.000057633337,0.00012066019],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029214553,0.00018785198,0.0003035027,0.00013686334,0.00047580595,0.00014679515,0.00029598997,0.00006140773,0.025736356],"category_scores_gemma":[0.00036617005,0.00019964541,0.00011169005,0.0008241913,0.000028314826,0.00016724685,0.0002417759,0.00037630636,0.000056407152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009146365,0.00023384721,0.007858481,0.000019687863,0.0001488889,0.0000109274115,0.00019422501,0.001454573,0.9846143,0.0007204504,0.0037330117,0.0009201325],"study_design_scores_gemma":[0.00041027184,0.00006618162,0.008246279,0.0000074983714,0.00018384728,0.000012158717,0.005786825,0.00029159908,0.9769724,0.0023518887,0.005236226,0.00043484333],"about_ca_topic_score_codex":0.001801125,"about_ca_topic_score_gemma":0.000030793115,"teacher_disagreement_score":0.02567995,"about_ca_system_score_codex":0.000522918,"about_ca_system_score_gemma":0.000087257664,"threshold_uncertainty_score":0.9751543},"labels":[],"label_agreement":null},{"id":"W4307548857","doi":"10.3390/foods11213347","title":"Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries","year":2022,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Blanching; Postharvest; Food science; Artificial neural network; Chemistry; Computer science; Botany; Biology; Artificial intelligence","score_opus":0.01993652095721988,"score_gpt":0.26449393037973495,"score_spread":0.24455740942251508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307548857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960989,0.0004191748,0.0011069359,0.00002569637,0.000056429493,0.000034087858,0.000036939855,0.000027339067,0.0021945168],"genre_scores_gemma":[0.9980931,0.000005802824,0.0016495263,0.000013887945,0.00007459465,0.000007804812,0.000034963436,0.000011039711,0.00010923518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931157,0.00002806831,0.00023258201,0.00014602693,0.00015363857,0.0001281028],"domain_scores_gemma":[0.999514,0.000106146195,0.00020437855,0.00011201383,0.000038308197,0.000025160158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009292016,0.000083050996,0.00020238663,0.00007288458,0.00016488871,0.000012486225,0.00008241642,0.000035662964,0.0004684967],"category_scores_gemma":[0.00007783102,0.00008950337,0.00005337725,0.00043910232,0.00006292037,0.00006301596,0.000093478884,0.00013206081,1.0286862e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038041017,0.00026580188,0.087582625,0.00021960666,0.00040261992,0.000007859917,0.0018562493,0.71576697,0.19019763,0.0005678926,0.00012654073,0.002625803],"study_design_scores_gemma":[0.000993182,0.0004492396,0.02527267,0.000050322913,0.00062632724,0.000053822474,0.0062545426,0.19435252,0.77064914,0.00061662635,0.00012711354,0.00055448234],"about_ca_topic_score_codex":0.00010383267,"about_ca_topic_score_gemma":0.000022891501,"teacher_disagreement_score":0.5804515,"about_ca_system_score_codex":0.00003415357,"about_ca_system_score_gemma":0.000021951972,"threshold_uncertainty_score":0.51297104},"labels":[],"label_agreement":null},{"id":"W4307794493","doi":"10.21748/pqtj3002","title":"Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy","year":2022,"lang":"en","type":"article","venue":"Proceedings of 2022 AOCS Annual Meeting &amp; Expo","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Partial least squares regression; Amino acid; Chemistry; Calibration; Near-infrared spectroscopy; Methionine; Histidine; Reflectivity; Near infrared reflectance spectroscopy; Tyrosine; Chromatography; Analytical Chemistry (journal); Mathematics; Biochemistry; Biology; Optics; Physics; Statistics","score_opus":0.03406428259275972,"score_gpt":0.2776190996124329,"score_spread":0.2435548170196732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307794493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951066,0.0021531954,0.000014531912,0.00006177386,0.000036135807,0.00019613006,0.00016831374,0.000044359862,0.002218988],"genre_scores_gemma":[0.9947374,0.000068646936,0.0040342016,0.000016856553,0.00005714935,0.0000620427,0.000021678828,0.00003375602,0.0009682706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979912,0.000012075239,0.0006053005,0.00051369827,0.00050885475,0.0003688689],"domain_scores_gemma":[0.9989686,0.0000414788,0.0005763803,0.00012155478,0.00021087365,0.0000811031],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004305838,0.00023601265,0.00048657344,0.0002532755,0.00026639865,0.000070513655,0.00022565969,0.00009819025,0.00011548345],"category_scores_gemma":[0.0003729065,0.00027054278,0.00006347941,0.00086346234,0.00018056696,0.00034042468,0.00033020685,0.00041053086,4.1873946e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023918251,0.00013730518,0.045732703,0.0004701615,0.00005628102,0.0000010585261,0.002171895,0.0000054308002,0.9508962,0.00004903515,0.00015214487,0.00008862479],"study_design_scores_gemma":[0.0011450353,0.00021706005,0.0027477646,0.00026021738,0.00010763931,0.000030777734,0.009323792,0.00038313938,0.98347014,0.0006553199,0.0013717746,0.0002873457],"about_ca_topic_score_codex":0.00020809869,"about_ca_topic_score_gemma":0.000009323256,"teacher_disagreement_score":0.04298494,"about_ca_system_score_codex":0.0001720427,"about_ca_system_score_gemma":0.000037793096,"threshold_uncertainty_score":0.99997467},"labels":[],"label_agreement":null},{"id":"W4309288204","doi":"10.3390/bios12111043","title":"A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef","year":2022,"lang":"en","type":"article","venue":"Biosensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Alanine; Near-infrared spectroscopy; Infrared; Chemistry; Remote sensing; Optics; Physics; Biochemistry; Geology","score_opus":0.022648700998367084,"score_gpt":0.2639472706927731,"score_spread":0.241298569694406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309288204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991099,0.00022339684,0.00009383239,0.00013914223,0.000025821439,0.00007963297,0.00003358807,0.000011886583,0.00028276033],"genre_scores_gemma":[0.99877614,0.00000192718,0.00074789097,0.000007943313,0.0000163336,0.000021520038,0.000045453242,0.000008578153,0.00037424054],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991939,0.000020541516,0.00024804857,0.00015117814,0.00026267982,0.00012362246],"domain_scores_gemma":[0.99932194,0.00018304928,0.00022707606,0.00014949759,0.00010042876,0.00001803185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014912394,0.00008368815,0.00022402497,0.00019534955,0.00010411394,0.000007915433,0.00010142244,0.000017269847,0.00014088364],"category_scores_gemma":[0.000056922654,0.00006578689,0.00011404427,0.0010373434,0.00006359618,0.00003642968,0.000028674165,0.00010314067,1.6986719e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027114962,0.0002003512,0.6561377,0.00002181382,0.00043728226,0.0000023678826,0.0005764015,0.037126254,0.3048992,0.00023535146,0.000021284026,0.00007087475],"study_design_scores_gemma":[0.0026677253,0.00012505804,0.18459521,0.000018459914,0.0011156267,0.000007409413,0.0075860224,0.08933803,0.7142247,0.000119186014,0.000016805625,0.00018578708],"about_ca_topic_score_codex":0.00028175616,"about_ca_topic_score_gemma":0.0000063547377,"teacher_disagreement_score":0.47154248,"about_ca_system_score_codex":0.000111104084,"about_ca_system_score_gemma":0.000030761305,"threshold_uncertainty_score":0.26827115},"labels":[],"label_agreement":null},{"id":"W4309650834","doi":"10.1007/978-3-031-07179-9_1","title":"Machine Learning Application in Food Safety, Production, and Quality","year":2022,"lang":"en","type":"book-chapter","venue":"Food microbiology and food safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Supply chain; Automation; Computer science; Quality (philosophy); Production (economics); Food processing; Food safety; Risk analysis (engineering); Food quality; Artificial intelligence; Engineering; Business; Marketing","score_opus":0.01962774259678988,"score_gpt":0.25067929641964737,"score_spread":0.2310515538228575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309650834","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08558012,0.20072038,0.0008756622,0.0040889317,0.00076775736,0.0020658604,0.008909941,0.00087278505,0.69611853],"genre_scores_gemma":[0.9453494,0.006778508,0.00010636829,0.000121365236,0.0002242195,0.00005085001,0.0027392914,0.00007706804,0.044552945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99778754,0.000065382555,0.00071269687,0.0010105479,0.000080329235,0.00034353053],"domain_scores_gemma":[0.9988588,0.00015732727,0.00045102093,0.00040241593,0.00005412016,0.000076309916],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043100867,0.00046475872,0.00079418154,0.00025598437,0.00039412998,0.000014926256,0.00020836036,0.0006839809,0.0012444872],"category_scores_gemma":[0.00010492086,0.00048499976,0.00012639152,0.000141276,0.00027819021,0.00006128382,0.0003218546,0.0013946814,0.000008504366],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005402544,0.0010820474,0.037988517,0.0048520197,0.009625155,0.000015917965,0.0027670274,0.00017422905,0.37030417,0.5347067,0.00096727593,0.032114383],"study_design_scores_gemma":[0.0019482684,0.0017538125,0.00066416676,0.00010172292,0.0005314687,0.00030758634,0.00029467986,0.000006683049,0.017811645,0.008410934,0.96661526,0.0015537442],"about_ca_topic_score_codex":0.000034754008,"about_ca_topic_score_gemma":0.000676382,"teacher_disagreement_score":0.965648,"about_ca_system_score_codex":0.00017397525,"about_ca_system_score_gemma":0.000057030727,"threshold_uncertainty_score":0.99976015},"labels":[],"label_agreement":null},{"id":"W4309676589","doi":"","title":"Assessment of two indigestible markers for improving the accuracy of measurement of feed intake by cattle fed ryegrass","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Animal science; Computer science; Agronomy; Biology","score_opus":0.0230379402388769,"score_gpt":0.28717800011111616,"score_spread":0.26414005987223926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309676589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8233389,0.0040491982,0.12578277,0.0027534289,0.00013724616,0.0010183775,0.0007315541,0.000105076906,0.04208342],"genre_scores_gemma":[0.9769472,0.00024059533,0.0209444,0.000013382937,0.000015727042,0.00010214185,0.0003281509,0.000034293495,0.0013741091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99691296,0.0006255825,0.00092388387,0.00050171325,0.0007429481,0.0002929335],"domain_scores_gemma":[0.9914754,0.002029895,0.0019817955,0.0017732027,0.002656308,0.00008337331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0047818455,0.00029665942,0.00061724364,0.00017760835,0.00015997716,0.0000727985,0.0014477455,0.00021473334,0.00025358933],"category_scores_gemma":[0.0027699529,0.0002605259,0.0003408623,0.0004472636,0.00028540197,0.000067624846,0.0006721473,0.00047313937,5.760468e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057475692,0.0013984,0.015002768,0.0021235552,0.0007498728,1.7767688e-7,0.0018338979,0.00020131186,0.95788234,0.0033999493,0.0011440222,0.01620622],"study_design_scores_gemma":[0.00073738233,0.0000011540085,0.002928734,0.0006619918,0.0003492419,7.7830043e-7,0.00039955718,0.003077729,0.99085927,0.00026389508,0.0004942246,0.0002260666],"about_ca_topic_score_codex":0.0035606134,"about_ca_topic_score_gemma":0.000716265,"teacher_disagreement_score":0.15360828,"about_ca_system_score_codex":0.00030735065,"about_ca_system_score_gemma":0.0006624306,"threshold_uncertainty_score":0.9999847},"labels":[],"label_agreement":null},{"id":"W4309797119","doi":"10.1115/icef2022-88851","title":"Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder","year":2022,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Fault detection and isolation; Computer science; Fast Fourier transform; Random forest; Encoder; Crank; Cylinder head; Multilayer perceptron; Condition monitoring; Combustion; Dimensionality reduction; Internal combustion engine; Automotive engineering; Artificial intelligence; Algorithm; Engineering; Artificial neural network; Actuator","score_opus":0.018602370343866453,"score_gpt":0.2727417588284853,"score_spread":0.2541393884846188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309797119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96949077,0.00026147396,0.029627187,0.000026786724,0.000026640964,0.000021539861,0.000008950866,0.000057126854,0.0004795451],"genre_scores_gemma":[0.9989173,0.000013088905,0.0007981341,0.000029751574,0.00003553346,0.000012444254,0.000016414871,0.000012235064,0.00016514119],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993388,0.000024033909,0.00016292794,0.00017837729,0.00017903074,0.00011682525],"domain_scores_gemma":[0.9996383,0.00010667214,0.0000716382,0.000094343915,0.000037283186,0.000051757946],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009287961,0.00009610649,0.00015159894,0.00013489732,0.00014662945,0.000018314402,0.0000768085,0.0000447603,0.003346628],"category_scores_gemma":[0.00008974422,0.00009334469,0.000050483166,0.00026833708,0.0000418011,0.0000685693,0.00005109939,0.0002316623,4.5542572e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020616481,0.00078352453,0.22022757,0.0001349474,0.0001285241,0.000010463463,0.00014116202,0.107300445,0.66585755,0.000050647915,0.000015133673,0.0051438664],"study_design_scores_gemma":[0.00035719216,0.00014968845,0.0003294803,0.000004225169,0.0001005561,0.000012499207,0.00031784296,0.32154995,0.67695814,0.000019924724,0.00010895168,0.0000915179],"about_ca_topic_score_codex":0.0005002717,"about_ca_topic_score_gemma":0.000032635504,"teacher_disagreement_score":0.21989809,"about_ca_system_score_codex":0.0000674441,"about_ca_system_score_gemma":0.000012796321,"threshold_uncertainty_score":0.99756444},"labels":[],"label_agreement":null},{"id":"W4310030236","doi":"10.1007/s11947-022-02939-5","title":"Automated Detection of Mechanical Damage in Flaxseeds Using Radiographic Imaging and Machine Learning","year":2022,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Foundation for Innovation","keywords":"Artificial intelligence; Support vector machine; Convolutional neural network; Pattern recognition (psychology); Classifier (UML); Computer science; Linear discriminant analysis; Machine learning; Feature selection; Deep learning","score_opus":0.009859686814554772,"score_gpt":0.24318364579196883,"score_spread":0.23332395897741406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310030236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99483407,0.0044832877,0.0002207761,0.00007665763,0.000010904845,0.000025355606,0.000007381221,0.00029120882,0.000050344057],"genre_scores_gemma":[0.99968773,0.000116371746,0.00014686219,0.000006508505,0.000006514809,0.000009918897,0.000005261665,0.000010904519,0.000009925511],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932534,0.000009416846,0.00017781492,0.00023412702,0.000084436106,0.00016889533],"domain_scores_gemma":[0.99975175,0.000017047269,0.000104698316,0.00008636106,0.000019167175,0.000021005259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009204943,0.0001024717,0.00021267001,0.0008537619,0.00015870167,0.00000931397,0.000107937805,0.00009622405,0.000040969073],"category_scores_gemma":[0.000039851766,0.00010489726,0.000022989643,0.0018280464,0.000115137074,0.00004288492,0.00017722315,0.0003814274,5.0830067e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025093643,0.00004292437,0.06981356,0.00009903268,0.000032459775,0.000006888839,0.00003171567,0.000020834384,0.9275255,0.00010379433,3.058251e-7,0.0022978878],"study_design_scores_gemma":[0.0005320519,0.00016329711,0.0005809817,0.000013508082,0.0000676753,0.0001651648,0.0016114026,0.078592084,0.9173191,0.0007429204,0.00006637208,0.00014542832],"about_ca_topic_score_codex":0.000087559194,"about_ca_topic_score_gemma":0.00002363841,"teacher_disagreement_score":0.07857125,"about_ca_system_score_codex":0.000027501344,"about_ca_system_score_gemma":0.000010560006,"threshold_uncertainty_score":0.42775863},"labels":[],"label_agreement":null},{"id":"W4310474075","doi":"10.1007/s12161-022-02425-w","title":"Fusion of Spectral and Textural Data of Hyperspectral Imaging for Glycine Content Prediction in Beef Using SFCN Algorithms","year":2022,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Variable elimination; Computer science; Pattern recognition (psychology); Residual; Support vector machine; Convolutional neural network; Algorithm; Matrix (chemical analysis); Artificial intelligence; Mathematics; Chemistry; Machine learning","score_opus":0.18417581297384616,"score_gpt":0.41880590215061886,"score_spread":0.2346300891767727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310474075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9230206,0.0017376234,0.07347088,0.00023130678,0.00008679921,0.00014561594,0.0006546069,0.000027281196,0.00062530686],"genre_scores_gemma":[0.83212215,0.000013991392,0.1675737,0.0000142727995,0.000094930714,0.000006309585,0.000077526915,0.000015321792,0.00008179656],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984737,0.000077575,0.00051310903,0.00041344712,0.00024964797,0.0002725123],"domain_scores_gemma":[0.9989587,0.0003646093,0.00017903688,0.0003646061,0.00005965237,0.000073400595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007544387,0.00014925304,0.0005039986,0.0002459859,0.000080463215,0.000008518468,0.00028952459,0.00004855376,0.00036671793],"category_scores_gemma":[0.00045414816,0.00014106593,0.000113402544,0.00060552056,0.0001260468,0.00010805563,0.00031541506,0.00025086856,4.9368854e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049031584,0.00048149796,0.027203375,0.0002800428,0.00037404348,0.000006360417,0.00021785755,0.0002922333,0.9581844,0.0016128562,0.00004658531,0.010810389],"study_design_scores_gemma":[0.0017305595,0.00039767034,0.0026833229,0.000031220236,0.00071918796,0.000047779078,0.003216698,0.6962556,0.29347062,0.0011398357,0.000102076185,0.00020540023],"about_ca_topic_score_codex":0.00014235047,"about_ca_topic_score_gemma":0.000004205148,"teacher_disagreement_score":0.6959634,"about_ca_system_score_codex":0.00013016487,"about_ca_system_score_gemma":0.000047546626,"threshold_uncertainty_score":0.5752502},"labels":[],"label_agreement":null},{"id":"W4312095680","doi":"10.1038/s41598-022-26343-3","title":"Deep-learning framework for fully-automated recognition of TiO2 polymorphs based on Raman spectroscopy","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Anatase; Raman spectroscopy; Computer science; Rutile; Artificial intelligence; Convolutional neural network; Identification (biology); Deep learning; Microelectronics; Throughput; Materials science; Machine learning; Nanotechnology; Chemistry; Physics; Chemical engineering; Engineering; Telecommunications; Optics","score_opus":0.017360027503270464,"score_gpt":0.28449002488332026,"score_spread":0.2671299973800498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312095680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95484054,0.00037073513,0.02045957,0.00022688883,0.0031480298,0.00040734466,0.000053691507,0.0009663052,0.01952687],"genre_scores_gemma":[0.9872342,0.0000015833033,0.009314668,0.000057330366,0.00008966779,0.00012558504,0.0006528436,0.000036339057,0.00248779],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997461,0.000041551673,0.0005795691,0.00077793887,0.00074316,0.00039676638],"domain_scores_gemma":[0.99807745,0.00025535177,0.00072500535,0.0006928628,0.00015070493,0.00009862955],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00091238046,0.00018949265,0.00031717925,0.00043114214,0.00086024695,0.0001212457,0.00020252468,0.00009962947,0.010388489],"category_scores_gemma":[0.0008227804,0.00020137106,0.00024138573,0.0014614674,0.00014859406,0.000065775945,0.00006560835,0.0003766181,0.000012184012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033723013,0.00096834375,0.00764858,0.00030640248,0.00016606513,0.00018988195,0.00034713594,0.008209343,0.9678971,0.00020817338,0.0112031475,0.0025185563],"study_design_scores_gemma":[0.0002974059,0.0001667838,0.000110521156,0.000043438326,0.00015717882,0.000047863647,0.0006405396,0.027138157,0.957173,0.009731791,0.0042083818,0.00028492167],"about_ca_topic_score_codex":0.000019207177,"about_ca_topic_score_gemma":0.000001452276,"teacher_disagreement_score":0.032393627,"about_ca_system_score_codex":0.00019552182,"about_ca_system_score_gemma":0.00013452256,"threshold_uncertainty_score":0.9905161},"labels":[],"label_agreement":null},{"id":"W4312164547","doi":"10.1002/cjs.11755","title":"Asymptotic distribution of one‐component partial least squares regression estimators in high dimensions","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Nacional del Litoral; Fondo para la Investigación Científica y Tecnológica; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Mathematics; Asymptotic distribution; Estimator; Partial least squares regression; Statistics; Linear regression; Univariate; Applied mathematics; Regression analysis; Asymptotic analysis; Confidence interval; Least-squares function approximation; Linear model; Infinity; Multivariate statistics; Mathematical analysis","score_opus":0.017008338233824784,"score_gpt":0.24531014762481693,"score_spread":0.22830180939099215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312164547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99196434,0.0006031194,0.0050266036,0.00019298289,0.00019682117,0.000029510065,0.0018875656,0.0000039206166,0.00009516339],"genre_scores_gemma":[0.998245,0.000018215047,0.0014217085,0.000017211549,0.000048859132,0.0000021517776,0.00017716546,0.00001208015,0.00005758026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998731,0.000040468403,0.00053289003,0.000108996865,0.00032613656,0.00026051668],"domain_scores_gemma":[0.99884546,0.00014807103,0.00041318277,0.00013663697,0.00012594236,0.000330721],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016097503,0.00011168018,0.00031356618,0.0002701726,0.0001839842,0.000016885357,0.00019846522,0.00004469864,0.0018609382],"category_scores_gemma":[0.00033921967,0.0001125365,0.00005856993,0.0004115119,0.000098682074,0.000047251793,0.000026626642,0.0003822137,0.0000017547542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000677302,0.0016261438,0.7249194,0.00078363495,0.0010430905,0.0042548133,0.0024228955,0.08182488,0.04227491,0.06772832,0.064765245,0.0076793926],"study_design_scores_gemma":[0.015970523,0.0034564445,0.54305017,0.0023770405,0.0050775204,0.0016426591,0.019696902,0.0351639,0.30374908,0.03722263,0.028652703,0.0039404454],"about_ca_topic_score_codex":0.0051582837,"about_ca_topic_score_gemma":0.0017007357,"teacher_disagreement_score":0.26147416,"about_ca_system_score_codex":0.0005761972,"about_ca_system_score_gemma":0.0007420394,"threshold_uncertainty_score":0.9990515},"labels":[],"label_agreement":null},{"id":"W4312177580","doi":"10.18280/ria.360517","title":"Grape Leaves Segmentation Using an Improved Graph-Based Approach","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Graph; Artificial intelligence; Computer science; Pattern recognition (psychology); Theoretical computer science","score_opus":0.06476616816127528,"score_gpt":0.30780887838035953,"score_spread":0.24304271021908425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312177580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6391591,0.0005808148,0.35431713,0.00006869691,0.00012837382,0.00016539157,0.00003902643,0.00019798479,0.0053434796],"genre_scores_gemma":[0.9928237,0.00001225561,0.0056517725,0.000119678254,0.000080763304,0.000069894704,0.00011618032,0.000032836408,0.0010929137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984771,0.00004620973,0.00037934957,0.0005092236,0.00023847033,0.00034967414],"domain_scores_gemma":[0.9990977,0.000065588,0.00018089144,0.00049842,0.00005533946,0.000102052174],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023183577,0.00019561221,0.00022439341,0.00024742808,0.0005874206,0.000067997236,0.00039958218,0.00006052297,0.0041479133],"category_scores_gemma":[0.000032723976,0.00021756967,0.00015767566,0.0012016132,0.00009195443,0.00014459992,0.000078438825,0.00030122555,0.000014869449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004696638,0.00072095834,0.001166476,0.000092643924,0.000045783298,0.000004819036,0.00058836536,0.26608655,0.72846764,0.00026070152,0.000045687804,0.0024733827],"study_design_scores_gemma":[0.0000454646,0.000057624773,0.0000035467274,0.0000031401717,0.00006381849,0.000010857845,0.0065841232,0.44280657,0.54978555,0.00027133262,0.00018534272,0.00018263578],"about_ca_topic_score_codex":0.00013484519,"about_ca_topic_score_gemma":0.0000034011362,"teacher_disagreement_score":0.3536646,"about_ca_system_score_codex":0.00016197289,"about_ca_system_score_gemma":0.000060073744,"threshold_uncertainty_score":0.99676245},"labels":[],"label_agreement":null},{"id":"W4312677689","doi":"10.2139/ssrn.4284415","title":"Evaluation of Artificial Neural Network Models for Predictive Monitoring of Biogas Production from Cassava Wastewater: Comparison of Training Algorithms","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Biogas; Artificial neural network; Anaerobic digestion; Engineering; Algorithm; Computer science; Process engineering; Machine learning; Waste management; Chemistry","score_opus":0.0896033635322523,"score_gpt":0.34091349296260315,"score_spread":0.25131012943035086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312677689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98779523,0.0045973887,0.0069824704,0.000043730193,0.00031312898,0.00013514461,0.00005002193,0.000012395512,0.00007045988],"genre_scores_gemma":[0.99831843,0.00009987586,0.00062425685,6.00827e-7,0.0008355997,0.00003124785,0.000033663437,0.000020528278,0.000035803576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731886,0.00010539521,0.0006811234,0.00022686005,0.0009116353,0.00075612747],"domain_scores_gemma":[0.99845463,0.000087518434,0.0008482731,0.00015774806,0.00041735487,0.000034488723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023254524,0.00013762292,0.00042422884,0.00015304603,0.00021347363,0.000008375042,0.00021594671,0.00008027778,0.000075115444],"category_scores_gemma":[0.0001188258,0.0001405453,0.00018585597,0.0004184342,0.000049688642,0.00013897115,0.000040362695,0.0009999792,6.196978e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004557739,0.0002503473,0.0040814513,0.000019473116,0.000986678,1.4665316e-7,0.0020519067,0.7970832,0.17314929,0.00050912396,0.000015564461,0.021397023],"study_design_scores_gemma":[0.00073767436,0.0005548698,0.00006922663,0.000025844138,0.0014548,0.000018746983,0.03621985,0.33511418,0.5374592,0.08820962,0.000003931398,0.00013206141],"about_ca_topic_score_codex":0.00011702559,"about_ca_topic_score_gemma":0.00002165395,"teacher_disagreement_score":0.46196905,"about_ca_system_score_codex":0.0008644517,"about_ca_system_score_gemma":0.0009302711,"threshold_uncertainty_score":0.5731271},"labels":[],"label_agreement":null},{"id":"W4312812887","doi":"10.1130/abs/2022am-382979","title":"UNDERSTANDING BIOCHAR-METAL INTERACTIONS USING COMBINED SPECTROSCOPIC AND SURFACE COMPLEXATION MODELLING APPROACHES","year":2022,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Biochar; Metal; Environmental chemistry; Environmental science; Materials science; Computer science; Chemistry; Chemical engineering; Metallurgy; Engineering; Pyrolysis","score_opus":0.17028370320104427,"score_gpt":0.2935777004058517,"score_spread":0.12329399720480741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312812887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9402002,0.00026270893,0.057706665,0.0003136802,0.000016194486,0.00014323926,0.000008288538,0.000101912476,0.0012470746],"genre_scores_gemma":[0.94226295,0.000035014175,0.057397876,0.000059312148,0.000023734627,0.000015959155,0.00008435357,0.00001630108,0.00010450306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985416,0.00002812031,0.00033754713,0.0003983234,0.00033136868,0.00036305533],"domain_scores_gemma":[0.99914896,0.00014345923,0.0004147019,0.00016571133,0.000035734152,0.00009144053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013509486,0.00021468787,0.0003914521,0.000032698583,0.00054726226,0.00004629756,0.0001588797,0.000068277106,0.00084898534],"category_scores_gemma":[0.000009269736,0.00018241964,0.00014568034,0.00059108506,0.00056454685,0.00012120846,0.00014402588,0.00049910636,0.0000013872792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075607246,0.005264115,0.1314667,0.0007395955,0.0030204533,0.000022549377,0.0054568686,0.7238944,0.12451908,0.001891328,0.00024122672,0.0027275959],"study_design_scores_gemma":[0.003241466,0.0026768062,0.0023556775,0.00009678634,0.0013407543,0.00013970264,0.09751354,0.82443714,0.060626443,0.0048222463,0.0011357871,0.0016136668],"about_ca_topic_score_codex":0.0006175839,"about_ca_topic_score_gemma":0.0000024477972,"teacher_disagreement_score":0.12911102,"about_ca_system_score_codex":0.00021417509,"about_ca_system_score_gemma":0.00003780924,"threshold_uncertainty_score":0.92957944},"labels":[],"label_agreement":null},{"id":"W4312847433","doi":"10.1007/978-3-030-95864-0_10","title":"Chapter 10: Canonical Correlation Analysis","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Canonical correlation; Mathematics; Scalar (mathematics); Canonical analysis; Applied mathematics; Statistics; Geometry","score_opus":0.01994776396526779,"score_gpt":0.2561964109061987,"score_spread":0.23624864694093092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312847433","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060352868,0.0007849623,0.00039125473,0.00010324614,0.000048259266,0.000032688935,0.000089287634,0.00014256431,0.9983474],"genre_scores_gemma":[0.030465992,0.00024132912,0.00010633321,0.0001277192,0.00016629962,0.000009170445,0.00084208086,0.000054354376,0.9679867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820435,0.0000032597402,0.00043788156,0.00059316534,0.00052648725,0.00023483286],"domain_scores_gemma":[0.9987895,0.00011537763,0.00027395293,0.00063335645,0.00006719162,0.000120613164],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007324577,0.00036590098,0.00067431177,0.00057939487,0.00014700477,0.000035770274,0.00030610536,0.00040251596,0.8474583],"category_scores_gemma":[0.000024348881,0.0003568942,0.000771057,0.00023536966,0.00007007352,0.000054308464,0.00016146273,0.0006285254,0.0006647565],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011249797,0.00010534898,0.001398349,0.000092224436,0.016392315,0.00012262474,0.00006518744,0.00054049696,0.0005377257,0.9625057,0.015784446,0.002343108],"study_design_scores_gemma":[0.00023688146,0.00003233811,0.000031839616,0.000006790898,0.011704323,0.00000906986,0.000029327517,0.0006917697,0.0010711158,0.0020929894,0.9833473,0.0007462565],"about_ca_topic_score_codex":0.00016496427,"about_ca_topic_score_gemma":0.00009441474,"teacher_disagreement_score":0.96756285,"about_ca_system_score_codex":0.00028435446,"about_ca_system_score_gemma":0.00006688331,"threshold_uncertainty_score":0.9998883},"labels":[],"label_agreement":null},{"id":"W4312970635","doi":"10.2139/ssrn.4241643","title":"Evaluation of Artificial Neural Network Models for Predictive Monitoring of Biogas Production from Cassava Wastewater: Comparison of Training Algorithms","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial neural network; Biogas production; Biogas; Production (economics); Wastewater; Machine learning; Engineering; Training (meteorology); Environmental science; Artificial intelligence; Algorithm; Agricultural engineering; Biochemical engineering; Computer science; Waste management; Anaerobic digestion; Biology; Ecology; Economics; Meteorology; Geography","score_opus":0.0896033635322523,"score_gpt":0.34091349296260315,"score_spread":0.25131012943035086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312970635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98779523,0.0045973887,0.0069824704,0.000043730193,0.00031312898,0.00013514461,0.00005002193,0.000012395512,0.00007045988],"genre_scores_gemma":[0.99831843,0.00009987586,0.00062425685,6.00827e-7,0.0008355997,0.00003124785,0.000033663437,0.000020528278,0.000035803576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731886,0.00010539521,0.0006811234,0.00022686005,0.0009116353,0.00075612747],"domain_scores_gemma":[0.99845463,0.000087518434,0.0008482731,0.00015774806,0.00041735487,0.000034488723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023254524,0.00013762292,0.00042422884,0.00015304603,0.00021347363,0.000008375042,0.00021594671,0.00008027778,0.000075115444],"category_scores_gemma":[0.0001188258,0.0001405453,0.00018585597,0.0004184342,0.000049688642,0.00013897115,0.000040362695,0.0009999792,6.196978e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004557739,0.0002503473,0.0040814513,0.000019473116,0.000986678,1.4665316e-7,0.0020519067,0.7970832,0.17314929,0.00050912396,0.000015564461,0.021397023],"study_design_scores_gemma":[0.00073767436,0.0005548698,0.00006922663,0.000025844138,0.0014548,0.000018746983,0.03621985,0.33511418,0.5374592,0.08820962,0.000003931398,0.00013206141],"about_ca_topic_score_codex":0.00011702559,"about_ca_topic_score_gemma":0.00002165395,"teacher_disagreement_score":0.46196905,"about_ca_system_score_codex":0.0008644517,"about_ca_system_score_gemma":0.0009302711,"threshold_uncertainty_score":0.5731271},"labels":[],"label_agreement":null},{"id":"W4313035297","doi":"10.54216/jisiot.070103","title":"Intelligent Wheat Types Classification Model Using New Voting Classifier","year":2022,"lang":"en","type":"article","venue":"Journal of Intelligent Systems and Internet of Things","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Support vector machine; Classifier (UML); Random forest; Decision tree; Artificial neural network; Pattern recognition (psychology)","score_opus":0.06801303523235548,"score_gpt":0.3047197847633324,"score_spread":0.23670674953097692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313035297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8010534,0.010347567,0.18348826,0.00019946629,0.0006594663,0.00009117005,0.000006483789,0.00002241612,0.004131753],"genre_scores_gemma":[0.99125147,0.00022961646,0.0012014409,0.000063471736,0.00018163484,0.0000017183492,0.0000026522011,0.000024173674,0.00704383],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979123,0.000037218968,0.0011348425,0.00020039863,0.00051585387,0.0001993745],"domain_scores_gemma":[0.9981087,0.00008504675,0.0012904368,0.00015996158,0.0002144018,0.00014149668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000543458,0.00018742743,0.0005116668,0.00034120475,0.00008960085,0.00008107457,0.00039335832,0.000087999935,0.00062486157],"category_scores_gemma":[0.00011067096,0.00015990023,0.00022440439,0.00022934699,0.000049145598,0.00024938752,0.00015688037,0.0005105805,0.0000019824843],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000979545,0.00091304525,0.017059017,0.0016328191,0.0028038672,0.000047961046,0.017269308,0.09482314,0.81423986,0.01781879,0.021048313,0.0113643315],"study_design_scores_gemma":[0.00031375527,0.000264161,0.000020695228,0.00043167072,0.00046242433,0.0004893838,0.010914288,0.7714374,0.20811178,0.0004401458,0.006799715,0.0003145656],"about_ca_topic_score_codex":0.00047396668,"about_ca_topic_score_gemma":0.0000012563803,"teacher_disagreement_score":0.6766143,"about_ca_system_score_codex":0.0003474224,"about_ca_system_score_gemma":0.00012176559,"threshold_uncertainty_score":0.6841796},"labels":[],"label_agreement":null},{"id":"W4313422391","doi":"10.1615/intjmedmushrooms.2022046298","title":"Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning","year":2022,"lang":"en","type":"article","venue":"International journal of medicinal mushrooms","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lentinula; Mushroom; Polysaccharide; Near-infrared spectroscopy; Deep learning; Artificial intelligence; Convolutional neural network; Mathematics; Auricularia; Edible mushroom; Computer science; Food science; Pattern recognition (psychology); Chemistry; Biology; Biochemistry","score_opus":0.029846706779670967,"score_gpt":0.297669230318085,"score_spread":0.26782252353841407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313422391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98265594,0.004147407,0.008172291,0.00085908495,0.00063121493,0.00013712856,0.000017781904,0.000027597585,0.0033515266],"genre_scores_gemma":[0.99722743,0.00013295314,0.0016210641,0.00021636634,0.00043139874,0.000024601059,0.00004425253,0.000038141206,0.00026377512],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99303025,0.00027126365,0.0013562305,0.00036102848,0.004582573,0.00039865225],"domain_scores_gemma":[0.9959618,0.00032041827,0.0015586969,0.00022518061,0.0017520944,0.00018184165],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027323305,0.000334675,0.00074409106,0.0008805187,0.00015996829,0.000055619992,0.0009795979,0.00010350856,0.008871146],"category_scores_gemma":[0.0009831776,0.00027449895,0.00024622932,0.0008476441,0.00028417126,0.0002502905,0.00015367438,0.0015742991,0.0000040037103],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008911912,0.0035346022,0.22265191,0.00018224218,0.005595633,0.0022991844,0.006841452,0.036427785,0.6593893,0.00031641143,0.0015370663,0.052312482],"study_design_scores_gemma":[0.057641543,0.011492395,0.06697687,0.0030345598,0.009341208,0.010636692,0.08685775,0.19142716,0.54453415,0.0041990248,0.011265075,0.0025935795],"about_ca_topic_score_codex":0.000460038,"about_ca_topic_score_gemma":0.000079797544,"teacher_disagreement_score":0.15567505,"about_ca_system_score_codex":0.0014794309,"about_ca_system_score_gemma":0.0006706068,"threshold_uncertainty_score":0.99997073},"labels":[],"label_agreement":null},{"id":"W4313483445","doi":"10.1016/j.asoc.2022.109970","title":"Broad learning system with Takagi–Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Artificial intelligence; Artificial neural network; Identification (biology); Computer science; Fuzzy logic; Convolutional neural network; Process (computing); Computation; Pattern recognition (psychology); Machine learning; Deep learning; Algorithm","score_opus":0.012097562896747974,"score_gpt":0.24802888407914303,"score_spread":0.23593132118239507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313483445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8145433,0.0001475541,0.14879006,0.000052540425,0.00017757864,0.0006202052,0.00003643097,0.0010808544,0.034551434],"genre_scores_gemma":[0.99256647,5.160567e-7,0.005767822,0.000098044075,0.000230287,0.00020724488,0.00023226731,0.00009055614,0.0008068185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974452,0.000045347326,0.00054802926,0.00076762005,0.000612972,0.00058083073],"domain_scores_gemma":[0.99820894,0.00047735902,0.00057887647,0.0005358031,0.00008959045,0.00010943523],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00054784893,0.0003443523,0.00049090036,0.00020182748,0.0015640461,0.0002454267,0.00047225182,0.00009609956,0.0001546533],"category_scores_gemma":[0.00005146251,0.00035793922,0.00013714416,0.0010255036,0.00005049121,0.00004750608,0.00010257365,0.0005694006,0.00003438395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016487003,0.00047793862,0.028364588,0.002150008,0.00066399656,0.000040153405,0.0016468174,0.64604783,0.30011398,0.013451959,0.0021134617,0.0032805966],"study_design_scores_gemma":[0.006031311,0.00052145525,0.0007996405,0.0002483559,0.0006962013,0.000051118244,0.017446417,0.570429,0.3906064,0.00015551932,0.011067141,0.0019474269],"about_ca_topic_score_codex":0.000041868243,"about_ca_topic_score_gemma":0.000001869952,"teacher_disagreement_score":0.17802311,"about_ca_system_score_codex":0.00078407564,"about_ca_system_score_gemma":0.00016882956,"threshold_uncertainty_score":0.9998873},"labels":[],"label_agreement":null},{"id":"W4316463982","doi":"10.18280/ts.390634","title":"Detection of Breast Cancer Using Modified Markov Model","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Closeness; Cluster analysis; Exploit; Computer science; Segmentation; Maxima and minima; Process (computing); Markov chain; Variable (mathematics); Algorithm; Artificial intelligence; Pattern recognition (psychology); Data mining; Mathematics; Machine learning","score_opus":0.027621055842258036,"score_gpt":0.27365106722609245,"score_spread":0.2460300113838344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316463982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97711116,0.00019851484,0.02023711,0.000047379974,0.000030188427,0.00004574454,0.00022912366,0.000033504813,0.00206727],"genre_scores_gemma":[0.9992135,0.000010143895,0.00021603254,0.000050248924,0.00007785032,0.00004206354,0.000011103537,0.000015871996,0.00036319354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990252,0.000013135702,0.00023612165,0.0001963424,0.00034230924,0.00018689012],"domain_scores_gemma":[0.9996418,0.000017938426,0.00013690576,0.000119276156,0.0000395764,0.000044519573],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009467287,0.00011478616,0.00017316994,0.000115931885,0.00017341497,0.000009805572,0.00015123106,0.00003220897,0.011310286],"category_scores_gemma":[0.000002198502,0.00012206621,0.00010047774,0.0003405666,0.00002940548,0.00005786891,0.00006358463,0.00014412026,5.7732206e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013549933,0.00014684383,0.0006540808,0.00004514975,0.00011011214,0.0000014904964,0.00009298553,0.10959928,0.88762796,0.000033542194,0.00006227361,0.0014907712],"study_design_scores_gemma":[0.00051338854,0.000018154116,0.00019153922,0.0000050942936,0.00019230996,0.000012114032,0.0003392777,0.49721077,0.50126386,0.00009385704,0.000023957382,0.00013565535],"about_ca_topic_score_codex":0.00029240968,"about_ca_topic_score_gemma":0.000007051284,"teacher_disagreement_score":0.3876115,"about_ca_system_score_codex":0.00021643908,"about_ca_system_score_gemma":0.000060844926,"threshold_uncertainty_score":0.9895935},"labels":[],"label_agreement":null},{"id":"W4317632884","doi":"10.1016/j.chemolab.2023.104766","title":"Calibration transfer by likelihood maximization: A standard-free approach capable of handling non-overlapping wavelength ranges","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Computer science; Bootstrapping (finance); Partial least squares regression; Context (archaeology); Transfer of learning; Maximization; Multivariate statistics; Set (abstract data type); Chemometrics; Data mining; Statistics; Artificial intelligence; Machine learning; Mathematics; Mathematical optimization; Econometrics","score_opus":0.015326347276313474,"score_gpt":0.23075944048634803,"score_spread":0.21543309321003457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317632884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76506156,0.028059313,0.19787878,0.00011885775,0.00046684995,0.00056193495,0.0014847211,0.00042591014,0.0059420853],"genre_scores_gemma":[0.9939891,0.004341224,0.00021946113,0.000026434796,0.00015784161,0.000051849467,0.00026543922,0.00005772935,0.0008908927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977033,0.000027932147,0.00070441834,0.0005139454,0.00061928853,0.00043110596],"domain_scores_gemma":[0.99853635,0.00018522724,0.00019063329,0.00048534886,0.00041356406,0.0001888698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058032083,0.0003124278,0.00067750574,0.0008740197,0.00017140225,0.00017528624,0.00033710187,0.00028798435,0.000114025795],"category_scores_gemma":[0.00034349295,0.00029855224,0.00009997816,0.006527579,0.000088175104,0.0002349208,0.00007491616,0.00022968612,0.000004981342],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032597498,0.0008438991,0.02017371,0.013565686,0.0019795373,0.000022307784,0.0039481083,0.0019985002,0.89075893,0.002734499,0.057755828,0.005892996],"study_design_scores_gemma":[0.0009039121,0.000096746415,0.000021464688,0.00011755866,0.00019409214,0.000003718844,0.0039955904,0.033119433,0.9539524,0.00005352929,0.0070804944,0.00046109827],"about_ca_topic_score_codex":0.00008972988,"about_ca_topic_score_gemma":0.0000012538686,"teacher_disagreement_score":0.22892758,"about_ca_system_score_codex":0.00013773116,"about_ca_system_score_gemma":0.00011737781,"threshold_uncertainty_score":0.99994665},"labels":[],"label_agreement":null},{"id":"W4318541765","doi":"","title":"Ultrasound-Assisted Extraction of Anthocyanins from Haskap (Lonicera caerulea L.) Berries Using a Deep Eutectic Solvent (DES) DES Extraction of Anthocyanins from Haskap Berries","year":2021,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Extraction (chemistry); Anthocyanin; Solvent; Chemistry; Solvent extraction; Berry; Botany; Chromatography; Food science; Organic chemistry; Biology","score_opus":0.2476354265506929,"score_gpt":0.5328370228211473,"score_spread":0.2852015962704544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318541765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94496197,0.04428644,0.0075375754,0.00003019356,0.0004355529,0.00017783458,0.00049321714,0.00007509391,0.0020021095],"genre_scores_gemma":[0.98100704,0.013078953,0.004975259,0.000034491884,0.00031205476,0.000021482803,0.00018551356,0.00011678822,0.00026840015],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947524,0.0003074827,0.0020826873,0.0009644968,0.0012122774,0.0006806327],"domain_scores_gemma":[0.992659,0.0018220391,0.0030600738,0.0009903533,0.0011188448,0.00034971122],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062859786,0.00070268585,0.0018761514,0.00086280215,0.00076875224,0.0010254714,0.0015280658,0.0004631242,0.028689612],"category_scores_gemma":[0.0014958361,0.00073479506,0.0007005175,0.0024001244,0.0008473097,0.0033874488,0.0004877313,0.0009275779,0.0000062575423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044159003,0.0010781139,0.20166594,0.00031635907,0.0015639366,0.000089264235,0.0009969961,0.0004598115,0.79101276,0.000012059095,0.00024862276,0.0021145358],"study_design_scores_gemma":[0.000812701,0.000017625141,0.22948936,0.0008158063,0.0015493132,0.00014384449,0.0032155886,0.000292926,0.76039857,0.002452754,0.00026607155,0.0005454535],"about_ca_topic_score_codex":0.025302771,"about_ca_topic_score_gemma":0.0037944652,"teacher_disagreement_score":0.036045074,"about_ca_system_score_codex":0.0007771335,"about_ca_system_score_gemma":0.00061448326,"threshold_uncertainty_score":0.9995103},"labels":[],"label_agreement":null},{"id":"W4318977075","doi":"10.1007/978-3-031-10602-6_10","title":"Unified Spectral Framework and Maximum Variance Unfolding","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Isomap; Principal component analysis; Spectral clustering; Multidimensional scaling; Pattern recognition (psychology); Mathematics; Dimensionality reduction; Cluster analysis; Diffusion map; Artificial intelligence; Nonlinear dimensionality reduction; Laplace operator; Variance (accounting); Embedding; Computer science; Statistics; Mathematical analysis","score_opus":0.028052884579374677,"score_gpt":0.26765309459575115,"score_spread":0.23960021001637646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977075","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015916846,0.00094913447,0.001432128,0.00021212938,0.00013692249,0.00004210825,0.0000366524,0.00043773477,0.996594],"genre_scores_gemma":[0.007483354,0.0012977296,0.0022398895,0.00014362889,0.00057125284,0.000004246165,0.000043978675,0.00011883786,0.9880971],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984248,0.0000020416096,0.00031959135,0.0006036953,0.0002828945,0.000366966],"domain_scores_gemma":[0.99887735,0.00028095383,0.00016338672,0.00049664784,0.000038023318,0.00014364233],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006953712,0.0004268035,0.00055406423,0.00021220112,0.00011058727,0.00009637369,0.00027367403,0.00080275617,0.017876482],"category_scores_gemma":[0.000080479804,0.00041506806,0.00016526887,0.00014354383,0.00010944097,0.000051114494,0.00014011294,0.00084834447,0.00039902827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020209127,0.000011547585,0.000091060334,0.00023724996,0.0005311578,0.00012445709,0.000039248913,0.0000020881555,0.0015275867,0.9931272,0.0035321706,0.0007560168],"study_design_scores_gemma":[0.00031847318,0.000032009197,0.000027164855,0.00039115353,0.0008364308,0.00004088451,0.0001277419,0.00004840587,0.024634441,0.91098714,0.061325487,0.001230689],"about_ca_topic_score_codex":0.00003008657,"about_ca_topic_score_gemma":0.000009067589,"teacher_disagreement_score":0.08214009,"about_ca_system_score_codex":0.00009319938,"about_ca_system_score_gemma":0.000048770027,"threshold_uncertainty_score":0.9998301},"labels":[],"label_agreement":null},{"id":"W4319067970","doi":"10.2139/ssrn.4347438","title":"Chemometrics Tools for the Non-Targeted Research of Food Bacteria By Raman Spectroscopy","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Chemometrics; Raman spectroscopy; Analytical Chemistry (journal); Food science; Computational biology; Data science; Computer science; Biochemical engineering; Biotechnology; Chemistry; Biology; Environmental chemistry; Engineering; Physics; Machine learning; Optics","score_opus":0.05122967538511761,"score_gpt":0.35202104656969146,"score_spread":0.3007913711845739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319067970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.979189,0.009033769,0.007264004,0.0015050771,0.00019115671,0.0003100915,0.00012513345,0.00010548975,0.0022762856],"genre_scores_gemma":[0.98317534,0.009652878,0.00016367214,0.000029078004,0.0005302845,0.000051850933,0.00007346729,0.00006808988,0.0062553487],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947629,0.00004686929,0.00056187843,0.0003671868,0.00081330544,0.0034478698],"domain_scores_gemma":[0.99700207,0.0016738534,0.00030437956,0.000498504,0.0003900499,0.00013113847],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0037983698,0.00025803756,0.00044647435,0.0006954923,0.0005987078,0.00021158735,0.0011084267,0.00020971356,0.0005162908],"category_scores_gemma":[0.001580902,0.00019355315,0.000341769,0.0045306245,0.00017779619,0.00022072872,0.000119061544,0.002535306,0.000036990878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022559344,0.0001589204,0.0005532448,0.00008395386,0.001130097,0.0000013770474,0.00012611657,0.000009253833,0.9726748,0.0050054146,0.016036274,0.003994951],"study_design_scores_gemma":[0.0012670782,0.000675813,0.00016584115,0.000017359202,0.00019692314,0.00004325196,0.004530066,0.00021096792,0.9549761,0.03085134,0.0068081347,0.00025716517],"about_ca_topic_score_codex":0.000037101992,"about_ca_topic_score_gemma":0.00004107344,"teacher_disagreement_score":0.025845924,"about_ca_system_score_codex":0.00096752733,"about_ca_system_score_gemma":0.0013472111,"threshold_uncertainty_score":0.9997659},"labels":[],"label_agreement":null},{"id":"W4319068021","doi":"10.2139/ssrn.4347412","title":"Understanding the Impact of a Non-Ionic Surfactant Alkylphenol Ethoxylate on Surface-Enhanced Raman Spectroscopic Analysis of Pesticides on Apple Surfaces","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Alkylphenol; Pulmonary surfactant; Raman spectroscopy; Pesticide; Environmental chemistry; Ionic bonding; Chemistry; Chromatography; Organic chemistry; Ion; Optics; Physics","score_opus":0.03787223013898332,"score_gpt":0.3266583014420428,"score_spread":0.28878607130305944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319068021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906603,0.0005316205,0.005083329,0.00012515188,0.000035292054,0.000096902455,0.000036303634,0.000057366884,0.0033737295],"genre_scores_gemma":[0.9959727,0.0030845748,0.000023192146,0.000008039954,0.00005152643,0.0000031446682,0.000027009095,0.00004604801,0.00078374887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99610347,0.00012244785,0.0007182754,0.00039881337,0.0007204765,0.0019365078],"domain_scores_gemma":[0.9972598,0.001223569,0.0007702345,0.0005292153,0.00010568023,0.000111531976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018124143,0.000398946,0.0009259063,0.00073955784,0.0003162327,0.00007303566,0.00062874623,0.00018290924,0.00067955896],"category_scores_gemma":[0.00022143735,0.00026650913,0.0008683333,0.00427035,0.00020610905,0.00009727735,0.00005607979,0.0018750834,0.000020610229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006606487,0.00041066212,0.027093988,0.00006126316,0.015787799,0.00001117586,0.0011387559,0.39519194,0.5525533,0.0067127026,0.00013467128,0.00024312412],"study_design_scores_gemma":[0.0036795617,0.0033149046,0.028935999,0.00034506968,0.00849292,0.000052792333,0.031377245,0.054368585,0.79601055,0.07212097,0.000006805877,0.0012945607],"about_ca_topic_score_codex":0.00036272022,"about_ca_topic_score_gemma":0.0002058489,"teacher_disagreement_score":0.34082335,"about_ca_system_score_codex":0.0020534976,"about_ca_system_score_gemma":0.001168571,"threshold_uncertainty_score":0.9999787},"labels":[],"label_agreement":null},{"id":"W4319080202","doi":"10.3390/foods12030672","title":"Predicting the Moisture Ratio of a Hami Melon Drying Process Using Image Processing Technology","year":2023,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Shrinkage; Moisture; Water content; Process (computing); Melon; Mathematics; Population; Agricultural engineering; Process engineering; Materials science; Engineering; Computer science; Composite material; Statistics; Horticulture; Geotechnical engineering; Biology","score_opus":0.021980387995402313,"score_gpt":0.3214960072538457,"score_spread":0.2995156192584434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319080202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992258,0.00064537296,0.0016386824,0.0003028413,0.000030272364,0.00006449828,0.000010027442,0.000349585,0.0047007245],"genre_scores_gemma":[0.9988405,0.0000111624495,0.0007210859,0.000016409389,0.00011800422,0.000020514208,0.000009102562,0.000025300564,0.00023791636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989946,0.000007874547,0.00025734172,0.00023673948,0.00022378913,0.000279655],"domain_scores_gemma":[0.9993409,0.000056967943,0.00021421241,0.00024389732,0.00011871408,0.000025287394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014966707,0.00013765432,0.00021680487,0.0002126966,0.00027808905,0.000048987098,0.00031084885,0.00015344063,0.00007006282],"category_scores_gemma":[0.00030573868,0.000103279395,0.00005964502,0.0024960593,0.00014084185,0.00013168204,0.00008956817,0.00028694735,0.000007804723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016445043,0.000041810883,0.04179273,0.0007652046,0.00010403777,0.000008903,0.0014730146,0.00034121238,0.95360893,0.00005849777,0.00007838311,0.0017108424],"study_design_scores_gemma":[0.00017895244,0.000017306564,0.00025173454,0.000106475454,0.00014766816,0.000014706726,0.0076213004,0.022028044,0.96867496,0.00076657033,0.00006620636,0.00012608313],"about_ca_topic_score_codex":0.000020596748,"about_ca_topic_score_gemma":0.000006020035,"teacher_disagreement_score":0.041541,"about_ca_system_score_codex":0.000043104024,"about_ca_system_score_gemma":0.0000951924,"threshold_uncertainty_score":0.42116114},"labels":[],"label_agreement":null},{"id":"W4320012707","doi":"10.7451/cbe.2022.64.7.1","title":"Rapid assessment of canola spoilage under sub-optimal storage condition using FTIR spectroscopy","year":2022,"lang":"en","type":"article","venue":"Canadian Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Canola; Principal component analysis; Partial least squares regression; Fourier transform infrared spectroscopy; Chemometrics; Environmental science; Mathematics; Chemistry; Food science; Statistics; Engineering; Chromatography","score_opus":0.013411641853537864,"score_gpt":0.24405884250338417,"score_spread":0.2306472006498463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320012707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99474895,0.00082652294,0.0015113101,0.000030800875,0.00031097588,0.00009299102,0.00042248252,0.00007943278,0.001976519],"genre_scores_gemma":[0.9986486,0.000011723237,0.0008454373,0.000028861405,0.00012941244,0.00002495349,0.00011753679,0.000054058848,0.00013943802],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984974,0.000023661025,0.0003540231,0.00030798127,0.000304648,0.0005122757],"domain_scores_gemma":[0.9991277,0.00004109794,0.00014808716,0.00033630303,0.000040255123,0.0003065712],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021663499,0.00023322996,0.00037302487,0.00060121535,0.00021172526,0.000038188457,0.0002552179,0.000093282986,0.0019286554],"category_scores_gemma":[0.00001623219,0.00029249798,0.00010389307,0.0007588381,0.000024725072,0.0000833182,0.000045007648,0.00034421374,0.0000022129743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002890794,0.000016201426,0.00071251293,0.00019378406,0.0001281253,0.00004917047,0.0000467798,0.04909539,0.9490196,0.00054771354,0.0001799051,0.000007940655],"study_design_scores_gemma":[0.00058891193,0.000072553026,0.0014454691,0.00008494613,0.0001980439,0.00013370013,0.0014784221,0.06244791,0.9283415,0.00000510683,0.004541914,0.0006615202],"about_ca_topic_score_codex":0.018724477,"about_ca_topic_score_gemma":0.0024755995,"teacher_disagreement_score":0.020678084,"about_ca_system_score_codex":0.0027389156,"about_ca_system_score_gemma":0.00059364794,"threshold_uncertainty_score":0.99995273},"labels":[],"label_agreement":null},{"id":"W4320016182","doi":"10.1016/j.compchemeng.2023.108160","title":"Linear hybrid models of distillation towers","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Multiplicative function; Nonlinear system; Distillation; Computer science; Tower; Work (physics); Fractionating column; Linear model; Algorithm; Artificial neural network; Biological system; Applied mathematics; Mathematics; Chemistry; Physics; Artificial intelligence; Mathematical analysis; Machine learning; Thermodynamics; Engineering; Chromatography","score_opus":0.014799387653406718,"score_gpt":0.2319911793083139,"score_spread":0.21719179165490718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320016182","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.831021,0.0001724702,0.16702175,0.000048680013,0.00010695446,0.000025408515,0.000013379593,0.0004942651,0.001096097],"genre_scores_gemma":[0.9955029,0.000020009718,0.0041687153,0.000009338835,0.000135091,0.000003182439,0.000060162998,0.00002152335,0.00007909448],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992339,0.000001045119,0.00020612609,0.00019103898,0.00015481988,0.00021305584],"domain_scores_gemma":[0.9995595,0.00010691977,0.000043934015,0.00018516996,0.000028408824,0.00007601664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034901157,0.00012684066,0.00021207881,0.00011177995,0.000014255692,0.000008854913,0.00017524652,0.000056878926,0.000058624777],"category_scores_gemma":[0.000050350074,0.00013605002,0.000101524885,0.00048157416,0.000022360471,0.00006194788,0.00008198633,0.0001265952,0.000010429781],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068526233,0.000019095747,0.000066347755,0.00017461993,0.00007669488,0.00000715588,0.00005669812,0.2844397,0.71300256,0.00023226878,0.00067706744,0.0012408935],"study_design_scores_gemma":[0.000095172705,0.0000021882827,0.0000055016676,0.00001830707,0.000018409151,0.0000020910973,0.000005561566,0.52509546,0.47442937,0.000071316965,0.00017186742,0.00008474676],"about_ca_topic_score_codex":0.0000044848416,"about_ca_topic_score_gemma":7.753312e-9,"teacher_disagreement_score":0.24065575,"about_ca_system_score_codex":0.000050902177,"about_ca_system_score_gemma":0.00000902015,"threshold_uncertainty_score":0.55479586},"labels":[],"label_agreement":null},{"id":"W4322211690","doi":"10.5194/egusphere-egu23-15539","title":"Prediction of Pb and Zn in urban soil using VIS-NIR-SWIR spectroscopy","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Environmental science; Pollutant; Heavy metals; Environmental chemistry; Pollution; Soil Pollutants; Soil test; Human health; Soil contamination; Soil water; Chemistry; Soil science; Ecology","score_opus":0.05611180542871913,"score_gpt":0.3053167615314308,"score_spread":0.24920495610271165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322211690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767412,0.0010331515,0.0031113098,0.00007185139,0.0001922895,0.00010169259,0.00014962802,0.00022664279,0.018372253],"genre_scores_gemma":[0.9914041,0.00092610763,0.003373055,0.000020234282,0.00033551152,0.000018063114,0.00012879824,0.00006306965,0.0037310713],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99809957,0.000016381713,0.00058572204,0.00065356283,0.00031145342,0.000333316],"domain_scores_gemma":[0.9989685,0.0000926796,0.0002848119,0.00051120616,0.000057147172,0.00008566586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017322332,0.00032822535,0.00064546167,0.00048823643,0.000044979686,0.000049731378,0.00024577408,0.0005137631,0.0008222313],"category_scores_gemma":[0.00010211695,0.0003329864,0.00014717269,0.00056443794,0.00010414379,0.00006173073,0.00046132234,0.00069515524,0.00000716909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006337015,0.0002022485,0.1463033,0.0015718497,0.00037452191,0.000018888277,0.00035517977,0.0007314057,0.8483821,0.00020193422,0.0017100743,0.0000851071],"study_design_scores_gemma":[0.0004670543,0.000026201502,0.0033294207,0.00026197272,0.00031463307,0.0000058768046,0.0005495526,0.010508844,0.9825389,0.0016061632,0.00007464673,0.00031675195],"about_ca_topic_score_codex":0.0019469884,"about_ca_topic_score_gemma":0.00012546008,"teacher_disagreement_score":0.14297388,"about_ca_system_score_codex":0.0002587953,"about_ca_system_score_gemma":0.00013985345,"threshold_uncertainty_score":0.9999122},"labels":[],"label_agreement":null},{"id":"W4322704938","doi":"10.2139/ssrn.4372717","title":"Factor Analysis for Signal Modeling and Noise Characterization in Spectro-Kinetic Data","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Kinetic energy; Characterization (materials science); Noise (video); SIGNAL (programming language); Computer science; Biological system; Physics; Materials science; Nanotechnology; Artificial intelligence","score_opus":0.03601600223528536,"score_gpt":0.29723838869292735,"score_spread":0.261222386457642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322704938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88990337,0.000764197,0.10875672,0.00022085926,0.000018278208,0.00005397942,0.00014450272,0.000050045473,0.00008807785],"genre_scores_gemma":[0.99539393,0.0028727134,0.00008890073,0.00001862567,0.0002419841,0.0000069243843,0.0005791202,0.00002547779,0.00077235105],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761444,0.000016649705,0.00036017466,0.00038342417,0.0002133558,0.001411949],"domain_scores_gemma":[0.9993597,0.00007683242,0.00014152331,0.00029494433,0.00005045252,0.00007652936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005810558,0.00017347423,0.00033207526,0.00070269586,0.00013503309,0.00009190038,0.00039416665,0.000096405965,0.00025109894],"category_scores_gemma":[0.000094653966,0.00016553121,0.000107651904,0.0016687545,0.00001977162,0.00025063552,0.00008518057,0.0007478377,0.000005709949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003449344,0.00024764464,0.13842404,0.00014594162,0.004886912,0.000018194825,0.00044225887,0.007982365,0.82819414,0.005381369,0.00002542293,0.013906754],"study_design_scores_gemma":[0.0017439653,0.00012180054,0.008170628,0.000022728505,0.0018105978,0.00006717677,0.0016825793,0.95366,0.0116266105,0.020381127,0.00014516772,0.00056757923],"about_ca_topic_score_codex":0.000042697273,"about_ca_topic_score_gemma":0.00030220964,"teacher_disagreement_score":0.9456777,"about_ca_system_score_codex":0.00036586617,"about_ca_system_score_gemma":0.00036441677,"threshold_uncertainty_score":0.67501664},"labels":[],"label_agreement":null},{"id":"W4323844304","doi":"10.18280/isi.280119","title":"Papaya Fruit Maturity Estimation Using Wavelet and ConvNET","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maturity (psychological); Estimation; Wavelet; Horticulture; Biology; Artificial intelligence; Mathematics; Computer science; Engineering; Psychology","score_opus":0.022584814258978902,"score_gpt":0.27020794165936074,"score_spread":0.24762312740038184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323844304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98310876,0.000113681665,0.005202671,0.000035284513,0.00006781816,0.00007347861,0.000046504025,0.00036169987,0.010990087],"genre_scores_gemma":[0.99729407,0.000059308906,0.0020289898,0.00006892969,0.00005018521,0.00001434908,0.00029398245,0.00001104877,0.00017912139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990236,0.000010462378,0.00038252695,0.000116501324,0.00020739122,0.0002594732],"domain_scores_gemma":[0.99938214,0.00007121109,0.00020748508,0.0001746773,0.00009390476,0.00007060339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019160434,0.00015233364,0.00019930032,0.00027634404,0.00025114763,0.0002088039,0.00009975628,0.00014352973,0.00033151708],"category_scores_gemma":[0.0002743653,0.00015162681,0.000043647608,0.0007307092,0.0001016533,0.0013981581,0.00006426444,0.00012866956,0.00014930552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055929285,0.00028696412,0.097949594,0.020671222,0.0018191113,0.00007787494,0.05792114,0.0128721995,0.17536682,0.02592158,0.019942624,0.58661157],"study_design_scores_gemma":[0.0017881558,0.00007487256,0.022862598,0.00037790756,0.00041776116,0.00021955607,0.008538584,0.6903567,0.24789634,0.02052937,0.0056593195,0.0012788713],"about_ca_topic_score_codex":0.00009446672,"about_ca_topic_score_gemma":0.0000024218743,"teacher_disagreement_score":0.67748445,"about_ca_system_score_codex":0.00017541315,"about_ca_system_score_gemma":0.00003843761,"threshold_uncertainty_score":0.6183162},"labels":[],"label_agreement":null},{"id":"W4324130132","doi":"10.1111/1541-4337.13131","title":"Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review","year":2023,"lang":"en","type":"review","venue":"Comprehensive Reviews in Food Science and Food Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quality (philosophy); Computer science; Food quality; Product (mathematics); Food products; Food science; Mathematics","score_opus":0.16912665911050628,"score_gpt":0.45110753799271724,"score_spread":0.28198087888221096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324130132","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007585951,0.9956354,0.0011234538,0.000034467157,0.00003959773,0.0020940958,0.00076046435,0.000009067512,0.0002959],"genre_scores_gemma":[0.0005105713,0.9985189,0.00020844559,0.000049188293,0.00003528319,0.0006176667,0.0000227251,0.000021827556,0.000015427038],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963241,0.00015071042,0.0023014008,0.00054528494,0.00041846908,0.00025998714],"domain_scores_gemma":[0.994493,0.001632336,0.0024716381,0.0008549479,0.00047860213,0.000069495465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025032172,0.00034002008,0.0035071373,0.0002291451,0.0001252438,0.00002227826,0.00071164226,0.00009372412,0.000009185242],"category_scores_gemma":[0.0005722097,0.00021922727,0.00037157687,0.00218033,0.00085550995,0.00006203041,0.00025818587,0.00023764222,4.6462355e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026086238,0.0000859822,0.00013896877,0.4668838,0.00024610513,9.0290406e-8,0.000043741325,1.6471435e-7,0.00009591495,0.0033751512,0.000055223398,0.5290722],"study_design_scores_gemma":[0.00017884946,0.00007474216,0.000121511,0.06032646,0.0014911179,0.000011063849,0.00021606479,0.000047383342,0.000024023871,0.00005511352,0.9371829,0.00027078865],"about_ca_topic_score_codex":0.000022791039,"about_ca_topic_score_gemma":0.0000036769216,"teacher_disagreement_score":0.93712765,"about_ca_system_score_codex":0.00008878062,"about_ca_system_score_gemma":0.00035592917,"threshold_uncertainty_score":0.8939828},"labels":[],"label_agreement":null},{"id":"W4327739947","doi":"10.3390/foods12061273","title":"An Automated Image Processing Module for Quality Evaluation of Milled Rice","year":2023,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Solidity; Roundness (object); Random forest; Image processing; Classifier (UML); Computer science; Pattern recognition (psychology); Machine vision; Identification (biology); Perimeter; Mathematics; Machine learning; Image (mathematics)","score_opus":0.08778733265688007,"score_gpt":0.4551177888889029,"score_spread":0.3673304562320229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327739947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99499434,0.00015393874,0.0009865123,0.000036264737,0.000021359298,0.00008326412,0.000043976772,0.0004731013,0.0032072642],"genre_scores_gemma":[0.997247,0.0000035058122,0.00216924,0.000009462617,0.000060029,0.00005613483,0.00016612343,0.000014977146,0.0002735191],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905384,0.000028331257,0.00023484393,0.00019662222,0.000323859,0.00016248267],"domain_scores_gemma":[0.99921405,0.00007060398,0.00014487894,0.00021963615,0.00031287843,0.0000379495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072235113,0.00008355022,0.0001800758,0.00009743956,0.00007522321,0.000021444457,0.00013160417,0.000075209755,0.00024305112],"category_scores_gemma":[0.00039954344,0.00008035946,0.00006505898,0.0007564402,0.000028775868,0.00011125676,0.000015171006,0.00004431091,0.000008360691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033554432,0.000121395395,0.000999384,0.00048555378,0.00005573518,2.0513187e-7,0.00023130914,0.00022960141,0.9949191,0.000025244617,0.00076496456,0.0021339427],"study_design_scores_gemma":[0.00060923834,0.000029300396,0.004527209,0.000010135095,0.00017903006,2.9107238e-7,0.00042024284,0.31322333,0.6803201,0.000557522,0.000029396548,0.00009425494],"about_ca_topic_score_codex":0.000031692533,"about_ca_topic_score_gemma":0.0000033682686,"teacher_disagreement_score":0.31459904,"about_ca_system_score_codex":0.000055269476,"about_ca_system_score_gemma":0.00007831025,"threshold_uncertainty_score":0.32769638},"labels":[],"label_agreement":null},{"id":"W4328103776","doi":"10.1016/j.microc.2023.108653","title":"Evaluation of the effects of vacuum cooling on moisture contents, colour and texture of mushroom (Agaricus Bisporus) using hyperspectral imaging method","year":2023,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Agaricus bisporus; Chewiness; Hyperspectral imaging; Partial least squares regression; Mushroom; Lightness; VNIR; Chemistry; Moisture; Smoothing; Food science; Horticulture; Mathematics; Analytical Chemistry (journal); Remote sensing; Artificial intelligence; Computer science; Environmental chemistry; Biology; Statistics; Geology","score_opus":0.021651488717588183,"score_gpt":0.3216301615631144,"score_spread":0.2999786728455262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328103776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961678,0.0030701507,0.00033907653,0.00009896164,0.00007496069,0.00007138111,0.000012871931,0.000010813908,0.00015400237],"genre_scores_gemma":[0.9985978,0.00010267482,0.0011055637,0.00003612522,0.00009598283,0.0000014158535,0.0000032618975,0.000016604958,0.000040553685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984286,0.000085085376,0.00041847286,0.00019378778,0.0006422934,0.0002318068],"domain_scores_gemma":[0.9984912,0.00034443816,0.0004956632,0.000179136,0.00042166098,0.0000678666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006401826,0.00016484731,0.0003998756,0.00015669878,0.000091113725,0.000016795278,0.00024268814,0.0001334324,0.00007016677],"category_scores_gemma":[0.00073269074,0.0001169368,0.00020698944,0.0006391321,0.00011590758,0.000043899498,0.00008008586,0.00049023697,3.389302e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067094305,0.00008919089,0.0039170044,0.00019088257,0.00022792521,0.0000042565794,0.00026852952,0.000087258115,0.9937249,0.000010080149,0.00020239994,0.0012104963],"study_design_scores_gemma":[0.0011597099,0.000024358986,0.0022542023,0.00030281558,0.0010809711,0.00011116685,0.00088627794,0.0034036878,0.9903261,0.00033343205,0.000011229532,0.000106033556],"about_ca_topic_score_codex":0.00003395915,"about_ca_topic_score_gemma":7.632235e-7,"teacher_disagreement_score":0.0033987677,"about_ca_system_score_codex":0.00015474379,"about_ca_system_score_gemma":0.000098568606,"threshold_uncertainty_score":0.4768544},"labels":[],"label_agreement":null},{"id":"W4353031484","doi":"10.1017/9781107588493.010","title":"Principal Components and Canonical Correlation","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Canonical correlation; Principal component analysis; Covariance; Mathematics; Correlation; Canonical analysis; Covariance matrix; Statistics; Multivariate statistics; Generalization; Uncorrelated; Curse of dimensionality; Variance (accounting); Pattern recognition (psychology); Artificial intelligence; Computer science; Mathematical analysis","score_opus":0.03606843864701287,"score_gpt":0.2254794853385694,"score_spread":0.18941104669155653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031484","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003980041,0.00010099746,0.00007975844,0.00000806773,0.00008247747,0.000073790376,0.0002334087,0.00020972651,0.99523175],"genre_scores_gemma":[0.015725058,0.0001585922,0.000025629834,0.000014801127,0.00008260504,3.94226e-7,0.00018094062,0.000046543933,0.9837654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889046,0.0000071514582,0.00016641383,0.00046102892,0.00024616212,0.00022878614],"domain_scores_gemma":[0.9991475,0.0001061974,0.00017640118,0.00033545803,0.000065849184,0.00016860185],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004018061,0.00028916073,0.00037858283,0.00017060828,0.0001619152,0.00003205769,0.00025886705,0.00047078822,0.000038220736],"category_scores_gemma":[0.000014587909,0.0003510266,0.00013798081,0.000013709551,0.00020813443,0.000041872005,0.0003491816,0.00052329485,0.0000293943],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016145488,0.00002080306,0.00023142924,0.00031306047,0.0007163844,0.00041041948,0.000038720907,0.0000068134077,0.0020659694,0.9843666,0.011274381,0.00039398097],"study_design_scores_gemma":[0.0011239169,0.000030244635,0.00028460077,0.00017978527,0.0014673689,0.0000380037,0.000048306858,0.0004981155,0.0028236944,0.000021315389,0.99267423,0.00081040733],"about_ca_topic_score_codex":0.0002448902,"about_ca_topic_score_gemma":0.000004342973,"teacher_disagreement_score":0.98434526,"about_ca_system_score_codex":0.00021093874,"about_ca_system_score_gemma":0.00006671849,"threshold_uncertainty_score":0.9998942},"labels":[],"label_agreement":null},{"id":"W4353055724","doi":"10.1016/j.jfca.2023.105290","title":"Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network","year":2023,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Council of Agricultural Research","keywords":"Adulterant; Hyperspectral imaging; Partial least squares regression; Calibration; Mathematics; Pattern recognition (psychology); Preprocessor; Near-infrared spectroscopy; Artificial intelligence; Biological system; Statistics; Chemistry; Computer science; Chromatography; Physics; Optics; Biology","score_opus":0.024204980059959365,"score_gpt":0.2814391631904493,"score_spread":0.25723418313048996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353055724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99482,0.0010646089,0.003843095,0.0001698983,0.000026869056,0.000027256183,0.000016594804,0.000012644077,0.000019027486],"genre_scores_gemma":[0.99638397,0.000061822764,0.0033671104,0.000018925626,0.000107481676,0.0000011104225,0.000041700532,0.000008986213,0.000008898576],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987526,0.00007085699,0.0004754966,0.00018721922,0.00034460824,0.0001692278],"domain_scores_gemma":[0.99902606,0.00008921516,0.0004898293,0.000072534145,0.0002384052,0.00008393312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020311505,0.0001462776,0.00044902074,0.0004911263,0.00019221437,0.00006653702,0.000050922405,0.000054316602,0.00004213276],"category_scores_gemma":[0.000021973261,0.00012180718,0.00016793003,0.0015359945,0.00010087877,0.00026044957,0.000021926417,0.00022847227,2.0393377e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013736609,0.00016114346,0.042220045,0.000060332193,0.0016747187,0.000030438785,0.00044042335,0.1634771,0.78994584,0.000078736004,0.000008627791,0.0005289563],"study_design_scores_gemma":[0.0013441687,0.00035472264,0.014103854,0.00018616936,0.0021584267,0.00010172457,0.0012008037,0.84888387,0.13131775,0.00016513767,0.0000017643465,0.0001816391],"about_ca_topic_score_codex":0.000034720855,"about_ca_topic_score_gemma":0.00004782139,"teacher_disagreement_score":0.68540674,"about_ca_system_score_codex":0.00007353573,"about_ca_system_score_gemma":0.00005526391,"threshold_uncertainty_score":0.49671525},"labels":[],"label_agreement":null},{"id":"W4353100172","doi":"10.54097/hset.v34i.5440","title":"Mobile Phone Price Prediction with Feature Reduction","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dimensionality reduction; Computer science; Feature selection; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Pearson product-moment correlation coefficient; Principal component analysis; Correlation; Mobile phone; Multilayer perceptron; Data mining; Feature extraction; Machine learning; Artificial neural network; Statistics; Mathematics","score_opus":0.004458293926302623,"score_gpt":0.21825019203279825,"score_spread":0.21379189810649563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99760437,0.00030591295,0.00019302523,0.0003587172,0.000089502675,0.000034668497,0.000003350565,0.0006505004,0.00075997505],"genre_scores_gemma":[0.9980261,0.00034179373,0.0007317169,0.0000010178082,0.000042427848,0.000042130883,0.000003210107,0.000007850263,0.00080377754],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992043,8.396507e-7,0.00008089444,0.00030199488,0.00014632117,0.00026567714],"domain_scores_gemma":[0.9997078,0.000011962454,0.000025814921,0.00018400978,0.000033338674,0.000037066024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009488829,0.00009306048,0.00011278695,0.0010193901,0.000096943324,0.00002429819,0.00015902394,0.00012800208,0.000009151808],"category_scores_gemma":[0.00004025585,0.00007384919,0.000007683177,0.005715002,0.00022433736,0.00013603376,0.00004641677,0.00019188721,0.000018975554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056464723,0.000024322404,0.0015240879,0.000034839446,0.000010655384,0.000016627919,0.00015635535,0.0020349657,0.99165124,0.0037294666,0.00025405365,0.0005577191],"study_design_scores_gemma":[0.0002449978,0.000065424116,0.00103854,0.000036319998,0.000014507516,0.00010174788,0.00047366,0.006620732,0.9837156,0.00012803148,0.0074110744,0.00014936521],"about_ca_topic_score_codex":0.000004579032,"about_ca_topic_score_gemma":6.8465545e-7,"teacher_disagreement_score":0.007935661,"about_ca_system_score_codex":0.00007159678,"about_ca_system_score_gemma":0.000022940281,"threshold_uncertainty_score":0.30114827},"labels":[],"label_agreement":null},{"id":"W4353114697","doi":"10.48550/arxiv.2303.11691","title":"A versatile classification tool for galactic activity using optical and infrared colors","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; York University; Office of Science; Johns Hopkins University; Carnegie Mellon University; College of Engineering, Michigan State University; Harvard University; Ohio State University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; Princeton University; Brookhaven National Laboratory; European Commission; U.S. Department of Energy","keywords":"Sky; Active galactic nucleus; Galaxy; Astrophysics; Physics; Redshift; Infrared; Classifier (UML); Random forest; Population; Luminous infrared galaxy; Luminosity; Astronomy; Artificial intelligence; Computer science","score_opus":0.17979448122449304,"score_gpt":0.2568250814130947,"score_spread":0.07703060018860164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353114697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96570426,0.000020240182,0.03200967,0.00004201854,0.00008946381,0.00016997411,0.00007263117,0.00016532619,0.0017264099],"genre_scores_gemma":[0.9951445,0.00008585236,0.0005350655,0.000009835741,0.00007832416,0.000003725018,0.000051847026,0.000032447075,0.004058434],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873394,0.000019251496,0.00014562812,0.00075077335,0.000070521564,0.00027990673],"domain_scores_gemma":[0.99871564,0.00039201957,0.0002221324,0.0004642338,0.00010137066,0.00010459162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011309683,0.00024515635,0.00035164042,0.0002215617,0.00016437222,0.00007041766,0.0002549148,0.0004418797,0.00014750776],"category_scores_gemma":[0.00027066882,0.00030449295,0.00016907345,0.00044256842,0.00014133428,0.00014561511,0.00035059708,0.00041671522,0.0000123496975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069823586,0.002698216,0.2921629,0.012223283,0.008643747,0.0006916153,0.0016147143,0.109334916,0.49979874,0.05755944,0.0058883782,0.0024016928],"study_design_scores_gemma":[0.002183748,0.00008388808,0.014378195,0.00016092202,0.0034612461,0.0000071897675,0.0014327738,0.8942772,0.06599051,0.016314045,0.00038127048,0.0013290187],"about_ca_topic_score_codex":0.00009227012,"about_ca_topic_score_gemma":0.000012159216,"teacher_disagreement_score":0.78494227,"about_ca_system_score_codex":0.00045944593,"about_ca_system_score_gemma":0.00016936852,"threshold_uncertainty_score":0.9999407},"labels":[],"label_agreement":null},{"id":"W4360874190","doi":"10.1016/j.heliyon.2023.e14763","title":"Correlation of tangible quality parameters of vegetable-based transformer fluids","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"Cape Breton University","keywords":"Vegetable oil; Transformer oil; Pulp and paper industry; Acid value; Mineral oil; Transformer; Kinematics; Materials science; Viscosity; Process engineering; Biochemical engineering; Chemistry; Environmental science; Biological system; Chemical engineering; Composite material; Food science; Engineering; Organic chemistry; Physics; Electrical engineering","score_opus":0.036428021156599065,"score_gpt":0.31277353679189385,"score_spread":0.2763455156352948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360874190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98232335,0.0009442508,0.004828104,0.000062828665,0.000045298508,0.00004095719,0.000042327338,0.00007844876,0.011634422],"genre_scores_gemma":[0.99785525,0.00020553371,0.00043406387,0.00002013965,0.000013819984,0.0000074291047,0.00006882577,0.000012098922,0.0013828604],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999059,0.000014468479,0.00035880637,0.00014822806,0.0002486202,0.00017083857],"domain_scores_gemma":[0.99936986,0.00018500301,0.00012246532,0.00022238817,0.00006228431,0.00003799091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017826653,0.000094488445,0.0002617025,0.00023243912,0.000027376791,0.0000044744083,0.00010195525,0.00010938982,0.00072215643],"category_scores_gemma":[0.00009108256,0.000090324844,0.00014598158,0.0012570823,0.000059501668,0.00005260306,0.000005742024,0.000086137705,0.000032935266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008996626,0.00009312534,0.03575172,0.0015612298,0.00007795527,9.114022e-7,0.00013989762,0.00133749,0.9600862,0.00027414222,0.000094448624,0.0004928918],"study_design_scores_gemma":[0.00045639294,0.000030645162,0.0028982563,0.000080304664,0.00009743226,2.5068474e-7,0.00018521327,0.0009976407,0.99486667,0.00007586606,0.0002168477,0.000094457755],"about_ca_topic_score_codex":0.000064453125,"about_ca_topic_score_gemma":0.000005257192,"teacher_disagreement_score":0.03478047,"about_ca_system_score_codex":0.000031286636,"about_ca_system_score_gemma":0.00004318926,"threshold_uncertainty_score":0.7907107},"labels":[],"label_agreement":null},{"id":"W4361028307","doi":"10.1016/j.postharvbio.2023.112334","title":"Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp","year":2023,"lang":"en","type":"article","venue":"Postharvest Biology and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Mean squared error; Pulp (tooth); Mathematics; Near-infrared spectroscopy; Artificial intelligence; Correlation coefficient; Multispectral image; Support vector machine; Coefficient of determination; Pattern recognition (psychology); Computer science; Statistics; Optics; Physics","score_opus":0.023066223928252236,"score_gpt":0.32402672578184744,"score_spread":0.3009605018535952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361028307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99689,0.0005264515,0.00019058846,0.0013625572,0.000015546431,0.00012900274,0.00004919119,0.00030959284,0.0005270355],"genre_scores_gemma":[0.9987116,0.00001912451,0.0005228957,0.000027944276,0.000020816136,0.00008338023,0.00045966054,0.000017823475,0.00013677763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989003,0.000041031253,0.00024525687,0.00037060463,0.000099425364,0.0003433973],"domain_scores_gemma":[0.999078,0.0002291762,0.00020061307,0.0002404689,0.0002106419,0.00004114349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039306292,0.00017006623,0.00037550737,0.0002516196,0.00016034074,0.000012917879,0.00016467307,0.00023285819,0.000099070036],"category_scores_gemma":[0.00060805463,0.00014701241,0.00005673888,0.00085463445,0.00055503065,0.000043840886,0.000047376525,0.00025903166,0.0000033179692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023875371,0.00008336811,0.41730112,0.00008534436,0.000119038305,0.0000012881045,0.000030900897,0.000025833313,0.57781434,0.0029732988,0.000014726015,0.0013119619],"study_design_scores_gemma":[0.0032317943,0.0002859882,0.01499689,0.000019956793,0.00032499243,0.000009676004,0.00039198762,0.017823908,0.95286965,0.009615121,0.00016538549,0.0002646554],"about_ca_topic_score_codex":0.000053781878,"about_ca_topic_score_gemma":0.000028766757,"teacher_disagreement_score":0.40230423,"about_ca_system_score_codex":0.000041176485,"about_ca_system_score_gemma":0.000089486624,"threshold_uncertainty_score":0.5994992},"labels":[],"label_agreement":null},{"id":"W4361214465","doi":"10.3390/s23073523","title":"Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Kernel (algebra); Mycotoxin; Fusarium; Segmentation; Mathematics; Computer science; Horticulture; Biology; Botany","score_opus":0.01713925369930059,"score_gpt":0.28215655054399585,"score_spread":0.26501729684469527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361214465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9831736,0.00032911246,0.000011024882,0.000068723384,0.0000122418805,0.000015044311,0.000060078262,0.00010700649,0.01622318],"genre_scores_gemma":[0.9878702,0.00021874228,0.00018265648,0.0000051452407,0.000027210644,0.0000011050781,0.000083364,0.000016968126,0.011594621],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989746,0.00002577263,0.00029031522,0.00022597869,0.00025115567,0.00023216108],"domain_scores_gemma":[0.9992439,0.00019610312,0.00016361465,0.0002764205,0.00006664903,0.000053324005],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013695032,0.00013181285,0.00049656065,0.0006731344,0.00004938385,0.000008659317,0.000163185,0.00007581211,0.0021720561],"category_scores_gemma":[0.0002519385,0.0001232478,0.0003227572,0.0034698423,0.00009998378,0.000031743733,0.000044054395,0.00019357559,0.00002051069],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003169957,0.000057460427,0.15354638,0.00008775902,0.0021058903,0.000016297574,0.0004966703,0.013626173,0.82967037,0.000077185745,0.00012628261,0.00015785807],"study_design_scores_gemma":[0.00023362787,0.000022081022,0.014444376,0.000008450341,0.0020249852,0.0000012609114,0.002143305,0.014013446,0.96683735,0.000040220904,0.00009252488,0.0001383566],"about_ca_topic_score_codex":0.00036469862,"about_ca_topic_score_gemma":0.000012319359,"teacher_disagreement_score":0.139102,"about_ca_system_score_codex":0.000028875425,"about_ca_system_score_gemma":0.000013742052,"threshold_uncertainty_score":0.9987401},"labels":[],"label_agreement":null},{"id":"W4362569774","doi":"10.23977/acss.2023.070211","title":"Detection of Strawberry Quality Based on Near Infrared Spectroscopy","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Sugar; Maturity (psychological); Near-infrared spectroscopy; Computer science; Environmental science; Materials science; Chemistry; Food science; Optics; Physics","score_opus":0.024313274615717105,"score_gpt":0.3201923960712082,"score_spread":0.2958791214554911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362569774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93666136,0.003148516,0.055008244,0.000022872513,0.000325758,0.00011703552,0.00003034396,0.00014712437,0.0045387293],"genre_scores_gemma":[0.9989883,0.00021119954,0.00048620213,0.00002652952,0.0001329224,0.000016075674,0.000009805497,0.000011627905,0.00011733817],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987508,0.00005079089,0.00043522264,0.00030146167,0.0002464336,0.0002152807],"domain_scores_gemma":[0.99910027,0.0003830149,0.000187544,0.00023999199,0.000038487753,0.000050671842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924566,0.00014837622,0.00038518317,0.00017416774,0.000063520165,0.000051666204,0.00012920432,0.00008661514,0.000040369974],"category_scores_gemma":[0.000027105614,0.00013528888,0.000063540036,0.00067287876,0.00006364054,0.00012304306,0.000027698181,0.00013429539,0.0000064969763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049994036,0.0005351478,0.07289956,0.0044324207,0.00019658629,0.000059551545,0.0007216381,0.28296337,0.6121153,0.000812558,0.00037348032,0.024390435],"study_design_scores_gemma":[0.0012819475,0.0003325752,0.005116866,0.0003625895,0.00003366021,0.000004208273,0.00035046495,0.5381451,0.45103014,0.00090721896,0.0019994392,0.00043576313],"about_ca_topic_score_codex":0.00008042655,"about_ca_topic_score_gemma":0.0000106083335,"teacher_disagreement_score":0.25518176,"about_ca_system_score_codex":0.000039367005,"about_ca_system_score_gemma":0.000020628715,"threshold_uncertainty_score":0.551692},"labels":[],"label_agreement":null},{"id":"W4362574778","doi":"10.1016/j.chemolab.2023.104818","title":"Maximum likelihood factor analysis for resolution of noisy smartphone based diffuse reflectance data from CdS pigments","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Principal component analysis; Chemometrics; RGB color model; Diffuse reflection; Multivariate statistics; Diffuse reflectance infrared fourier transform; Analytical Chemistry (journal); Reflectivity; Materials science; Cadmium sulfide; Pigment; Partial least squares regression; Principal component regression; Biological system; Computer science; Chemistry; Mathematics; Statistics; Optics; Artificial intelligence; Chromatography; Physics","score_opus":0.08077864368649768,"score_gpt":0.3221780964083346,"score_spread":0.24139945272183694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362574778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92946154,0.012313048,0.046382587,0.000059620812,0.0004156538,0.00029372107,0.0105302585,0.00020331496,0.00034024005],"genre_scores_gemma":[0.99528044,0.0009671961,0.0004362916,0.00003603324,0.0002007589,0.00005750108,0.0025908605,0.000049829407,0.00038106547],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970741,0.000037648344,0.00088013,0.0008929766,0.0006121995,0.0005028935],"domain_scores_gemma":[0.99669147,0.0006134199,0.0005640994,0.0013857409,0.00050955894,0.0002356856],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005250789,0.0003501911,0.00090656057,0.0018733968,0.00014631411,0.00013050645,0.00074210507,0.00029447893,0.00024047629],"category_scores_gemma":[0.0010007527,0.0003341213,0.00018389779,0.013979442,0.00008784572,0.00018938165,0.00020517192,0.00019085871,0.000025750493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047100114,0.0015823403,0.32108426,0.0030378315,0.008295239,0.000016964867,0.00046893719,0.0004978886,0.6466038,0.00010564742,0.015428398,0.0024076547],"study_design_scores_gemma":[0.0018929294,0.00019387096,0.0052676955,0.00014196716,0.0028209642,5.027987e-7,0.0015271215,0.11651397,0.84816706,0.00013313923,0.022357393,0.0009833565],"about_ca_topic_score_codex":0.00017675186,"about_ca_topic_score_gemma":0.00002584463,"teacher_disagreement_score":0.31581655,"about_ca_system_score_codex":0.00019564336,"about_ca_system_score_gemma":0.000122030964,"threshold_uncertainty_score":0.99991107},"labels":[],"label_agreement":null},{"id":"W4365453223","doi":"10.3390/foods12081629","title":"Performance of a Handheld Near-Infrared Spectroscopy Device to Predict Pork Primal Belly Fat Iodine Value and Loin Lean Intramuscular Fat Content","year":2023,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Swine Innovation Porc","keywords":"Loin; Intramuscular fat; Near-infrared spectroscopy; Iodine value; Calibration; Lean tissue; Food science; Chemistry; Animal science; Mathematics; Adipose tissue; Biology; Biochemistry","score_opus":0.027037726592633173,"score_gpt":0.26929375274075007,"score_spread":0.2422560261481169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365453223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925493,0.000580547,0.00019189443,0.00021790755,0.00007641679,0.00015806835,0.000056212706,0.00018145889,0.005988195],"genre_scores_gemma":[0.99448526,0.00021399898,0.002322394,0.00014066965,0.00021896101,0.000028001936,0.00006352902,0.000038160906,0.0024890553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981974,0.000013201838,0.00042123705,0.00045271273,0.00040535102,0.0005101255],"domain_scores_gemma":[0.9990229,0.000086554086,0.00013809396,0.00044732713,0.000087535904,0.0002176019],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016872238,0.0002787699,0.00049367885,0.00018154674,0.00016448663,0.000061807135,0.00030787406,0.00015618869,0.00054761406],"category_scores_gemma":[0.00016054079,0.0002605324,0.00013050767,0.0011446727,0.00013446475,0.000107765794,0.00019034659,0.00024912393,0.000054552325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005172611,0.00016675683,0.09547838,0.000810226,0.00046280888,0.000026656971,0.00088023994,0.00024029663,0.8954401,0.00018679092,0.004885192,0.0009052732],"study_design_scores_gemma":[0.0010542177,0.00063900906,0.009520934,0.00013133726,0.00022497147,0.000010943592,0.00048723267,0.0037625926,0.9816002,0.00007822299,0.0021730033,0.0003173226],"about_ca_topic_score_codex":0.00007832521,"about_ca_topic_score_gemma":0.0000086360415,"teacher_disagreement_score":0.08616009,"about_ca_system_score_codex":0.000091956055,"about_ca_system_score_gemma":0.000055304237,"threshold_uncertainty_score":0.9999847},"labels":[],"label_agreement":null},{"id":"W4366506633","doi":"10.11159/iceptp23.192","title":"Quantification of Hydrocarbon Contamination in Soil Using Hyperspectral Data and Deep Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S.-Middle East Partnership Initiative; American University of Beirut","keywords":"Hyperspectral imaging; Contamination; Environmental science; Remote sensing; Hydrocarbon; Geology; Chemistry","score_opus":0.013938642019223468,"score_gpt":0.23396946440915023,"score_spread":0.22003082238992677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366506633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992784,0.00034069084,0.0000056135946,0.000027077156,0.000042654596,0.000042897955,0.0000103348875,0.000022845152,0.00022947675],"genre_scores_gemma":[0.99940616,0.00029145592,0.00009954078,0.0000022517904,0.000017421893,0.000002252853,0.000016245462,0.000014839658,0.00014984178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992641,0.0000017986665,0.00019826644,0.0002333062,0.00015675122,0.00014575997],"domain_scores_gemma":[0.99967957,0.000048789156,0.00014006633,0.00009901333,0.000004944757,0.000027636648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008526242,0.00012647695,0.00018345704,0.00018574279,0.000055841,0.000020303112,0.00018581175,0.000041611624,0.000016425705],"category_scores_gemma":[0.00004976329,0.0001105673,0.000023147342,0.00030095648,0.00008357958,0.00015866786,0.0001517527,0.00016380499,1.4803247e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017443981,0.000008967428,0.06371803,0.0002090372,0.00003750299,3.4784986e-7,0.00014976512,0.0053897887,0.92948,0.0003039799,0.0000023437487,0.0006827679],"study_design_scores_gemma":[0.00033793933,0.000013753502,0.04291659,0.00012447788,0.00007202201,0.000006372073,0.0012260241,0.44249377,0.5125599,0.00007567776,0.00001963678,0.0001538343],"about_ca_topic_score_codex":0.000025319163,"about_ca_topic_score_gemma":0.000012508413,"teacher_disagreement_score":0.437104,"about_ca_system_score_codex":0.000052091364,"about_ca_system_score_gemma":0.000002007723,"threshold_uncertainty_score":0.45088038},"labels":[],"label_agreement":null},{"id":"W4366522197","doi":"10.5267/j.ccl.2023.2.005","title":"Development of fast analytical method for the detection and quantification of Moroccan picholine extra virgin olive oil adulteration using MIR spectroscopy and chemometrics tools","year":2023,"lang":"en","type":"article","venue":"Current Chemistry Letters","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chemometrics; Principal component analysis; Partial least squares regression; Olive oil; Principal component regression; Chemistry; Chromatography; Analytical Chemistry (journal); Calibration; Biological system; Pattern recognition (psychology); Food science; Statistics; Mathematics; Artificial intelligence; Computer science","score_opus":0.0751603903004456,"score_gpt":0.3607738363761056,"score_spread":0.28561344607565997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366522197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8514838,0.00085848756,0.14731847,0.00015159178,0.00004238249,0.000059118967,0.000039926217,0.000028109822,0.000018075742],"genre_scores_gemma":[0.982329,0.0005196838,0.016729832,0.000011400199,0.00017511145,0.00004231555,0.00013433037,0.000024281457,0.000034053628],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987013,0.000010442969,0.0004742272,0.00035062266,0.00024195474,0.00022147516],"domain_scores_gemma":[0.9989784,0.00031710538,0.00030665498,0.00022246517,0.00011349377,0.0000619332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029465757,0.00019396201,0.00029870603,0.00014874697,0.00013725678,0.000050164756,0.00013606813,0.000086660504,0.000029843912],"category_scores_gemma":[0.00029289286,0.00016674711,0.00007874599,0.0009189907,0.000118506876,0.00010738321,0.000051576677,0.00017089245,3.9713018e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045493754,0.00004531008,0.00034180575,0.0012050624,0.00010728322,1.9411105e-7,0.00023707644,0.00009569056,0.97458804,0.0000073172782,0.000018127987,0.023308625],"study_design_scores_gemma":[0.00040701916,0.0000027788928,0.00035184436,0.0000695462,0.00025059702,0.000004704205,0.00040481164,0.051637374,0.94651884,0.0000074079353,0.0001921688,0.00015293175],"about_ca_topic_score_codex":0.000008621664,"about_ca_topic_score_gemma":0.0000012993288,"teacher_disagreement_score":0.13084514,"about_ca_system_score_codex":0.000101761776,"about_ca_system_score_gemma":0.000040173993,"threshold_uncertainty_score":0.679975},"labels":[],"label_agreement":null},{"id":"W4366596622","doi":"10.3390/agronomy13041167","title":"Current Optical Sensing Applications in Seeds Vigor Determination","year":2023,"lang":"en","type":"article","venue":"Agronomy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"","keywords":"CLARITY; Agricultural engineering; Agriculture; Crop; Yield (engineering); Process (computing); Seed testing; Germination; Environmental science; Computer science; Agronomy; Engineering; Materials science; Biology; Ecology","score_opus":0.02071920250256189,"score_gpt":0.3094095658444627,"score_spread":0.28869036334190085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366596622","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9709526,0.00028176926,0.007909079,0.00015365999,0.000028015758,0.00007587503,0.0000034518257,0.00017255727,0.020423047],"genre_scores_gemma":[0.998385,0.000030422108,0.00082355755,0.000008808465,0.00009260185,0.000027862154,0.000069709975,0.00000918729,0.00055286405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941427,0.0000038300664,0.0001439464,0.00017719509,0.00008261297,0.00017815003],"domain_scores_gemma":[0.99965006,0.000103218124,0.000033057167,0.00015077357,0.00001930579,0.000043579657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056002016,0.00007819781,0.00010406804,0.0001645042,0.000051475443,0.000023822498,0.00008112675,0.000047879592,0.00016997116],"category_scores_gemma":[0.000024375671,0.000079015874,0.000043043896,0.00073616096,0.000028201515,0.000058366404,0.000037345468,0.00011539779,0.0001647499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022875707,0.00053601904,0.18544231,0.00045273273,0.00013203605,0.000043377808,0.0012214793,0.00021174656,0.097073644,0.0070834355,0.0034064,0.70437396],"study_design_scores_gemma":[0.0014409497,0.000023309023,0.06629187,0.00010643857,0.00032981412,0.000020123962,0.003351546,0.016943242,0.8050455,0.005913053,0.09940311,0.0011310593],"about_ca_topic_score_codex":0.00001451443,"about_ca_topic_score_gemma":0.000007451882,"teacher_disagreement_score":0.7079718,"about_ca_system_score_codex":0.000086328524,"about_ca_system_score_gemma":0.000022479502,"threshold_uncertainty_score":0.32221738},"labels":[],"label_agreement":null},{"id":"W4367040796","doi":"10.1016/j.chemolab.2023.104841","title":"Exploring the scores: Procrustes analysis for comprehensive exploration of multivariate data","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Data mining; Overfitting; Principal component analysis; Procrustes analysis; Cluster analysis; Hierarchical clustering; Projection pursuit; Preprocessor; Projection (relational algebra); Multivariate statistics; Artificial intelligence; Pattern recognition (psychology); Exploratory data analysis; Visualization; Machine learning; Artificial neural network","score_opus":0.35375863775162236,"score_gpt":0.36373001267784605,"score_spread":0.009971374926223686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367040796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9400114,0.012808362,0.043633334,0.00014593305,0.0005530515,0.0007324432,0.0014248,0.00034803397,0.0003426827],"genre_scores_gemma":[0.9955875,0.0030173813,0.00016597958,0.000017196004,0.00017481273,0.00020381395,0.00049196277,0.000029929444,0.00031141323],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981765,0.000029539558,0.00061830174,0.0005115565,0.00037077646,0.0002932826],"domain_scores_gemma":[0.9969218,0.001040492,0.00037877046,0.0008892504,0.00067654415,0.00009317623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055455643,0.00022467134,0.00056052045,0.001275685,0.00019449664,0.00013702156,0.0005845394,0.00008507028,0.000017561913],"category_scores_gemma":[0.00101996,0.00016649181,0.00011104233,0.012920373,0.00010245548,0.0005391974,0.00022993724,0.00013516066,0.0000076041074],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077892205,0.0017365108,0.18228094,0.024567684,0.040443957,0.0000375431,0.014190523,0.038213175,0.6237909,0.024078704,0.02771426,0.022166835],"study_design_scores_gemma":[0.0008404435,0.00019072607,0.0023034874,0.00016892159,0.0037139629,0.0000017093415,0.032260034,0.17573635,0.7200911,0.00008984284,0.06371059,0.0008928057],"about_ca_topic_score_codex":0.00009963799,"about_ca_topic_score_gemma":0.0000047962285,"teacher_disagreement_score":0.17997746,"about_ca_system_score_codex":0.000052932395,"about_ca_system_score_gemma":0.000054982633,"threshold_uncertainty_score":0.6789339},"labels":[],"label_agreement":null},{"id":"W4367182640","doi":"10.1007/s11694-023-01935-3","title":"Discrimination of black tea fermentation degree based on multi-data fusion of near-infrared spectroscopy and machine vision","year":2023,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Black tea; Principal component analysis; Fermentation; Linear discriminant analysis; Near-infrared spectroscopy; Chemistry; Artificial intelligence; Fusion; Spectrometer; Calibration; Mathematics; Pattern recognition (psychology); Biological system; Food science; Computer science; Optics; Statistics; Physics","score_opus":0.11786311361707548,"score_gpt":0.32070540626768507,"score_spread":0.2028422926506096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367182640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9826498,0.000076031276,0.016531521,0.00021911511,0.0000908293,0.00011635046,0.00014081932,0.000020055566,0.00015545182],"genre_scores_gemma":[0.9978206,0.00021725582,0.001154568,0.000022099672,0.00006796461,0.0000017581236,0.00064950745,0.000020646583,0.000045586425],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978169,0.00005232212,0.0007590452,0.00021319232,0.0010125574,0.0001459559],"domain_scores_gemma":[0.99786806,0.00004107907,0.0012500804,0.00031026805,0.00046131376,0.000069223446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072791043,0.00016936885,0.00035368686,0.00040673206,0.0000737668,0.00004158385,0.00022861868,0.00008964646,0.00011847042],"category_scores_gemma":[0.00035290245,0.0001472927,0.00008180904,0.00060600904,0.00005774192,0.0003904785,0.000052146373,0.00014985971,0.0000017341398],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003728586,0.00040507395,0.004744375,0.0003272336,0.00008401668,0.0000015966723,0.00021054155,0.00007446492,0.99126244,0.0000060106254,0.00006125986,0.0024501425],"study_design_scores_gemma":[0.0015637184,0.0005517677,0.20826669,0.00031892315,0.00024604602,0.0000021348965,0.00013506298,0.021098254,0.7676608,0.000022078513,0.000031628824,0.00010289843],"about_ca_topic_score_codex":0.0000048814086,"about_ca_topic_score_gemma":0.0000044696944,"teacher_disagreement_score":0.22360164,"about_ca_system_score_codex":0.00013970424,"about_ca_system_score_gemma":0.00009485541,"threshold_uncertainty_score":0.60064214},"labels":[],"label_agreement":null},{"id":"W4367187019","doi":"10.18280/mmep.100226","title":"Rice Foreign Object Classification Based on Integrated Color and Textural Feature Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Feature (linguistics); Computer science; Pattern recognition (psychology); Object (grammar); Computer vision; Linguistics","score_opus":0.03952378692402381,"score_gpt":0.2489918961343502,"score_spread":0.20946810921032638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367187019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57803196,0.0003270523,0.41958004,0.000113510294,0.00001524578,0.0000948778,0.000008350376,0.0005419172,0.0012870679],"genre_scores_gemma":[0.9849228,0.000052124426,0.014385718,0.00001230163,0.000028481008,0.00001805129,0.00003823865,0.000036524565,0.00050576153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991378,0.000007671858,0.00018105436,0.00026306778,0.00015068594,0.00025969296],"domain_scores_gemma":[0.9994581,0.000256838,0.000049441984,0.00011990456,0.00002795817,0.000087757195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017342804,0.00020009719,0.00025003395,0.00017310164,0.00012624946,0.00008286055,0.00006553187,0.00014104544,0.000028098997],"category_scores_gemma":[0.00011210211,0.00016286997,0.000042752283,0.00047464986,0.000026817728,0.000049427403,0.000022168315,0.00038328953,0.000006320557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014120106,0.000029158,0.00014240385,0.0009303648,0.000038005604,0.0000029089822,0.00014169345,0.9553274,0.04188116,0.001367618,0.0000101305795,0.00011504132],"study_design_scores_gemma":[0.00021143595,0.000026143161,0.00001277113,0.00019996858,0.000074168565,0.000008842534,0.00010921461,0.99308115,0.0051704613,0.00085222203,0.000068494366,0.00018512912],"about_ca_topic_score_codex":0.00001349598,"about_ca_topic_score_gemma":1.6301237e-7,"teacher_disagreement_score":0.40689087,"about_ca_system_score_codex":0.000044914286,"about_ca_system_score_gemma":0.000009761991,"threshold_uncertainty_score":0.6641644},"labels":[],"label_agreement":null},{"id":"W4375824621","doi":"10.1007/978-1-0716-3151-5_15","title":"Analysis of Complex Carbohydrate Composition in Plant Cell Wall Using Fourier Transform Mid-Infrared Spectroscopy","year":2023,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Fourier transform infrared spectroscopy; Principal component analysis; Fourier transform; Infrared spectroscopy; Infrared; Analytical Chemistry (journal); Spectroscopy; Fourier transform spectroscopy; Materials science; Chemistry; Biological system; Optics; Mathematics; Computer science; Artificial intelligence; Chromatography; Physics; Organic chemistry; Biology","score_opus":0.0414495313229251,"score_gpt":0.3907937114376515,"score_spread":0.3493441801147264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375824621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695047,0.00024928496,0.30149952,0.000049389906,0.00003034685,0.000069675196,0.00006368929,0.00003793658,0.0029531387],"genre_scores_gemma":[0.57804847,0.000106022846,0.42097434,0.000097136035,0.000016307331,0.000018865898,0.0006375624,0.000028912598,0.00007235379],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813974,0.0002998393,0.00058163406,0.00042413417,0.00011495968,0.00043970582],"domain_scores_gemma":[0.99914736,0.00025442222,0.00016523541,0.00034794016,0.00003131423,0.00005373759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006161151,0.00021586451,0.00081283407,0.0016027207,0.000032866792,0.000009476758,0.00026237895,0.00026219772,0.00033782903],"category_scores_gemma":[0.000050238996,0.00022017675,0.00026044852,0.0043961643,0.0001136316,0.000022613194,0.00005163437,0.0002526883,0.0000018179915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057292837,0.00008676453,0.012841215,0.000054455006,0.00035904115,0.000047822734,0.0001984752,0.0029203554,0.983223,0.000069165,0.0000042314778,0.00013818154],"study_design_scores_gemma":[0.00048446853,0.000030298062,0.00083038356,0.000009976125,0.0005908402,0.000001549061,0.00013657889,0.08843246,0.907677,0.0015527182,0.000059574795,0.00019413047],"about_ca_topic_score_codex":0.0004787361,"about_ca_topic_score_gemma":0.000052841322,"teacher_disagreement_score":0.11947483,"about_ca_system_score_codex":0.00015734497,"about_ca_system_score_gemma":0.000038781796,"threshold_uncertainty_score":0.89785475},"labels":[],"label_agreement":null},{"id":"W4376112483","doi":"10.26434/chemrxiv-2023-qtzzd","title":"Rapid Fluorescence EEM spectra acquisition using super-cycle Hadamard-Transform Multiplexing","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; University of Victoria","keywords":"Hadamard transform; Multiplexing; Excitation; Spectral line; Data acquisition; Computer science; Algorithm; Chemistry; Analytical Chemistry (journal); Biological system; Mathematics; Physics; Telecommunications; Mathematical analysis","score_opus":0.05142029836711797,"score_gpt":0.2996217063099022,"score_spread":0.24820140794278422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376112483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97215295,0.003169277,0.004463599,0.00061105815,0.0008120437,0.000363722,0.00012320586,0.0016356477,0.016668487],"genre_scores_gemma":[0.9882537,0.0010990476,0.006860544,0.00007593374,0.001313095,0.000062406165,0.00055442395,0.00019974624,0.00158109],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963765,0.000021021036,0.00074259075,0.0013071799,0.00064373214,0.0009089739],"domain_scores_gemma":[0.9979525,0.00018063943,0.00029995755,0.0011651324,0.00014707331,0.00025470377],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028442117,0.0007429631,0.0009042035,0.000389335,0.0003114889,0.000250771,0.0009111379,0.00089276186,0.0037052694],"category_scores_gemma":[0.00015001612,0.0008146883,0.00067126116,0.0007942031,0.00020436816,0.00018412965,0.0004839881,0.0014012806,0.00017754587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008195555,0.00022655066,0.0030534102,0.001887085,0.00068373256,0.00012075082,0.00054238795,0.0009786012,0.9895176,0.000060261842,0.00091601384,0.0019316323],"study_design_scores_gemma":[0.0005414129,0.000011814638,0.00037787287,0.00041894088,0.00049309444,0.000017935223,0.00048080066,0.0129653225,0.9815948,0.001798328,0.00036268996,0.00093697023],"about_ca_topic_score_codex":0.0004308521,"about_ca_topic_score_gemma":0.000016828086,"teacher_disagreement_score":0.016100753,"about_ca_system_score_codex":0.00066772057,"about_ca_system_score_gemma":0.0002254539,"threshold_uncertainty_score":0.9994304},"labels":[],"label_agreement":null},{"id":"W4376135837","doi":"10.1364/ol.485451","title":"Efficient line shape estimation by ghost spectroscopy","year":2023,"lang":"en","type":"article","venue":"Optics Letters","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"H2020 Future and Emerging Technologies; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Optics; Spectroscopy; Line (geometry); Inversion (geology); Algorithm; Computer science; Mathematics; Physics; Geometry; Geology","score_opus":0.011894529504839973,"score_gpt":0.27113008962853463,"score_spread":0.25923556012369464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376135837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9833927,0.00009638261,0.0060727727,0.0042972616,0.00009848711,0.00004296531,0.00004172896,0.00035127616,0.0056064148],"genre_scores_gemma":[0.9888955,0.000054538024,0.0069444967,0.0013649961,0.00023249365,0.000018029288,0.0003335476,0.00005111015,0.0021052922],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987836,0.0000056705835,0.00022124819,0.00030085252,0.0002990249,0.00038960393],"domain_scores_gemma":[0.99940324,0.00009853196,0.000085822234,0.0002973444,0.000024351439,0.000090717105],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009022804,0.00017791815,0.00019014595,0.00015095266,0.00011849664,0.00007155294,0.00020796225,0.00008133312,0.0010926238],"category_scores_gemma":[0.000096188036,0.00017985175,0.00008550854,0.000865851,0.00006658687,0.000028842498,0.00005484718,0.0001969618,0.00061471417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009632894,0.000055596873,0.00025108262,0.00004130675,0.00005194414,0.000015056065,0.000057054658,0.010718774,0.9596206,0.00007822144,0.028894404,0.00020632407],"study_design_scores_gemma":[0.0002895377,0.000016948929,0.00005657452,0.000012449854,0.00009342108,0.0000033122164,0.00007898424,0.34084213,0.6575528,0.000027225535,0.00080354424,0.00022313393],"about_ca_topic_score_codex":0.000010743284,"about_ca_topic_score_gemma":3.58815e-7,"teacher_disagreement_score":0.33012334,"about_ca_system_score_codex":0.000106941625,"about_ca_system_score_gemma":0.0000141858645,"threshold_uncertainty_score":0.99982053},"labels":[],"label_agreement":null},{"id":"W4376638888","doi":"10.18280/isi.280204","title":"Modeling Artificial Neural Network of Insect’s Proliferation During Cocoa Beans Storage","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Insect; Artificial neural network; Biology; Computer science; Horticulture; Biological system; Artificial intelligence; Botany","score_opus":0.028111225463682134,"score_gpt":0.25199098010345494,"score_spread":0.2238797546397728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376638888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98889506,0.00006155439,0.007291074,0.00002032827,0.00011840023,0.000107268184,0.000021718084,0.00030875573,0.0031758249],"genre_scores_gemma":[0.9991465,0.000023347979,0.00014801684,0.000021850512,0.0002472819,0.000041410047,0.0002606781,0.000015296795,0.0000956094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998537,0.000016353992,0.0006939613,0.00012345808,0.00029284583,0.0003363823],"domain_scores_gemma":[0.99924886,0.000037014022,0.00025101137,0.00020817066,0.0001976668,0.000057280027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021909266,0.00016340871,0.00025215288,0.0002286751,0.00029504046,0.00011186303,0.00015000961,0.00013603715,0.00014918286],"category_scores_gemma":[0.00019802083,0.00016996061,0.000093670315,0.0011124879,0.000055006596,0.0012517014,0.000057152927,0.000161924,0.000045315493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016847505,0.000030350211,0.0047251396,0.0011429868,0.000108878885,0.0000035561927,0.0050564525,0.95713496,0.027867584,0.0008658588,0.00019213413,0.0027036122],"study_design_scores_gemma":[0.00031751764,0.000036565394,0.0011201211,0.000093706236,0.00007417229,0.000012158969,0.00217091,0.89980274,0.09438035,0.0016901596,0.000034398534,0.0002671803],"about_ca_topic_score_codex":0.000058418616,"about_ca_topic_score_gemma":0.000015837233,"teacher_disagreement_score":0.06651276,"about_ca_system_score_codex":0.00020482946,"about_ca_system_score_gemma":0.00005746724,"threshold_uncertainty_score":0.69307923},"labels":[],"label_agreement":null},{"id":"W4377019721","doi":"10.3390/pr11051551","title":"Impact of Data Grouping on the Multivariate Analysis of Several Concrete Plants","year":2023,"lang":"en","type":"article","venue":"Processes","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs","keywords":"Principal component analysis; Collinearity; Raw data; Multivariate statistics; Computer science; Blocking (statistics); Data mining; Block (permutation group theory); Novelty; Multivariate analysis; Process (computing); Data analysis; Factory (object-oriented programming); Dimension (graph theory); Curse of dimensionality; Statistics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.09005318576208371,"score_gpt":0.38112466369661335,"score_spread":0.29107147793452964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377019721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965323,0.00032249474,0.00012008945,0.00003710394,0.000006453825,0.000023451305,0.00067873904,0.00004591482,0.0022334114],"genre_scores_gemma":[0.9990966,0.00026609094,0.000046596633,0.000013189051,0.000025753725,0.0000034703435,0.00031805725,0.000008818511,0.00022142396],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915594,0.00001003664,0.0002242826,0.00022306989,0.00021909284,0.00016756433],"domain_scores_gemma":[0.9984742,0.00069476635,0.00021336104,0.00050838955,0.00008066407,0.00002864259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015279933,0.00010935425,0.00032487322,0.00024194442,0.000048885617,0.00001468543,0.0005660284,0.00004685957,0.0008896457],"category_scores_gemma":[0.000697903,0.00006796115,0.00010190079,0.0028621021,0.00005511197,0.00010558515,0.00013289085,0.00008761893,0.000009345044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090845587,0.000283151,0.49114704,0.0049470663,0.045318287,0.00003104469,0.0042803935,0.008555297,0.4359835,0.0002071496,0.006920369,0.0014182603],"study_design_scores_gemma":[0.0008280158,0.00012086359,0.05664596,0.0001909344,0.0052496498,0.0000027007918,0.0019327606,0.066983886,0.86708194,0.0002634222,0.00020248552,0.0004974036],"about_ca_topic_score_codex":0.00043076745,"about_ca_topic_score_gemma":0.0000109240555,"teacher_disagreement_score":0.43450108,"about_ca_system_score_codex":0.000017943075,"about_ca_system_score_gemma":0.00007698476,"threshold_uncertainty_score":0.97409964},"labels":[],"label_agreement":null},{"id":"W4378084446","doi":"10.1002/cjce.24938","title":"Application of artificial neural network for prediction of 10 crude oil properties","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"API gravity; Artificial neural network; Mean squared error; Light crude oil; Mean absolute percentage error; Distillation; Oil refinery; Environmental science; Fourier transform infrared spectroscopy; Petroleum industry; Petroleum; Mathematics; Crude oil; Biological system; Petroleum engineering; Statistics; Computer science; Chemistry; Engineering; Artificial intelligence; Waste management; Chromatography; Environmental engineering; Chemical engineering; Organic chemistry","score_opus":0.019809804407349925,"score_gpt":0.21595697662233312,"score_spread":0.1961471722149832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378084446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983713,0.00042044112,0.0007631156,0.00021294742,0.000067822664,0.000022678298,0.000034398105,0.000014648161,0.00009265193],"genre_scores_gemma":[0.99927914,0.0000033455706,0.0001688396,0.000005566055,0.0004293867,0.000006048644,0.000009324956,0.000012533245,0.000085815744],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993217,0.0000023219807,0.000326876,0.00005497317,0.00011604109,0.00017807221],"domain_scores_gemma":[0.99945265,0.00007214205,0.00015285035,0.00010047111,0.00011955299,0.000102306876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014882683,0.000069800066,0.00017885848,0.00009483823,0.000032703192,0.000009336238,0.00018404506,0.000065315144,0.000037419602],"category_scores_gemma":[0.00021964131,0.00005396033,0.000102732956,0.00037978415,0.00004910664,0.00003614817,0.0000070084266,0.00013410252,6.5864265e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026974201,0.000004513486,0.0002002994,0.0001401787,0.000066134904,6.0665565e-7,0.000073710944,0.0498627,0.94799244,0.000105843006,0.00049408246,0.001032508],"study_design_scores_gemma":[0.0001069827,0.000014755022,0.000059785427,0.00004181588,0.00008988326,0.0000083937175,0.000027664038,0.07214831,0.92692965,0.00009529286,0.00043064836,0.000046824418],"about_ca_topic_score_codex":0.00020375085,"about_ca_topic_score_gemma":0.000031616793,"teacher_disagreement_score":0.022285605,"about_ca_system_score_codex":0.00007034666,"about_ca_system_score_gemma":0.000105873056,"threshold_uncertainty_score":0.22004384},"labels":[],"label_agreement":null},{"id":"W4378418349","doi":"10.18280/ria.370223","title":"Computer Intelligence-Based Fruit Grading: A Review","year":2023,"lang":"en","type":"review","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Grading (engineering); Computer science; Artificial intelligence; Biology; Ecology","score_opus":0.1644413169745071,"score_gpt":0.38164438412090823,"score_spread":0.21720306714640114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378418349","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9753787e-7,0.9614126,0.032306217,0.00013060975,0.0003619494,0.0005273534,0.00008260883,0.0005389358,0.004639425],"genre_scores_gemma":[0.0000060671446,0.98516107,0.00095230015,0.00026401677,0.0006949464,0.0003225963,0.00037613252,0.00021210288,0.012010774],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946312,0.00010067024,0.0022957213,0.0014826637,0.0005072359,0.0009825672],"domain_scores_gemma":[0.99540854,0.0012792392,0.0009767782,0.001828447,0.00018232605,0.00032465672],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00065191847,0.0010663115,0.0034899344,0.00065975013,0.00025446227,0.00016228778,0.0018390212,0.00063586264,0.00912211],"category_scores_gemma":[0.00045503743,0.00094846706,0.0019462405,0.0041677924,0.00023390228,0.000093797695,0.00029135484,0.0012608175,0.010115342],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024970598,0.00016946773,0.0000012716713,0.16123682,0.0003494283,0.00009200017,0.0000333018,0.00023719025,0.000003176239,0.00053978636,0.007745402,0.82958966],"study_design_scores_gemma":[0.000012944213,0.00004406094,1.0650954e-8,0.08418328,0.0024469113,0.000057476303,0.000049962484,0.0058706417,0.0015403506,0.00026030734,0.90455866,0.0009753905],"about_ca_topic_score_codex":0.000032882203,"about_ca_topic_score_gemma":0.0000052155196,"teacher_disagreement_score":0.8968133,"about_ca_system_score_codex":0.00038419056,"about_ca_system_score_gemma":0.00035844906,"threshold_uncertainty_score":0.9992966},"labels":[],"label_agreement":null},{"id":"W4378781202","doi":"10.1177/00037028231176805","title":"Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra","year":2023,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Resolution (logic); Gaussian; Spectral line; Hyperspectral imaging; Spectral resolution; Data set; Algorithm; Set (abstract data type); Raman spectroscopy; Gaussian process; Computer science; Computational physics; Chemistry; Artificial intelligence; Optics; Physics","score_opus":0.04208574699617193,"score_gpt":0.3251948479935088,"score_spread":0.2831091009973369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378781202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9368987,0.0014634728,0.041688897,0.00030247902,0.000086182874,0.00041834192,0.000052305968,0.00038872001,0.018700901],"genre_scores_gemma":[0.99174124,0.00018748567,0.0071579358,0.000056925837,0.00016686165,0.00011853207,0.00022486039,0.000038137012,0.0003080429],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830174,0.000011259408,0.00043851303,0.0004996036,0.00029855594,0.00045034208],"domain_scores_gemma":[0.99886286,0.00035167055,0.00029072483,0.0003438801,0.00006666151,0.000084178326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025670973,0.00024952993,0.0004312394,0.0003262409,0.00021432311,0.00003919552,0.00016339785,0.00014284647,0.0005189181],"category_scores_gemma":[0.000105883126,0.0002543643,0.0001415297,0.00097338826,0.00011479367,0.00005692653,0.000037773076,0.00016821832,0.000019546984],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019256005,0.00008167779,0.00032844595,0.00020382684,0.0001318807,7.689741e-7,0.00010916717,0.00025191193,0.99484986,0.002662709,0.0008852523,0.0003019174],"study_design_scores_gemma":[0.001114548,0.00011790883,0.0008404082,0.000021512222,0.0003442399,6.7640576e-7,0.0006142315,0.009025675,0.98644674,0.0008991072,0.00033322195,0.00024175429],"about_ca_topic_score_codex":0.00003859419,"about_ca_topic_score_gemma":0.0000052637974,"teacher_disagreement_score":0.05484252,"about_ca_system_score_codex":0.00015222706,"about_ca_system_score_gemma":0.0000609066,"threshold_uncertainty_score":0.9999909},"labels":[],"label_agreement":null},{"id":"W4379384013","doi":"10.1088/1755-1315/1183/1/012003","title":"Optical characterization of NIR spectra for chemomectric model of cocoa pod husk fermented for animal feed","year":2023,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Husk; Fermentation; Food science; Near-infrared spectroscopy; Chemistry; Mathematics; Starch; Analytical Chemistry (journal); Physics; Chromatography; Botany; Biology; Optics","score_opus":0.02120774676385831,"score_gpt":0.24245204399002732,"score_spread":0.221244297226169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379384013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973359,0.000013580129,0.001411189,0.00009652985,0.0000145993645,0.00016653405,0.0003025732,0.000024336994,0.0006347432],"genre_scores_gemma":[0.99759156,0.00017048564,0.0011923303,0.000012635319,0.000017480903,0.00003098937,0.00013125151,0.000008418698,0.0008448423],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887663,0.0000023847829,0.00023717972,0.0003186308,0.00024418905,0.0003209976],"domain_scores_gemma":[0.9995684,0.00003571947,0.00012534735,0.00014445477,0.000024771613,0.0001013072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012502873,0.00013262188,0.00024021778,0.0001239512,0.00014620888,0.000029767863,0.00019405618,0.000053092357,0.0001355332],"category_scores_gemma":[0.000050758284,0.00012653769,0.000056694367,0.00042566902,0.000690341,0.00026224088,0.000072263654,0.000050107028,0.0000015536233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015074763,0.000056938592,0.0005344641,0.00008043141,0.000012067342,2.0629989e-7,0.00011212201,0.000016570297,0.9970268,0.0008976399,0.0000019917518,0.0011100136],"study_design_scores_gemma":[0.0004226213,0.00021567472,0.013285337,0.000010407746,0.000038521015,0.000002293328,0.0004706305,0.018946698,0.9662753,0.0001779608,0.000026278884,0.00012829491],"about_ca_topic_score_codex":0.0000046658374,"about_ca_topic_score_gemma":0.0000014753547,"teacher_disagreement_score":0.030751524,"about_ca_system_score_codex":0.000025836553,"about_ca_system_score_gemma":0.00006707143,"threshold_uncertainty_score":0.51600575},"labels":[],"label_agreement":null},{"id":"W4380081943","doi":"10.2139/ssrn.4474480","title":"Portable Hyperspectral Imaging Coupled with Multivariate Analysis for Real-Time Prediction of Plant-Based Meat Analogues Quality","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Multivariate statistics; Quality (philosophy); Multivariate analysis; Computer science; Artificial intelligence; Remote sensing; Environmental science; Data mining; Pattern recognition (psychology); Machine learning; Geography; Physics","score_opus":0.02691467345024028,"score_gpt":0.2976354621454462,"score_spread":0.2707207886952059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380081943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8178028,0.0025704459,0.1735591,0.00044878092,0.00017334562,0.00047819404,0.003532936,0.0005425875,0.00089183444],"genre_scores_gemma":[0.9894284,0.0018796332,0.0021304314,0.000011356227,0.00040183615,0.000053709275,0.0032027143,0.00010997424,0.0027819707],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950055,0.000081741564,0.001117994,0.0008163122,0.0007385947,0.002239885],"domain_scores_gemma":[0.9967171,0.00037435704,0.001598358,0.0006648772,0.00047973462,0.00016557549],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0020616204,0.00055846345,0.0014666705,0.0010550433,0.0002644536,0.000112341986,0.0006079388,0.00034838438,0.00025472938],"category_scores_gemma":[0.00022400956,0.00048647472,0.0010001009,0.0012525581,0.00011972671,0.00012167994,0.00008764766,0.0024393867,0.0000037959755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005982338,0.0016645377,0.3809578,0.0018458096,0.091959596,0.00006732652,0.0007285531,0.13662383,0.37297928,0.0063740164,0.0005137044,0.00030319183],"study_design_scores_gemma":[0.016136788,0.0012342621,0.030390717,0.00091382256,0.08760543,0.0002855901,0.016601633,0.58084583,0.19506532,0.06655672,0.00007511075,0.004288796],"about_ca_topic_score_codex":0.004218619,"about_ca_topic_score_gemma":0.0016090318,"teacher_disagreement_score":0.44422197,"about_ca_system_score_codex":0.00161721,"about_ca_system_score_gemma":0.0033559895,"threshold_uncertainty_score":0.999862},"labels":[],"label_agreement":null},{"id":"W4380200828","doi":"10.1016/j.heliyon.2023.e17115","title":"Detection of dextran, maltodextrin and soluble starch in the adulterated Lycium barbarum polysaccharides (LBPs) using Fourier-transform infrared spectroscopy (FTIR) and machine learning models","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Key Research and Development Program of Ningxia; Science and Technology Department of Ningxia","keywords":"Maltodextrin; Fourier transform infrared spectroscopy; Starch; Chemistry; Partial least squares regression; Artificial intelligence; Polysaccharide; Principal component analysis; Adulterant; Support vector machine; Mathematics; Chromatography; Food science; Computer science; Machine learning; Spray drying; Biochemistry; Chemical engineering","score_opus":0.039077442547220675,"score_gpt":0.2767035886306504,"score_spread":0.23762614608342975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380200828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99170583,0.0048169442,0.0018980051,0.00009477876,0.000032179618,0.00013068007,0.000015319954,0.000084116495,0.0012221298],"genre_scores_gemma":[0.9950391,0.003930138,0.0004886949,0.000022897859,0.0000548401,0.000017789047,0.000018495179,0.000031377025,0.00039668762],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983478,0.00007551161,0.00038364742,0.0003705813,0.00034589964,0.0004765643],"domain_scores_gemma":[0.99930906,0.00023267796,0.000099172954,0.0002163597,0.000073508316,0.000069229805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005835637,0.00021412161,0.00032046688,0.0005074543,0.0002729661,0.00008969282,0.00016027965,0.00016127659,0.00008142043],"category_scores_gemma":[0.00008380506,0.00018171707,0.000049825376,0.0015598917,0.00012077154,0.00025282113,0.000058795908,0.0005860974,0.000002244486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023306819,0.000048179765,0.005759584,0.0005994019,0.000045632733,0.000011889033,0.0015863101,0.000576175,0.9902852,0.000067633126,0.000004375193,0.0007825276],"study_design_scores_gemma":[0.00090136094,0.0001635975,0.0010279806,0.00016426877,0.00011180817,0.000025731038,0.00205574,0.09906943,0.8947587,0.0013940205,0.00009279299,0.00023455257],"about_ca_topic_score_codex":0.0005295955,"about_ca_topic_score_gemma":0.0002338605,"teacher_disagreement_score":0.098493256,"about_ca_system_score_codex":0.00007529818,"about_ca_system_score_gemma":0.000042225252,"threshold_uncertainty_score":0.74102074},"labels":[],"label_agreement":null},{"id":"W4380303725","doi":"10.1109/tsmc.2023.3281474","title":"A Soft Sensor for Estimating Tire Cornering Properties for Intelligent Tires","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; National Natural Science Foundation of China","keywords":"Accelerometer; Notation; Acceleration; Mathematics; Computer science; Artificial intelligence; Algorithm; Engineering; Physics","score_opus":0.05480054578835583,"score_gpt":0.2805920665660529,"score_spread":0.22579152077769707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380303725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24579686,0.0026399882,0.7435108,0.00011234536,0.0027393564,0.002059085,0.0004859053,0.0010887388,0.0015669247],"genre_scores_gemma":[0.9699211,0.00007455199,0.0002765148,0.00001061207,0.00033865374,0.0010933147,0.000014810883,0.00007770729,0.028192746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982498,0.000022348126,0.00057287066,0.00046198585,0.0002448309,0.0004481059],"domain_scores_gemma":[0.99891037,0.0003324734,0.00018201335,0.0003044201,0.00013587756,0.00013482754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024033105,0.0003054524,0.00050108,0.00023776937,0.0003935863,0.0002558264,0.00016141268,0.00018545953,0.000015194912],"category_scores_gemma":[0.000028489456,0.0002694333,0.00017464002,0.00030319538,0.000069731694,0.000060685452,0.0000028902157,0.00014753334,0.00002914785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013534697,0.0011682664,0.000579665,0.068763256,0.0052690776,0.000051347313,0.012331276,0.4891405,0.28437248,0.0018969619,0.014094696,0.120978996],"study_design_scores_gemma":[0.0007651409,0.00018765134,0.0000025630568,0.0008261372,0.00038507054,0.0000587349,0.0095662335,0.9277635,0.05365865,0.000009155864,0.006253165,0.00052399555],"about_ca_topic_score_codex":0.0002614436,"about_ca_topic_score_gemma":0.000015073729,"teacher_disagreement_score":0.7432343,"about_ca_system_score_codex":0.0001095966,"about_ca_system_score_gemma":0.000034320998,"threshold_uncertainty_score":0.9999758},"labels":[],"label_agreement":null},{"id":"W4380369378","doi":"10.1016/j.jfca.2023.105465","title":"Detection of common adulterants in olive oils by bench top 60 MHz 1H NMR with partial least squares regression","year":2023,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"","keywords":"Adulterant; Olive oil; Partial least squares regression; Olea; Chemometrics; Food science; Vegetable oil; Chemistry; Olive trees; Edible oil; Oleuropein; Chromatography; Mathematics; Botany; Biology; Statistics","score_opus":0.009976264638781457,"score_gpt":0.2701087706548473,"score_spread":0.26013250601606586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380369378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977921,0.0007414146,0.0011521921,0.0001248781,0.000012301504,0.000016564249,0.00002614944,0.000011796857,0.00012256623],"genre_scores_gemma":[0.9993575,0.00041084364,0.000089272224,0.00001936617,0.00004703555,0.0000017658888,0.00003452999,0.000006691451,0.00003299077],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886763,0.00004523783,0.0004741625,0.00014320806,0.0003247304,0.00014501547],"domain_scores_gemma":[0.99911386,0.00006874064,0.0004951377,0.00011034498,0.00012665348,0.00008526778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001377753,0.00012695414,0.00048280958,0.00061241724,0.0000809139,0.000036130223,0.00009786386,0.00007814449,0.00010730763],"category_scores_gemma":[0.000012471244,0.00009533409,0.00018692264,0.0017938775,0.00004565197,0.00013994427,0.000019452058,0.00020396548,8.841162e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058643374,0.00033291223,0.048701312,0.00010571004,0.0013795679,0.000032675303,0.00039840167,0.0011198262,0.9440914,0.000004317284,0.00008136902,0.003166083],"study_design_scores_gemma":[0.0011061978,0.00044773257,0.007216531,0.00018621338,0.0015007025,0.000031823678,0.0008932762,0.004804146,0.9836031,0.0000489229,0.000022200102,0.00013911269],"about_ca_topic_score_codex":0.00007722228,"about_ca_topic_score_gemma":0.00008443834,"teacher_disagreement_score":0.04148478,"about_ca_system_score_codex":0.000045242108,"about_ca_system_score_gemma":0.000014507817,"threshold_uncertainty_score":0.38876113},"labels":[],"label_agreement":null},{"id":"W4381112559","doi":"10.1016/j.rineng.2023.101234","title":"Fusion of heterogeneous industrial data using polygon generation &amp; deep learning","year":2023,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada","funders":"Natural Resources Canada; Office of Energy Research and Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Polygon (computer graphics); Raw data; Sensor fusion; Process (computing); Data mining; Fusion; Artificial intelligence; Deep learning; Rendering (computer graphics); Machine learning","score_opus":0.11499035372798397,"score_gpt":0.315596184932195,"score_spread":0.20060583120421102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381112559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99564624,0.0002337591,0.0035941044,0.000008821036,0.00010572242,0.000025831716,0.000027881097,0.00011169322,0.00024592012],"genre_scores_gemma":[0.99793214,0.000111296395,0.0009354365,0.0000011629587,0.00039108284,0.0000014224273,0.0004790334,0.000021379852,0.00012703669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901253,0.0000089433815,0.00033012233,0.00025770068,0.00017400288,0.00021668433],"domain_scores_gemma":[0.99941295,0.0000858769,0.00008567461,0.00036283903,0.000016830363,0.000035828045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022617771,0.00011026491,0.00017651897,0.0002906805,0.00003577599,0.000017941886,0.00021994485,0.00013904672,0.00003854521],"category_scores_gemma":[0.0007272753,0.000121335244,0.000030143507,0.0008946424,0.000008978376,0.00008480764,0.00016573665,0.00024476447,0.000005781236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015128585,0.0000068411573,0.0004904769,0.000026428594,0.000016587006,0.000004375996,0.00006607088,0.5046243,0.49351552,0.0000012266382,0.000018835757,0.0012142177],"study_design_scores_gemma":[0.0004881013,0.000006252573,0.00003672834,0.00004365253,0.000025971638,0.0000046592563,0.000047598984,0.67997175,0.31849328,9.1978006e-7,0.0007633737,0.000117705786],"about_ca_topic_score_codex":0.000090664296,"about_ca_topic_score_gemma":0.000016705184,"teacher_disagreement_score":0.17534745,"about_ca_system_score_codex":0.00007802925,"about_ca_system_score_gemma":0.000016570677,"threshold_uncertainty_score":0.49479076},"labels":[],"label_agreement":null},{"id":"W4381245652","doi":"10.48550/arxiv.2306.09418","title":"A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images","year":2023,"lang":"en","type":"review","venue":"arXiv (Cornell University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Hyperspectral imaging; Computer science; Convolutional neural network; Multispectral image; Artificial intelligence; Bottleneck; Precision agriculture; Pattern recognition (psychology); Spatial analysis; Field (mathematics); Object detection; Preprocessor; Remote sensing; Machine learning; Agriculture","score_opus":0.15129156743721567,"score_gpt":0.2887066900830957,"score_spread":0.13741512264588002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381245652","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018914304,0.9932402,0.0035860783,0.000008419696,0.000028365537,0.00045655255,0.00036589708,0.000102795515,0.00032026056],"genre_scores_gemma":[0.02908057,0.96970767,0.0006950185,0.000032862372,0.000070679875,0.0000056788963,0.00027756512,0.000052131716,0.00007783147],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9982251,0.00013054337,0.0005222925,0.0006732906,0.00014305126,0.00030575768],"domain_scores_gemma":[0.99738914,0.0005938921,0.0010136643,0.00048689492,0.00039702046,0.0001193847],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012634766,0.0004359923,0.001627298,0.0003678476,0.000111885696,0.000012073872,0.00034804273,0.00026816656,0.00012659299],"category_scores_gemma":[0.00009658378,0.000445291,0.0006527941,0.0019540596,0.0005064786,0.0000888988,0.00008315436,0.00034592257,0.0000029947867],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053760054,0.0012590451,0.006668801,0.9568334,0.0044326163,0.00035141263,0.00002606365,0.00708972,0.0051477263,0.002036268,0.0012658557,0.014351454],"study_design_scores_gemma":[0.008319859,0.0011094414,0.0025337625,0.51216966,0.12540066,0.00009476699,0.0007635225,0.26192144,0.022257086,0.0014308656,0.054500766,0.009498156],"about_ca_topic_score_codex":0.00011084658,"about_ca_topic_score_gemma":0.0000022561183,"teacher_disagreement_score":0.44466376,"about_ca_system_score_codex":0.0003381048,"about_ca_system_score_gemma":0.0005014393,"threshold_uncertainty_score":0.9997999},"labels":[],"label_agreement":null},{"id":"W4382395051","doi":"10.18280/ts.400331","title":"An Integrative Approach for Mineral Nutrient Quantification in Dioscorea Leaves: Uniting Image Processing and Machine Learning","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dioscorea; Image processing; Mineral; Artificial intelligence; Agricultural engineering; Computer science; Image (mathematics); Machine learning; Environmental science; Engineering; Biology; Ecology; Medicine","score_opus":0.03832157212453446,"score_gpt":0.3041399035728955,"score_spread":0.26581833144836103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382395051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89331615,0.00022229894,0.10477339,0.00006557815,0.000006807333,0.00017431083,0.000029382785,0.00012595736,0.001286143],"genre_scores_gemma":[0.9950277,0.000030638068,0.0037082448,0.000017128934,0.000090930946,0.0001185669,0.0006938756,0.000023729857,0.00028914562],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887943,0.000027908,0.00029437878,0.00036074544,0.00016309165,0.000274427],"domain_scores_gemma":[0.9995694,0.00009624701,0.00012998734,0.00008260641,0.00005912778,0.000062599516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003073552,0.00017547296,0.00021901914,0.0002540503,0.00017770522,0.00010756778,0.000115519455,0.000058964506,0.00006456154],"category_scores_gemma":[0.00006866683,0.00015459664,0.000044213848,0.0006476424,0.00006657682,0.00021638435,0.000020902038,0.0002004429,0.0000011464148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013838596,0.00031828225,0.018799381,0.00042594603,0.000045311353,0.000004200153,0.0032561284,0.0008154286,0.96831566,0.0005489429,0.000042554002,0.0072897538],"study_design_scores_gemma":[0.0013429885,0.00013864085,0.0022345057,0.00008265833,0.00009269241,0.0000038539256,0.017334389,0.5594187,0.41874567,0.00019826816,0.00010279893,0.0003048489],"about_ca_topic_score_codex":0.000054855333,"about_ca_topic_score_gemma":0.000012182185,"teacher_disagreement_score":0.5586033,"about_ca_system_score_codex":0.00006787711,"about_ca_system_score_gemma":0.000025523652,"threshold_uncertainty_score":0.6304268},"labels":[],"label_agreement":null},{"id":"W4382395330","doi":"10.18280/ts.400325","title":"Advanced Prediction of Crop Diseases Using Cetalatran-Optimized Deep KNN in Multispectral Imaging","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multispectral image; Artificial intelligence; Computer science; Remote sensing; Pattern recognition (psychology); Geology","score_opus":0.016974916856379488,"score_gpt":0.27202614098358996,"score_spread":0.2550512241272105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382395330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931143,0.00047251605,0.0053140814,0.000041478717,0.000050505805,0.00010641101,0.000112513146,0.00015429738,0.0006339299],"genre_scores_gemma":[0.998219,0.00006534663,0.001368534,0.0000156227,0.00010165413,0.000021044783,0.00010722208,0.000025302126,0.00007625078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985338,0.0000184586,0.0004578804,0.00031210377,0.0003078338,0.00036993233],"domain_scores_gemma":[0.9994986,0.00008563823,0.00013859804,0.00015409793,0.000040108836,0.00008294334],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009768785,0.00018797789,0.00031964242,0.00032169741,0.000073164614,0.000022800852,0.0001502486,0.00004907346,0.0021166136],"category_scores_gemma":[0.000033435972,0.0001935213,0.0001590994,0.0008453357,0.0000748399,0.00019386475,0.000032550324,0.00012995403,0.000006837513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020924566,0.00030541274,0.074323,0.00017633622,0.00013002496,0.000041567895,0.00030757498,0.036684163,0.8857997,0.000041334577,0.00004912328,0.0019324964],"study_design_scores_gemma":[0.0053776097,0.00004483934,0.032552373,0.00011805375,0.00042884555,0.00000756288,0.001274339,0.44339347,0.51619595,0.00020598213,0.000049442402,0.0003515493],"about_ca_topic_score_codex":0.00006098375,"about_ca_topic_score_gemma":0.000006289008,"teacher_disagreement_score":0.4067093,"about_ca_system_score_codex":0.00013717316,"about_ca_system_score_gemma":0.000035648023,"threshold_uncertainty_score":0.99879557},"labels":[],"label_agreement":null},{"id":"W4382402535","doi":"10.3390/horticulturae9070750","title":"Interpretation of Hyperspectral Images Using Integrated Gradients to Detect Bruising in Lemons","year":2023,"lang":"en","type":"article","venue":"Horticulturae","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Pixel; Convolutional neural network; Computer science; Pattern recognition (psychology); Environmental science; Remote sensing; Geology","score_opus":0.021952311368863643,"score_gpt":0.304497515510904,"score_spread":0.2825452041420403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382402535","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977371,0.00013180356,0.00040097113,0.00005434879,0.000039722567,0.000051507654,0.000013513695,0.000091913724,0.0014791195],"genre_scores_gemma":[0.99769634,0.000019903156,0.0019210355,0.00001704037,0.000025340742,0.0000064137494,0.000022397151,0.000013075264,0.00027846306],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990326,0.000013313213,0.0002961168,0.00023229426,0.00015473898,0.0002709023],"domain_scores_gemma":[0.9995905,0.000050944538,0.00007934331,0.00014110522,0.00007295597,0.000065140106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007353369,0.00013199501,0.00023489946,0.00030013017,0.000041863233,0.00003088538,0.00013402777,0.00006457834,0.00013027643],"category_scores_gemma":[0.0002825365,0.000110148794,0.00008415762,0.0019206777,0.000036724545,0.00010287428,0.000040708117,0.00013357379,0.00002340671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030091662,0.000023567063,0.0053185653,0.00002927709,0.0000414285,0.000011014496,0.0009805922,0.0005576373,0.99167407,0.0000104667415,0.00011091239,0.0012123811],"study_design_scores_gemma":[0.00019754603,0.00002301501,0.011880266,0.000084448984,0.00007540422,0.000006807488,0.004122772,0.0025546695,0.98079884,0.00008607877,0.000017277678,0.00015290082],"about_ca_topic_score_codex":0.00041333635,"about_ca_topic_score_gemma":0.000098729164,"teacher_disagreement_score":0.010875252,"about_ca_system_score_codex":0.00017320922,"about_ca_system_score_gemma":0.000021444348,"threshold_uncertainty_score":0.44917375},"labels":[],"label_agreement":null},{"id":"W4382541734","doi":"10.18280/mmep.100343","title":"Rapid Detection of Patchouli Oil Adulteration Using Support Vector Machine Classification and Discriminant Analysis with Near-Infrared Spectroscopy","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Direktorat Riset dan Pengabdian Masyarakat; Universitas Syiah Kuala","keywords":"Patchouli; Linear discriminant analysis; Support vector machine; Pattern recognition (psychology); Artificial intelligence; Infrared spectroscopy; Quadratic classifier; Principal component analysis; Mathematics; Chromatography; Computer science; Chemistry; Essential oil; Organic chemistry","score_opus":0.032520587604969314,"score_gpt":0.24416592748174135,"score_spread":0.21164533987677203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382541734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54426384,0.00014401849,0.45532098,0.000016250853,0.0000069738344,0.000026620555,0.0000072473317,0.00009770724,0.00011637818],"genre_scores_gemma":[0.98285407,0.0002119164,0.016694745,0.0000016815168,0.000020592915,0.000018352985,0.000035646783,0.000026153508,0.0001368416],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990221,0.0000060323723,0.00031714415,0.00026436622,0.00017836249,0.00021195384],"domain_scores_gemma":[0.99953073,0.00007247358,0.000098518845,0.00017829878,0.000041137548,0.00007885685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014788474,0.00017494158,0.00034736836,0.00021154585,0.00009262466,0.0000734672,0.000051379622,0.00008498612,0.00004135613],"category_scores_gemma":[0.000026139365,0.00014216857,0.00005793558,0.0007874939,0.000045753517,0.00009444655,0.000020333298,0.00013496545,0.0000015020632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048126432,0.000091627044,0.00069991575,0.0026579837,0.0006161093,0.0000030511203,0.0014465792,0.47686526,0.51630944,0.00042430006,0.0000017222338,0.0008358512],"study_design_scores_gemma":[0.00018875147,0.00004516939,0.00013920262,0.00008169013,0.00078675046,0.0000072454636,0.00009025804,0.94160414,0.056607738,0.00028199417,0.000010078138,0.00015695766],"about_ca_topic_score_codex":0.000035222398,"about_ca_topic_score_gemma":0.0000020668372,"teacher_disagreement_score":0.4647389,"about_ca_system_score_codex":0.000032686912,"about_ca_system_score_gemma":0.0000107288715,"threshold_uncertainty_score":0.57974666},"labels":[],"label_agreement":null},{"id":"W4383069805","doi":"10.1139/cjfas-2023-0045","title":"The future of fish age estimation: deep machine learning coupled with Fourier transform near-infrared spectroscopy of otoliths","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Marine Fisheries Service; National Oceanic and Atmospheric Administration","keywords":"Otolith; Mean squared error; Fourier transform; Pattern recognition (psychology); Artificial intelligence; Biological system; Data set; Mathematics; Convolutional neural network; Absorbance; Partial least squares regression; Statistics; Fish <Actinopterygii>; Computer science; Fishery; Biology; Optics; Physics","score_opus":0.010739336284139834,"score_gpt":0.23474064511236145,"score_spread":0.2240013088282216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383069805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903362,0.0018444522,0.00033978163,0.003368139,0.00007925851,0.000044249828,0.000009674657,0.0000070203732,0.003971247],"genre_scores_gemma":[0.9963112,0.00042730075,0.002331134,0.000051265306,0.00007391876,0.0000017120306,0.000003877582,0.000008000067,0.0007915951],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989655,0.000013868733,0.0003459116,0.000102456266,0.00031721915,0.0002550491],"domain_scores_gemma":[0.9991254,0.00021604101,0.00031401694,0.000094335854,0.000060678463,0.00018950892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039349822,0.00010682015,0.000275624,0.00012956493,0.0005246682,0.0001607014,0.00028137804,0.00004426128,0.00031661228],"category_scores_gemma":[0.00017544266,0.00006505851,0.00006508624,0.0009771341,0.000988229,0.00017551964,0.000006294899,0.00017219955,3.3015849e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097027054,0.00015990205,0.76637965,0.0017782144,0.0023441324,0.0009800772,0.04531821,0.010696789,0.0129298,0.0031673966,0.016664885,0.13861065],"study_design_scores_gemma":[0.0045218775,0.0040268665,0.033716712,0.00089586567,0.0013138095,0.00070233515,0.10586506,0.68445534,0.077173315,0.016974825,0.06893212,0.0014219033],"about_ca_topic_score_codex":0.0013749179,"about_ca_topic_score_gemma":0.013919487,"teacher_disagreement_score":0.732663,"about_ca_system_score_codex":0.000028883944,"about_ca_system_score_gemma":0.0005857395,"threshold_uncertainty_score":0.77674},"labels":[],"label_agreement":null},{"id":"W4383112966","doi":"10.2139/ssrn.4500633","title":"Early Detection of Citrus Anthracnose Caused by Colletotrichum Gloeosporioides Using Hyperspectral Imaging","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Colletotrichum gloeosporioides; Hyperspectral imaging; Colletotrichum; Biology; Horticulture; Botany; Geography; Remote sensing","score_opus":0.01671051592971536,"score_gpt":0.28116856867378454,"score_spread":0.2644580527440692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383112966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96994716,0.020365713,0.008260747,0.00015189698,0.00045088422,0.00011547866,0.00008174582,0.00018415137,0.0004422411],"genre_scores_gemma":[0.9870305,0.008930335,0.00014380938,0.000012629325,0.00070692494,0.000009378988,0.00003083425,0.00014347643,0.0029920992],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946382,0.000062132756,0.0009738666,0.00071461685,0.00075858633,0.0028526045],"domain_scores_gemma":[0.99758625,0.00013271101,0.0012731628,0.00053613225,0.0002959819,0.00017573815],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00095994794,0.00060153456,0.0009526886,0.00075478066,0.00038402787,0.00018263384,0.0007704418,0.00047185237,0.00020563423],"category_scores_gemma":[0.00024098864,0.0006314553,0.0006327571,0.0011170383,0.00016449965,0.0001773102,0.00026900438,0.005783289,0.000011458657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001977701,0.00025365283,0.011139162,0.00024217545,0.0024625852,0.000058863592,0.0002968821,0.00043256424,0.9819198,0.00039790926,0.000165863,0.002432732],"study_design_scores_gemma":[0.0021879298,0.00021336363,0.0008817899,0.00028844975,0.0028399539,0.00061161193,0.007949896,0.0024308835,0.91636467,0.06449724,0.00014463592,0.0015895887],"about_ca_topic_score_codex":0.0024084668,"about_ca_topic_score_gemma":0.00022431965,"teacher_disagreement_score":0.065555185,"about_ca_system_score_codex":0.0042539625,"about_ca_system_score_gemma":0.0028454764,"threshold_uncertainty_score":0.9996137},"labels":[],"label_agreement":null},{"id":"W4383425981","doi":"10.1016/b978-0-323-95295-8.00005-8","title":"Wheat quality testing, including near-infrared spectroscopy","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Island University","funders":"","keywords":"Quality (philosophy); Agricultural engineering; Near infrared reflectance spectroscopy; Moisture; Quality assessment; Wheat flour; Process engineering; Near-infrared spectroscopy; Environmental science; Biochemical engineering; Biotechnology; Food science; Business; Marketing; Materials science; Engineering; Chemistry; Psychology; Biology","score_opus":0.07763656279662538,"score_gpt":0.3291919795495752,"score_spread":0.2515554167529498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383425981","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005482242,0.0010112912,0.0000062096506,0.000048274513,0.0002662447,0.00017878183,0.00015718621,0.000937036,0.99684674],"genre_scores_gemma":[0.00074869685,0.0001283967,0.0022025702,0.00021360413,0.0010164368,0.000044732282,0.00010445949,0.00033084495,0.99521023],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959525,0.000022175169,0.0011151459,0.0011852077,0.00087446196,0.0008505499],"domain_scores_gemma":[0.99661654,0.0007207663,0.00073817256,0.001393817,0.00021308509,0.00031764287],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004181627,0.00096815557,0.0014438018,0.0003086473,0.0005086836,0.0002578974,0.00082453224,0.0009090614,0.004640847],"category_scores_gemma":[0.0005852662,0.0009957005,0.00060502294,0.00015247265,0.00035111132,0.0000668915,0.00052066724,0.0014935393,0.0013108036],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024616343,0.00015256926,0.0019891458,0.0045120935,0.004330587,0.00077048107,0.0011853763,0.00000826498,0.097522244,0.015414855,0.014567103,0.8593011],"study_design_scores_gemma":[0.00076392427,0.00008065075,0.00006025686,0.0009161879,0.0012189362,0.000034455155,0.00007308231,0.00003236697,0.05307714,0.062306713,0.87932354,0.0021127225],"about_ca_topic_score_codex":0.000012002817,"about_ca_topic_score_gemma":0.000027919683,"teacher_disagreement_score":0.86475646,"about_ca_system_score_codex":0.00051691296,"about_ca_system_score_gemma":0.00034617618,"threshold_uncertainty_score":0.9994668},"labels":[],"label_agreement":null},{"id":"W4383499307","doi":"10.1101/2023.07.05.547761","title":"<i>RamanSPy</i> : An open-source Python package for integrative Raman spectroscopy data analysis","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Engineering and Physical Sciences Research Council; Royal Academy of Engineering; Imperial College London; UK Research and Innovation","keywords":"Python (programming language); Workflow; Raman spectroscopy; Computer science; Open source; Modular design; Data science; Software; Software engineering; Programming language; Physics; Database","score_opus":0.050424384496079416,"score_gpt":0.3134699798776756,"score_spread":0.2630455953815962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383499307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65296775,0.002700193,0.30912316,0.0010844474,0.0014972078,0.0029139007,0.024152104,0.0051718,0.00038943294],"genre_scores_gemma":[0.95920706,0.0006040691,0.036171805,0.0003116841,0.0013724503,0.00080316124,0.00022579043,0.0006943936,0.0006095971],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9928267,0.0001410919,0.0011780027,0.003847455,0.00072386576,0.0012828667],"domain_scores_gemma":[0.988919,0.00041995556,0.0011491344,0.008235671,0.00064545666,0.00063075847],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0013471356,0.0013571164,0.0022351234,0.0010837475,0.00050525455,0.0016927095,0.0072554643,0.0011142901,0.00065457635],"category_scores_gemma":[0.00098174,0.0013741525,0.0005354514,0.0039531346,0.00026447224,0.0007093431,0.004450044,0.001660851,0.00010264528],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002020832,0.00068628567,0.010912781,0.0007521864,0.006782165,0.00005665188,0.000044246743,0.00012410754,0.9739559,0.0005707579,0.005910534,0.0000022745687],"study_design_scores_gemma":[0.0010138314,0.00012309554,0.0033382953,0.00023560307,0.008876502,1.3100466e-8,0.00010515237,0.0038994136,0.97184074,0.000032331904,0.008551791,0.001983243],"about_ca_topic_score_codex":0.0007323909,"about_ca_topic_score_gemma":0.00016046953,"teacher_disagreement_score":0.3062393,"about_ca_system_score_codex":0.00056224986,"about_ca_system_score_gemma":0.0007654089,"threshold_uncertainty_score":0.999918},"labels":[],"label_agreement":null},{"id":"W4384134832","doi":"10.1007/978-3-030-85040-1_120","title":"Fractal Geometry and the Downscaling of Sun-Induced Chlorophyll Fluorescence Imagery","year":2023,"lang":"en","type":"book-chapter","venue":"Encyclopedia of earth sciences series/Encyclopedia of earth sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Downscaling; Fractal; Chlorophyll fluorescence; Remote sensing; Geometry; Environmental science; Fluorescence; Geology; Physics; Mathematics; Optics; Climate change; Mathematical analysis; Oceanography","score_opus":0.019658236247847728,"score_gpt":0.2551461581522457,"score_spread":0.23548792190439793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384134832","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23764542,0.0032588053,0.000011234942,0.00026598966,0.00082326157,0.00038342975,0.00023323616,0.00009909648,0.7572795],"genre_scores_gemma":[0.50486016,0.08449005,0.007988351,0.000121784586,0.0015886108,0.000059728478,0.00006573924,0.00019777857,0.4006278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99206066,0.00007274646,0.0021817642,0.0016420282,0.002938704,0.0011040937],"domain_scores_gemma":[0.99341816,0.0022080916,0.0025687995,0.0009866761,0.000495395,0.0003228693],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026046708,0.00095955614,0.0021161675,0.0013478234,0.00083373365,0.00017721055,0.0026093335,0.00058020937,0.0021395916],"category_scores_gemma":[0.0015465246,0.0006902903,0.0006528297,0.0026722967,0.014333207,0.0011242249,0.00083645136,0.00095429417,0.000040578172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00424302,0.001917578,0.20620307,0.015526068,0.0042946627,0.0005239683,0.019638142,0.0028962914,0.13703638,0.5391519,0.0032113087,0.06535757],"study_design_scores_gemma":[0.018947884,0.014181081,0.067012966,0.013483175,0.010328416,0.0008802688,0.03554559,0.0052947095,0.42004958,0.18937321,0.20180851,0.023094604],"about_ca_topic_score_codex":0.00060853414,"about_ca_topic_score_gemma":0.00029311326,"teacher_disagreement_score":0.35665175,"about_ca_system_score_codex":0.0000094811385,"about_ca_system_score_gemma":0.0012207307,"threshold_uncertainty_score":0.9995548},"labels":[],"label_agreement":null},{"id":"W4384161830","doi":"10.1016/j.chemolab.2023.104915","title":"Spectral sensor fusion for prediction of Li and Zr in rocks: Neural network and PLS methods","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geoscience BC; University of British Columbia","funders":"","keywords":"Fusion; Partial least squares regression; Pattern recognition (psychology); Mean squared error; Sensor fusion; Convolutional neural network; Artificial neural network; Artificial intelligence; Computer science; Near-infrared spectroscopy; Machine learning; Mathematics; Statistics; Physics; Optics","score_opus":0.0406857204496112,"score_gpt":0.3196314234110144,"score_spread":0.2789457029614032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384161830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708269,0.023230406,0.0047028484,0.000034007586,0.00031504888,0.00026164448,0.00022948153,0.00008276736,0.00031689796],"genre_scores_gemma":[0.9926906,0.005682858,0.00061993365,0.000022011796,0.00033400144,0.000044627024,0.000053582455,0.000034333832,0.0005180441],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984031,0.000044044795,0.0005724716,0.00042889273,0.00019608246,0.00035545893],"domain_scores_gemma":[0.998642,0.0006224242,0.00022130097,0.00019887066,0.0001697691,0.00014558065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008500456,0.00022403762,0.00053815806,0.0008097245,0.00009977059,0.00007865554,0.000096021955,0.0002282036,0.000021780548],"category_scores_gemma":[0.00053611427,0.00020864236,0.000052462656,0.0041043875,0.00008612189,0.000106303254,0.000085745036,0.0001821427,8.993133e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003402268,0.00031666062,0.66166586,0.0074978475,0.00060770934,0.000025953539,0.0015689292,0.0032156906,0.29844326,0.0015080925,0.005585085,0.019224707],"study_design_scores_gemma":[0.00238887,0.0007750466,0.019853102,0.00046836594,0.00053486356,0.000043483746,0.010146137,0.22219293,0.7148341,0.0006238956,0.027025614,0.0011136216],"about_ca_topic_score_codex":0.000047187958,"about_ca_topic_score_gemma":0.0000026568957,"teacher_disagreement_score":0.64181274,"about_ca_system_score_codex":0.00006524279,"about_ca_system_score_gemma":0.000028440787,"threshold_uncertainty_score":0.8508188},"labels":[],"label_agreement":null},{"id":"W4384564283","doi":"10.1016/j.chemolab.2023.104916","title":"Factor analysis for signal modeling and noise characterization in spectro-kinetic data","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Heteroscedasticity; Noise (video); Mathematics; Smoothing; Statistics; Algorithm; Computer science; Artificial intelligence","score_opus":0.07453700199509453,"score_gpt":0.30497535897681277,"score_spread":0.23043835698171825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384564283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95519567,0.003869502,0.03856613,0.000033236844,0.00009041815,0.00018883891,0.0018484239,0.000096475516,0.00011133663],"genre_scores_gemma":[0.99625593,0.0018717027,0.000071530325,0.000021683256,0.0001662203,0.000036011676,0.0012397458,0.000031830423,0.00030531688],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981074,0.000018126842,0.0005579689,0.0006731088,0.0002749074,0.00036853403],"domain_scores_gemma":[0.99875486,0.00022503194,0.00017710195,0.0005003054,0.00019022389,0.00015245283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042654737,0.00024656756,0.0005621647,0.0022327255,0.00009543619,0.00021977759,0.00031121683,0.00017788095,0.000103619175],"category_scores_gemma":[0.00033305437,0.00024010772,0.000058705435,0.00994974,0.000038085123,0.00022074617,0.00015797788,0.00015889935,0.000006733797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096337106,0.0002846752,0.35565427,0.0024208077,0.001993247,0.000023078466,0.00071034214,0.004311706,0.6300498,0.0007141694,0.00020606066,0.0035354851],"study_design_scores_gemma":[0.00039887594,0.00005426486,0.0037455857,0.000040912026,0.0005520787,0.0000016875341,0.0009955921,0.933056,0.058533233,0.000025815783,0.0021345506,0.00046143014],"about_ca_topic_score_codex":0.00005270584,"about_ca_topic_score_gemma":0.0000063888647,"teacher_disagreement_score":0.92874426,"about_ca_system_score_codex":0.00009633773,"about_ca_system_score_gemma":0.000045693752,"threshold_uncertainty_score":0.97913086},"labels":[],"label_agreement":null},{"id":"W4384929752","doi":"10.3390/app13148391","title":"Exploration of Machine Learning Algorithms for pH and Moisture Estimation in Apples Using VIS-NIR Imaging","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mean squared error; Hyperspectral imaging; Artificial intelligence; Feature selection; Random forest; Partial least squares regression; Machine learning; Artificial neural network; Mathematics; Computer science; Statistics","score_opus":0.050240361094921776,"score_gpt":0.3343668322107889,"score_spread":0.2841264711158671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384929752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9411787,0.00037482491,0.05656531,0.00013788512,0.000020021953,0.00010473243,0.000007932819,0.00007531093,0.001535294],"genre_scores_gemma":[0.98946846,0.000029329602,0.010383049,0.000009267195,0.000021047525,0.000020965113,0.000024451914,0.0000051015145,0.00003833226],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993546,0.0000037692637,0.00014879713,0.00020192828,0.00014891053,0.00014202241],"domain_scores_gemma":[0.99969757,0.0001248275,0.000095350006,0.000048058813,0.000015940632,0.000018280842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002926793,0.00007128098,0.00012366424,0.0002011372,0.00016770791,0.000040314637,0.000087244334,0.000028225246,0.000013163369],"category_scores_gemma":[0.000065119566,0.00006418115,0.00001725408,0.0009587564,0.000119576485,0.00017771329,0.00003012012,0.00006287438,8.2727524e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013108058,0.000028752265,0.017567419,0.00015320511,0.000013070472,7.464994e-7,0.0013304725,0.052423943,0.90966886,0.0019151738,0.000024035431,0.016861195],"study_design_scores_gemma":[0.00020453811,0.0000073364886,0.00032750668,0.0000142423,0.000019296785,8.4613265e-7,0.0035795732,0.69432336,0.29876453,0.0026322033,0.000037983682,0.00008855686],"about_ca_topic_score_codex":0.00010093431,"about_ca_topic_score_gemma":0.00001591758,"teacher_disagreement_score":0.64189947,"about_ca_system_score_codex":0.000019131516,"about_ca_system_score_gemma":0.000020005566,"threshold_uncertainty_score":0.26172313},"labels":[],"label_agreement":null},{"id":"W4385071202","doi":"10.1177/14759217231186357","title":"A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines","year":2023,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Entropy (arrow of time); Computer science; Feature extraction; Algorithm; Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Data mining","score_opus":0.03211733575480371,"score_gpt":0.37780800320499947,"score_spread":0.3456906674501958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385071202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99669933,0.00038240483,0.0015513272,0.00067075895,0.00018295822,0.00022223522,0.0001705199,0.00009780226,0.000022679602],"genre_scores_gemma":[0.99400675,0.00011033976,0.005257033,0.000024587735,0.00035880477,0.0000755373,0.00007131492,0.000014154823,0.00008147228],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990561,0.000013576915,0.00017798935,0.00029574407,0.00021049246,0.0002460927],"domain_scores_gemma":[0.999178,0.0003242428,0.00018980725,0.00014405695,0.00006457011,0.00009932401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012591961,0.00014485876,0.0002512198,0.00013883476,0.00039309822,0.000010463034,0.000106279214,0.00010562844,0.000014830644],"category_scores_gemma":[0.000062139545,0.00013342629,0.000073695825,0.00040606645,0.000013360828,0.00008288918,0.00005581059,0.00018104677,6.870992e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041001648,0.000066986184,0.5772605,0.0027269374,0.000106784,0.0000010927541,0.00060735585,0.0022404895,0.34736478,0.0000948037,0.00018822766,0.06893203],"study_design_scores_gemma":[0.00096717285,0.00010144299,0.20735388,0.00009833176,0.00011029614,0.0000035322169,0.0005193496,0.06252703,0.727769,0.000212411,0.00011269384,0.00022487683],"about_ca_topic_score_codex":0.00044252272,"about_ca_topic_score_gemma":0.000010050672,"teacher_disagreement_score":0.3804042,"about_ca_system_score_codex":0.00014489544,"about_ca_system_score_gemma":0.000025678113,"threshold_uncertainty_score":0.54409665},"labels":[],"label_agreement":null},{"id":"W4385078005","doi":"10.18280/isi.280315","title":"Hybrid Enhanced Featured AlexNet for Milled Rice Grain Identification","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Materials science; Botany; Biology","score_opus":0.014583477192984952,"score_gpt":0.2650419016043131,"score_spread":0.25045842441132815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9043328,0.00023290355,0.07203263,0.00014255811,0.00039220412,0.00043156734,0.0003129717,0.0010104317,0.021111928],"genre_scores_gemma":[0.9930818,0.000077378485,0.00065339677,0.00008922162,0.00012344947,0.00029267583,0.002225164,0.000019119469,0.0034377652],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986297,0.000011851323,0.0005748596,0.00017912824,0.00024968083,0.00035477924],"domain_scores_gemma":[0.9988593,0.00013035219,0.00037277985,0.00030489507,0.00026165554,0.000071043134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030889676,0.00018808409,0.00024299033,0.00039208116,0.0003121287,0.00020576827,0.00024437474,0.00012408113,0.00019553851],"category_scores_gemma":[0.0007413972,0.00019146022,0.00013878124,0.0009214301,0.000066831846,0.001126027,0.00003808215,0.00011031574,0.0003633738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000384931,0.00010052147,0.00038990847,0.0040340726,0.0005795959,0.0000035997218,0.008091695,0.0006126911,0.8532574,0.0027155979,0.077228196,0.052601773],"study_design_scores_gemma":[0.000853645,0.000029191384,0.0012584173,0.00005249027,0.00012243137,0.000009789302,0.0018621106,0.0051096305,0.9782515,0.0049174973,0.0071884175,0.0003448483],"about_ca_topic_score_codex":0.000022668932,"about_ca_topic_score_gemma":0.0000023361476,"teacher_disagreement_score":0.124994114,"about_ca_system_score_codex":0.0002356957,"about_ca_system_score_gemma":0.00004756414,"threshold_uncertainty_score":0.7807521},"labels":[],"label_agreement":null},{"id":"W4385232660","doi":"10.1016/j.fbp.2023.07.006","title":"Rapid and non-destructive detection of hard to cook chickpeas using NIR hyperspectral imaging and machine learning","year":2023,"lang":"en","type":"article","venue":"Food and Bioproducts Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College; University of Guelph","funders":"","keywords":"Hyperspectral imaging; Convolutional neural network; Pattern recognition (psychology); Artificial intelligence; Partial least squares regression; Mean squared error; Mathematics; Artificial neural network; Root mean square; Biological system; Computer science; Biology; Engineering; Statistics","score_opus":0.021589696015538798,"score_gpt":0.2535402163090572,"score_spread":0.23195052029351843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385232660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99254614,0.00682325,0.00009841896,0.00015292864,0.000027116681,0.00005344011,0.000006986547,0.00008295323,0.00020876288],"genre_scores_gemma":[0.9983695,0.00022548153,0.0010710865,0.00001518217,0.00016801879,0.0000023839823,0.000005521784,0.00002261299,0.00012019207],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897003,0.000008254706,0.00017680107,0.0004613951,0.00013014732,0.00025334326],"domain_scores_gemma":[0.9996076,0.000019049916,0.000114190625,0.000093871146,0.00007624072,0.00008903807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013736365,0.00017455965,0.00024891042,0.00025787158,0.00030750743,0.00009339024,0.000051734183,0.000053357533,0.000009632207],"category_scores_gemma":[0.00013109799,0.00016128377,0.000022036545,0.00083009445,0.00012888381,0.00019071324,0.000080774706,0.00018824071,5.710496e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043642947,0.000011750586,0.010066654,0.00036668725,0.000036294194,0.000002350583,0.00079793204,0.0000059398644,0.9624811,0.0000033147683,0.0000014182301,0.026182916],"study_design_scores_gemma":[0.00032815282,0.00009513378,0.0047900453,0.00010255731,0.00011744027,0.00008609789,0.0023568603,0.002347982,0.9893603,0.00014275021,0.0000580618,0.00021467532],"about_ca_topic_score_codex":0.000053701006,"about_ca_topic_score_gemma":0.0000034250631,"teacher_disagreement_score":0.026879143,"about_ca_system_score_codex":0.000027098216,"about_ca_system_score_gemma":0.000030441775,"threshold_uncertainty_score":0.65769607},"labels":[],"label_agreement":null},{"id":"W4385597366","doi":"10.1002/cem.3511","title":"Methodology adjusting for least squares regression slope in the application of multiplicative scatter correction to near‐infrared spectra of forage feed samples","year":2023,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Innovate UK; Bangor University; Biotechnology and Biological Sciences Research Council; Aberystwyth University","keywords":"Statistics; Partial least squares regression; Mathematics; Least-squares function approximation; Ordinary least squares; Regression; Multiplicative function; Spectral line; Regression analysis; Linear regression; Goodness of fit; Physics; Mathematical analysis","score_opus":0.09433355330875362,"score_gpt":0.37795164120027597,"score_spread":0.2836180878915223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385597366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85441,0.0005619207,0.14287812,0.0007489766,0.0001743535,0.00029151194,0.0000381435,0.000023245706,0.00087371754],"genre_scores_gemma":[0.9698885,0.000075625685,0.029508838,0.000060543352,0.00017017909,0.000027367967,0.000019632651,0.000022043218,0.00022723562],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813026,0.00006890977,0.0008999174,0.00020694803,0.00044399648,0.00024995688],"domain_scores_gemma":[0.99412936,0.0036452704,0.001318506,0.0002967149,0.0005472852,0.0000628919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016492591,0.00016212487,0.00059470406,0.0014971498,0.00007522025,0.000021854241,0.00049474044,0.00014060676,0.00004051631],"category_scores_gemma":[0.007510216,0.00011723821,0.00024420687,0.006991521,0.000076613775,0.000109823864,0.000059404105,0.00032539744,0.0000019983795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067223335,0.0003193549,0.038199414,0.0005079307,0.0001641557,0.0000033982385,0.002827446,0.0012690411,0.90550315,0.00006325665,0.0055881725,0.04488242],"study_design_scores_gemma":[0.00097097404,0.00021567846,0.02602566,0.00011448178,0.00018076035,0.000027678027,0.005842297,0.0019793261,0.96292555,0.00063587463,0.0009383131,0.00014340277],"about_ca_topic_score_codex":0.00005002608,"about_ca_topic_score_gemma":0.000008901061,"teacher_disagreement_score":0.11547852,"about_ca_system_score_codex":0.00012579952,"about_ca_system_score_gemma":0.00007563475,"threshold_uncertainty_score":0.89909726},"labels":[],"label_agreement":null},{"id":"W4385728839","doi":"10.20944/preprints202308.0823.v1","title":"An Adaptive Partial Least Square Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Thresholding; Cluster analysis; Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Regression; Data set; Range (aeronautics); k-means clustering; Computer science; Image (mathematics); Engineering","score_opus":0.17880890331318972,"score_gpt":0.38692528485841154,"score_spread":0.20811638154522183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385728839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97106665,0.0008057141,0.01552356,0.00036624414,0.00032704626,0.0007266905,0.00062788103,0.0012540168,0.00930217],"genre_scores_gemma":[0.99252754,0.00007257956,0.002240552,0.000043047887,0.0006040331,0.00068206893,0.0014207552,0.00017336059,0.0022360822],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947868,0.00011315832,0.00081989425,0.0026419177,0.0006455862,0.0009926205],"domain_scores_gemma":[0.9964944,0.00015973557,0.0006126965,0.002116744,0.00023676344,0.00037967038],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068714074,0.0008357459,0.0010294596,0.00024540108,0.0004347012,0.00011736723,0.0013672798,0.00072311534,0.0010179064],"category_scores_gemma":[0.00045395206,0.0008268,0.00066897756,0.0004003449,0.0002469059,0.00023893408,0.0012928838,0.0018706587,0.0001196981],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011323442,0.0010782339,0.6990352,0.001314641,0.00071282627,0.000014814158,0.0012540987,0.0009901492,0.29312274,0.00008937728,0.0005829087,0.0006726722],"study_design_scores_gemma":[0.0011444081,0.00003798407,0.036762357,0.00029861866,0.0008210966,0.000008080916,0.0052831084,0.038306307,0.91404635,0.0014551547,0.00037004444,0.0014664733],"about_ca_topic_score_codex":0.0004444986,"about_ca_topic_score_gemma":0.000007674556,"teacher_disagreement_score":0.6622729,"about_ca_system_score_codex":0.00065286533,"about_ca_system_score_gemma":0.00023581005,"threshold_uncertainty_score":0.9998953},"labels":[],"label_agreement":null},{"id":"W4385794319","doi":"10.26434/chemrxiv-2023-kt0sx","title":"Discriminating Extra Virgin Olive Oils from Common Edible Oils: Comparable Performance of PLS-DA Models Trained on Low-Field and High-Field 1H NMR Data","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"Olive oil; Partial least squares regression; Adulterant; Olea; Edible oil; Chemometrics; Oil field; Proton NMR; NMR spectra database; Chemistry; Pulp and paper industry; Food science; Mathematics; Chromatography; Spectral line; Horticulture; Statistics; Physics; Engineering; Biology; Organic chemistry; Petroleum engineering","score_opus":0.11277943548050332,"score_gpt":0.323700714031006,"score_spread":0.21092127855050266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385794319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878855,0.0011979959,0.001464916,0.0003221163,0.00020033833,0.00013946519,0.00030053567,0.00019067519,0.008298483],"genre_scores_gemma":[0.99430126,0.0015504179,0.001447222,0.00014955047,0.00042258555,0.000052044175,0.0012196399,0.00008338888,0.0007738805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99678785,0.00002845013,0.00082653837,0.001259446,0.0005279504,0.0005697714],"domain_scores_gemma":[0.99571335,0.0012508376,0.0005879052,0.0021594083,0.00009097092,0.00019749968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030158096,0.0006191476,0.0012503496,0.00024494535,0.00017116829,0.00014938942,0.0015831676,0.0006778958,0.00070623273],"category_scores_gemma":[0.00031043417,0.00061799097,0.00016759677,0.00037481278,0.00013779935,0.00026771464,0.001532213,0.0014792038,0.000020116557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002139431,0.0027836557,0.017278763,0.027319215,0.0043557365,0.00020125651,0.006382106,0.011842117,0.87972623,0.0023570077,0.030069003,0.015545497],"study_design_scores_gemma":[0.0007542768,0.0000863057,0.00013351446,0.0015253662,0.0005170951,0.0000018752818,0.0007372241,0.07083333,0.92134553,0.0034133978,0.000018036797,0.0006340391],"about_ca_topic_score_codex":0.0034731207,"about_ca_topic_score_gemma":0.000094558905,"teacher_disagreement_score":0.058991216,"about_ca_system_score_codex":0.000094760966,"about_ca_system_score_gemma":0.00012191526,"threshold_uncertainty_score":0.9996271},"labels":[],"label_agreement":null},{"id":"W4386307345","doi":"10.18280/ts.400413","title":"Implementation of YOLOv5 for Real-Time Maturity Detection and Identification of Pineapples","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maturity (psychological); Identification (biology); Computer science; Biology; Psychology; Botany","score_opus":0.01705943825642313,"score_gpt":0.30453781034589583,"score_spread":0.2874783720894727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386307345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99676603,0.000028959363,0.0027254885,0.00003990476,0.000011335384,0.00011731703,0.00012997081,0.0000378371,0.00014316538],"genre_scores_gemma":[0.99937844,0.00007132916,0.00016585282,0.0000026426187,0.000042600805,0.000045894954,0.00013272249,0.00000795833,0.00015256512],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925584,0.0000073117744,0.00034775646,0.00014342804,0.00014046824,0.00010517794],"domain_scores_gemma":[0.999529,0.00007480278,0.00022607262,0.00008117083,0.00006711233,0.000021812702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020405678,0.00007208639,0.00014540595,0.00013009402,0.000045631605,0.000009703182,0.000057424924,0.000041737214,0.0006075955],"category_scores_gemma":[0.000013112033,0.000073589246,0.00005466155,0.00028085615,0.000034730805,0.00006173174,0.000014338321,0.000025148765,0.000002902761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042165888,0.00003394204,0.0020140312,0.00024679367,0.00006760205,9.239052e-8,0.00015761988,0.000010562973,0.99388456,0.00012367783,0.00017118914,0.0032477777],"study_design_scores_gemma":[0.0005030387,0.000052510222,0.01685644,0.0000058826813,0.00013224073,5.4940034e-7,0.0010290148,0.0013927749,0.9795923,0.00032246404,0.000051066734,0.00006168754],"about_ca_topic_score_codex":0.00010320007,"about_ca_topic_score_gemma":0.000014245306,"teacher_disagreement_score":0.014842408,"about_ca_system_score_codex":0.000023141103,"about_ca_system_score_gemma":0.000012183697,"threshold_uncertainty_score":0.66527444},"labels":[],"label_agreement":null},{"id":"W4386451280","doi":"10.53555/sfs.v10i1.1427","title":"EVALUATION OF CARDIOPROTECTIVE EFFECT OF CYPERUS TRICEPS( CYPERACEAE) AGAINST ISOPROTERENOL INDUCED MYOCARDIAL INFARCTION IN ALBINO RATS","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Isoprenaline; Cyperus; Myocardial infarction; Medicine; Pharmacology; Histopathology; Traditional medicine; Internal medicine; Stimulation; Botany; Pathology; Biology","score_opus":0.1719261863163902,"score_gpt":0.34018557976838615,"score_spread":0.16825939345199595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386451280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974188,0.00033260803,0.000004313702,0.000046463116,0.00020966244,0.00015231161,0.000011269531,0.0000073629903,0.0018171902],"genre_scores_gemma":[0.9997195,0.0001429907,0.000024785266,0.000004700402,0.00006720177,0.000015702035,0.0000036775189,0.0000075474964,0.000013923547],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996536,0.0007497785,0.00081108755,0.0002126375,0.0014504256,0.0002400354],"domain_scores_gemma":[0.9977328,0.00073812454,0.0007409285,0.00014593749,0.000597731,0.000044448145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013050256,0.0001570215,0.00067344116,0.00096449506,0.0000576945,0.000036000074,0.00033181312,0.00014595126,0.0000950853],"category_scores_gemma":[0.004449261,0.00012516418,0.00019464924,0.0042580827,0.00024740733,0.0004435066,0.000051098294,0.0003391622,0.0000014104127],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028806616,0.00005652136,0.7560804,0.00006494203,0.00014603396,0.000005115363,0.0008096523,0.0035947694,0.23087297,5.9376885e-7,0.000037378646,0.00804358],"study_design_scores_gemma":[0.0012476912,0.00054837175,0.59840465,0.00010327443,0.00012166017,0.000006966873,0.0011829354,0.0005179468,0.39764202,0.000085913285,0.000005689463,0.00013287982],"about_ca_topic_score_codex":0.0018555217,"about_ca_topic_score_gemma":0.0003715156,"teacher_disagreement_score":0.16676903,"about_ca_system_score_codex":0.0002667127,"about_ca_system_score_gemma":0.00041548864,"threshold_uncertainty_score":0.53265023},"labels":[],"label_agreement":null},{"id":"W4386636172","doi":"10.1021/acs.analchem.3c02348","title":"Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic–Mineral Mixtures","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mira Geoscience (Canada); University of British Columbia","funders":"Terramera; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Partial least squares regression; Raman spectroscopy; Chemistry; Principal component analysis; Artificial intelligence; Soil water; Biological system; Pattern recognition (psychology); Machine learning; Computer science; Soil science; Environmental science","score_opus":0.03070900664626969,"score_gpt":0.3117169863319497,"score_spread":0.28100797968568003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386636172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98314935,0.00015092742,0.00020545462,0.0008231785,0.000012123383,0.000044938828,0.00004868557,0.00012917917,0.015436173],"genre_scores_gemma":[0.99451405,0.000055224256,0.000060251146,0.00013311704,0.00008362784,0.0000054012216,0.00036229723,0.00002800505,0.0047580153],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785817,0.000024590574,0.0005987892,0.00051889947,0.00047051118,0.0005290377],"domain_scores_gemma":[0.99862885,0.00038976167,0.00014829317,0.0005544493,0.00008641177,0.00019220798],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002663491,0.00028854088,0.00075216126,0.00033190404,0.00011508367,0.000053910826,0.00060559233,0.00018797762,0.018153239],"category_scores_gemma":[0.0009508643,0.00023030455,0.0003254396,0.0077523664,0.00020922512,0.00003695708,0.0002077684,0.00060795085,0.00009571761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000392571,0.00012356622,0.15073147,0.00009666392,0.0013405452,0.000044407956,0.000114685055,0.00070696115,0.8457545,0.00015372594,0.0008603448,0.000033860895],"study_design_scores_gemma":[0.00082870276,0.000026531998,0.031375982,0.00003299111,0.003773716,0.000012659502,0.0011343119,0.05670231,0.90339065,0.0001463664,0.0019415625,0.00063422514],"about_ca_topic_score_codex":0.00019673836,"about_ca_topic_score_gemma":0.0001048184,"teacher_disagreement_score":0.119355485,"about_ca_system_score_codex":0.00012165349,"about_ca_system_score_gemma":0.00006393326,"threshold_uncertainty_score":0.9827443},"labels":[],"label_agreement":null},{"id":"W4386714095","doi":"10.1002/cjce.25076","title":"Deep chemometrics using one‐dimensional convolutional neural networks for predicting crude oil properties from FTIR spectral data","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Chemometrics; Convolutional neural network; Interpretability; Biological system; Artificial neural network; Principal component analysis; Fourier transform infrared spectroscopy; Computer science; Artificial intelligence; Principal component regression; Pattern recognition (psychology); Fourier transform; Preprocessor; Mean squared error; Mathematics; Statistics; Machine learning; Optics; Physics","score_opus":0.05514006406698218,"score_gpt":0.2426008797624582,"score_spread":0.187460815695476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386714095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99471015,0.0026144625,0.001715884,0.00046114885,0.0002758279,0.000030358422,0.00010596755,0.000047116635,0.000039079325],"genre_scores_gemma":[0.9956787,0.000008674288,0.0023160412,0.00006125599,0.001721033,0.0000027595397,0.00011990887,0.000044006538,0.00004758107],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834394,0.0000069087623,0.0004775761,0.00022524502,0.0003541411,0.00059218646],"domain_scores_gemma":[0.9984826,0.00042443085,0.00019100601,0.00032054217,0.00016334045,0.00041806608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031884914,0.00020647213,0.0003474377,0.00029682898,0.00018912123,0.00010539316,0.00083959155,0.00015912458,0.00015382287],"category_scores_gemma":[0.0011993742,0.0001721705,0.00014917532,0.0009196324,0.00010397964,0.00019097528,0.000094164476,0.0006380531,0.0000016201323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006253573,0.000021388349,0.0013908608,0.0001020076,0.00060087047,0.000039747432,0.00011572524,0.402211,0.5942507,0.00005456782,0.00069977005,0.00045081828],"study_design_scores_gemma":[0.00037307682,0.000008158228,0.00008145097,0.00007642555,0.00024325326,0.0000698791,0.000036984955,0.8728104,0.12594602,0.00004822165,0.00012039485,0.00018571226],"about_ca_topic_score_codex":0.001064542,"about_ca_topic_score_gemma":0.000107059524,"teacher_disagreement_score":0.4705994,"about_ca_system_score_codex":0.00040999812,"about_ca_system_score_gemma":0.00036306988,"threshold_uncertainty_score":0.7020909},"labels":[],"label_agreement":null},{"id":"W4386727275","doi":"10.18280/ijdne.180422","title":"Rapid and Non-Destructive Prediction of Animal Feed Nutritive Parameters Using Near Infrared Spectroscopy and Multivariate Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","keywords":"Partial least squares regression; Multivariate statistics; Principal component analysis; Mean squared error; Principal component regression; Mathematics; Smoothing; Statistics; Regression analysis; Second derivative; Multivariate analysis; Analytical Chemistry (journal); Biological system; Chemistry; Chromatography; Biology","score_opus":0.015727531573480922,"score_gpt":0.28447901481777377,"score_spread":0.2687514832442928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386727275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874673,0.00079188566,0.011279771,0.00007237729,0.00009582225,0.00004369921,0.00012826083,0.0000116860165,0.00010919726],"genre_scores_gemma":[0.9838043,0.0013731763,0.014665333,0.000022990786,0.000078122815,9.69827e-7,0.000025846266,0.0000127173635,0.000016539658],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988898,0.000028374132,0.00041797073,0.000202087,0.00031139137,0.00015039831],"domain_scores_gemma":[0.9987467,0.00028676123,0.0004910961,0.00007347045,0.00032593252,0.00007606163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023500597,0.00016044397,0.00039395172,0.000714623,0.00007584216,0.00009766824,0.00014499482,0.0002153876,0.00002464968],"category_scores_gemma":[0.00017475715,0.00014812397,0.00012359965,0.00070231565,0.00014032375,0.00028784995,0.00006171418,0.0004305031,1.7073467e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029920582,0.00019598992,0.08721224,0.00011242064,0.0118343765,0.0001554016,0.001567707,0.0023876184,0.89187306,0.00019153181,0.000083170235,0.0013944138],"study_design_scores_gemma":[0.0038215967,0.0006431297,0.12709983,0.00020503717,0.0039688884,0.0003681538,0.0027007982,0.7087008,0.14670749,0.0053487523,0.0000273589,0.0004082188],"about_ca_topic_score_codex":0.0000320428,"about_ca_topic_score_gemma":0.0000019851427,"teacher_disagreement_score":0.7451656,"about_ca_system_score_codex":0.000098521145,"about_ca_system_score_gemma":0.000057964535,"threshold_uncertainty_score":0.60403204},"labels":[],"label_agreement":null},{"id":"W4386880288","doi":"10.1016/j.jfca.2023.105701","title":"Detection of aflatoxin contamination in single kernel almonds using multispectral imaging system","year":2023,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Multispectral image; Linear discriminant analysis; Support vector machine; Hyperspectral imaging; Contamination; Kernel (algebra); Artificial intelligence; Aflatoxin; Pattern recognition (psychology); Mathematics; Computer science; Chemistry; Food science; Biology","score_opus":0.016222480989224186,"score_gpt":0.26705092066100145,"score_spread":0.25082843967177726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386880288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98810637,0.00047525484,0.01117535,0.0000254249,0.000022995526,0.000012945582,0.000006064484,0.000016915888,0.00015867241],"genre_scores_gemma":[0.9996743,0.000030815532,0.0002214525,0.0000044288804,0.000042027434,5.1930954e-7,0.000007939367,0.0000063543557,0.000012130849],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902964,0.000029715162,0.00049806054,0.000108676395,0.00021716121,0.00011676065],"domain_scores_gemma":[0.9992096,0.00005788781,0.00048851344,0.0000716728,0.00012455633,0.000047803893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020636963,0.0000902552,0.0003691186,0.0013276209,0.000054788703,0.00003252902,0.00006416359,0.000048111913,0.000019572823],"category_scores_gemma":[0.000016081902,0.000087669745,0.00021584156,0.0020149075,0.000026518459,0.00015632372,0.000014459026,0.00012211595,3.8350302e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038262864,0.00007408417,0.023804482,0.00007133221,0.00052214286,0.000017037213,0.0001375286,0.001594181,0.9729382,0.000026035244,0.0000010465097,0.0007756596],"study_design_scores_gemma":[0.00062057097,0.00005897687,0.014063921,0.000089019726,0.0015041976,0.00006731093,0.0017816751,0.1278178,0.85387444,0.000026594218,0.0000014847128,0.000094029565],"about_ca_topic_score_codex":0.00003907528,"about_ca_topic_score_gemma":0.000016980766,"teacher_disagreement_score":0.12622361,"about_ca_system_score_codex":0.00018151042,"about_ca_system_score_gemma":0.000011660152,"threshold_uncertainty_score":0.35750684},"labels":[],"label_agreement":null},{"id":"W4387048904","doi":"10.1007/978-3-031-43605-5_11","title":"Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Near-infrared spectroscopy; Content (measure theory); Calibration; Mathematics; Food science; Wheat flour; Chemistry; Computer science; Statistics; Biology; Artificial intelligence","score_opus":0.041574999321110265,"score_gpt":0.28583462590202435,"score_spread":0.2442596265809141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387048904","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012131399,0.0018574477,0.4286575,0.0011631064,0.0004130678,0.0036890507,0.0007814688,0.00067276903,0.5506342],"genre_scores_gemma":[0.9574877,0.0004554081,0.036110837,0.00007618617,0.00007943315,0.00046861434,0.00057936786,0.00003087503,0.004711606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985411,0.000007636479,0.00078306795,0.00021269932,0.00027657562,0.00017890194],"domain_scores_gemma":[0.9981751,0.00010279307,0.0004385645,0.0009134875,0.00032204195,0.000048015798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005736253,0.00016110745,0.00028382338,0.0007269328,0.00022718328,0.00013865612,0.0008795279,0.00011029265,0.0000070407873],"category_scores_gemma":[0.00006278419,0.0001693573,0.000060399212,0.00051749515,0.0004542155,0.0011594342,0.00032686125,0.00027269358,0.00000990118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001274882,0.00021031382,0.006271435,0.0034659058,0.00012793294,9.827999e-7,0.008303668,0.0023185778,0.014425713,0.813772,0.00041303446,0.15056291],"study_design_scores_gemma":[0.0011644906,0.000043037096,0.0021849377,0.0008395105,0.000055279415,0.000013769106,0.0010691339,0.97805303,0.005802532,0.0028604881,0.0075072283,0.00040653374],"about_ca_topic_score_codex":0.000054459386,"about_ca_topic_score_gemma":0.00000837336,"teacher_disagreement_score":0.9757345,"about_ca_system_score_codex":0.0003251225,"about_ca_system_score_gemma":0.00017081395,"threshold_uncertainty_score":0.69061905},"labels":[],"label_agreement":null},{"id":"W4387051562","doi":"10.1007/s11694-023-02142-w","title":"Identification of the authenticity and geographical origin of Iranian lime juice by targeted method and machine learning","year":2023,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"College of Agriculture Natural Resources, University of Tehran; Iran National Science Foundation","keywords":"Lime; Sugar; Citric acid; Sucrose; Food science; Artificial intelligence; Mathematics; Chemistry; Machine learning; Computer science; Materials science; Metallurgy","score_opus":0.03335244715674548,"score_gpt":0.26994122774241064,"score_spread":0.23658878058566515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387051562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989547,0.0003864945,0.009673737,0.0002463947,0.000049335613,0.000047070007,0.00002735965,0.000010441665,0.0000121598105],"genre_scores_gemma":[0.99946654,0.0003225555,0.00006987804,0.000009960289,0.000034326415,0.0000012627982,0.000019242787,0.000009453297,0.000066799475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986015,0.00008131401,0.0005650818,0.000112821355,0.0005410073,0.0000982983],"domain_scores_gemma":[0.99847794,0.000044004104,0.001018862,0.00009923408,0.00031329814,0.000046693232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009913355,0.00009483266,0.0002474106,0.00018578966,0.00007703117,0.000025165224,0.00011208107,0.000068572794,0.00003396638],"category_scores_gemma":[0.00036010897,0.0000717359,0.00007905142,0.0006460576,0.000045340486,0.00012913819,0.000027569751,0.0001897069,1.8654875e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044582895,0.00006621323,0.039135415,0.00013275904,0.00017154428,1.6033053e-7,0.00019785656,0.000005088184,0.95948404,0.000020201878,0.000007782105,0.0007343751],"study_design_scores_gemma":[0.00037718387,0.00007225361,0.15850541,0.000056192155,0.00030079225,0.000005851826,0.00007063095,0.0006930218,0.8396276,0.00005532515,0.00017808286,0.000057640074],"about_ca_topic_score_codex":0.0000065523545,"about_ca_topic_score_gemma":0.0000014782992,"teacher_disagreement_score":0.11985642,"about_ca_system_score_codex":0.000028509901,"about_ca_system_score_gemma":0.000027340313,"threshold_uncertainty_score":0.2925305},"labels":[],"label_agreement":null},{"id":"W4387060405","doi":"10.1177/09670335231202258","title":"Investigation of <i>Fusarium</i> damage in wheat using hyperspectral imaging: An independent component analysis approach","year":2023,"lang":"en","type":"article","venue":"Journal of Near Infrared Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Mitacs","keywords":"Hyperspectral imaging; Fusarium; Computer science; Independent component analysis; Population; Component (thermodynamics); Principal component analysis; Remote sensing; Pattern recognition (psychology); Environmental science; Artificial intelligence; Physics; Geography; Biology","score_opus":0.03723462715687706,"score_gpt":0.2992278567112334,"score_spread":0.26199322955435633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387060405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995514,0.00026115807,0.001101247,0.00011713352,0.00007008973,0.000060932485,0.000021813523,0.00004563754,0.00280802],"genre_scores_gemma":[0.9841745,0.000085440246,0.015255009,0.000054594555,0.00017801796,0.000002170513,0.00008998718,0.000037724905,0.00012257817],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970395,0.00009425794,0.0011315718,0.0003555971,0.00084357883,0.00053547043],"domain_scores_gemma":[0.998314,0.00007592124,0.00067225646,0.00046779236,0.00022229878,0.0002477069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007291674,0.00029379537,0.00092241046,0.0017187683,0.000119689714,0.00013465366,0.0005543059,0.00015318536,0.0003904927],"category_scores_gemma":[0.000074428666,0.00025854606,0.00042146363,0.0034978027,0.00020320335,0.0005495155,0.000090340036,0.0007175262,0.0000053295835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114570576,0.00016090032,0.13601878,0.000059979215,0.00045275412,0.000071529474,0.0012407511,0.007949293,0.853747,0.000047923055,0.00012848193,0.000008022556],"study_design_scores_gemma":[0.0011562137,0.00009913873,0.02347727,0.000028415547,0.0011132228,0.000060143055,0.0030538458,0.042009525,0.92781323,0.0008646869,0.000034974586,0.00028931265],"about_ca_topic_score_codex":0.00019680413,"about_ca_topic_score_gemma":0.000012295974,"teacher_disagreement_score":0.11254152,"about_ca_system_score_codex":0.00049614196,"about_ca_system_score_gemma":0.00024652274,"threshold_uncertainty_score":0.99998665},"labels":[],"label_agreement":null},{"id":"W4387187073","doi":"10.3390/agriculture13101913","title":"Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System","year":2023,"lang":"en","type":"article","venue":"Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"PEAR; Titratable acid; Chemistry; Cold storage; Horticulture; Biological system; Food science; Biology","score_opus":0.02518969056451621,"score_gpt":0.2568820303945331,"score_spread":0.2316923398300169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387187073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954049,0.0009845407,0.000007204414,0.0000414716,0.00006136352,0.000048778136,0.00001778378,0.0003681128,0.0030658483],"genre_scores_gemma":[0.9981282,0.000013280159,0.00007027991,0.0000044626354,0.0003032174,0.0000057112716,0.000012448756,0.000016660393,0.0014457757],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987527,0.000022019907,0.00032222844,0.00027386125,0.00030621278,0.0003229394],"domain_scores_gemma":[0.9994392,0.000022666414,0.00018747301,0.00020139992,0.00008922523,0.000060032766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012676574,0.00017245555,0.00029822777,0.00008522661,0.00018182497,0.000040258103,0.00018047343,0.000086899396,0.00006169544],"category_scores_gemma":[0.00007996538,0.000118009244,0.00013877019,0.00084163673,0.00004214946,0.00014223703,0.000082682134,0.00021702834,0.000009579958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009732338,0.000019849447,0.06473217,0.00088915724,0.00007199987,0.000013761309,0.0004916712,0.00037898374,0.93326426,0.00003724928,0.00008356468,0.0000076056435],"study_design_scores_gemma":[0.00023150246,0.0000030858714,0.024582813,0.00024532032,0.0001394465,0.00005201493,0.017723614,0.0006911844,0.95608264,0.0000023339444,0.00003528596,0.00021074137],"about_ca_topic_score_codex":0.00040704556,"about_ca_topic_score_gemma":0.000008442728,"teacher_disagreement_score":0.04014936,"about_ca_system_score_codex":0.00016888896,"about_ca_system_score_gemma":0.000027992453,"threshold_uncertainty_score":0.48122773},"labels":[],"label_agreement":null},{"id":"W4387308816","doi":"10.20944/preprints202309.2092.v1","title":"Sensitivity Analysis of Intensity-Modulated Plastic Optical Fiber Sensors for Effective Ageing Detection in Rapeseed Transformer Oil","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Glasgow Caledonian University","keywords":"Optical fiber; Transformer; Materials science; Repeatability; Computer science; Electronic engineering; Environmental science; Voltage; Electrical engineering; Telecommunications; Engineering","score_opus":0.060843377619938216,"score_gpt":0.33741245702137773,"score_spread":0.2765690794014395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387308816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99512255,0.000023874341,0.0028980265,0.000047434107,0.0001726549,0.00026938767,0.00011827921,0.00023011952,0.0011176579],"genre_scores_gemma":[0.9982503,0.00004224065,0.00010558221,0.000009017597,0.00008359709,0.00021003268,0.00021103946,0.00007381749,0.0010143968],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99662006,0.00011072639,0.0009052604,0.0013798933,0.0004208799,0.00056318264],"domain_scores_gemma":[0.9961658,0.0020534499,0.00042175179,0.0008398556,0.00038125445,0.00013788047],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096163363,0.0005439816,0.0017272647,0.0015510242,0.00008885399,0.00002102287,0.00025632046,0.00086118793,0.00052187964],"category_scores_gemma":[0.0025802548,0.0005889819,0.0010893227,0.0022321888,0.00016487383,0.000075148106,0.00032810625,0.0012746119,0.000086917185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008873485,0.00029189457,0.10579028,0.0012898634,0.007560927,0.00006983914,0.0006555283,0.06687167,0.8152886,0.0000030064423,0.0000011775995,0.0012898865],"study_design_scores_gemma":[0.0005311639,0.000014280375,0.2658538,0.00014477533,0.0045339293,0.0000031836955,0.00017937293,0.031149266,0.6971302,0.0000694203,0.000008023208,0.00038258935],"about_ca_topic_score_codex":0.0010403752,"about_ca_topic_score_gemma":0.00065194,"teacher_disagreement_score":0.1600635,"about_ca_system_score_codex":0.00050107273,"about_ca_system_score_gemma":0.000071848546,"threshold_uncertainty_score":0.99965614},"labels":[],"label_agreement":null},{"id":"W4387385786","doi":"10.2139/ssrn.4591751","title":"Demonstration of the Mathematical Equivalency of Partial Least Squares and the Beer-Lambert Law for Estimating Protein Concentrations from Spectroscopic Data, and its Specific Applications in Bioseparations Processing","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Beer–Lambert law; Law; Algorithm; Computer science; Chemistry; Mathematics; Physics; Optics","score_opus":0.04380161974184971,"score_gpt":0.3295573821240925,"score_spread":0.2857557623822428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387385786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70204395,0.016178245,0.27587163,0.0023896117,0.00007216984,0.0020416714,0.0006579843,0.000044319986,0.00070041267],"genre_scores_gemma":[0.9959885,0.00057177,0.0026280275,0.0000040616537,0.00023044438,0.00019283427,0.00012522723,0.000023801183,0.00023536565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978684,0.000062953775,0.00082231034,0.0003742903,0.0002817417,0.0005902704],"domain_scores_gemma":[0.99824286,0.00038841178,0.0007765379,0.00042974896,0.00012280089,0.00003962265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078638445,0.00021855155,0.00044960095,0.00007534675,0.0003156108,0.000119020115,0.00055257854,0.00016561597,0.000024966577],"category_scores_gemma":[0.00021447071,0.00014928714,0.00009752902,0.00027940882,0.00037920027,0.0001451365,0.00024777662,0.0012265514,5.3320264e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017356005,0.00039896637,0.0028045531,0.0013988301,0.000656276,4.1926037e-7,0.0017391499,0.0006585803,0.10964381,0.8812022,0.000029751003,0.0012938712],"study_design_scores_gemma":[0.0019856154,0.000059565806,0.00024161878,0.00087849674,0.0010098739,0.00002430674,0.0047780867,0.08923091,0.114952445,0.7864514,0.000030845524,0.000356823],"about_ca_topic_score_codex":0.000111917485,"about_ca_topic_score_gemma":0.00061235053,"teacher_disagreement_score":0.2939445,"about_ca_system_score_codex":0.0001684338,"about_ca_system_score_gemma":0.0010381412,"threshold_uncertainty_score":0.60877526},"labels":[],"label_agreement":null},{"id":"W4387396618","doi":"10.1016/j.fertnstert.2023.08.233","title":"HIGHER METAPHASE II (MII) OOCYTE QUALITY IDENTIFIED BY AN ARTIFICIAL INTELLIGENCE (AI) OOCYTE IMAGE ANALYSIS TOOL IN GnRH ANTAGONIST VERSUS GnRH AGONIST CONTROLLED OVARIAN STIMULATION (COS) PROTOCOLS","year":2023,"lang":"en","type":"article","venue":"Fertility and Sterility","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CReATe Fertility Centre","funders":"","keywords":"Oocyte; Blastocyst; Andrology; Agonist; Medicine; Gynecology; Biology; Internal medicine; Embryo; Embryogenesis; Receptor","score_opus":0.0910762221018124,"score_gpt":0.4050465827340153,"score_spread":0.31397036063220285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387396618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919544,0.000083957355,0.00357784,0.0002564099,0.00012010523,0.002453171,0.00068269036,0.00028859737,0.0005828659],"genre_scores_gemma":[0.99640846,0.000009091692,0.00013711597,0.000065276385,0.00007132048,0.0014889336,0.0009979359,0.000025714366,0.0007961271],"study_design_codex":"randomized_trial","study_design_gemma":"observational","domain_scores_codex":[0.9949386,0.00047632738,0.0017687406,0.0014136871,0.00067012006,0.00073249405],"domain_scores_gemma":[0.99732876,0.00045312027,0.00043318723,0.0013033975,0.00023578084,0.00024575563],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023012727,0.00054350524,0.0015429235,0.00037680723,0.00069029455,0.0005825414,0.00047844154,0.00037340636,0.0022244204],"category_scores_gemma":[0.0006974988,0.000529279,0.00049572473,0.0027262145,0.00044942205,0.0007505128,0.00025445092,0.00051285914,0.000029979918],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.56597483,0.0051633525,0.064796954,0.000986779,0.003536379,0.00010556124,0.0017668342,0.000535801,0.3505372,0.00027269614,0.00022090842,0.0061026653],"study_design_scores_gemma":[0.0069865077,0.00028516198,0.7638965,0.000022659358,0.0018734832,8.0964634e-7,0.0008750647,0.044296566,0.1767035,0.003595526,0.00037890914,0.0010852595],"about_ca_topic_score_codex":0.0022101905,"about_ca_topic_score_gemma":0.0018766831,"teacher_disagreement_score":0.6990996,"about_ca_system_score_codex":0.0003066109,"about_ca_system_score_gemma":0.00011522114,"threshold_uncertainty_score":0.99971586},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"agree"},{"id":"W4387575714","doi":"10.1002/cjce.25107","title":"Dynamic stationary subspace analysis based on Gaussian mixture models for ironmaking process monitoring","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Subspace topology; Mixture model; Gaussian; Computer science; Gaussian process; Projection (relational algebra); Divergence (linguistics); Algorithm; Artificial intelligence; Chemistry","score_opus":0.012348849990795331,"score_gpt":0.2588435124833114,"score_spread":0.24649466249251606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387575714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793046,0.0005587085,0.01807256,0.0013925041,0.00011887857,0.000068312744,0.000061424566,0.00006625825,0.0003567232],"genre_scores_gemma":[0.9988612,0.000002972391,0.00081382255,0.000025627322,0.00014221268,0.000009081564,0.000020553163,0.000030624797,0.00009389576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989984,0.000003802274,0.00025530352,0.00013451019,0.0002481779,0.00035980588],"domain_scores_gemma":[0.9990768,0.00024653957,0.00013104643,0.00016798648,0.00009913351,0.00027853678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017562867,0.00015492659,0.00026305421,0.00056733063,0.000106508334,0.00005735762,0.00032946828,0.000102547514,0.00005566984],"category_scores_gemma":[0.00019906914,0.00012607146,0.00022855724,0.0014174226,0.000027183229,0.000086049666,0.0000057357825,0.00035877115,0.0000011628478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017159202,0.0000058781884,0.00043253205,0.00008947727,0.00031821983,0.0000205539,0.00020215829,0.9715026,0.027222494,0.000052426207,0.00004174892,0.00009473542],"study_design_scores_gemma":[0.00023797233,0.000011102861,0.00010976905,0.00007488742,0.0005588773,0.0000073866895,0.00014563907,0.8635021,0.13484228,0.00029532678,0.00004658317,0.00016809205],"about_ca_topic_score_codex":0.00009130675,"about_ca_topic_score_gemma":0.00004177614,"teacher_disagreement_score":0.10800053,"about_ca_system_score_codex":0.00035424644,"about_ca_system_score_gemma":0.0002374853,"threshold_uncertainty_score":0.5141045},"labels":[],"label_agreement":null},{"id":"W4387718857","doi":"10.1364/ais.2023.jm2b.3","title":"Reconciling Cepstral and Spectral spectroscopic fits","year":2023,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Cepstrum; Baseline (sea); Absorbance; Frequency domain; Computer science; Mel-frequency cepstrum; Spectroscopy; Domain (mathematical analysis); Absorption (acoustics); Transmittance; Artificial intelligence; Speech recognition; Biological system; Mathematics; Physics; Optics; Feature extraction; Computer vision; Mathematical analysis","score_opus":0.02664477235392568,"score_gpt":0.2849064190494302,"score_spread":0.2582616466955045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387718857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86297494,0.00019750246,0.00003941304,0.00029037,0.000045695913,0.000019474579,0.0000046135233,0.0003859015,0.13604206],"genre_scores_gemma":[0.95334476,0.00019582432,0.00051581685,0.00007283666,0.00021495807,0.000004832094,0.000015790232,0.000016344662,0.045618825],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898636,0.000003975606,0.00017164186,0.0003036425,0.00013975675,0.00039462175],"domain_scores_gemma":[0.99957377,0.00007944886,0.00003553703,0.00018593794,0.00001576238,0.000109575856],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007326086,0.0001431101,0.00019680265,0.0001332507,0.0000981794,0.000055575372,0.000119650154,0.00008512906,0.008665817],"category_scores_gemma":[0.00006214042,0.00013095795,0.00006221225,0.0005919434,0.00005506939,0.000082874794,0.00004972171,0.00016888761,0.00034772957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033728553,0.000071481394,0.17252707,0.00023537109,0.00025433686,0.000100151854,0.00035361407,0.000017500137,0.80508274,0.005474399,0.013397409,0.0024522035],"study_design_scores_gemma":[0.0004303278,0.000031171086,0.010576882,0.0000121958665,0.0000757526,0.000018312392,0.00079523405,0.00051528204,0.9836225,0.0020201316,0.0016185212,0.000283703],"about_ca_topic_score_codex":0.00004968936,"about_ca_topic_score_gemma":0.00001745504,"teacher_disagreement_score":0.17853975,"about_ca_system_score_codex":0.00004602099,"about_ca_system_score_gemma":0.000025508167,"threshold_uncertainty_score":0.99224037},"labels":[],"label_agreement":null},{"id":"W4387843169","doi":"10.1016/j.compag.2023.108348","title":"Early detection of citrus anthracnose caused by Colletotrichum gloeosporioides using hyperspectral imaging","year":2023,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Thresholding; Artificial intelligence; Support vector machine; Histogram; Pattern recognition (psychology); Spectral imaging; Postharvest; Grayscale; Feature (linguistics); Mathematics; Computer science; Remote sensing; Pixel; Horticulture; Biology; Image (mathematics); Geography","score_opus":0.0073050060488072275,"score_gpt":0.23652317961471808,"score_spread":0.22921817356591084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387843169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916186,0.0072642392,0.00054087176,0.00008049389,0.00006759978,0.000056074667,0.000012641038,0.00009307239,0.00026640508],"genre_scores_gemma":[0.998249,0.0011740384,0.00016954118,0.00002351201,0.00008786922,0.0000039124075,0.000024563353,0.000014240703,0.00025334273],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987772,0.000013264822,0.00025343252,0.00033132784,0.00017693009,0.00044782215],"domain_scores_gemma":[0.99955267,0.00006812773,0.00012893713,0.00013054235,0.000053594747,0.00006613633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008617781,0.00019849479,0.0003112437,0.00018724543,0.0001266694,0.000054191234,0.00015471167,0.00012747126,0.0000155839],"category_scores_gemma":[0.000019681025,0.00016700498,0.00007961974,0.0017032353,0.000056849505,0.00010087669,0.000055493867,0.00032364542,0.0000012142674],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019562183,0.000060454724,0.0074792574,0.00005627405,0.00007045815,0.000017079145,0.00026594233,0.00020051173,0.9890995,0.0000977921,0.0012616629,0.0013715313],"study_design_scores_gemma":[0.0012615647,0.00009620789,0.008623271,0.000066904635,0.00016746258,0.000045345012,0.0011300208,0.012025818,0.97399604,0.00052749156,0.0015179941,0.0005418961],"about_ca_topic_score_codex":0.00022326404,"about_ca_topic_score_gemma":0.000030439429,"teacher_disagreement_score":0.015103451,"about_ca_system_score_codex":0.00021778684,"about_ca_system_score_gemma":0.00003374612,"threshold_uncertainty_score":0.6810266},"labels":[],"label_agreement":null},{"id":"W4388032213","doi":"10.18280/ts.400528","title":"Hyper Spectral Imaging and Optimized Neural Networks for Early Detection of Grapevine Viral Disease","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease; Artificial neural network; Artificial intelligence; Computer science; Virology; Pattern recognition (psychology); Medicine; Internal medicine","score_opus":0.012321030638779392,"score_gpt":0.24736716544665258,"score_spread":0.23504613480787317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388032213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9813733,0.0005121356,0.017502183,0.00013748808,0.000049339076,0.00012501235,0.00003284374,0.000103094215,0.0001646058],"genre_scores_gemma":[0.9991181,0.000040741703,0.00020620583,0.000043618726,0.0002614163,0.00004744223,0.000049176644,0.000022133681,0.00021117802],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901354,0.000010318373,0.00025878294,0.00025218105,0.0001636532,0.0003014947],"domain_scores_gemma":[0.99954325,0.000098609955,0.00009482538,0.00010942159,0.000038384187,0.00011550597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010691733,0.00015716322,0.00022406595,0.00014093204,0.00009965677,0.000038182396,0.000096920056,0.00003519512,0.00036493156],"category_scores_gemma":[0.000021914599,0.0001487849,0.0001510993,0.00031512108,0.00006772443,0.000114426344,0.00003012114,0.000094604795,0.0000011062152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002432757,0.00028777125,0.063684665,0.00039015783,0.00042919072,0.00002720351,0.0001862127,0.012193773,0.9069984,0.0002150264,0.0005955283,0.0125593385],"study_design_scores_gemma":[0.005013785,0.00017791952,0.04480533,0.000032086184,0.0009049227,0.0000065071386,0.00026784625,0.7524261,0.19522615,0.00042047774,0.000226929,0.0004919388],"about_ca_topic_score_codex":0.000021843245,"about_ca_topic_score_gemma":0.0000014426271,"teacher_disagreement_score":0.74023235,"about_ca_system_score_codex":0.000027495345,"about_ca_system_score_gemma":0.000011608441,"threshold_uncertainty_score":0.6067272},"labels":[],"label_agreement":null},{"id":"W4388482205","doi":"10.48550/arxiv.2311.03256","title":"$λ$-Griffiths polynomials: Bispectrality and biorthogonality","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of Science; Centre National de la Recherche Scientifique; Université Savoie Mont Blanc","keywords":"Bivariate analysis; Orthogonal polynomials; Biorthogonal system; Generalization; Mathematics; Pure mathematics; Discrete orthogonal polynomials; Classical orthogonal polynomials; Hahn polynomials; Jacobi polynomials; Value (mathematics); Wilson polynomials; Gegenbauer polynomials; Mathematical analysis; Statistics; Computer science","score_opus":0.14131103135860856,"score_gpt":0.23196446809773016,"score_spread":0.0906534367391216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388482205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889398,0.00038315196,0.0013630032,0.00021131276,0.00017400742,0.00009360528,0.00030952186,0.0005494976,0.007976068],"genre_scores_gemma":[0.9851544,0.0010481548,0.00007883773,0.00004616667,0.00020290434,0.000001335549,0.00009946478,0.000046022284,0.013322727],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976688,0.00005295198,0.00030441303,0.0013767576,0.00011120826,0.00048586144],"domain_scores_gemma":[0.99811596,0.0002134021,0.00030474947,0.0010067291,0.00008899575,0.00027015732],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025362102,0.00046119216,0.00065075635,0.00030934208,0.00019000443,0.00009464861,0.0006446997,0.0006254819,0.0010443348],"category_scores_gemma":[0.00013198493,0.00052932627,0.00033164123,0.00080552575,0.0003148139,0.00010451644,0.0010743319,0.000899948,0.000112578695],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007504775,0.0011407239,0.87761617,0.0041577513,0.005193265,0.0020396274,0.0005130498,0.009973815,0.02027601,0.06802903,0.009547755,0.0007623431],"study_design_scores_gemma":[0.009331283,0.00027985167,0.25840652,0.0010978809,0.0156833,0.000097321885,0.0061664367,0.05141642,0.24299036,0.388211,0.012731194,0.013588398],"about_ca_topic_score_codex":0.0015231639,"about_ca_topic_score_gemma":0.00021879385,"teacher_disagreement_score":0.61920965,"about_ca_system_score_codex":0.00026776805,"about_ca_system_score_gemma":0.00015589297,"threshold_uncertainty_score":0.99986887},"labels":[],"label_agreement":null},{"id":"W4388505392","doi":"10.1109/tii.2023.3326533","title":"Design of Tobacco Leaves Classifier Through Fuzzy Clustering-Based Neural Networks With Multiple Histogram Analyses of Images","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; Ministry of Education, Libya","keywords":"Artificial intelligence; Pattern recognition (psychology); Cluster analysis; Computer science; Softmax function; Fuzzy clustering; Overfitting; Fuzzy logic; Artificial neural network; Machine learning; Histogram; Data mining; Mathematics","score_opus":0.12121762527546233,"score_gpt":0.3202231574571817,"score_spread":0.1990055321817194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388505392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06267754,0.00002485052,0.93569344,0.000041254116,0.00019386501,0.00018460154,0.00010078893,0.0001799761,0.0009036956],"genre_scores_gemma":[0.99571717,0.00004661888,0.003786259,0.000038927185,0.00006633815,0.000033811015,0.000025670146,0.000028531684,0.00025665454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820155,0.000032441687,0.00088163326,0.00014569322,0.0003940012,0.00034467806],"domain_scores_gemma":[0.99821746,0.0006356906,0.0005080605,0.00042024223,0.00014068617,0.00007787248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014717139,0.0002809445,0.00054612756,0.0003839542,0.00014352571,0.000039764986,0.0002806454,0.00032821245,0.00021017298],"category_scores_gemma":[0.000037496487,0.00023489771,0.00020750378,0.0018499659,0.0002521633,0.00030307492,0.0000034597585,0.00053605967,0.0000056269764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005253916,0.00016040928,0.0003499627,0.00008789813,0.00032081787,0.0000019311922,0.000268958,0.99152446,0.0038406102,9.4604843e-7,0.000584352,0.0023342518],"study_design_scores_gemma":[0.0020067042,0.00030877977,0.000015966465,0.00008001518,0.00054924306,0.0000031391592,0.0019221717,0.40643466,0.5883522,0.0000045640118,0.000058535737,0.00026401316],"about_ca_topic_score_codex":0.00021843213,"about_ca_topic_score_gemma":0.000029500708,"teacher_disagreement_score":0.93303967,"about_ca_system_score_codex":0.00008862308,"about_ca_system_score_gemma":0.00011541973,"threshold_uncertainty_score":0.9578851},"labels":[],"label_agreement":null},{"id":"W4388551001","doi":"10.18280/ijdne.180513","title":"Estimating Gurjun Oil Adulteration in Aceh Patchouli Oil Using NIRS and Multivariate Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Direktorat Riset dan Pengabdian Masyarakat; Universitas Syiah Kuala","keywords":"Patchouli; Multivariate statistics; Multivariate analysis; Environmental science; Mathematics; Statistics; Food science; Biology; Essential oil","score_opus":0.016584738185283585,"score_gpt":0.31422249625573934,"score_spread":0.2976377580704557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388551001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.968755,0.0007125878,0.029692074,0.00036501657,0.00023156864,0.000008225527,0.000019572393,0.000018775507,0.00019715998],"genre_scores_gemma":[0.9788275,0.000578102,0.020040143,0.00007323608,0.0002283997,0.0000010273102,0.000029806692,0.000013310885,0.00020847878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988331,0.000028966546,0.00046414122,0.0001839602,0.00032898498,0.0001608261],"domain_scores_gemma":[0.9989956,0.00023976916,0.00038442694,0.00007579918,0.0002355823,0.00006881137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003775816,0.00014557531,0.00029602548,0.0009311358,0.00006422113,0.00013707607,0.00019067882,0.00020878631,0.00004600302],"category_scores_gemma":[0.00032997475,0.00013258711,0.00010420365,0.00085754745,0.000031002943,0.0003013052,0.000056602443,0.0005178737,9.274349e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009453011,0.00039997153,0.17384493,0.0002851164,0.008168017,0.0010775344,0.0034239404,0.44781294,0.27441984,0.00053397747,0.00024320307,0.08884523],"study_design_scores_gemma":[0.00079662557,0.000018482226,0.0028480105,0.00010865874,0.00047738352,0.000097032826,0.00034081793,0.9919124,0.00262726,0.00058433984,0.000025958272,0.00016303352],"about_ca_topic_score_codex":0.000061620456,"about_ca_topic_score_gemma":0.000046875302,"teacher_disagreement_score":0.54409945,"about_ca_system_score_codex":0.00014240487,"about_ca_system_score_gemma":0.000047744583,"threshold_uncertainty_score":0.54067457},"labels":[],"label_agreement":null},{"id":"W4389042690","doi":"10.1016/j.foodchem.2023.138093","title":"Automated sequential SPME addressing the displacement effect in food samples","year":2023,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Narodowe Centrum Nauki","keywords":"Displacement (psychology); Food science; Chemistry; Computer science; Biological system; Biology; Psychology","score_opus":0.05500135408181836,"score_gpt":0.3242687735768955,"score_spread":0.26926741949507715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389042690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894712,0.0005199998,0.0000113482765,0.00021762175,0.00005854852,0.00007099645,0.00009398329,0.0008269524,0.008729358],"genre_scores_gemma":[0.99819803,0.000027165159,0.000024718847,0.00003925109,0.0002418145,0.00008516214,0.0002465532,0.00003997836,0.0010973454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99824816,0.000023429831,0.000341469,0.00043523754,0.00037676079,0.00057493296],"domain_scores_gemma":[0.9989553,0.00028379683,0.00012975668,0.00051226333,0.000025330335,0.000093525145],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023085326,0.00029330188,0.00032742033,0.00007019793,0.0001836103,0.00010312682,0.00046598542,0.00020775246,0.0014372582],"category_scores_gemma":[0.00021533207,0.00022764996,0.0001651134,0.0010886634,0.00010597235,0.000060606082,0.00019442971,0.00038681153,0.00006170828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055727593,0.00007279676,0.003898315,0.00060132734,0.00028471337,0.000021463211,0.00015279053,0.0001497041,0.99030167,0.0000129808395,0.0041965283,0.0002520065],"study_design_scores_gemma":[0.00076562236,0.000050505067,0.0003876022,0.00008692227,0.00010009401,0.000009600484,0.000376623,0.0011159288,0.99574935,0.00010831447,0.0009831823,0.00026626675],"about_ca_topic_score_codex":0.000026860265,"about_ca_topic_score_gemma":0.000014732797,"teacher_disagreement_score":0.00872682,"about_ca_system_score_codex":0.00018553984,"about_ca_system_score_gemma":0.00006525429,"threshold_uncertainty_score":0.99947554},"labels":[],"label_agreement":null},{"id":"W4389052687","doi":"10.1016/j.fbio.2023.103392","title":"SR-FTIR microspectroscopy: Emerging 2D structural-chemical analytical technique for food quality and safety monitoring","year":2023,"lang":"en","type":"article","venue":"Food Bioscience","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Fourier transform infrared spectroscopy; Synchrotron; Chemistry; Food industry; Food products; Food safety; Food quality; Chemical imaging; Fourier transform; Process engineering; Biochemical engineering; Analytical Chemistry (journal); Computer science; Food science; Optics; Environmental chemistry; Physics; Engineering; Artificial intelligence","score_opus":0.04799328493532124,"score_gpt":0.36562773443974245,"score_spread":0.3176344495044212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389052687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960885,0.0002590987,0.0018696258,0.0005001745,0.000115328214,0.00016593376,0.00019183339,0.0002897206,0.0005197617],"genre_scores_gemma":[0.99457026,0.00007455735,0.0048197554,0.000037411977,0.00019948109,0.000056230598,0.000017996304,0.000022290822,0.00020201178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977376,0.000012834868,0.0004233299,0.0007595032,0.0003689729,0.0006977961],"domain_scores_gemma":[0.9989644,0.00024137455,0.00013505184,0.0003819655,0.00007744257,0.0001997379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038565663,0.00026259397,0.00036855417,0.00019595424,0.00037958336,0.0001099152,0.00044920132,0.00018543801,0.00006039401],"category_scores_gemma":[0.0004949201,0.00023842855,0.00014651446,0.0016516784,0.0003319836,0.00016014194,0.00023257834,0.0002601355,0.0000047749295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035262037,0.000016762453,0.01088515,0.00010664253,0.000037027985,0.0000010682016,0.000060572802,0.0000012051547,0.98754436,0.0009828227,0.00007393405,0.00025521807],"study_design_scores_gemma":[0.00029840227,0.00012604657,0.003992155,0.000027832773,0.00005642346,0.000010608187,0.00029842547,0.00027120436,0.9924509,0.0018340098,0.00031527242,0.0003187013],"about_ca_topic_score_codex":0.000010114419,"about_ca_topic_score_gemma":0.0000029675175,"teacher_disagreement_score":0.006892995,"about_ca_system_score_codex":0.00014952627,"about_ca_system_score_gemma":0.000060214817,"threshold_uncertainty_score":0.9722834},"labels":[],"label_agreement":null},{"id":"W4389058493","doi":"10.22541/essoar.170110651.16979152/v1","title":"UAV-based Multispectral Imaging for High-throughput Phenotyping of Dry Bean Breeding Trials","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Alberta Pulse Growers Commission","keywords":"Multispectral image; Throughput; Dry bean; Remote sensing; Computer science; Agronomy; Environmental science; Biology; Artificial intelligence; Geography","score_opus":0.10961708485879158,"score_gpt":0.37208997575600616,"score_spread":0.2624728908972146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389058493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06428862,0.0022868258,0.915134,0.0016784421,0.0018587406,0.0010810705,0.0018532507,0.0017520583,0.010066972],"genre_scores_gemma":[0.9614966,0.00006980404,0.032221787,0.00010666762,0.0013897476,0.00018389792,0.00071652443,0.00016725417,0.0036477514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962965,0.000042271047,0.0015475076,0.0010450875,0.0003912786,0.000677329],"domain_scores_gemma":[0.9954613,0.0021934013,0.0011672975,0.0008150776,0.00023744369,0.00012551156],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012444789,0.0006116206,0.0019982983,0.00051841396,0.00015278102,0.0001267099,0.0007392992,0.0004441567,0.0015489286],"category_scores_gemma":[0.0021519104,0.000571409,0.0010754894,0.0004543731,0.00012831073,0.000083505474,0.00042829386,0.0006518887,0.0000117117825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007841204,0.00070603064,0.01646158,0.011533705,0.004642521,0.000025734646,0.00070738915,0.00675788,0.92810535,0.0066476287,0.010705013,0.012923047],"study_design_scores_gemma":[0.0019446649,0.000017629149,0.000090385016,0.00040409193,0.0011376262,4.885241e-7,0.0007694279,0.033285283,0.953982,0.0075319186,0.00014250209,0.00069398363],"about_ca_topic_score_codex":0.0022858775,"about_ca_topic_score_gemma":0.0000646713,"teacher_disagreement_score":0.8972079,"about_ca_system_score_codex":0.00032260138,"about_ca_system_score_gemma":0.00027353174,"threshold_uncertainty_score":0.9996737},"labels":[],"label_agreement":null},{"id":"W4389223262","doi":"10.1128/aem.01673-23","title":"Species identification and strain discrimination of fermentation yeasts <i>Saccharomyces cerevisiae</i> and <i>Saccharomyces uvarum</i> using Raman spectroscopy and convolutional neural networks","year":2023,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Saccharomyces cerevisiae; Yeast; Fermentation; Strain (injury); Wine; Biology; Raman spectroscopy; Food science; Saccharomyces; Biological system; Artificial intelligence; Biochemistry; Computer science; Physics; Optics","score_opus":0.012417992411162882,"score_gpt":0.22759640172911028,"score_spread":0.2151784093179474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389223262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979634,0.0012967015,0.00021214323,0.0000906812,0.000026372974,0.00009746376,0.0002038689,0.00002538862,0.00008392912],"genre_scores_gemma":[0.9966882,0.0021610868,0.000188735,0.00004123318,0.00005357885,0.000009370262,0.0007324713,0.0000133723715,0.000111984285],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990918,0.000015765778,0.00025873806,0.0003729003,0.000054276286,0.00020650211],"domain_scores_gemma":[0.9996399,0.000055330882,0.00016203987,0.0000857637,0.00000356284,0.00005337234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087223154,0.00017852795,0.00024403393,0.000090824455,0.00015647178,0.000039354803,0.000053201526,0.00011399182,0.00009376366],"category_scores_gemma":[0.0000035260873,0.00017620783,0.000022359538,0.00010350001,0.0004996365,0.00009817386,0.00010693788,0.00009937089,0.0000012952041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024957795,0.000026901396,0.01995153,0.00008216571,0.000045216377,7.1898654e-7,0.0001884898,0.000025055893,0.97861075,0.0002813585,0.00002879118,0.0007340329],"study_design_scores_gemma":[0.001441329,0.00007568744,0.20129704,0.00003126911,0.00035103702,0.00013586396,0.005987795,0.004389689,0.78497195,0.0007516831,0.00010327894,0.00046337474],"about_ca_topic_score_codex":0.000019282807,"about_ca_topic_score_gemma":0.0000037883597,"teacher_disagreement_score":0.19363883,"about_ca_system_score_codex":0.00003528583,"about_ca_system_score_gemma":0.0000039040833,"threshold_uncertainty_score":0.7185547},"labels":[],"label_agreement":null},{"id":"W4389341989","doi":"10.1016/j.jfca.2023.105895","title":"Predictive techniques for authenticating and quantifying crude palm oil adulterated with leaf extract and food color − An ultra-violet visible spectrophotometric approach","year":2023,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Palm oil; Edible oil; Ultra violet; Food science; Chemistry; Materials science; Optoelectronics","score_opus":0.028343561170609696,"score_gpt":0.3003453746652452,"score_spread":0.2720018134946355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389341989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9775597,0.000897736,0.02098681,0.00006794501,0.000005620705,0.000052559808,0.00007207945,0.000061897634,0.00029567227],"genre_scores_gemma":[0.9922217,0.0006735477,0.0068649715,0.000024417353,0.00007086784,0.000016227184,0.00007761814,0.000017830782,0.000032808133],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987387,0.000036805686,0.00043337097,0.000305774,0.00026140612,0.00022394182],"domain_scores_gemma":[0.99878913,0.0001931693,0.00047720905,0.00012980282,0.00024467902,0.00016603133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306508,0.00019718986,0.00055579795,0.0014404322,0.00025927267,0.00021710903,0.00010703858,0.00009727155,0.000016546412],"category_scores_gemma":[0.00004468751,0.00015553307,0.0001440657,0.0027824908,0.000083421364,0.00024798737,0.000015848356,0.00022316261,1.1339512e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006599793,0.0004864939,0.02330913,0.00044592758,0.0057394765,0.000012652353,0.0007742132,0.00013900377,0.9644961,0.00013073781,0.000027028795,0.0037793047],"study_design_scores_gemma":[0.0018165743,0.003596911,0.0068571623,0.0001714845,0.0076041897,0.00024974486,0.0044619045,0.043020535,0.9315964,0.00016007292,0.00002288016,0.00044211777],"about_ca_topic_score_codex":0.000005686527,"about_ca_topic_score_gemma":0.0000031541754,"teacher_disagreement_score":0.042881534,"about_ca_system_score_codex":0.000043568278,"about_ca_system_score_gemma":0.000024005918,"threshold_uncertainty_score":0.63424546},"labels":[],"label_agreement":null},{"id":"W4389887343","doi":"10.36939/ir.202312181457","title":"Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Mitacs","keywords":"Hyperspectral imaging; Convolutional neural network; Multispectral image; Artificial intelligence; Computer science; Pattern recognition (psychology); Pixel; Focus (optics); Process (computing); RGB color model; Remote sensing; Computer vision; Geography","score_opus":0.09378798428540702,"score_gpt":0.31969013239756344,"score_spread":0.22590214811215642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389887343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99100614,0.0015579303,0.000041172338,0.000071392235,0.0003763263,0.000118348195,0.00011032298,0.00015769956,0.0065606465],"genre_scores_gemma":[0.98115253,0.0008559159,0.0007028882,0.000020728728,0.0007685129,0.00031076005,0.0055984776,0.000069244095,0.010520914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986984,0.000006432883,0.00030681796,0.0004430552,0.0001750914,0.00037017613],"domain_scores_gemma":[0.9993982,0.00022124164,0.00013861971,0.00013611437,0.00004812256,0.000057713412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000741215,0.00023084607,0.00029989769,0.00021763824,0.00011393258,0.00006001925,0.00012961653,0.00013188274,0.00016369035],"category_scores_gemma":[0.000060129332,0.00024395314,0.00007596027,0.00028640238,0.000023946885,0.00015223413,0.000013634098,0.00026604932,0.0000054389625],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033573657,0.00093207275,0.67688584,0.008315752,0.0020240725,0.00015350668,0.0074558193,0.004666816,0.20358896,0.017189581,0.020979809,0.054450396],"study_design_scores_gemma":[0.004959716,0.000074644195,0.5317527,0.0012854296,0.001179982,0.000031635,0.1422112,0.17140645,0.13936743,0.0033304861,0.00095015357,0.0034501443],"about_ca_topic_score_codex":0.00019398094,"about_ca_topic_score_gemma":0.00048659949,"teacher_disagreement_score":0.16673964,"about_ca_system_score_codex":0.00012888726,"about_ca_system_score_gemma":0.00004925388,"threshold_uncertainty_score":0.9948121},"labels":[],"label_agreement":null},{"id":"W4390398480","doi":"10.3390/foods13010120","title":"Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging","year":2023,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Hyperspectral imaging; Partial least squares regression; Chemometrics; VNIR; Germination; Principal component analysis; Linear discriminant analysis; Mathematics; Artificial intelligence; Computer science; Statistics; Machine learning; Botany; Biology","score_opus":0.027674845398714856,"score_gpt":0.356173815239152,"score_spread":0.32849896984043714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390398480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996108,0.000086692045,0.00019027224,0.00010115522,0.000021559881,0.000025339552,0.0000071763475,0.00004755535,0.0034122348],"genre_scores_gemma":[0.9974188,0.000016412845,0.002404268,0.0000131784,0.00003443649,0.0000032696844,0.0000048408815,0.000010384106,0.000094382885],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919546,0.000014545335,0.00020456806,0.00022271226,0.00014485755,0.00021787414],"domain_scores_gemma":[0.9995942,0.00008557972,0.00005262574,0.00019888619,0.000021698104,0.00004700626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022255984,0.00009447615,0.00022158165,0.00016176826,0.00003383058,0.000014384974,0.00010171656,0.00005413321,0.00025065878],"category_scores_gemma":[0.00007050879,0.000095120806,0.000053320047,0.00071596325,0.00006234668,0.000062938,0.00009415685,0.00015632404,9.77621e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071379463,0.00008940974,0.22730124,0.00013992992,0.000029197954,0.000022914133,0.00010293683,0.00003906306,0.77097374,0.0009994105,0.0000225322,0.00027250923],"study_design_scores_gemma":[0.0008620096,0.000051035917,0.21989763,0.000048763297,0.00015592918,0.000017999542,0.00307718,0.09446011,0.67762446,0.0034078362,0.00006211312,0.00033495223],"about_ca_topic_score_codex":0.00012703921,"about_ca_topic_score_gemma":0.000006542647,"teacher_disagreement_score":0.094421044,"about_ca_system_score_codex":0.0001093225,"about_ca_system_score_gemma":0.000048530324,"threshold_uncertainty_score":0.38789138},"labels":[],"label_agreement":null},{"id":"W4390480461","doi":"10.2139/ssrn.4681154","title":"Assessment of Tomatoes’ Freshness and Wholeness Using Digital Imaging and Machine Learning Techniques","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Conestoga College","funders":"","keywords":"Digital imaging; Computer science; Computer vision; Artificial intelligence; Human–computer interaction; Digital image; Image processing; Image (mathematics)","score_opus":0.010164302255840222,"score_gpt":0.2991489435216496,"score_spread":0.2889846412658094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390480461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9204635,0.06407529,0.01284737,0.00011620187,0.0000710654,0.00006649276,0.000041213672,0.00012521433,0.0021936276],"genre_scores_gemma":[0.98892826,0.00935639,0.00069172,0.000006794336,0.00022440017,0.0000051886914,0.000023717741,0.00006633484,0.0006972149],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975668,0.000027480164,0.00050075474,0.00046849728,0.00033738159,0.0010990951],"domain_scores_gemma":[0.9990891,0.000069831556,0.00043989718,0.00020656525,0.000101966456,0.00009262649],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00064746715,0.00038253932,0.0006094689,0.00040567748,0.00017085619,0.00058112,0.0002651941,0.00021069437,0.000041884337],"category_scores_gemma":[0.000057262492,0.0003485982,0.00016437573,0.00022066556,0.0001334443,0.00020353742,0.00083865743,0.005305016,2.5793733e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011662198,0.0004549128,0.5663961,0.0067596124,0.006243302,0.00020516389,0.0005993792,0.00041363123,0.27075928,0.032576144,0.000022503413,0.115453325],"study_design_scores_gemma":[0.0017433508,0.0002885058,0.0014150715,0.0041871383,0.0050341035,0.010469297,0.012259278,0.04362815,0.2667863,0.65021384,0.00084139005,0.0031335587],"about_ca_topic_score_codex":0.0001569552,"about_ca_topic_score_gemma":0.000025668094,"teacher_disagreement_score":0.6176377,"about_ca_system_score_codex":0.00080448284,"about_ca_system_score_gemma":0.0013733769,"threshold_uncertainty_score":0.9998966},"labels":[],"label_agreement":null},{"id":"W4390493422","doi":"10.1109/induscon58041.2023.10374963","title":"A Decision Support Tool to Analyze Food Properties from Near Infrared Spectroscopy*","year":2023,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Calibration; Near-infrared spectroscopy; Computer science; Process analytical technology; Partial least squares regression; Sample (material); Chemometrics; Quality (philosophy); Machine learning; Predictive modelling; Artificial intelligence; Data mining; Process engineering; Mathematics; Statistics; Chemistry; Engineering; Chromatography; Work in process; Physics","score_opus":0.02446400907851599,"score_gpt":0.2776643329495592,"score_spread":0.25320032387104324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390493422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95417607,0.00009930466,0.0013658231,0.00026002564,0.00007984934,0.00008745416,0.000073241776,0.0006024775,0.043255784],"genre_scores_gemma":[0.95259553,0.000045774304,0.010407208,0.0004943948,0.00022073711,0.000052169376,0.00011228502,0.000049577924,0.03602229],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803954,0.000007909559,0.00039864363,0.0005559847,0.0004810007,0.00051690073],"domain_scores_gemma":[0.99890625,0.00011995186,0.00006404097,0.00064086495,0.000078827594,0.00019006683],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000108808345,0.00026115146,0.00039147786,0.00025437452,0.00017345499,0.00021446498,0.00043166406,0.00014399049,0.020783773],"category_scores_gemma":[0.00037932777,0.00021028529,0.00017288543,0.0019571811,0.000050832743,0.00015474526,0.000227238,0.0001831225,0.0029776208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035271805,0.0001906392,0.014614533,0.000053343458,0.0005613194,0.00005370212,0.0008665821,0.00012385925,0.79808766,0.00014986855,0.18167117,0.0032746184],"study_design_scores_gemma":[0.00046869126,0.00014596658,0.0022549604,0.00002419783,0.00012567177,0.0000022489787,0.0007783008,0.000708808,0.9710413,0.0011891171,0.022865562,0.00039515467],"about_ca_topic_score_codex":0.00016539323,"about_ca_topic_score_gemma":0.00006469609,"teacher_disagreement_score":0.17295368,"about_ca_system_score_codex":0.00011726487,"about_ca_system_score_gemma":0.000113342634,"threshold_uncertainty_score":0.9977987},"labels":[],"label_agreement":null},{"id":"W4390607267","doi":"10.2139/ssrn.4685190","title":"Neutralnet: Development and Testing of a Machine Learning Solution for Pulse Shape Discrimination","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pulse (music); Computer science; Artificial intelligence; Telecommunications","score_opus":0.027542071160749932,"score_gpt":0.28714915811573427,"score_spread":0.25960708695498436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390607267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9415274,0.04176242,0.0144214155,0.00034804965,0.00012314384,0.00014537589,0.000019266534,0.00008828687,0.0015646141],"genre_scores_gemma":[0.99308246,0.001683185,0.0026541376,0.000006182202,0.00025795482,0.000027000682,0.000081765385,0.000047248064,0.0021600812],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977781,0.000018529407,0.00051106117,0.0003574772,0.000232581,0.0011022378],"domain_scores_gemma":[0.99910957,0.00012837214,0.0004492167,0.00010611566,0.0001441943,0.00006251298],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00088465755,0.00027251532,0.0003706328,0.00029345794,0.00021814654,0.0000914209,0.00020191142,0.00022668751,0.000057742738],"category_scores_gemma":[0.00030368645,0.00025252905,0.0001444718,0.00021549444,0.00003945715,0.000050929353,0.00024653316,0.0033987502,0.000001424603],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030326637,0.00035897427,0.0082910545,0.006497593,0.0036566656,0.00001081146,0.0022766439,0.0008808788,0.16266167,0.016918767,0.00005434148,0.7980893],"study_design_scores_gemma":[0.0023728702,0.0006769367,0.0007729636,0.0020115396,0.0045702164,0.0010224143,0.0046181288,0.18636803,0.32705805,0.4670338,0.0015443899,0.0019506655],"about_ca_topic_score_codex":0.00008466885,"about_ca_topic_score_gemma":0.00018933536,"teacher_disagreement_score":0.79613864,"about_ca_system_score_codex":0.0010279506,"about_ca_system_score_gemma":0.0017082924,"threshold_uncertainty_score":0.99999267},"labels":[],"label_agreement":null},{"id":"W4390695134","doi":"10.1002/fft2.345","title":"Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging","year":2024,"lang":"en","type":"article","venue":"Food Frontiers","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Agricultural Science and Technology Innovation Program; Chinese Academy of Agricultural Sciences","keywords":"Traceability; Hyperspectral imaging; Linear discriminant analysis; Sample (material); Mathematics; Food science; Pattern recognition (psychology); Artificial intelligence; Statistics; Computer science; Biology; Chemistry; Chromatography","score_opus":0.013800228333873548,"score_gpt":0.2597214158947465,"score_spread":0.24592118756087297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390695134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9816836,0.004571748,0.012456382,0.00003908108,0.00017856098,0.000039220857,0.00001890561,0.0001284793,0.00088401977],"genre_scores_gemma":[0.9936239,0.000016567896,0.0061412384,0.00000576644,0.00009041931,0.0000026442924,0.000006954153,0.000014773659,0.00009774764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928874,0.000010633572,0.00018139073,0.0002483362,0.00011727433,0.00015364694],"domain_scores_gemma":[0.999722,0.000019335428,0.000044561988,0.00013381342,0.000032573396,0.00004773685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007513896,0.00011150729,0.00016275115,0.00011060964,0.000084464926,0.00009502002,0.000054016655,0.00005987406,0.000075021046],"category_scores_gemma":[0.000042458974,0.00011264645,0.000075125405,0.00032911732,0.00009674651,0.00022519406,0.000013120463,0.00014314179,8.593162e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009313799,0.00006952536,0.020777427,0.00048019187,0.0003442969,0.00000596156,0.001958201,0.00006175994,0.966796,0.00008206583,0.0004410098,0.008890429],"study_design_scores_gemma":[0.00029317968,0.000056512767,0.00045249215,0.000034891353,0.00028532482,0.000016695934,0.00254096,0.11986179,0.8747859,0.0010040638,0.0004693028,0.0001988755],"about_ca_topic_score_codex":0.00005606616,"about_ca_topic_score_gemma":0.0000044171566,"teacher_disagreement_score":0.119800024,"about_ca_system_score_codex":0.00015270892,"about_ca_system_score_gemma":0.00004432287,"threshold_uncertainty_score":0.4593589},"labels":[],"label_agreement":null},{"id":"W4390744831","doi":"10.3389/fpls.2023.1303429","title":"Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms","year":2024,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Camelina sativa; Sensitivity (control systems); Fluorescence; Camelina; Computer science; Algorithm; Artificial intelligence; Biology; Agronomy; Optics; Physics; Engineering; Crop","score_opus":0.007073424625033881,"score_gpt":0.24238295130964718,"score_spread":0.2353095266846133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390744831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8987757,0.0005376249,0.10013602,0.000015068195,0.00016493346,0.00009517599,0.00011911539,0.000042154803,0.000114192226],"genre_scores_gemma":[0.98121053,0.0000786609,0.018589806,0.0000026545847,0.00003200938,0.000004693931,0.000019880617,0.0000125930055,0.000049166978],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99882376,0.000012765564,0.00019603276,0.00042067477,0.00026820845,0.00027858146],"domain_scores_gemma":[0.9995597,0.00007852893,0.00010250216,0.000099489625,0.00009165344,0.00006813158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037536392,0.00016252283,0.00028268073,0.00047811284,0.00016585292,0.00007830325,0.00009210256,0.00006484558,0.0000069148923],"category_scores_gemma":[0.000121346675,0.00012842551,0.0000338762,0.0014000423,0.00034894934,0.0002668394,0.000039573326,0.0002713484,1.4931658e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011451353,0.000022704466,0.020488825,0.00013178954,0.00004571589,0.00004359697,0.00025897252,0.00018909237,0.9773162,0.000005642637,0.000017677465,0.0013652573],"study_design_scores_gemma":[0.00017598007,0.00004586082,0.00047054118,0.00010420967,0.000049492228,0.00006761409,0.0008775167,0.46895102,0.5291268,0.000021003263,0.000011137685,0.00009880974],"about_ca_topic_score_codex":0.0003780182,"about_ca_topic_score_gemma":0.000057571022,"teacher_disagreement_score":0.46876192,"about_ca_system_score_codex":0.00019263168,"about_ca_system_score_gemma":0.00026347782,"threshold_uncertainty_score":0.52370405},"labels":[],"label_agreement":null},{"id":"W4390745798","doi":"10.2139/ssrn.4689448","title":"Integrating Parafac and Pso Fitting Model: A New View into Fluorescence Spectroscopic Analysis for Adulterant Quantification of Lubricant Oil","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adulterant; Lubricant; Oil analysis; Fluorescence; Biological system; Computer science; Chemistry; Chromatography; Petroleum engineering; Engineering; Optics; Physics; Organic chemistry; Biology","score_opus":0.01985180212176636,"score_gpt":0.3109421682596861,"score_spread":0.2910903661379198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390745798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47152805,0.07365382,0.45270276,0.0011265578,0.00019647357,0.00019047539,0.00006426172,0.00013500605,0.00040260234],"genre_scores_gemma":[0.9575561,0.026716432,0.012821134,0.000020753661,0.00038915884,0.000040418487,0.00007544781,0.00006907738,0.002311447],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99574906,0.00004372722,0.0012345755,0.00089844875,0.00046042097,0.0016137689],"domain_scores_gemma":[0.99775165,0.00019226591,0.0010753395,0.0005464125,0.00025470703,0.00017960009],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0011971986,0.0005501327,0.00122752,0.00075339625,0.00022347289,0.0002551688,0.0006587568,0.00034252266,0.00006749511],"category_scores_gemma":[0.00030267786,0.00048175233,0.0007958962,0.0010782803,0.000098637334,0.000089574474,0.00032033332,0.0038403287,0.0000027856256],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005559081,0.0004991664,0.0040536677,0.009517945,0.027907511,0.000013251527,0.004990604,0.01950147,0.68248695,0.07254086,0.00035535704,0.17757732],"study_design_scores_gemma":[0.00086834683,0.00020150759,0.0000261758,0.0014815165,0.0139498105,0.00006739709,0.0038054092,0.40079483,0.22928925,0.34834614,0.000077662866,0.0010919272],"about_ca_topic_score_codex":0.00092575944,"about_ca_topic_score_gemma":0.000834178,"teacher_disagreement_score":0.48602808,"about_ca_system_score_codex":0.0012912804,"about_ca_system_score_gemma":0.0034321712,"threshold_uncertainty_score":0.9997634},"labels":[],"label_agreement":null},{"id":"W4390749878","doi":"10.3390/foods13020231","title":"Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Hyperspectral imaging; Partial least squares regression; Principal component analysis; Linear discriminant analysis; Food science; Protein quality; Mathematics; Pattern recognition (psychology); Artificial intelligence; Chemistry; Statistics; Computer science","score_opus":0.043954416865897704,"score_gpt":0.3474826830252241,"score_spread":0.30352826615932643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390749878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9832987,0.0005817576,0.00473803,0.00008876784,0.00011193889,0.00002297772,0.000058714915,0.00013308,0.010966003],"genre_scores_gemma":[0.99801785,0.000021185651,0.0005530098,0.000022606939,0.00019368448,0.0000019390677,0.00006958747,0.000019209785,0.0011009134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913913,0.000009333876,0.00027854257,0.00022117588,0.00016777331,0.00018401841],"domain_scores_gemma":[0.999609,0.000037293656,0.00008184152,0.00019355534,0.000037588823,0.000040727035],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010444873,0.00011322089,0.00019683584,0.00009628793,0.000054801447,0.000065073466,0.00010921226,0.000057021993,0.0009869995],"category_scores_gemma":[0.000035550685,0.000109856686,0.00011653519,0.00043908917,0.00004644637,0.00015262538,0.000028461249,0.000120714685,0.0000077490995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010626593,0.000033383985,0.0031499844,0.00019848337,0.000076717406,0.0000042047936,0.00018438995,0.000002804844,0.9942696,0.00048543242,0.00002660982,0.0015577647],"study_design_scores_gemma":[0.000096963755,0.0000074196787,0.0016810906,0.000041128693,0.00011785573,0.00001145702,0.00028020155,0.0032692843,0.993692,0.00027188438,0.00038356017,0.00014719034],"about_ca_topic_score_codex":0.00015684502,"about_ca_topic_score_gemma":0.0000019935708,"teacher_disagreement_score":0.014719133,"about_ca_system_score_codex":0.00008573099,"about_ca_system_score_gemma":0.000046364094,"threshold_uncertainty_score":0.9999262},"labels":[],"label_agreement":null},{"id":"W4390828111","doi":"10.1002/cem.3531","title":"Implications of confounding from unmodeled interactions between explanatory variables when using latent variable regression models to make inferences","year":2024,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Center for Complementary and Integrative Health; National Institutes of Health; Office of Dietary Supplements","keywords":"Latent variable; Confounding; Econometrics; Regression analysis; Statistics; Regression; Variable (mathematics); Latent variable model; Mathematics; Linear regression; Computer science","score_opus":0.11151190975266735,"score_gpt":0.36280783935058086,"score_spread":0.2512959295979135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390828111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.631278,0.0034706525,0.35609573,0.0003529384,0.00035227436,0.00006162985,0.00020689184,0.00005012424,0.008131786],"genre_scores_gemma":[0.9684236,0.00021350401,0.030499652,0.000035454654,0.00038811823,0.00000297026,0.000025217612,0.000028019285,0.00038346247],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980105,0.000022076425,0.000959605,0.00024796618,0.0005065574,0.0002532696],"domain_scores_gemma":[0.99739635,0.0010145216,0.0005852045,0.0002874727,0.0005009659,0.00021549454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003936303,0.00021229008,0.00060913805,0.001981691,0.000102332146,0.0001495422,0.00044525648,0.00016602247,0.00078658113],"category_scores_gemma":[0.0004263123,0.00017645763,0.00020009278,0.003307296,0.000036849236,0.00046642128,0.00010819582,0.00049687794,0.0000043349683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013315826,0.00051441556,0.04738226,0.000732849,0.0034091047,0.000033248936,0.0023713722,0.01917001,0.9031326,0.008000206,0.004871603,0.010249162],"study_design_scores_gemma":[0.0012198427,0.00017207804,0.0009307394,0.0026950012,0.0042159664,0.00013152226,0.0022586412,0.041329056,0.80280554,0.13269702,0.010531604,0.0010130089],"about_ca_topic_score_codex":0.00017690932,"about_ca_topic_score_gemma":0.0000026910898,"teacher_disagreement_score":0.33714563,"about_ca_system_score_codex":0.00043432895,"about_ca_system_score_gemma":0.00033817926,"threshold_uncertainty_score":0.8612512},"labels":[],"label_agreement":null},{"id":"W4390849523","doi":"10.2139/ssrn.4694336","title":"Chemical and Physical Characterization of 'Egusi' Seeds and Prediction of Fat Content by Nir Spectroscopy","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chemistry; Sphericity; Food science; Proximate; Chemical composition; Mathematics","score_opus":0.010999985501826027,"score_gpt":0.24614885209561332,"score_spread":0.2351488665937873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390849523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912701,0.0067890435,0.0010031568,0.00017401471,0.00008732352,0.00008015084,0.00019634637,0.000034710305,0.00036515557],"genre_scores_gemma":[0.98720306,0.011328961,0.000035009176,0.000008565401,0.0004884227,0.000008154859,0.00016917037,0.00004063926,0.0007179942],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978267,0.00002212261,0.0005487373,0.0004215123,0.00036368633,0.00081722776],"domain_scores_gemma":[0.99900484,0.00004256361,0.00052178535,0.00020869047,0.000121021556,0.00010111458],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00025699372,0.00032955947,0.0006902139,0.00019212085,0.000048047514,0.000056163724,0.00020022914,0.0003239369,0.000052329895],"category_scores_gemma":[0.000044960583,0.0002979236,0.0001940291,0.00020922013,0.00018747749,0.000068481364,0.00028594403,0.0028658086,8.9091725e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096347016,0.00018536637,0.0014640852,0.00047639973,0.0007712782,9.001356e-7,0.00018206678,7.3123135e-7,0.9944859,0.0016230067,0.000041592404,0.00067231903],"study_design_scores_gemma":[0.0005073987,0.00017879288,0.00015949963,0.00019661854,0.00095004996,0.00011879951,0.00056271424,0.00052281376,0.96893245,0.02762833,0.000030269293,0.0002122636],"about_ca_topic_score_codex":0.000048246515,"about_ca_topic_score_gemma":0.000003610237,"teacher_disagreement_score":0.026005324,"about_ca_system_score_codex":0.0005335727,"about_ca_system_score_gemma":0.0004966895,"threshold_uncertainty_score":0.9999473},"labels":[],"label_agreement":null},{"id":"W4391215599","doi":"10.1021/acs.est.3c06447","title":"Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra","year":2024,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and ICT, South Korea; Mitacs; Alberta Innovates; National Research Foundation of Korea; University of Toronto; National Research Foundation; Government of Ontario","keywords":"Raman spectroscopy; Surface-enhanced Raman spectroscopy; Pollutant; Artificial intelligence; Analytical Chemistry (journal); Chemistry; Environmental chemistry; Biological system; Computer science; Raman scattering; Optics","score_opus":0.006270581555452644,"score_gpt":0.24527147017846518,"score_spread":0.23900088862301253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391215599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9680873,0.0009254098,0.02969097,0.00014915605,0.000044428427,0.00007088413,0.00001337203,0.00007397488,0.00094446796],"genre_scores_gemma":[0.9988422,0.00016447519,0.00072843506,0.0000024257536,0.000008897343,0.0000046851846,0.000011931243,0.000008899903,0.00022805417],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887466,0.0000060477623,0.00029372642,0.00034673774,0.0002765227,0.00020228175],"domain_scores_gemma":[0.9995175,0.000031594034,0.00015881314,0.0002496684,0.000010403988,0.0000320215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014406137,0.000111920075,0.0001783348,0.00020922963,0.000073918716,0.000012857984,0.00031033772,0.00012706516,0.00091091037],"category_scores_gemma":[0.000032639196,0.00010494412,0.00004049404,0.0012085204,0.0011394478,0.00018896708,0.000052226955,0.0001484891,0.000016840952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054296765,0.00008876129,0.003603832,0.000026071195,0.0000074495947,0.0000015903827,0.000113783484,0.0000053088334,0.98426574,0.00048923836,9.978419e-7,0.011391785],"study_design_scores_gemma":[0.0001337188,0.00008071397,0.0038065722,0.000016215907,0.000047903322,0.00001212726,0.00053623546,0.008165338,0.9867398,0.00031454398,0.000051408624,0.00009539296],"about_ca_topic_score_codex":0.00000963138,"about_ca_topic_score_gemma":0.0000052707815,"teacher_disagreement_score":0.030754859,"about_ca_system_score_codex":0.00032913807,"about_ca_system_score_gemma":0.00004021365,"threshold_uncertainty_score":0.99738294},"labels":[],"label_agreement":null},{"id":"W4391305279","doi":"10.3390/foods13030433","title":"Non-Destructive Assessment of Microstructural Changes in Kabuli Chickpeas during Storage","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); University of Lethbridge; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba; Indian Council of Agricultural Research; Canadian Light Source","keywords":"Principal component analysis; Relative humidity; Hyperspectral imaging; Partial least squares regression; Materials science; Endosperm; Moisture; Humidity; Mathematics; Biology; Botany; Remote sensing; Composite material; Meteorology; Statistics","score_opus":0.009502208240756507,"score_gpt":0.302430561746721,"score_spread":0.2929283535059645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391305279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898218,0.0008867727,0.000029631601,0.000075821,0.00012757414,0.00003808481,0.000037723035,0.00005080571,0.008931815],"genre_scores_gemma":[0.99844176,0.00004707272,0.00049705995,0.000007688793,0.00015933503,0.000012001951,0.0000157168,0.00002059522,0.00079879246],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915224,0.000006434146,0.00018109885,0.0002714952,0.00016197006,0.00022674883],"domain_scores_gemma":[0.99965066,0.000036851558,0.000054263684,0.0001940201,0.000020543535,0.00004367482],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000051710056,0.00014997557,0.0002463738,0.00021922504,0.00003399993,0.00003475965,0.00015699591,0.00009500727,0.001106851],"category_scores_gemma":[0.000013442461,0.00013831613,0.0000767556,0.00057314284,0.00006152549,0.00007209314,0.00006755885,0.00026520863,0.0000040950895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001546003,0.000030400819,0.028554877,0.0006132993,0.00014191351,0.00004287926,0.0005070654,0.000022907727,0.9695125,0.00021095756,0.000037531896,0.0003101633],"study_design_scores_gemma":[0.00030760065,0.00004061513,0.087801576,0.000113667214,0.000071477836,0.000023584305,0.00059568667,0.00044848217,0.91002923,0.00029046444,0.000095455835,0.00018217805],"about_ca_topic_score_codex":0.00008014278,"about_ca_topic_score_gemma":0.000048156806,"teacher_disagreement_score":0.059483334,"about_ca_system_score_codex":0.00017652258,"about_ca_system_score_gemma":0.00006055316,"threshold_uncertainty_score":0.9998063},"labels":[],"label_agreement":null},{"id":"W4391454135","doi":"10.2139/ssrn.4713847","title":"3d-Ghostnet: A Novel Spatial-Spectral Algorithm to Improve Foodborne Bacteria Classification Coupled with Hyperspectral Microscopic Imaging Technology","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.007844160921576495,"score_gpt":0.2577018064135073,"score_spread":0.24985764549193082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391454135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76465166,0.02128004,0.20386577,0.005925514,0.0010605804,0.00069145404,0.00026817402,0.0008176978,0.0014390913],"genre_scores_gemma":[0.9740104,0.002331659,0.01843634,0.00011213893,0.0018783716,0.0001703619,0.0001487954,0.00028444594,0.0026274943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99276084,0.000024660669,0.0009381516,0.0015803268,0.00062579487,0.0040702],"domain_scores_gemma":[0.9975922,0.000051091316,0.0006799522,0.0010110423,0.00036172528,0.0003039762],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00061661704,0.001022279,0.0010975087,0.0012899769,0.00031393074,0.00056735263,0.0012776348,0.0007242544,0.00037268447],"category_scores_gemma":[0.00008789089,0.0009229771,0.0003760568,0.001271501,0.00021852428,0.0001179184,0.00060439645,0.009978745,0.00008355688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023870527,0.00039449494,0.00061150605,0.00023344647,0.002433404,0.000058999478,0.00022340448,0.000056797482,0.96756667,0.0048694406,0.000078887475,0.023234224],"study_design_scores_gemma":[0.00817288,0.0024269002,0.00084855437,0.0016875245,0.008187911,0.008421142,0.014084844,0.038671542,0.75308293,0.155695,0.0021219274,0.0065988386],"about_ca_topic_score_codex":0.0005176841,"about_ca_topic_score_gemma":0.0007024615,"teacher_disagreement_score":0.21448375,"about_ca_system_score_codex":0.005851601,"about_ca_system_score_gemma":0.0056106644,"threshold_uncertainty_score":0.99932206},"labels":[],"label_agreement":null},{"id":"W4391680080","doi":"10.54103/2039-4942/19954","title":"RECONSTRUCTION OF THE MULTIELEMENT APPARATUS OF THE CONODONT &lt;em&gt;GLADIGONDOLELLA TETHYDIS&lt;/em&gt; (HUCKRIEDE) USING MULTIVARIATE STATISTICAL ANALYSIS; IMPLICATIONS FOR TAXONOMY, STRATIGRAPHY, AND EVOLUTION","year":2024,"lang":"en","type":"article","venue":"RIVISTA ITALIANA DI PALEONTOLOGIA E STRATIGRAFIA","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Conodont; Taxonomy (biology); Paleontology; Stratigraphy; Multivariate statistics; Geology; Statistical analysis; Biology; Biostratigraphy; Zoology; Statistics; Mathematics; Tectonics","score_opus":0.03012252230568381,"score_gpt":0.2892336243851305,"score_spread":0.2591111020794467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391680080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8969647,0.0031168808,0.094120614,0.00011461903,0.00018891852,0.00091926515,0.0024423795,0.000080203405,0.0020523728],"genre_scores_gemma":[0.9927378,0.00003287023,0.0067937844,0.0000072118482,0.00004980327,0.00014112103,0.00008570476,0.000018966886,0.00013271045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972678,0.00011775768,0.0010797473,0.00073248113,0.00033079763,0.00047142044],"domain_scores_gemma":[0.9976217,0.00051945157,0.0006518064,0.000841699,0.00023459799,0.00013074592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031691475,0.00042259516,0.0007176513,0.00027059714,0.00035661194,0.00017388306,0.00047939928,0.00026819945,0.000095095886],"category_scores_gemma":[0.00025588254,0.00029634027,0.0005730291,0.0015699879,0.0006744141,0.00017452386,0.00006782601,0.00030106702,5.1750925e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013414228,0.00034400384,0.44751325,0.0009669573,0.004657274,0.0000024475737,0.00043191668,0.00058398716,0.4225081,0.11764651,0.00039401214,0.0048173955],"study_design_scores_gemma":[0.0012698019,0.00016582718,0.8892577,0.00023278802,0.008186393,0.000038068054,0.0027830058,0.03440244,0.057574455,0.004822535,0.00045496854,0.0008120148],"about_ca_topic_score_codex":0.0005971458,"about_ca_topic_score_gemma":0.008563066,"teacher_disagreement_score":0.44174445,"about_ca_system_score_codex":0.0001562756,"about_ca_system_score_gemma":0.00031456084,"threshold_uncertainty_score":0.99994886},"labels":[],"label_agreement":null},{"id":"W4391731569","doi":"10.1016/j.compag.2024.108720","title":"Intelligent detection and control of quality deterioration of fresh aquatic products in the supply chain: A review","year":2024,"lang":"en","type":"review","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research","keywords":"Supply chain; Quality (philosophy); Risk analysis (engineering); Food chain; Food quality; Biochemical engineering; Business; Environmental science; Computer science; Biotechnology; Engineering; Biology; Ecology; Marketing; Food science","score_opus":0.026311196209690302,"score_gpt":0.31206529840564046,"score_spread":0.28575410219595015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391731569","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004760234,0.9985962,0.00026097975,0.00014348468,0.00003319253,0.00043439853,0.000013505228,0.000008266549,0.00003397589],"genre_scores_gemma":[0.015576951,0.9841362,0.00003772651,0.00004679971,0.00006339015,0.000058528443,0.00004517969,0.000009238517,0.000025981515],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998393,0.00013117821,0.0007516415,0.00035507738,0.00017537779,0.00019369654],"domain_scores_gemma":[0.9991389,0.00018207204,0.00039976757,0.00021449149,0.00004257087,0.000022201302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005212052,0.00027600199,0.0012507905,0.00012804517,0.000022652952,0.000029853727,0.00020134616,0.00020780519,0.000004184808],"category_scores_gemma":[0.000092999326,0.00014846049,0.00014745122,0.0010498124,0.000050023493,0.000034355297,0.0000418899,0.00059217203,3.6699987e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019953377,0.00023798423,0.000044147106,0.31285825,0.00046816104,0.0000083329915,0.00065047876,0.000002836746,0.0024679757,0.0007067495,0.00094228215,0.6815928],"study_design_scores_gemma":[0.001538762,0.0009378029,0.00011345995,0.17852014,0.013094475,0.00046151242,0.00088558317,0.0007001293,0.009738109,0.002132363,0.7896671,0.002210541],"about_ca_topic_score_codex":0.00005266806,"about_ca_topic_score_gemma":0.00020615717,"teacher_disagreement_score":0.78872484,"about_ca_system_score_codex":0.00012201692,"about_ca_system_score_gemma":0.00007566081,"threshold_uncertainty_score":0.60540426},"labels":[],"label_agreement":null},{"id":"W4391757440","doi":"10.1021/acssensors.3c02512","title":"Method for Glucose and Fructose Quantitation in Beverages Using an Off-the-Shelf Glucose Test Strip","year":2024,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Ministry of Advanced Education, Government of Alberta; Grand Challenges Canada; Canada Foundation for Innovation; University of Alberta","keywords":"Fructose; Chemistry; Fructolysis; Chromatography; Monosaccharide; Biochemistry; Food science","score_opus":0.03857829925479872,"score_gpt":0.35280454217255763,"score_spread":0.3142262429177589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391757440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914876,0.0054990905,0.002307589,0.0002251504,0.000052288553,0.00010354916,0.000048735343,0.000085266474,0.00019072274],"genre_scores_gemma":[0.98679864,0.00041586324,0.012045236,0.000065740984,0.00015153897,0.000012406232,0.000039255377,0.000037209797,0.00043411116],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989113,0.000033126875,0.00025845063,0.00038363322,0.00014986405,0.00026363335],"domain_scores_gemma":[0.9983374,0.0012549653,0.00006083072,0.00023347345,0.000049342663,0.00006400653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029775948,0.00017960131,0.0002234287,0.00020056216,0.00011985025,0.00015019697,0.00013080092,0.0001289579,0.000063247935],"category_scores_gemma":[0.00046173795,0.00014908607,0.00006521268,0.00061436876,0.000053053354,0.00023193707,0.000033349483,0.00019902851,0.0000043070427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040095056,0.00014022242,0.0026618412,0.00026983436,0.000104025,0.000029280613,0.0011992863,0.000523705,0.98166066,0.00060312613,0.00011673419,0.0126511585],"study_design_scores_gemma":[0.00039097518,0.0000562538,0.00055924186,0.000045380155,0.00023976539,0.00003282767,0.002200596,0.066479675,0.9265492,0.0016358398,0.0015693131,0.00024093056],"about_ca_topic_score_codex":0.00023372963,"about_ca_topic_score_gemma":0.00006720337,"teacher_disagreement_score":0.06595597,"about_ca_system_score_codex":0.000082523264,"about_ca_system_score_gemma":0.000044218505,"threshold_uncertainty_score":0.6079554},"labels":[],"label_agreement":null},{"id":"W4391759542","doi":"10.2139/ssrn.4714936","title":"Multiclass Online Counting for Strawberry Fruits Based on Tracking-by-Detection","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Tracking (education); Computer science; Artificial intelligence; Computer vision; Mathematics; Horticulture; Psychology; Biology","score_opus":0.017185025499108268,"score_gpt":0.30164334889444433,"score_spread":0.28445832339533605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391759542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9037289,0.02153055,0.065275095,0.0019857867,0.001621939,0.0005581798,0.00095344847,0.0006612758,0.0036848446],"genre_scores_gemma":[0.9929012,0.0014989447,0.00019049438,0.00011489789,0.0017769625,0.0000439942,0.00023792486,0.00015235996,0.0030832302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954549,0.000030458268,0.0006961464,0.00077500154,0.0005938898,0.0024495677],"domain_scores_gemma":[0.9983902,0.00028383487,0.00056053104,0.00039834247,0.00023286552,0.00013420885],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010118987,0.00058773434,0.00061170483,0.00042957588,0.0003020731,0.00034461747,0.000565379,0.00070632534,0.0002160122],"category_scores_gemma":[0.00034962903,0.00056082895,0.0006841075,0.0003488265,0.00005192752,0.000065430395,0.000118979035,0.009439094,0.000018688237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003292981,0.0052366853,0.0011958063,0.0077088405,0.013177216,0.000089384856,0.0005254139,0.026460141,0.68461114,0.0073435735,0.0050694537,0.24528934],"study_design_scores_gemma":[0.0041119405,0.0009539506,0.000041449846,0.0011954549,0.0043817963,0.0002808898,0.0031309593,0.1391587,0.6023145,0.23703659,0.0049440265,0.0024497593],"about_ca_topic_score_codex":0.00009643853,"about_ca_topic_score_gemma":0.00046477426,"teacher_disagreement_score":0.24283959,"about_ca_system_score_codex":0.003592093,"about_ca_system_score_gemma":0.002474052,"threshold_uncertainty_score":0.99968433},"labels":[],"label_agreement":null},{"id":"W4391852605","doi":"10.2139/ssrn.4727614","title":"A Platform for Continuous Monitoring of the Degradation of Aluminum Beverage Can Lids in Realistic Conditions Through Electrochemical Impedance Spectroscopy","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Dielectric spectroscopy; Degradation (telecommunications); Electrochemistry; Electrical impedance; Materials science; Aluminium; Spectroscopy; Computer science; Environmental science; Chemical engineering; Metallurgy; Chemistry; Electrical engineering; Physics; Engineering; Telecommunications; Electrode","score_opus":0.017112860401330938,"score_gpt":0.3049111828813187,"score_spread":0.28779832247998777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391852605","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9842475,0.008473365,0.0048823035,0.0003270137,0.0003230646,0.00030046262,0.0002093087,0.000037787428,0.0011991817],"genre_scores_gemma":[0.99321085,0.0037336105,0.000658911,0.000010873268,0.00051378476,0.00008401817,0.00010148987,0.00006971339,0.0016167356],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961747,0.00002961365,0.0010735238,0.00049760105,0.0004944167,0.0017301609],"domain_scores_gemma":[0.997963,0.00027448699,0.00095982617,0.00051850383,0.00022295189,0.00006119653],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005979193,0.00043515392,0.00088466576,0.00024710534,0.00012829638,0.00005847145,0.0008389558,0.0004535051,0.00003889593],"category_scores_gemma":[0.00038074728,0.0003669205,0.0006087582,0.0006430623,0.0001692128,0.00007458792,0.00025394504,0.0048654354,6.4939013e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025975626,0.0002879948,0.008283573,0.0012041442,0.0014828179,0.000005226693,0.0005715593,0.00014243413,0.94451433,0.042732168,0.00016650316,0.00034948922],"study_design_scores_gemma":[0.00054666126,0.00010229841,0.00009152237,0.0004602253,0.0005879683,0.00010105908,0.0015918633,0.00011354297,0.6579922,0.3381433,0.000016045784,0.00025330973],"about_ca_topic_score_codex":0.000488876,"about_ca_topic_score_gemma":0.00057693076,"teacher_disagreement_score":0.29541114,"about_ca_system_score_codex":0.0028848206,"about_ca_system_score_gemma":0.003804104,"threshold_uncertainty_score":0.9998783},"labels":[],"label_agreement":null},{"id":"W4392105329","doi":"10.1007/s11947-024-03343-x","title":"Application of Portable NIR Spectroscopy for Instant Prediction of the Product Quality of Apple Slices During Hot Air–Assisted Radio Frequency Drying","year":2024,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China","keywords":"Instant; Spectroscopy; Materials science; Quality (philosophy); Product (mathematics); Chemistry; Process engineering; Analytical Chemistry (journal); Food science; Chromatography; Engineering; Physics","score_opus":0.016249838702421214,"score_gpt":0.278172783237533,"score_spread":0.2619229445351118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392105329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99189883,0.0058889887,0.0011570362,0.00018072338,0.000026570939,0.00018726269,0.0002531538,0.00012574499,0.0002817138],"genre_scores_gemma":[0.9989989,0.00013241493,0.0006550367,0.0000015991369,0.00003583404,0.00008893936,0.00002450104,0.00001210623,0.000050713483],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989259,0.0000040324526,0.00045323782,0.0003228582,0.00013613515,0.00015782271],"domain_scores_gemma":[0.99920547,0.000028062173,0.00033572194,0.00030531568,0.00010993338,0.000015525498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012170377,0.00012221669,0.00032303267,0.00021834993,0.00007337808,0.0000064957553,0.00022906633,0.00016861405,0.0000087572425],"category_scores_gemma":[0.00007603767,0.000090617585,0.000073727344,0.0012393637,0.00022959735,0.0000694861,0.00004853154,0.00013741522,8.77412e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004427551,0.000088573004,0.0343096,0.0035917792,0.000132945,8.840699e-8,0.000062638515,0.0000014381621,0.95814234,0.0030260347,0.000004825275,0.00059547246],"study_design_scores_gemma":[0.00023767645,0.00010869134,0.0037925811,0.000089051675,0.00012533914,0.0000074452814,0.0006034764,0.0000609637,0.99249345,0.0023267723,0.00007762587,0.00007690404],"about_ca_topic_score_codex":0.00006755868,"about_ca_topic_score_gemma":0.000023647117,"teacher_disagreement_score":0.034351144,"about_ca_system_score_codex":0.00003508784,"about_ca_system_score_gemma":0.00005785484,"threshold_uncertainty_score":0.3695278},"labels":[],"label_agreement":null},{"id":"W4392166152","doi":"10.3390/s24051485","title":"An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Cluster analysis; Thresholding; Mathematics; Statistics; Pattern recognition (psychology); Artificial intelligence; Regression; Range (aeronautics); Data set; Computer science; Image (mathematics); Engineering","score_opus":0.028660267425214513,"score_gpt":0.3117697280785099,"score_spread":0.2831094606532954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392166152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9753971,0.0040834937,0.008475245,0.0003276637,0.00012717745,0.00015379814,0.0001758429,0.00057182764,0.010687859],"genre_scores_gemma":[0.9947767,0.000020892492,0.003126635,0.000035107445,0.00036517106,0.000034164404,0.00015258594,0.000052124145,0.0014366182],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821734,0.000033449858,0.0002622512,0.0007229909,0.00026416092,0.0004997976],"domain_scores_gemma":[0.99924916,0.000118709046,0.00006425634,0.00035458512,0.00005176051,0.00016153618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015607943,0.0002811858,0.00030209537,0.00011475081,0.00022690398,0.00016230476,0.00021999309,0.00014032263,0.00027855422],"category_scores_gemma":[0.00008057266,0.00023416257,0.00021881721,0.00035124755,0.00012964949,0.0002002378,0.000031480606,0.00035762112,0.000010644499],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010783802,0.0005998797,0.007346334,0.0006923513,0.00048322938,0.000041568142,0.002138999,0.00059854955,0.9679671,0.00087317283,0.0066028,0.011577635],"study_design_scores_gemma":[0.00040001227,0.00006705214,0.00018555392,0.000057919086,0.00027645755,0.000014618316,0.0073044645,0.26914266,0.72020656,0.0003180766,0.0015886152,0.00043800854],"about_ca_topic_score_codex":0.00008584986,"about_ca_topic_score_gemma":0.0000021767719,"teacher_disagreement_score":0.2685441,"about_ca_system_score_codex":0.00017529391,"about_ca_system_score_gemma":0.00005368503,"threshold_uncertainty_score":0.9548873},"labels":[],"label_agreement":null},{"id":"W4392200065","doi":"10.18280/isi.290128","title":"Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Agricultural engineering; Process (computing); Grain quality; Computer science; Artificial intelligence; Engineering; Agronomy; Biology","score_opus":0.0246959573271795,"score_gpt":0.30368856514530895,"score_spread":0.27899260781812946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392200065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87097144,0.00079538673,0.12712185,0.000050643313,0.00009688317,0.00017366176,0.00001895297,0.00013209115,0.00063905807],"genre_scores_gemma":[0.9980978,0.00001815138,0.0015538818,0.000040445088,0.00010135851,0.000034943158,0.00008766931,0.000013739829,0.00005201888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998933,0.000027135213,0.00047676783,0.00016187868,0.00016367619,0.00023749872],"domain_scores_gemma":[0.9994613,0.0001630157,0.000155489,0.00011272842,0.00007202242,0.000035434816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006262047,0.00016178757,0.00020715136,0.00019467446,0.00026564975,0.0004223245,0.00011177351,0.000106063184,0.000036032692],"category_scores_gemma":[0.00020854828,0.000118705444,0.000062013,0.0004546435,0.00008293464,0.001202383,0.000058915193,0.00023238557,0.0000027973044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007594665,0.00014989555,0.028959017,0.037778873,0.0005051205,0.0000127996655,0.05087822,0.011330674,0.22019093,0.005794343,0.00018594787,0.64345473],"study_design_scores_gemma":[0.0005033949,0.00006547946,0.0006868346,0.0003009973,0.00007095931,0.000050560404,0.0030403929,0.9613238,0.031684868,0.0013276194,0.0006592925,0.000285803],"about_ca_topic_score_codex":0.0002818693,"about_ca_topic_score_gemma":0.000015432212,"teacher_disagreement_score":0.94999313,"about_ca_system_score_codex":0.00018508095,"about_ca_system_score_gemma":0.00005670295,"threshold_uncertainty_score":0.48406675},"labels":[],"label_agreement":null},{"id":"W4392292723","doi":"10.18280/ijdne.190131","title":"Non-Destructive Prediction of Moisture and Fat Content in Cocoa Beans Using Near-Infrared Spectroscopy and Multivariate Regression Models","year":2024,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Syiah Kuala","keywords":"Partial least squares regression; Water content; Moisture; Principal component analysis; Linear regression; Multivariate statistics; Near-infrared spectroscopy; Principal component regression; Regression analysis; Mathematics; Coefficient of determination; Analytical Chemistry (journal); Environmental science; Chemistry; Statistics; Chromatography; Biology; Engineering","score_opus":0.023433252679625212,"score_gpt":0.294256015543316,"score_spread":0.2708227628636908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392292723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9556206,0.0065363874,0.037027046,0.00012764127,0.00025671953,0.000058637226,0.00007534152,0.000008638183,0.00028902674],"genre_scores_gemma":[0.98999715,0.0015528166,0.008214994,0.00002593402,0.00011757172,9.416147e-7,0.000010465936,0.000014211071,0.00006592764],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990314,0.000016290354,0.00038687506,0.0001870361,0.00026415056,0.000114200615],"domain_scores_gemma":[0.9993122,0.00013289039,0.00024095338,0.00005963523,0.00019898203,0.000055334363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015899645,0.00015282661,0.0002779965,0.00028615072,0.000040877694,0.000109121,0.00011759622,0.00024282171,0.000013842203],"category_scores_gemma":[0.00005660393,0.000121630386,0.00006070324,0.00014951784,0.00008653522,0.00033452007,0.000049307917,0.0006163103,5.0067268e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016983801,0.00021353854,0.027446534,0.00034561928,0.0016044457,0.0003634226,0.0029734685,0.005991575,0.9550315,0.0018530419,0.00019458859,0.0022838963],"study_design_scores_gemma":[0.0018363072,0.00016802229,0.007506639,0.0012313847,0.0003409456,0.00068594,0.0011308584,0.9122368,0.05579729,0.018809179,0.0000397702,0.00021687175],"about_ca_topic_score_codex":0.00006861569,"about_ca_topic_score_gemma":0.000007651947,"teacher_disagreement_score":0.90624523,"about_ca_system_score_codex":0.00015725571,"about_ca_system_score_gemma":0.000063855034,"threshold_uncertainty_score":0.49599433},"labels":[],"label_agreement":null},{"id":"W4392356840","doi":"10.18280/ria.380135","title":"A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Agricultural engineering; Computer science; Process engineering; Engineering; Environmental science; Machine learning","score_opus":0.0681366476061873,"score_gpt":0.4043207096942407,"score_spread":0.3361840620880534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392356840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99256766,0.0020600501,0.004479427,0.00033433942,0.0000601164,0.00020095299,0.00019585695,0.000010648999,0.000090922425],"genre_scores_gemma":[0.9995882,0.0000477105,0.00013682606,0.000010883661,0.00007150202,0.000023891736,0.000016007712,0.000009122979,0.00009584029],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991066,0.000019817353,0.00038785156,0.00018667897,0.00012812648,0.00017092789],"domain_scores_gemma":[0.9991555,0.00053349003,0.000054720196,0.00016652422,0.000057633002,0.000032083957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024041407,0.00009313506,0.00018424251,0.000087072694,0.00005696427,0.000063717926,0.0001621407,0.000029838171,0.00017199479],"category_scores_gemma":[0.00017514573,0.000060934202,0.00006990774,0.00036935107,0.00007545422,0.00006386016,0.000026552594,0.00007558553,0.0000012043287],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007419524,0.00016217423,0.01342495,0.0013078091,0.000079117395,0.000017505721,0.003022443,0.0013972769,0.97280604,0.0028288914,0.00021143576,0.0046681454],"study_design_scores_gemma":[0.000036202997,0.0000892642,0.00044446837,0.0001239436,0.0001661699,0.00001106335,0.0146759935,0.0174066,0.96637994,0.0005151394,0.00006796155,0.00008322944],"about_ca_topic_score_codex":0.00032005215,"about_ca_topic_score_gemma":0.000036579662,"teacher_disagreement_score":0.016009323,"about_ca_system_score_codex":0.000024987317,"about_ca_system_score_gemma":0.000042678108,"threshold_uncertainty_score":0.24848247},"labels":[],"label_agreement":null},{"id":"W4392367902","doi":"10.7554/elife.95802.1.sa3","title":"eLife Assessment: A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Institute of Mental Health; National Institutes of Health","keywords":"Photometry (optics); Dynamics (music); Statistical analysis; Computer science; Statistics; Mathematics; Physics; Astrophysics; Acoustics","score_opus":0.09234830621448911,"score_gpt":0.46223350541353875,"score_spread":0.3698851991990496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392367902","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035111688,0.15466227,0.41451034,0.0108514745,0.004274894,0.005629352,0.1603594,0.00070419017,0.24549691],"genre_scores_gemma":[0.0103120655,0.004908296,0.1761559,0.0012787489,0.00061785255,0.0015776557,0.05304757,0.00026584353,0.75183606],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955227,0.00004421707,0.0017490401,0.0010523777,0.001080723,0.00055098726],"domain_scores_gemma":[0.9965059,0.0015264922,0.00063185324,0.0009168345,0.00024534503,0.00017356088],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005835741,0.00065944024,0.0030905835,0.0018720974,0.00004008807,0.00008433654,0.0006256944,0.0009707006,0.04805383],"category_scores_gemma":[0.0010550582,0.00058423175,0.001202313,0.00531513,0.00010391311,0.00004946015,0.00020014097,0.0010739195,0.00002711847],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016710651,0.002140585,0.0011376331,0.02504089,0.028469749,0.00005419089,0.000042937034,0.000011905029,0.00007085422,0.008818644,0.9301476,0.0023939444],"study_design_scores_gemma":[0.030234165,0.00093496416,0.00018934144,0.01440272,0.1894774,0.000007856251,0.0019732553,0.05370822,0.010772915,0.009796391,0.6819729,0.0065298243],"about_ca_topic_score_codex":0.0007966548,"about_ca_topic_score_gemma":0.0003541901,"teacher_disagreement_score":0.5063392,"about_ca_system_score_codex":0.0011534231,"about_ca_system_score_gemma":0.00050000194,"threshold_uncertainty_score":0.9996609},"labels":[],"label_agreement":null},{"id":"W4392663606","doi":"10.1117/12.3002945","title":"Advancing recycling efficiency through hyperspectral identification of fabric blends","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Photon Etc (Canada); Institut National de la Recherche Scientifique","funders":"","keywords":"Hyperspectral imaging; Sorting; Principal component analysis; Multivariate statistics; Computer science; Pattern recognition (psychology); Regression; Artificial intelligence; Identification (biology); Set (abstract data type); Principal component regression; Range (aeronautics); Mean squared error; Algorithm; Mathematics; Materials science; Statistics; Machine learning","score_opus":0.013313044117687061,"score_gpt":0.30697988164627027,"score_spread":0.2936668375285832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392663606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73255736,0.008146952,0.05216657,0.00031570595,0.00023013003,0.000038200687,0.000009595178,0.00040809743,0.20612738],"genre_scores_gemma":[0.98976207,0.000257107,0.0015898122,0.000015330608,0.00011473528,0.0000041923863,0.000007971894,0.00001586134,0.008232924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989473,0.0000046834866,0.00034521736,0.00029232132,0.00019503375,0.00021540454],"domain_scores_gemma":[0.9995216,0.000112316775,0.00006510812,0.00023073521,0.000040140323,0.00003008337],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012429086,0.00010771075,0.00016477065,0.00012672001,0.000053800806,0.00004571539,0.00016058312,0.000065114014,0.0020020707],"category_scores_gemma":[0.000103502774,0.000095420466,0.000121645964,0.0009875352,0.00004340265,0.00018412265,0.00002436011,0.00013999357,0.00005201999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034200807,0.000040257226,0.00028235724,0.00019261503,0.000055225446,0.000003718316,0.00034572365,0.000089086476,0.991022,0.0065203058,0.00019929447,0.0012459793],"study_design_scores_gemma":[0.000058988255,0.000010375694,0.000032232078,0.000030558807,0.00012364786,0.000008225878,0.0015262326,0.0022896216,0.99356204,0.0017138455,0.0005318197,0.000112391426],"about_ca_topic_score_codex":0.00005383078,"about_ca_topic_score_gemma":0.0000023379478,"teacher_disagreement_score":0.2572047,"about_ca_system_score_codex":0.000093490766,"about_ca_system_score_gemma":0.000053424184,"threshold_uncertainty_score":0.99891025},"labels":[],"label_agreement":null},{"id":"W4392828296","doi":"10.1051/0004-6361/202449970","title":"<i>Euclid</i>: Testing photometric selection of emission-line galaxy targets","year":2024,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas; Staatssekretariat für Bildung, Forschung und Innovation; National Astronomical Observatory of Japan; Institut de Física d'Altes Energies; Norsk Romsenter; Agenția Spațială Română; European Space Agency; Agenzia Spaziale Italiana; Fundação para a Ciência e a Tecnologia; Magyar Tudományos Akadémia; Science and Technology Facilities Council; European Commission; Integrated Electronics Engineering Center, Binghamton University; Ministerio de Ciencia, Innovación y Universidades; National Aeronautics and Space Administration","keywords":"Physics; Galaxy; Redshift; Astrophysics; Photometric redshift; Spectrograph; Emission spectrum; Line (geometry); Photometry (optics); Astronomy; Spectral line; Stars","score_opus":0.013652727478460724,"score_gpt":0.2540347163307177,"score_spread":0.240381988852257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392828296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88281363,0.0014040276,0.11034937,0.000037394224,0.00008057318,0.00005432919,0.000039617546,0.00018340412,0.005037661],"genre_scores_gemma":[0.96406525,0.000025505442,0.034893427,0.0000066066655,0.00030498725,0.0000072240477,0.000025711006,0.000022995951,0.0006482758],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903786,0.000006683518,0.00024969463,0.00030534065,0.00014927372,0.00025114304],"domain_scores_gemma":[0.99947584,0.00015032756,0.00009258558,0.00012340723,0.00006532609,0.000092502174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053313142,0.00018215287,0.00025072283,0.00019634381,0.00009420029,0.000056591653,0.000099160265,0.00006283988,0.00042569372],"category_scores_gemma":[0.000048115526,0.00017010159,0.00009049048,0.0014297361,0.000056206714,0.00015621826,0.000054076612,0.00024909838,0.000010128714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025674504,0.00012954783,0.015576891,0.00031882405,0.0002412599,0.0000045051993,0.00005995067,0.0001840697,0.72367144,0.00018651347,0.00038670536,0.25921464],"study_design_scores_gemma":[0.0002541011,0.00012518094,0.0007167447,0.00009576508,0.00023503536,0.000009960864,0.00016351714,0.0027351554,0.98500496,0.00021737201,0.010224828,0.00021737644],"about_ca_topic_score_codex":0.000034683973,"about_ca_topic_score_gemma":2.0728093e-7,"teacher_disagreement_score":0.26133353,"about_ca_system_score_codex":0.00003797781,"about_ca_system_score_gemma":0.00006594112,"threshold_uncertainty_score":0.6936541},"labels":[],"label_agreement":null},{"id":"W4393315987","doi":"10.1002/cem.3541","title":"Selective protein quantification on continuous chromatography equipment with limited absorbance sensing: A partial least squares and statistical wavelength selection solution","year":2024,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Partial least squares regression; Absorbance; Chromatography; Chemometrics; Selection (genetic algorithm); Chemistry; Analytical Chemistry (journal); Computer science; Mathematics; Statistics; Artificial intelligence","score_opus":0.017549167765078626,"score_gpt":0.27051470290228546,"score_spread":0.2529655351372068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393315987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92292875,0.0011928563,0.074536584,0.00031977074,0.00008418405,0.0001341075,0.000019297247,0.00008770597,0.00069673604],"genre_scores_gemma":[0.9952572,0.00013443491,0.0040812697,0.000022657134,0.0002569224,0.0000051127136,0.000011653312,0.000032356675,0.00019836923],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811643,0.0000364682,0.00052906625,0.0003359884,0.0006719815,0.0003100779],"domain_scores_gemma":[0.99859655,0.00030474286,0.00039912036,0.00012026495,0.00043067164,0.00014866826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037468126,0.00023839386,0.00040626084,0.0013752361,0.0001287901,0.00021750653,0.00009383024,0.00016855818,0.000079763944],"category_scores_gemma":[0.0005378367,0.00019016721,0.00010032205,0.0038051622,0.00011273488,0.000206886,0.000016349159,0.0006261528,0.0000057981665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017057173,0.00091298146,0.004306604,0.001096474,0.0014519074,0.00015419263,0.0005824829,0.000106104824,0.9274259,0.002274008,0.003039815,0.05694378],"study_design_scores_gemma":[0.0010486478,0.0013317191,0.0030235332,0.00042073888,0.000531067,0.000447297,0.00039379875,0.014475486,0.9756539,0.00032457375,0.0019585502,0.00039070495],"about_ca_topic_score_codex":0.000013812446,"about_ca_topic_score_gemma":0.000002366257,"teacher_disagreement_score":0.07232846,"about_ca_system_score_codex":0.00029021257,"about_ca_system_score_gemma":0.00015138213,"threshold_uncertainty_score":0.7754794},"labels":[],"label_agreement":null},{"id":"W4393750204","doi":"10.5281/zenodo.8060806","title":"CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Analytics; Omics; Database; Data science; Computer science; Geography; Computational biology; Environmental science; Biology; Bioinformatics","score_opus":0.05104551453830425,"score_gpt":0.29536263307215493,"score_spread":0.24431711853385069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393750204","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019373684,0.00021179656,0.00030604086,0.00015999515,0.000091681526,0.00024820495,0.9946094,0.0005388012,0.0036403132],"genre_scores_gemma":[0.00009273109,0.0056213164,0.0002659146,0.00013424846,0.00040863242,1.752544e-7,0.99078345,0.0016123038,0.0010812244],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755615,0.00006271521,0.0004853397,0.00083228684,0.00037924707,0.00068424444],"domain_scores_gemma":[0.99782294,0.00014858125,0.00031479337,0.0010430006,0.00040293654,0.00026774587],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006466848,0.00036058223,0.00047344388,0.00052374095,0.0017408164,0.0009573889,0.0013992161,0.00029495452,0.006921159],"category_scores_gemma":[0.0016853282,0.0003968517,0.00013440306,0.00082828064,0.00022166179,0.00015634147,0.002190737,0.0006112788,0.006417205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100626894,0.00009808507,7.9636595e-7,0.0010156307,0.00020210462,0.000018898088,0.000027222191,0.0000056889266,0.0011070438,0.00015218402,0.9956065,0.001665212],"study_design_scores_gemma":[0.00066157436,0.00008363472,0.0000046866735,0.000062997664,0.00044212147,0.00005095997,0.00026112984,0.0003246767,0.0010595957,0.00007721074,0.9965648,0.00040659416],"about_ca_topic_score_codex":0.000042898304,"about_ca_topic_score_gemma":0.0000022890256,"teacher_disagreement_score":0.0054095197,"about_ca_system_score_codex":0.0002202624,"about_ca_system_score_gemma":0.000008824037,"threshold_uncertainty_score":0.99984837},"labels":[],"label_agreement":null},{"id":"W4393830807","doi":"10.5281/zenodo.8060807","title":"CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Analytics; Omics; Data science; Computer science; Database; Computational biology; Biology; Bioinformatics","score_opus":0.05104551453830425,"score_gpt":0.29536263307215493,"score_spread":0.24431711853385069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393830807","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019373684,0.00021179656,0.00030604086,0.00015999515,0.000091681526,0.00024820495,0.9946094,0.0005388012,0.0036403132],"genre_scores_gemma":[0.00009273109,0.0056213164,0.0002659146,0.00013424846,0.00040863242,1.752544e-7,0.99078345,0.0016123038,0.0010812244],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755615,0.00006271521,0.0004853397,0.00083228684,0.00037924707,0.00068424444],"domain_scores_gemma":[0.99782294,0.00014858125,0.00031479337,0.0010430006,0.00040293654,0.00026774587],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006466848,0.00036058223,0.00047344388,0.00052374095,0.0017408164,0.0009573889,0.0013992161,0.00029495452,0.006921159],"category_scores_gemma":[0.0016853282,0.0003968517,0.00013440306,0.00082828064,0.00022166179,0.00015634147,0.002190737,0.0006112788,0.006417205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100626894,0.00009808507,7.9636595e-7,0.0010156307,0.00020210462,0.000018898088,0.000027222191,0.0000056889266,0.0011070438,0.00015218402,0.9956065,0.001665212],"study_design_scores_gemma":[0.00066157436,0.00008363472,0.0000046866735,0.000062997664,0.00044212147,0.00005095997,0.00026112984,0.0003246767,0.0010595957,0.00007721074,0.9965648,0.00040659416],"about_ca_topic_score_codex":0.000042898304,"about_ca_topic_score_gemma":0.0000022890256,"teacher_disagreement_score":0.0054095197,"about_ca_system_score_codex":0.0002202624,"about_ca_system_score_gemma":0.000008824037,"threshold_uncertainty_score":0.99984837},"labels":[],"label_agreement":null},{"id":"W4393968636","doi":"10.1051/0004-6361/202450198","title":"SpectroTranslator: Deep-neural network algorithm for homogenising spectroscopic parameters","year":2024,"lang":"en","type":"preprint","venue":"Astronomy and Astrophysics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Australian Astronomical Optics-Macquarie; Research Executive Agency; Smithsonian Astrophysical Observatory; University of Colorado Boulder; Instituto de Astrofísica de Canarias; Office of Science; Max-Planck-Institut für Astronomie; Leibniz-Institut für Astrophysik Potsdam; Ministério da Ciência, Tecnologia e Inovação; Agence Nationale de la Recherche; University of Oxford; York University; Leibniz-Gemeinschaft; University of Notre Dame; European Regional Development Fund; Carnegie Mellon University; Universidad Nacional Autónoma de México; Alfred P. Sloan Foundation; University of Washington; European Space Agency; Johns Hopkins University; Carnegie Institution of Washington; University of Utah; Ohio State University; U.S. Department of Energy; Smithsonian Institution; New Mexico State University; University of Portsmouth; Vanderbilt University; Agencia Estatal de Investigación; Yale University; HORIZON EUROPE Framework Programme; Max-Planck-Institut für Astrophysik; European Commission","keywords":"Artificial neural network; Algorithm; Computer science; Deep neural networks; Artificial intelligence","score_opus":0.013208784648393318,"score_gpt":0.2538696365137058,"score_spread":0.2406608518653125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393968636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21326481,0.008829951,0.7746489,0.00013567362,0.0007773842,0.0004146086,0.0005659117,0.00029234707,0.0010704333],"genre_scores_gemma":[0.3565736,0.00011415203,0.6382581,0.000045568697,0.0035577996,0.00021604629,0.0005394997,0.00017490114,0.00052032975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971138,0.000017093618,0.0005129822,0.0011461583,0.00023199672,0.0009779667],"domain_scores_gemma":[0.9987648,0.00011864254,0.00025760007,0.0005864714,0.000050427214,0.00022206143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007744658,0.000775829,0.0009277788,0.00012173365,0.00029203188,0.00043683848,0.00041533526,0.00033786095,0.00015418339],"category_scores_gemma":[0.0000051176435,0.0008106611,0.0006440615,0.00028061296,0.00018082507,0.00008323623,0.00050693104,0.0013664962,0.000022302907],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009185152,0.00022305155,0.00057476794,0.0012298784,0.0031232508,0.00003564153,0.00018339849,0.00850835,0.006812136,0.0016968838,0.0008288731,0.9766919],"study_design_scores_gemma":[0.0060568387,0.0012996098,0.00033490674,0.0015392666,0.020729028,0.00010858517,0.0020361228,0.20504074,0.5530672,0.1621541,0.038919166,0.008714421],"about_ca_topic_score_codex":0.000038182505,"about_ca_topic_score_gemma":0.0000037100817,"teacher_disagreement_score":0.9679775,"about_ca_system_score_codex":0.00015891135,"about_ca_system_score_gemma":0.00011185004,"threshold_uncertainty_score":0.9994344},"labels":[],"label_agreement":null},{"id":"W4393977885","doi":"10.11591/eei.v13i3.5432","title":"Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon","year":2024,"lang":"en","type":"article","venue":"Bulletin of Electrical Engineering and Informatics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Ordinal Scale; Ordinal data; Scale (ratio); Grading (engineering); Statistics; Grading scale; Correlation; Mathematics; Artificial intelligence; Computer science; Medicine; Biology; Cartography; Ecology; Geography; Surgery; Geometry","score_opus":0.006964077054794532,"score_gpt":0.22396721397661593,"score_spread":0.21700313692182138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393977885","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99635017,0.0008178818,0.0022332603,0.0000692868,0.000017549783,0.000044642373,0.0000035845721,0.000019819576,0.000443823],"genre_scores_gemma":[0.99897254,0.00066264195,0.00030361142,0.000011568457,0.000012856413,0.0000035476771,0.0000017336324,0.0000054933985,0.00002601149],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994207,0.0000050034587,0.0003106858,0.000060651262,0.000102184866,0.000100743266],"domain_scores_gemma":[0.9996647,0.00014556797,0.00005178276,0.000070957016,0.000034607987,0.000032332115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001783137,0.00008800268,0.00019540622,0.00017577637,0.000014766723,0.000016940796,0.000024721367,0.000060255934,0.000019981722],"category_scores_gemma":[0.00023998515,0.00006707158,0.000028030374,0.00030142302,0.000027703958,0.000033654876,0.000027680844,0.00016841106,5.3470086e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008898421,0.0010224673,0.3958122,0.05657747,0.0021406813,0.000017585835,0.012450205,0.02884191,0.2760028,0.012500165,0.009836926,0.20390774],"study_design_scores_gemma":[0.0006913915,0.00041592034,0.06298631,0.0006443937,0.0002574182,0.0001060425,0.000071330396,0.15194738,0.7695292,0.00011215173,0.012844835,0.00039360343],"about_ca_topic_score_codex":0.00024553927,"about_ca_topic_score_gemma":0.0000019593538,"teacher_disagreement_score":0.49352643,"about_ca_system_score_codex":0.00001705846,"about_ca_system_score_gemma":0.000010697606,"threshold_uncertainty_score":0.27350998},"labels":[],"label_agreement":null},{"id":"W4394051810","doi":"10.5281/zenodo.6637598","title":"Tallo database","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Regina; University of British Columbia; Queen's University; University of Toronto","funders":"UK Research and Innovation","keywords":"Database; Computer science","score_opus":0.036997701199033714,"score_gpt":0.27474315731208787,"score_spread":0.23774545611305414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394051810","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050894425,0.00033891143,0.00004644792,0.00011069134,0.000081921935,0.000106183696,0.9450723,0.0005460614,0.053646587],"genre_scores_gemma":[0.00015023285,0.00064174016,0.00002922065,0.00018471121,0.0002667139,7.936837e-8,0.99383515,0.0013708859,0.0035212648],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758255,0.00012765802,0.00034051816,0.0007362275,0.0007163369,0.0004966868],"domain_scores_gemma":[0.997938,0.000041408388,0.00023848393,0.0013291985,0.00022677537,0.00022615082],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038430796,0.00030016253,0.00032361932,0.0004619888,0.0026896647,0.00068846066,0.0026479524,0.00016401648,0.9039071],"category_scores_gemma":[0.0008783383,0.00033985198,0.00012802218,0.0010281119,0.000158065,0.00015412676,0.0037844374,0.0010926056,0.009213888],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034964185,0.00021657659,1.1395634e-7,0.00027760185,0.0001537942,0.000065273816,0.00003055743,0.0000047766252,0.0018444,0.00006080295,0.99588567,0.00142548],"study_design_scores_gemma":[0.0003066661,0.00006706412,0.0000011024534,0.000016384392,0.00018081591,0.00012854558,0.00020799869,0.0000069014,0.0009408404,0.000017889692,0.99777585,0.0003499307],"about_ca_topic_score_codex":0.00009470092,"about_ca_topic_score_gemma":5.356322e-7,"teacher_disagreement_score":0.89469326,"about_ca_system_score_codex":0.00038476617,"about_ca_system_score_gemma":0.000008757992,"threshold_uncertainty_score":0.99990535},"labels":[],"label_agreement":null},{"id":"W4394531526","doi":"10.6084/m9.figshare.21082235","title":"Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Bayesian probability; Yield (engineering); Statistics; Computer science; Econometrics; Mathematics; Physics","score_opus":0.056886879262735154,"score_gpt":0.257235656133276,"score_spread":0.2003487768705408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394531526","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025875486,0.00034254664,0.0003293591,0.0000067981478,0.00012131996,0.00013956965,0.9976959,0.00015853341,0.0011800762],"genre_scores_gemma":[0.0010030896,0.000012336793,0.00005071437,0.00008188513,0.000567184,0.0002168931,0.9976207,0.000062647465,0.0003845387],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973625,0.000019029687,0.00040596016,0.00075720233,0.0010144155,0.00044089573],"domain_scores_gemma":[0.9984457,0.00015706495,0.0004283294,0.00065857026,0.00014417358,0.00016619374],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000042033356,0.0004829061,0.00043923382,0.00033637517,0.00047535633,0.00013245472,0.0004783916,0.00040993872,0.9266388],"category_scores_gemma":[0.00028233707,0.0004649199,0.00017911494,0.0007143707,0.000018311186,0.00017533843,0.00029856063,0.0009939764,0.00006663726],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064854445,0.00012452606,0.000009188209,0.0005586116,0.00025188221,0.000047539248,0.0000123588125,0.09562974,0.000075453405,2.8265646e-7,0.90320927,0.000016294507],"study_design_scores_gemma":[0.00057570334,0.00014443656,0.000007593828,0.001795012,0.0010680372,0.000117155825,0.000056482437,0.04181482,0.0026064082,0.000007856091,0.9508979,0.00090863043],"about_ca_topic_score_codex":0.00017031249,"about_ca_topic_score_gemma":0.000023722805,"teacher_disagreement_score":0.92657214,"about_ca_system_score_codex":0.00050120644,"about_ca_system_score_gemma":0.00045422587,"threshold_uncertainty_score":0.99978024},"labels":[],"label_agreement":null},{"id":"W4394616371","doi":"10.48130/grares-0024-0009","title":"Integrating spectral data and phylogeographic patterns to study plant genetic variation: a review","year":2024,"lang":"en","type":"review","venue":"Grass Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Agriculture and Agri-Food Canada","funders":"","keywords":"Phylogeography; Variation (astronomy); Evolutionary biology; Genetic data; Genetic variation; Data science; Biology; Geography; Computer science; Phylogenetics; Genetics; Sociology; Population; Demography; Physics","score_opus":0.25141154888845507,"score_gpt":0.4950840926579064,"score_spread":0.24367254376945136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394616371","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015737435,0.9969232,0.000046964215,0.00008866812,0.000055057124,0.0009266531,0.0010555024,0.00006819961,0.00067835645],"genre_scores_gemma":[0.00032808297,0.9973618,0.0002716573,0.00002859726,0.000466606,0.00041233265,0.0006745888,0.0000799821,0.00037636963],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955094,0.00033979042,0.0008255436,0.0015041737,0.0010969018,0.00072418805],"domain_scores_gemma":[0.99699014,0.0005128996,0.00013726999,0.001958681,0.00012369269,0.00027729222],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018120838,0.0005091907,0.0016891097,0.0009825091,0.00025027295,0.00036356898,0.0018819793,0.00021820569,0.00093310635],"category_scores_gemma":[0.00071949896,0.00036747078,0.00022359952,0.003683068,0.00006523468,0.000056410026,0.0018375199,0.0019696692,0.00016732476],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060441803,0.00040921976,0.0014864674,0.20750885,0.0024673715,0.00062640494,0.00023851485,2.1961492e-8,0.000011236002,0.000056991354,0.009680266,0.7775086],"study_design_scores_gemma":[0.00021552572,0.00028271275,0.00021369864,0.07358931,0.009368605,0.0002754858,0.00072925055,0.000044501525,0.000010075335,0.00015947895,0.91406935,0.0010419786],"about_ca_topic_score_codex":0.00037758186,"about_ca_topic_score_gemma":0.00019934689,"teacher_disagreement_score":0.9043891,"about_ca_system_score_codex":0.00015257597,"about_ca_system_score_gemma":0.00026257138,"threshold_uncertainty_score":0.99998015},"labels":[],"label_agreement":null},{"id":"W4394972048","doi":"10.1093/pasj/psae037","title":"Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning","year":2024,"lang":"en","type":"preprint","venue":"Publications of the Astronomical Society of Japan","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Smithsonian Astrophysical Observatory; University of Colorado Boulder; Office of Science; Max-Planck-Institut für Astronomie; Ministério da Ciência, Tecnologia e Inovação; U.S. Department of Energy; Smithsonian Institution; National Natural Science Foundation of China; University of Oxford; York University; Leibniz-Gemeinschaft; University of Notre Dame; Instituto de Astrofísica de Canarias; Astronomical Society of Japan; Carnegie Mellon University; Universidad Nacional Autónoma de México; Alfred P. Sloan Foundation; University of Washington; Johns Hopkins University; Carnegie Institution of Washington; University of Utah; Ohio State University; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; Max-Planck-Institut für Astrophysik","keywords":"Quasar; Dimensionality reduction; Artificial intelligence; Isomap; Computer science; Machine learning; Principal component analysis; Supermassive black hole; Astrophysics; Physics; Pattern recognition (psychology); Galaxy; Nonlinear dimensionality reduction","score_opus":0.030744767219097285,"score_gpt":0.29215147673991815,"score_spread":0.26140670952082085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394972048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99665934,0.0006905379,0.0010240622,0.0012009287,0.00007328559,0.000118927404,0.00014305527,0.000031626005,0.000058209924],"genre_scores_gemma":[0.9955988,0.00004495534,0.0038172088,0.000001133663,0.00007099101,0.00001364986,0.00016628034,0.000017110402,0.00026987953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984381,0.000034840104,0.00077647745,0.00037960263,0.00023981466,0.00013111984],"domain_scores_gemma":[0.9980263,0.00007442657,0.0010816309,0.0005499419,0.00022086145,0.000046845787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004236028,0.00016593173,0.0003608474,0.00009234574,0.000111894435,0.000031434396,0.0003372402,0.00021307405,0.00007795998],"category_scores_gemma":[0.00013386372,0.00014718104,0.00045319364,0.00035481664,0.00037953953,0.000029232984,0.0007068373,0.00057972467,9.637606e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021275147,0.00038293583,0.026409,0.00091550476,0.0005746673,3.1345615e-10,0.00039176104,0.103655756,0.86613655,0.0005502942,0.00010331303,0.0008589509],"study_design_scores_gemma":[0.00027216095,0.00001602546,0.047832582,0.00017852422,0.0010937571,0.0000016617346,0.0010026973,0.418747,0.5293977,0.0011618304,0.000057814887,0.00023824489],"about_ca_topic_score_codex":0.00042298122,"about_ca_topic_score_gemma":0.0000011943362,"teacher_disagreement_score":0.33673882,"about_ca_system_score_codex":0.00020023003,"about_ca_system_score_gemma":0.000121816425,"threshold_uncertainty_score":0.6001868},"labels":[],"label_agreement":null},{"id":"W4394987473","doi":"10.1101/2024.04.19.24306081","title":"Accurate cross-platform GWAS analysis via two-stage imputation","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Krembil Foundation; University of Toronto; National Institutes of Health; Memorial University of Newfoundland; Helene Morgan Babcock and Alfred Babcock Memorial Scholarship Trust; U.S. Department of Veterans Affairs","keywords":"Genome-wide association study; Imputation (statistics); Computer science; Machine learning; Biology; Single-nucleotide polymorphism; Missing data; Genetics","score_opus":0.030264536200897592,"score_gpt":0.35966847176354766,"score_spread":0.3294039355626501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394987473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96332246,0.001874931,0.006409772,0.00009982237,0.00034994454,0.00008495162,0.00026917408,0.00045897719,0.027129952],"genre_scores_gemma":[0.9763465,0.00013149022,0.00037940498,0.000059487265,0.00046555142,0.000062447216,0.0007807008,0.000075598306,0.021698844],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99700373,0.00001596485,0.0007666806,0.0011258556,0.00056901673,0.0005187347],"domain_scores_gemma":[0.99795216,0.00014908113,0.00045349897,0.0010765011,0.00018185162,0.00018688303],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003434795,0.0005772582,0.00089318963,0.000909854,0.00013940569,0.00055024924,0.00072684546,0.0005507799,0.007971615],"category_scores_gemma":[0.00014940357,0.00053533097,0.0009775306,0.002262792,0.00011910447,0.00010685811,0.0009535993,0.0014279081,0.00051986176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029854235,0.0007201961,0.53492725,0.009321257,0.053136855,0.0011517309,0.0023289095,0.20199387,0.18658099,0.001772424,0.0017719227,0.0059960485],"study_design_scores_gemma":[0.0009452714,0.00003419449,0.013392173,0.00015810027,0.02407004,0.000018706849,0.00034174378,0.1714894,0.7620149,0.021948786,0.003026528,0.0025601443],"about_ca_topic_score_codex":0.00045233825,"about_ca_topic_score_gemma":0.000105112034,"teacher_disagreement_score":0.5754339,"about_ca_system_score_codex":0.0003161737,"about_ca_system_score_gemma":0.00014995738,"threshold_uncertainty_score":0.99970984},"labels":[],"label_agreement":null},{"id":"W4395082281","doi":"10.3390/horticulturae10050435","title":"Sweet Cherry Fruit Firmness Evaluation Using Compression Distance Methods","year":2024,"lang":"en","type":"article","venue":"Horticulturae","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Regional Development Fund","keywords":"Horticulture; Compression (physics); Mathematics; Fruit set; Biology; Botany; Materials science; Pollination","score_opus":0.0801072975301359,"score_gpt":0.4371400497857878,"score_spread":0.3570327522556519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395082281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93353295,0.023032647,0.024451382,0.00016557409,0.00038916979,0.00012749924,0.000020224312,0.00036700693,0.017913558],"genre_scores_gemma":[0.99153686,0.00007071723,0.0059967744,0.000030450761,0.0002807716,0.000029801311,0.00004282134,0.000022493841,0.0019893239],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986419,0.00005184395,0.00027832758,0.0003929735,0.00038367865,0.00025124117],"domain_scores_gemma":[0.9993428,0.0001276887,0.000072034956,0.00025875025,0.00011711755,0.000081613274],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030292282,0.00018943699,0.0002325748,0.00006151937,0.00013817215,0.0001662483,0.00016300236,0.00013693271,0.0034783601],"category_scores_gemma":[0.00016078627,0.00013845142,0.00012770716,0.00064414873,0.000051770185,0.00020945125,0.000052072068,0.00022479387,0.00003869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010862037,0.000046509627,0.00026020352,0.00019696592,0.00013868867,0.000009136807,0.00040621855,0.00028743997,0.98705024,0.00016564546,0.0008816574,0.010546454],"study_design_scores_gemma":[0.00022091373,0.000009372567,0.0014140932,0.00022048719,0.00094406743,0.000025151825,0.0008674853,0.101214804,0.8800241,0.0006264102,0.014054564,0.0003785714],"about_ca_topic_score_codex":0.000046767986,"about_ca_topic_score_gemma":0.0000059716267,"teacher_disagreement_score":0.107026145,"about_ca_system_score_codex":0.00026087408,"about_ca_system_score_gemma":0.000049749102,"threshold_uncertainty_score":0.9974326},"labels":[],"label_agreement":null},{"id":"W4395478317","doi":"10.18280/isi.290209","title":"Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bilinear interpolation; Bayesian probability; Gaussian; Bayesian optimization; Gaussian process; Artificial intelligence; Computer science; Machine learning; Econometrics; Statistics; Mathematics; Environmental science; Pattern recognition (psychology); Physics","score_opus":0.019159751822032296,"score_gpt":0.25583290772964884,"score_spread":0.23667315590761653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395478317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40162107,0.00026445274,0.5956169,0.000055672856,0.00008669757,0.00023082735,0.00022426846,0.00047093126,0.0014292308],"genre_scores_gemma":[0.9606959,0.000015524329,0.038366586,0.000079450845,0.00009311369,0.000086053275,0.0006054539,0.000023963086,0.00003392841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987281,0.00001458532,0.0004763339,0.00021316,0.00024085565,0.0003269672],"domain_scores_gemma":[0.9992408,0.00015052453,0.00017170692,0.00019298786,0.00009246299,0.00015152355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017864478,0.00022232572,0.00023692838,0.00028386153,0.00025661063,0.00042176267,0.00013387646,0.00012981458,0.00021839117],"category_scores_gemma":[0.0002822832,0.00017283131,0.00010703967,0.00058746693,0.0001021247,0.0014216691,0.000029086954,0.0001339731,0.000006740629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019360428,0.00020419058,0.020423882,0.012552707,0.0015935174,0.00008202249,0.0071661198,0.9327461,0.0048787263,0.003859989,0.0006225197,0.013934171],"study_design_scores_gemma":[0.00029569288,0.00007268741,0.000085529085,0.00027882546,0.00036200485,0.000046497193,0.0010255717,0.9882663,0.00873977,0.00031715617,0.00025137368,0.0002586295],"about_ca_topic_score_codex":0.000026865788,"about_ca_topic_score_gemma":0.000002100145,"teacher_disagreement_score":0.5590749,"about_ca_system_score_codex":0.0002928003,"about_ca_system_score_gemma":0.00012727882,"threshold_uncertainty_score":0.70478565},"labels":[],"label_agreement":null},{"id":"W4395696310","doi":"10.1007/s11694-024-02504-y","title":"Miniaturized hand-held near-infrared spectroscopy and machine learning for precision monitoring of solid fat content","year":2024,"lang":"en","type":"article","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Partial least squares regression; Mean squared error; Content (measure theory); Correlation coefficient; Coefficient of determination; Analytical Chemistry (journal); Differential scanning calorimetry; Mathematics; Near-infrared spectroscopy; Spectroscopy; Chemistry; Linear regression; Statistics; Chromatography; Physics; Thermodynamics; Optics","score_opus":0.07450806278114032,"score_gpt":0.29266777654314174,"score_spread":0.21815971376200144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395696310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9736899,0.004268775,0.021259341,0.00013852118,0.00039607275,0.0001242006,0.000036538535,0.000026069563,0.000060586546],"genre_scores_gemma":[0.99659705,0.0007505469,0.0018471383,0.000008164119,0.0004819442,0.0000073585034,0.000027187454,0.000028548347,0.00025203172],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983029,0.000025285992,0.0006932014,0.00018621318,0.0006057915,0.00018664243],"domain_scores_gemma":[0.99864435,0.000085436106,0.00058686075,0.000102990845,0.00049192645,0.00008842087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048482633,0.00017963396,0.00043282678,0.00021348453,0.00012010504,0.0001660087,0.00011647491,0.00010172532,0.00006962144],"category_scores_gemma":[0.00041789483,0.00015050928,0.00018122671,0.00024219372,0.00003385427,0.00030142657,0.000022985345,0.00026446092,5.612391e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044008874,0.000082421735,0.002979813,0.0003941834,0.00037903918,0.0000022101724,0.0003466246,0.0000072181756,0.99352777,0.0000068694253,0.00002192525,0.0018118657],"study_design_scores_gemma":[0.0011041964,0.00055045856,0.005730737,0.00065880985,0.00042016205,0.000016243459,0.00011963552,0.0007670829,0.98904794,0.000055161807,0.0013993399,0.000130213],"about_ca_topic_score_codex":0.0000016134684,"about_ca_topic_score_gemma":4.580711e-7,"teacher_disagreement_score":0.022907186,"about_ca_system_score_codex":0.00015714642,"about_ca_system_score_gemma":0.00008082105,"threshold_uncertainty_score":0.61375904},"labels":[],"label_agreement":null},{"id":"W4396690222","doi":"10.15278/isms.2023.7001","title":"DIAGNOSTIC OF SMALL WEAK INTERACTIONS IN GASOLINE BLENDS BY ATTENUATED TOTAL REFLECTION INFRARED SPECTROSCOPY","year":2023,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada)","funders":"","keywords":"Attenuated total reflection; Gasoline; Spectroscopy; Infrared spectroscopy; Infrared; Materials science; Reflection (computer programming); Near-infrared spectroscopy; Total internal reflection; Optics; Optoelectronics; Chemistry; Physics; Computer science; Organic chemistry","score_opus":0.024281201521006882,"score_gpt":0.31431318921118445,"score_spread":0.2900319876901776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396690222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8983711,0.00011193191,0.0004887622,0.00029155775,0.00010839304,0.000052648717,0.000043698146,0.00025811794,0.100273766],"genre_scores_gemma":[0.9457781,0.0002064051,0.00031889896,0.000027919845,0.000089371024,0.000034526394,0.0002571957,0.000025110292,0.053262502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987999,0.000015161799,0.000415863,0.00029153866,0.00015556788,0.00032197582],"domain_scores_gemma":[0.9990264,0.00046441372,0.00012367252,0.00025612215,0.00006037575,0.0000689878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000081816484,0.0001684321,0.00028898145,0.00043689486,0.000048436934,0.000020720348,0.00013831361,0.00008302026,0.0042673745],"category_scores_gemma":[0.0005175559,0.00016749553,0.00009446934,0.0023117452,0.00004470139,0.00009909478,0.000057883448,0.00027548213,0.0001117877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005477911,0.00020475632,0.02201187,0.000058615864,0.00009201497,0.000008371773,0.00009438718,0.00013112035,0.9526849,0.0000737638,0.024444446,0.00014102418],"study_design_scores_gemma":[0.00055374746,0.000049517814,0.0029746844,0.000039678318,0.000077383644,0.0000112614225,0.0007992046,0.0017848678,0.9920304,0.0003003321,0.001190648,0.0001882904],"about_ca_topic_score_codex":0.00054891885,"about_ca_topic_score_gemma":0.00032657053,"teacher_disagreement_score":0.047406945,"about_ca_system_score_codex":0.00015219583,"about_ca_system_score_gemma":0.000046019788,"threshold_uncertainty_score":0.9966429},"labels":[],"label_agreement":null},{"id":"W4397002438","doi":"10.1080/17480272.2024.2351201","title":"A comparative study on the performance of terahertz, near-infrared, and hyperspectral spectroscopy for wood identification","year":2024,"lang":"en","type":"article","venue":"Wood Material Science and Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hyperspectral imaging; Terahertz radiation; Identification (biology); Spectroscopy; Terahertz spectroscopy and technology; Infrared; Near-infrared spectroscopy; Materials science; Remote sensing; Infrared spectroscopy; Analytical Chemistry (journal); Environmental science; Optics; Optoelectronics; Physics; Chemistry; Geography; Astronomy; Environmental chemistry; Botany; Biology; Organic chemistry","score_opus":0.017123860100967267,"score_gpt":0.2753845159675014,"score_spread":0.25826065586653413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397002438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991921,0.00012026665,0.000060403043,0.00005051694,0.00011489524,0.000108579945,0.000012607709,0.000038938033,0.0003016738],"genre_scores_gemma":[0.99957746,0.000016784628,0.00022439004,0.0000046855816,0.0000719516,0.000031164607,0.000001289227,0.000006022132,0.000066266904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993338,0.0000021398662,0.00012425413,0.00021501537,0.00015951355,0.0001652721],"domain_scores_gemma":[0.99975044,0.000048291313,0.000024092715,0.000113798116,0.000029407272,0.000033984863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002155566,0.00009376807,0.00013036546,0.000070640475,0.00014598439,0.000286917,0.0001265592,0.000020366806,0.00003645482],"category_scores_gemma":[0.000027785405,0.00006566159,0.00001591525,0.00034664024,0.000117426316,0.00016224741,0.000029540524,0.000057084697,0.0000013111573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001895897,0.000023361628,0.00008504684,0.00007747474,0.000018442206,3.4876948e-7,0.001263818,0.000030688512,0.9980694,0.00034182114,0.000021599686,0.000049050028],"study_design_scores_gemma":[0.00010092552,0.00014220702,0.0009880845,0.000029571665,0.000039636147,0.0000029943649,0.0013178788,0.012298017,0.9848788,0.000026555326,0.00009331035,0.00008203597],"about_ca_topic_score_codex":0.0000069443895,"about_ca_topic_score_gemma":3.4481812e-7,"teacher_disagreement_score":0.013190606,"about_ca_system_score_codex":0.000038659153,"about_ca_system_score_gemma":0.000029259949,"threshold_uncertainty_score":0.27667472},"labels":[],"label_agreement":null},{"id":"W4398144380","doi":"10.1021/acsomega.3c09262","title":"Introducing SpectraFit: An Open-Source Tool for Interactive Spectral Analysis","year":2024,"lang":"en","type":"article","venue":"ACS Omega","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Max-Planck-Gesellschaft","keywords":"Computer science; Software; Process (computing); Consistency (knowledge bases); Container (type theory); Interface (matter); Data mining; Operating system; Engineering","score_opus":0.017327724315160838,"score_gpt":0.3229809492662509,"score_spread":0.3056532249510901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398144380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9664691,0.0008520275,0.014858992,0.0006215971,0.00017072335,0.00016767182,0.00009536144,0.00038232756,0.016382162],"genre_scores_gemma":[0.9819423,0.000035269233,0.002677706,0.00013896333,0.00094586785,0.00006133312,0.00020130466,0.000055935292,0.0139413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981433,0.00001610854,0.00034236582,0.0008308747,0.0002094068,0.00045795745],"domain_scores_gemma":[0.99875754,0.00033917787,0.000083330415,0.00063579745,0.000070224545,0.000113955044],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025002396,0.00026700136,0.0004900693,0.0005029897,0.0001566521,0.00078858266,0.0007553175,0.00011733358,0.0013606327],"category_scores_gemma":[0.00020616775,0.00024838533,0.00026989993,0.0021396861,0.000051146835,0.0007081452,0.0001660285,0.00034603858,0.00006193053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000774747,0.0011894857,0.008409579,0.00066464045,0.02072832,0.00014029557,0.0052692844,0.0022933725,0.899588,0.012869048,0.021630226,0.026443027],"study_design_scores_gemma":[0.00066534133,0.00021874161,0.00057380716,0.000046852303,0.0054006698,0.000028810742,0.0017541915,0.011178906,0.9332474,0.0024011508,0.043642703,0.00084144174],"about_ca_topic_score_codex":0.00021530096,"about_ca_topic_score_gemma":0.000081888626,"teacher_disagreement_score":0.03365941,"about_ca_system_score_codex":0.00027310645,"about_ca_system_score_gemma":0.00006634092,"threshold_uncertainty_score":0.99999684},"labels":[],"label_agreement":null},{"id":"W4398245406","doi":"10.1002/ppj2.20103","title":"Estimating Fusarium head blight severity in winter wheat using deep learning and a spectral index","year":2024,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Fusarium; Index (typography); Winter wheat; Head (geology); Agronomy; Blight; Environmental science; Mathematics; Horticulture; Biology; Computer science","score_opus":0.02293843411685579,"score_gpt":0.28671018001187065,"score_spread":0.26377174589501484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398245406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98756844,0.0045420914,0.003517837,0.00030836515,0.00015762218,0.000025092797,0.000008792647,0.000049830014,0.003821951],"genre_scores_gemma":[0.9975463,0.00010038442,0.0010129825,0.0000362445,0.0008100197,9.919867e-7,0.000005104858,0.00002031257,0.00046766523],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887073,0.000037879465,0.00030924886,0.00019944289,0.0002093686,0.00037330625],"domain_scores_gemma":[0.999544,0.00016835137,0.00008573775,0.00009038092,0.000016096707,0.0000954061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037030014,0.00017669625,0.00024796312,0.00021932492,0.00025668633,0.00037360602,0.0001725755,0.0000739869,0.000909759],"category_scores_gemma":[0.0000549794,0.00012316063,0.000075718504,0.00036531087,0.00006136671,0.0002011065,0.00007703086,0.001283424,0.000008071627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005566551,0.00029983805,0.30153164,0.0009907428,0.001374921,0.002021559,0.016524766,0.016779263,0.6513819,0.00032345453,0.00042316102,0.007792095],"study_design_scores_gemma":[0.001096539,0.00007997297,0.0052386005,0.00074006146,0.0005153449,0.011169261,0.003867283,0.9456044,0.025756588,0.0036111267,0.0015688369,0.00075199304],"about_ca_topic_score_codex":0.000055623346,"about_ca_topic_score_gemma":0.000033225944,"teacher_disagreement_score":0.92882514,"about_ca_system_score_codex":0.0002014495,"about_ca_system_score_gemma":0.00005016769,"threshold_uncertainty_score":0.99612236},"labels":[],"label_agreement":null},{"id":"W4398781925","doi":"10.1080/03639045.2024.2358366","title":"A Novel image processing technique for weighted particle size distribution assessment","year":2024,"lang":"en","type":"article","venue":"Drug Development and Industrial Pharmacy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada); MPB Technologies & Communications (Canada); Université de Montréal","funders":"","keywords":"Computer science; Process (computing); Particle-size distribution; Particle size; Process engineering; Quality (philosophy); Reliability engineering; Distribution (mathematics); Critical quality attributes; Mathematics; Engineering; Physics","score_opus":0.04717013464909912,"score_gpt":0.34948043457786815,"score_spread":0.302310299928769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398781925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90221006,0.0018884434,0.08744407,0.0025358058,0.00036327235,0.0009277959,0.00026223477,0.00067733583,0.0036909909],"genre_scores_gemma":[0.9890421,0.000045314453,0.0076981755,0.000050996405,0.00043995428,0.00038345583,0.00024674876,0.000027111146,0.0020661158],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876446,0.000007925828,0.00035004513,0.00036274243,0.00019222478,0.00032261896],"domain_scores_gemma":[0.9995128,0.00015678068,0.00007063672,0.00007786254,0.00006540024,0.00011654241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003060369,0.00020602944,0.00020955519,0.000038973514,0.0002416646,0.000259891,0.000114233095,0.00008078155,0.00046984784],"category_scores_gemma":[0.00006932121,0.00018222301,0.000059531096,0.00047676373,0.000051008235,0.00029358745,0.000058344373,0.00029064118,0.0000050971457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009560865,0.00018992767,0.0013090959,0.00031171532,0.00017431992,0.000007203928,0.00026011953,2.4896198e-7,0.94121015,0.00030899732,0.008951275,0.047181364],"study_design_scores_gemma":[0.0013929361,0.0000060301118,0.00005434171,0.0000932945,0.00015184308,0.000005264223,0.00013328293,0.0012238524,0.9196151,0.000099346726,0.07697032,0.00025440424],"about_ca_topic_score_codex":0.000011750465,"about_ca_topic_score_gemma":6.280052e-7,"teacher_disagreement_score":0.08683208,"about_ca_system_score_codex":0.00021642087,"about_ca_system_score_gemma":0.00042467058,"threshold_uncertainty_score":0.7430839},"labels":[],"label_agreement":null},{"id":"W4399303930","doi":"10.1016/j.dcan.2024.05.008","title":"Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network","year":2024,"lang":"en","type":"article","venue":"Digital Communications and Networks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Convolutional neural network; Computer science; Nondestructive testing; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.019030363340923378,"score_gpt":0.26018380422420834,"score_spread":0.24115344088328497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399303930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9652019,0.021438353,0.0030282116,0.00065150607,0.00006921329,0.0001122311,0.000067873756,0.00012969771,0.009300997],"genre_scores_gemma":[0.998873,0.0002825026,0.00053838984,0.00004149166,0.00007918694,0.000014327999,0.000119633936,0.000015277827,0.000036211393],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992448,0.000020885514,0.0002173884,0.00023938493,0.00011068201,0.00016686489],"domain_scores_gemma":[0.99895966,0.0005582342,0.00007719654,0.00027886181,0.000062300554,0.00006373564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009456502,0.00013744841,0.00017363376,0.00010117962,0.00025673732,0.00018028733,0.000102882535,0.00007441651,0.000017400056],"category_scores_gemma":[0.000023503122,0.00014067409,0.000045085508,0.00027443178,0.00036564967,0.00024696597,0.00009489208,0.00029692685,4.787749e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025987318,0.0014511428,0.3298053,0.0010658384,0.0022491133,0.000030925275,0.0021413215,0.018610192,0.14042903,0.17714742,0.00054443884,0.32392654],"study_design_scores_gemma":[0.00033119853,0.000068577996,0.020136064,0.000119307115,0.00011146005,0.000038062695,0.0005157862,0.97614753,0.00044601108,0.0017873178,0.00011676166,0.00018192471],"about_ca_topic_score_codex":0.00001975374,"about_ca_topic_score_gemma":0.000010149556,"teacher_disagreement_score":0.95753735,"about_ca_system_score_codex":0.000054778015,"about_ca_system_score_gemma":0.000032775013,"threshold_uncertainty_score":0.5736523},"labels":[],"label_agreement":null},{"id":"W4399422353","doi":"10.1117/12.3013495","title":"Revisiting the liquid mull technique to derive the infrared optical constants of organic powders from transmission IR spectroscopy","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Spectroscopy; Infrared spectroscopy; Infrared; Materials science; Transmission (telecommunications); Analytical Chemistry (journal); Optoelectronics; Optics; Chemistry; Physics; Computer science; Environmental chemistry; Organic chemistry; Telecommunications","score_opus":0.011189818512733934,"score_gpt":0.27634932168602905,"score_spread":0.2651595031732951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399422353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4554911,0.0040464355,0.42336267,0.012046909,0.00008955484,0.00051487744,0.00007954454,0.00057190855,0.103797],"genre_scores_gemma":[0.9887709,0.00021468462,0.009392794,0.00039173165,0.00019597296,0.000026740501,0.000010773304,0.000035111974,0.0009612587],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986318,0.000029195433,0.0003824937,0.00035941575,0.00029927745,0.00029787046],"domain_scores_gemma":[0.9986434,0.0006886661,0.000056452147,0.00045302184,0.000056449022,0.00010201712],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020588904,0.0002125241,0.00028996455,0.000082682185,0.00013477224,0.0000867476,0.00040258974,0.00014260507,0.008638744],"category_scores_gemma":[0.00017814711,0.000114871546,0.00015899561,0.00094624783,0.00015817567,0.000077923636,0.00009285729,0.00041251825,0.00003691519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071417744,0.000016862741,0.000041862062,0.00006529239,0.00013902034,0.000014216359,0.0004280427,0.0000021311303,0.9969232,0.000921876,0.00095110066,0.00042496086],"study_design_scores_gemma":[0.00010081933,0.00004729607,0.000015311394,0.00019737009,0.00020171054,0.0000142274075,0.0022591194,0.00019408605,0.99357253,0.0005857284,0.0026558908,0.00015588735],"about_ca_topic_score_codex":0.00006552442,"about_ca_topic_score_gemma":0.0000041806547,"teacher_disagreement_score":0.53327984,"about_ca_system_score_codex":0.00009051731,"about_ca_system_score_gemma":0.00015056979,"threshold_uncertainty_score":0.9922675},"labels":[],"label_agreement":null},{"id":"W4399733584","doi":"10.1177/00037028241258111","title":"Characterization of Near Infrared-Dye Colored Fabrics Using Hyperspectral Imaging","year":2024,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Camouflage; Hyperspectral imaging; Dyeing; Near-infrared spectroscopy; Principal component analysis; Partial least squares regression; Infrared; Materials science; Characterization (materials science); Chemometrics; VNIR; Chemical imaging; Optics; Remote sensing; Chemistry; Nanotechnology; Chromatography; Computer science; Artificial intelligence; Geology; Composite material","score_opus":0.011642779191415056,"score_gpt":0.26492035963187544,"score_spread":0.2532775804404604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399733584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95150036,0.00066365075,0.009827767,0.000079667305,0.0001306988,0.000110676774,0.00006213708,0.00035731928,0.037267733],"genre_scores_gemma":[0.99220604,0.000101142716,0.006543598,0.00008161797,0.00031342765,0.00001775547,0.00012468256,0.000075724114,0.0005360023],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981769,0.000007970852,0.00044849745,0.00052926544,0.00034459733,0.00049281283],"domain_scores_gemma":[0.9992423,0.00006584335,0.00015173783,0.00038898573,0.000047510708,0.0001036258],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010469655,0.0003069291,0.0004365598,0.00024734117,0.00015199908,0.00021469571,0.00028341112,0.0001342872,0.0017425629],"category_scores_gemma":[0.000021174525,0.00031781755,0.00016062659,0.0012493901,0.00018861948,0.00018754134,0.00007275112,0.00034449648,0.00005374258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047138976,0.0000677975,0.00091331016,0.00017502959,0.00013501746,0.000015152533,0.00032809426,0.000017475819,0.9919172,0.006045232,0.00011946942,0.00021905969],"study_design_scores_gemma":[0.0002618977,0.00001664779,0.00018014651,0.000038420363,0.00028312206,0.00001764513,0.00032328034,0.006600679,0.9896438,0.0012009051,0.0011263409,0.0003070753],"about_ca_topic_score_codex":0.000038223607,"about_ca_topic_score_gemma":9.51218e-7,"teacher_disagreement_score":0.0407057,"about_ca_system_score_codex":0.0003064261,"about_ca_system_score_gemma":0.00018487974,"threshold_uncertainty_score":0.9999274},"labels":[],"label_agreement":null},{"id":"W4399855266","doi":"10.18280/isi.290313","title":"Classification of Density and Transparency of Needle Leaves Types Using AlexNet and VGG16 Architecture","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transparency (behavior); Architecture; Computer science; Geography; Archaeology","score_opus":0.02081492931759432,"score_gpt":0.2588769766904557,"score_spread":0.23806204737286135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399855266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852309,0.0020153692,0.009270275,0.00001469216,0.00002237415,0.000038421444,0.000027841517,0.00003640178,0.0033436818],"genre_scores_gemma":[0.9986256,0.00014606438,0.0011397279,0.000005450075,0.000019168992,0.0000021338067,0.000027639553,0.000005136879,0.000029117142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993876,0.0000073471915,0.00032842832,0.000080195205,0.00010861908,0.00008777203],"domain_scores_gemma":[0.9996188,0.00005425689,0.00013714933,0.00009496028,0.000066333356,0.000028457618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008589101,0.0000907144,0.00017123524,0.00019594662,0.000050388484,0.000045044693,0.000047804217,0.00008592093,0.000052649273],"category_scores_gemma":[0.00006199229,0.00008148747,0.000032313474,0.00029844223,0.0001538395,0.00044698454,0.000015095303,0.00007921543,9.821244e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012936059,0.000051268627,0.061710622,0.015108442,0.0004424007,0.0000018764515,0.027705811,0.00039728108,0.7999573,0.008003784,0.00007569271,0.086416185],"study_design_scores_gemma":[0.00036503226,0.000082075356,0.03492313,0.00072357134,0.00047052425,0.00008842714,0.0056100115,0.023464063,0.926653,0.006913846,0.00037626296,0.00033002347],"about_ca_topic_score_codex":0.00007752473,"about_ca_topic_score_gemma":0.0000055728055,"teacher_disagreement_score":0.12669577,"about_ca_system_score_codex":0.00003767201,"about_ca_system_score_gemma":0.000035244353,"threshold_uncertainty_score":0.33229628},"labels":[],"label_agreement":null},{"id":"W4399875432","doi":"10.1016/j.atech.2024.100492","title":"Calibrating lab and field reflectance spectra for nutrient estimation in potato plants using local support vector regression models","year":2024,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Reflectivity; Nutrient; Field (mathematics); Regression; Estimation; Support vector machine; Environmental science; Regression analysis; Mathematics; Biological system; Statistics; Remote sensing; Geography; Computer science; Biology; Artificial intelligence; Ecology; Optics; Engineering; Physics","score_opus":0.018803514756171598,"score_gpt":0.2963613699394522,"score_spread":0.27755785518328063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399875432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96650934,0.0017571575,0.029551942,0.0010894596,0.000079326535,0.00012261426,0.000017856257,0.0002923385,0.0005799608],"genre_scores_gemma":[0.9950911,0.00008239185,0.004452615,0.000023332002,0.000050926014,0.00003490734,0.000042174146,0.000011011509,0.00021155721],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998998,0.000005247652,0.0002471445,0.0003612472,0.00009894954,0.00028941067],"domain_scores_gemma":[0.999667,0.00009824963,0.00005950139,0.00011272973,0.000025662357,0.000036871123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004993121,0.00016991935,0.0002365245,0.00019116883,0.0000856754,0.000045319146,0.000112213405,0.00029535434,0.00004658242],"category_scores_gemma":[0.00005800448,0.00011628924,0.00004464137,0.00063304347,0.000052429597,0.00018837283,0.00005606658,0.00027971782,0.0000018156529],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074842304,0.00006675342,0.0013541558,0.0004203654,0.00008018905,0.00007218757,0.00037021647,0.00049778697,0.9795868,0.010357997,0.0011040411,0.006014713],"study_design_scores_gemma":[0.00024939972,0.00007560097,0.000047951635,0.00020520415,0.000043483044,0.00013747867,0.00097236974,0.09656628,0.8954912,0.0058937916,0.00011540648,0.00020183536],"about_ca_topic_score_codex":0.000035868732,"about_ca_topic_score_gemma":0.000043885022,"teacher_disagreement_score":0.096068494,"about_ca_system_score_codex":0.00014534115,"about_ca_system_score_gemma":0.000028648341,"threshold_uncertainty_score":0.47421375},"labels":[],"label_agreement":null},{"id":"W4399900549","doi":"10.59400/cai.v1i1.383","title":"Application of machine vision in drying process modeling of carrot slices","year":2023,"lang":"en","type":"article","venue":"Computing and artificial intelligence.","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Indian Institute of Technology Kharagpur","keywords":"Process (computing); Computer science; Artificial intelligence; Computer vision; Process engineering; Machine vision; Biological system; Engineering; Biology; Programming language","score_opus":0.04168343728122409,"score_gpt":0.3627486669179214,"score_spread":0.3210652296366973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399900549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9553815,0.00042895228,0.04363382,0.00003090724,0.000016816415,0.000034058146,0.0000026147886,0.0000423106,0.00042899195],"genre_scores_gemma":[0.9996538,0.000050049177,0.00022531717,0.000003321396,0.000040281597,0.0000020966559,0.000009227854,0.0000070057977,0.000008892896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914503,0.0000069605094,0.00040008363,0.0001925654,0.00012108708,0.00013424973],"domain_scores_gemma":[0.9995803,0.00010143414,0.00012899289,0.00010532515,0.00005929555,0.000024642537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021034222,0.00007908067,0.00019759695,0.00019381788,0.000056623452,0.00001316102,0.00011703059,0.000054030344,0.000018379273],"category_scores_gemma":[0.0000732616,0.00007666067,0.000033404558,0.000849192,0.000048649697,0.000038066682,0.000046121215,0.00009920538,0.0000025423744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005754719,0.00018564399,0.023791315,0.0009407628,0.00003620352,0.0000019568392,0.0039359014,0.39207253,0.3879033,0.0036898502,0.000002604839,0.18738239],"study_design_scores_gemma":[0.000011025383,0.000008498756,0.000030135114,0.000047891735,0.000008919748,2.9926017e-7,0.0015213778,0.62843394,0.36630502,0.003585885,9.127495e-7,0.000046103774],"about_ca_topic_score_codex":0.00027234651,"about_ca_topic_score_gemma":0.0000206228,"teacher_disagreement_score":0.2363614,"about_ca_system_score_codex":0.0000097818465,"about_ca_system_score_gemma":0.000012865529,"threshold_uncertainty_score":0.31261313},"labels":[],"label_agreement":null},{"id":"W4399925899","doi":"10.1016/bs.afnr.2024.06.009","title":"Frontiers of machine learning in smart food safety","year":2024,"lang":"en","type":"review","venue":"Advances in food and nutrition research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Food safety; Food science; Biology","score_opus":0.06377717577422787,"score_gpt":0.4193702606875574,"score_spread":0.3555930849133295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399925899","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007681393,0.9942621,0.0000106736725,0.000037735183,0.000048534315,0.00019528544,0.00014273121,0.000023648763,0.0052024964],"genre_scores_gemma":[0.002549639,0.9963778,0.00018075405,0.0000019562915,0.00007539015,0.00014352809,0.00016974976,0.000032639444,0.00046852892],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99795467,0.0001433513,0.0006100436,0.0004902387,0.00040488425,0.00039682118],"domain_scores_gemma":[0.99914753,0.00042126063,0.00010971493,0.00019490556,0.00005872062,0.000067841305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059851224,0.00023985302,0.0011130231,0.001395128,0.00006510564,0.00003223817,0.00026057832,0.00028422382,0.00010520888],"category_scores_gemma":[0.0002526308,0.00020762043,0.00017989542,0.0023548976,0.00018971591,0.00013206805,0.0001487676,0.0017530423,0.000005475077],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018791894,0.0006238648,0.00065307575,0.16797523,0.0003490737,0.000046492758,0.000099279954,0.0000021785277,0.000029718638,0.001198123,0.0007071246,0.8281279],"study_design_scores_gemma":[0.00043767923,0.00031301356,7.109581e-7,0.009088626,0.0001062362,0.000008663803,0.00032254012,0.00002025153,0.00035012665,0.001643132,0.9875126,0.00019640297],"about_ca_topic_score_codex":0.000022019218,"about_ca_topic_score_gemma":0.00014350076,"teacher_disagreement_score":0.9868055,"about_ca_system_score_codex":0.00020003128,"about_ca_system_score_gemma":0.000090725494,"threshold_uncertainty_score":0.84665155},"labels":[],"label_agreement":null},{"id":"W4400041224","doi":"10.18280/ts.410337","title":"Edge Architecture for High-Accuracy Disease Identification in Apple Plants Using Transfer Learning Approach","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Transfer of learning; Architecture; Artificial intelligence; Computer science; Enhanced Data Rates for GSM Evolution; Biology; Botany; Visual arts; Art","score_opus":0.027474676481194334,"score_gpt":0.2840671931491559,"score_spread":0.25659251666796157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400041224","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.701222,0.00070156547,0.29729477,0.00007811297,0.00004193479,0.00019770444,0.00015458031,0.000094802024,0.00021451064],"genre_scores_gemma":[0.9978479,0.0000211064,0.00061632675,0.000023874372,0.00027647737,0.00013259616,0.00061802054,0.000032972388,0.00043069205],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987224,0.000018658819,0.0003237914,0.0004186673,0.0002251733,0.0002913294],"domain_scores_gemma":[0.9995987,0.00014036274,0.00003276888,0.00011884142,0.000017428118,0.00009194217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016980182,0.00018592538,0.00019453808,0.00022720132,0.00010977527,0.0001274535,0.0001590148,0.0000711147,0.00072018016],"category_scores_gemma":[0.000027472834,0.00017610977,0.00011563375,0.00034176954,0.00003336583,0.0001233654,0.000013685256,0.00024539465,0.0000061874016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041406043,0.00049698085,0.0018341047,0.0020672553,0.00028935145,0.00002821356,0.0015479117,0.037936777,0.94450897,0.0018481194,0.00040156863,0.0086266855],"study_design_scores_gemma":[0.0026231115,0.000073460826,0.0020419091,0.00030888547,0.00093526364,0.000017487533,0.0015916837,0.24229129,0.73802495,0.0019928522,0.00902461,0.001074498],"about_ca_topic_score_codex":0.000033492037,"about_ca_topic_score_gemma":0.0000032682913,"teacher_disagreement_score":0.29667842,"about_ca_system_score_codex":0.00011327956,"about_ca_system_score_gemma":0.000059391044,"threshold_uncertainty_score":0.78854674},"labels":[],"label_agreement":null},{"id":"W4400288423","doi":"10.1121/10.0027341","title":"Probabilistic multiphysics inference with flexible coupling and data covariance estimation","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multiphysics; Covariance; Inference; Coupling (piping); Probabilistic logic; Computer science; Mathematics; Statistics; Artificial intelligence; Engineering; Finite element method; Structural engineering; Mechanical engineering","score_opus":0.03001103264760661,"score_gpt":0.320436311913465,"score_spread":0.29042527926585837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400288423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030100083,0.001501649,0.9664662,0.0016829788,0.00003911959,0.00004602946,0.000026806858,0.000021124917,0.00011602603],"genre_scores_gemma":[0.94680583,0.00045736984,0.052439317,0.000111349844,0.000090091395,5.436751e-7,0.0000018428078,0.000010098723,0.00008353122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921846,0.000010861606,0.00023476346,0.00010750278,0.0003051961,0.00012319203],"domain_scores_gemma":[0.99828064,0.0010020176,0.0002178212,0.00036108439,0.00009765939,0.00004079839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026254842,0.00009689663,0.00020912464,0.000009520592,0.00010943303,0.000041911677,0.0005477155,0.000037772374,0.000047951635],"category_scores_gemma":[0.00037340494,0.000046380836,0.000068803325,0.00040860596,0.00055105315,0.0001338088,0.00018086872,0.00038080648,0.0000010230926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044797658,0.0004983404,0.0010507596,0.0025845768,0.0024270364,0.000006050854,0.0023461143,0.752046,0.19308822,0.000289026,0.012370133,0.032845747],"study_design_scores_gemma":[0.00012669877,0.00005176538,0.00008329499,0.00019320652,0.00085082575,0.00003223254,0.0006089778,0.9905453,0.0060498742,0.0012390907,0.00015128667,0.00006745998],"about_ca_topic_score_codex":0.000028054457,"about_ca_topic_score_gemma":1.5311088e-7,"teacher_disagreement_score":0.9167058,"about_ca_system_score_codex":0.00004139549,"about_ca_system_score_gemma":0.00012930795,"threshold_uncertainty_score":0.20303783},"labels":[],"label_agreement":null},{"id":"W4400409431","doi":"10.1109/tafe.2024.3406329","title":"A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on AgriFood Electronics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Regina","funders":"","keywords":"Canola; Flea beetle; Crop; Flea; Agricultural engineering; Computer science; Biology; Agronomy; Engineering; Ecology","score_opus":0.013677072472837549,"score_gpt":0.27343869008671917,"score_spread":0.2597616176138816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400409431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28737277,0.0019715568,0.7087099,0.000095142306,0.00071542006,0.00018157768,0.00007733374,0.00037850026,0.00049782544],"genre_scores_gemma":[0.99765533,0.000081112375,0.0012435074,0.0000120828645,0.00023013291,0.000105100706,0.0000065381437,0.000051724423,0.00061445264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835914,0.00000850968,0.0003312324,0.00049662555,0.00024992062,0.0005545808],"domain_scores_gemma":[0.9993948,0.00017450597,0.00004277834,0.0002535216,0.0000504232,0.00008396823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223283,0.00025386433,0.00028440874,0.00027295534,0.00016545548,0.00012511376,0.00019743334,0.00020118144,0.00004354783],"category_scores_gemma":[0.0000023491943,0.00024090709,0.00022640638,0.00088604295,0.00002294532,0.00006684845,0.0000017600332,0.00056098663,0.000027003112],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020516614,0.0007555079,0.0000077235045,0.00096065603,0.00039106846,0.00001567797,0.00028156955,0.19269878,0.79195005,0.00038091306,0.00011086101,0.012242049],"study_design_scores_gemma":[0.0006505591,0.00025986886,0.000007787592,0.0002688623,0.00019794292,0.00003983574,0.000152506,0.11676771,0.880195,0.000008642769,0.0011735648,0.0002776886],"about_ca_topic_score_codex":0.00006474567,"about_ca_topic_score_gemma":0.000058546982,"teacher_disagreement_score":0.71028256,"about_ca_system_score_codex":0.0010972174,"about_ca_system_score_gemma":0.00016233577,"threshold_uncertainty_score":0.9823906},"labels":[],"label_agreement":null},{"id":"W4400654041","doi":"10.3390/engproc2024068045","title":"Robust Multitaper Tests for Detecting Frequency Modulated Signals","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Queen's University","funders":"","keywords":"Multitaper; Computer science; Test statistic; Statistic; Series (stratigraphy); Algorithm; Test (biology); Statistical hypothesis testing; Statistics; Mathematics; Geology","score_opus":0.0541122348441973,"score_gpt":0.31310648593683355,"score_spread":0.25899425109263624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400654041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8248083,0.0065001356,0.05870807,0.0003703317,0.00022135205,0.00018664236,0.00005142473,0.0016809426,0.10747278],"genre_scores_gemma":[0.97840035,0.000015402422,0.009436872,0.00006560252,0.00020086212,0.00003704028,0.000015937338,0.00003521205,0.011792749],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990395,0.0000032657993,0.0002132879,0.0003414615,0.000118175354,0.00028434425],"domain_scores_gemma":[0.99929744,0.000356809,0.000027554448,0.0001878084,0.00006377822,0.00006660987],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008535546,0.0001529922,0.00017046675,0.00012487578,0.00009238574,0.000105372186,0.00013671877,0.00012804163,0.0064492472],"category_scores_gemma":[0.00026780766,0.00012691681,0.00014734306,0.0005035168,0.000022022467,0.00011591617,0.000022494274,0.00014342045,0.000083335945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042653865,0.00003189115,0.0006042525,0.00016524953,0.00017271783,0.000009225394,0.000051091098,0.00028140016,0.995381,0.00038674838,0.0010784237,0.0018337248],"study_design_scores_gemma":[0.00018734335,0.000020691748,0.000039548133,0.00003985596,0.00016525172,0.000009684708,0.00019777886,0.03300618,0.9638819,0.0013383868,0.00087767356,0.00023567381],"about_ca_topic_score_codex":0.00009702941,"about_ca_topic_score_gemma":0.000019119278,"teacher_disagreement_score":0.153592,"about_ca_system_score_codex":0.00007787513,"about_ca_system_score_gemma":0.000034757588,"threshold_uncertainty_score":0.994459},"labels":[],"label_agreement":null},{"id":"W4400724587","doi":"10.1016/j.saa.2024.124844","title":"Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton","year":2024,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Ningxia University","keywords":"Hyperspectral imaging; Partial least squares regression; Norfloxacin; Fusion; Sensor fusion; Chemistry; Data set; Near-infrared spectroscopy; Pattern recognition (psychology); Support vector machine; Artificial intelligence; Computer science; Machine learning; Antibiotics; Optics; Ciprofloxacin","score_opus":0.01708658465626582,"score_gpt":0.28867160882839626,"score_spread":0.27158502417213043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400724587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9651177,0.009705515,0.019762218,0.00087764004,0.000054853088,0.0005703859,0.00057472073,0.00036368606,0.0029732971],"genre_scores_gemma":[0.9861012,0.00080200087,0.012162344,0.000053921136,0.00012392295,0.000059396625,0.0005574106,0.00009442773,0.00004535734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99698895,0.00004593563,0.00057502044,0.001303718,0.0003928636,0.0006934889],"domain_scores_gemma":[0.9987362,0.00008621454,0.00016232328,0.00078321324,0.000066520886,0.00016549982],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031002235,0.0005277829,0.0006607074,0.000598077,0.00014301369,0.0002527194,0.00042567778,0.00018893364,0.00005608555],"category_scores_gemma":[0.00005850578,0.00047053202,0.00012466602,0.0008695436,0.0003105762,0.00033985326,0.00023114888,0.00044491197,6.6749027e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033948038,0.00015153273,0.0015466122,0.00043365415,0.00023333443,0.00014212092,0.00013778935,8.9141616e-7,0.9941115,0.0022944207,0.00018368043,0.0004250345],"study_design_scores_gemma":[0.0009486089,0.00052414084,0.00023563711,0.00040333084,0.0004937633,0.00020382044,0.00041142973,0.002105127,0.99222857,0.0014907124,0.0005030704,0.0004517851],"about_ca_topic_score_codex":0.00012668587,"about_ca_topic_score_gemma":0.00009252034,"teacher_disagreement_score":0.020983534,"about_ca_system_score_codex":0.00013076229,"about_ca_system_score_gemma":0.00016483385,"threshold_uncertainty_score":0.99977463},"labels":[],"label_agreement":null},{"id":"W4400833011","doi":"10.1016/b978-0-323-95686-4.00009-5","title":"ML-based PCA methods to diagnose statistical distribution of blood glucose levels of diabetic patients","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algonquin College","funders":"","keywords":"Medicine; Distribution (mathematics); Internal medicine; Statistics; Mathematics; Mathematical analysis","score_opus":0.019547764712625038,"score_gpt":0.3121525320125235,"score_spread":0.2926047672998985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400833011","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007453993,0.0033552798,0.0013967489,0.000026649783,0.00021071923,0.00039891803,0.010650526,0.00009236528,0.9764148],"genre_scores_gemma":[0.281168,0.00005387639,0.008400717,0.000088818,0.00019279783,0.00009425651,0.0011495262,0.0001675588,0.70868444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99738806,0.000031598094,0.0009965579,0.0006349433,0.00058441353,0.00036439544],"domain_scores_gemma":[0.9975515,0.0007926326,0.00042731743,0.00070648507,0.0002885933,0.000233498],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024675133,0.00052757614,0.001156853,0.0002562547,0.000042866865,0.000025055368,0.000376524,0.00045915204,0.0029121027],"category_scores_gemma":[0.00046218166,0.000500179,0.0003980989,0.00010142195,0.00023606575,0.000019162415,0.00017625478,0.00050419325,0.000049819795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010713551,0.00046939784,0.00039383347,0.0042279772,0.0019355889,0.00005027148,0.000121463614,0.000004771089,0.020605776,0.017821776,0.00031638026,0.95394564],"study_design_scores_gemma":[0.0008530608,0.00031238556,0.00016242532,0.0011537949,0.005648627,0.0000015467878,0.000010703622,0.000021264028,0.8681387,0.011414338,0.11151552,0.0007676396],"about_ca_topic_score_codex":0.0000028347379,"about_ca_topic_score_gemma":0.000003619932,"teacher_disagreement_score":0.953178,"about_ca_system_score_codex":0.00014350512,"about_ca_system_score_gemma":0.00015707179,"threshold_uncertainty_score":0.999745},"labels":[],"label_agreement":null},{"id":"W4400852488","doi":"10.1021/acs.jcim.4c00128","title":"Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Terramera (Canada)","funders":"","keywords":"Fungicide; Graph; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Theoretical computer science; Biology; Botany","score_opus":0.04823447170410108,"score_gpt":0.31834488678423906,"score_spread":0.270110415080138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400852488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59281963,0.0010031133,0.40586448,0.000016133372,0.000060284936,0.000010015442,0.0000024699375,0.000011021949,0.00021283764],"genre_scores_gemma":[0.9976541,0.00007788653,0.0021255838,0.000035491237,0.00008323502,3.8172706e-7,0.000012209363,0.000006222171,0.0000049220703],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878514,0.000004540826,0.0008116314,0.00005646154,0.00020831711,0.0001339208],"domain_scores_gemma":[0.9993893,0.00008566626,0.00023579039,0.00006450396,0.0001393584,0.00008539787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014864097,0.000096978285,0.00023868616,0.00019728462,0.000041721047,0.00011185845,0.000098650045,0.000089284425,0.000065477885],"category_scores_gemma":[0.000081444465,0.000082265884,0.00014859602,0.00024694542,0.000028589124,0.0009375351,0.000044448814,0.00030152194,4.1202898e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092906266,0.000036929847,0.0002306698,0.0004706494,0.0002717007,0.0000043059767,0.0004284918,0.3737082,0.62216026,0.0008276896,0.00004726126,0.0017209352],"study_design_scores_gemma":[0.0001780631,0.000006897715,0.0000016935189,0.00020852983,0.00020419156,0.000068626,0.00015850003,0.9626547,0.035888188,0.00053854845,0.000016130949,0.00007590947],"about_ca_topic_score_codex":0.000010970202,"about_ca_topic_score_gemma":1.2033979e-7,"teacher_disagreement_score":0.5889465,"about_ca_system_score_codex":0.00005532277,"about_ca_system_score_gemma":0.000020517104,"threshold_uncertainty_score":0.33547056},"labels":[],"label_agreement":null},{"id":"W4400859859","doi":"10.3168/jds.2024-24911","title":"Rapid selection of milk mid-infrared spectra for creating a dairy cow population world representative spectral database","year":2024,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Valacta (Canada); Ste. Anne's Hospital","funders":"Indian Council of Agricultural Research; Interreg North-West Europe; Waalse Gewest","keywords":"Representativeness heuristic; Principal component analysis; Calibration; Extrapolation; Set (abstract data type); Selection (genetic algorithm); Data set; Computer science; Database; Data mining; Population; Mathematics; Statistics; Artificial intelligence","score_opus":0.033720742612324585,"score_gpt":0.34075705793744954,"score_spread":0.30703631532512493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400859859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803652,0.0019499888,0.008154248,0.00021657118,0.00043639762,0.00011183051,0.000061453175,0.00005245051,0.008651846],"genre_scores_gemma":[0.98079634,0.00006549775,0.016771063,0.000020659065,0.0005231799,0.0000029321543,0.0000132145615,0.000013876154,0.0017932634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980233,0.000019278745,0.0006546687,0.0003286607,0.00066823163,0.0003059084],"domain_scores_gemma":[0.9985164,0.00037660162,0.00051471783,0.00019899376,0.00026253072,0.00013075142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008361284,0.0001493372,0.0003212987,0.00083660736,0.00020823098,0.00014018512,0.0003827803,0.000042558604,0.00068380433],"category_scores_gemma":[0.00078833074,0.00012832822,0.00021141263,0.0028515118,0.00020422428,0.0010788784,0.000042899923,0.00031358624,0.0000017387943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022822048,0.00011890209,0.018131368,0.00025799338,0.00013996403,0.000024633917,0.00082760263,0.00017301342,0.97559506,0.00042760387,0.002959218,0.001116445],"study_design_scores_gemma":[0.00047458237,0.00015787447,0.0056136693,0.0002433443,0.00022388557,0.00012730385,0.0011311367,0.0055988347,0.9847066,0.0011925381,0.00037541185,0.00015481166],"about_ca_topic_score_codex":0.00005429311,"about_ca_topic_score_gemma":0.000020047073,"teacher_disagreement_score":0.012517698,"about_ca_system_score_codex":0.0003000663,"about_ca_system_score_gemma":0.00034075617,"threshold_uncertainty_score":0.7487178},"labels":[],"label_agreement":null},{"id":"W4401108328","doi":"10.3390/s24154944","title":"Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Nitrogen fixation; Rhizobia; Raman spectroscopy; Bacteria; Nitrogen; Sustainable agriculture; Chemistry; Materials science; Environmental chemistry; Nanotechnology; Biology; Agriculture; Ecology; Physics","score_opus":0.035687281708951836,"score_gpt":0.30872860263168284,"score_spread":0.273041320922731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401108328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905585,0.0011521996,0.00010669471,0.00011013903,0.00006142857,0.000043335367,0.0000059139084,0.00016309386,0.00779867],"genre_scores_gemma":[0.99785304,0.00005772553,0.0012313395,0.000024720286,0.00019884082,0.000004372772,0.000018767732,0.000018885672,0.0005923164],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990246,0.000017519133,0.00021519174,0.00032826758,0.00012153231,0.00029287755],"domain_scores_gemma":[0.99967724,0.000081588376,0.000037813188,0.00014429456,0.00001470159,0.000044386805],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009752541,0.00014884962,0.00019289537,0.00021858767,0.000054766053,0.00008268641,0.00010025102,0.0001354433,0.0011055697],"category_scores_gemma":[0.000073758725,0.00012647487,0.00009074973,0.0006822583,0.000050986713,0.00007102388,0.000030280853,0.00024195219,0.00006398481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000141603205,0.000042933454,0.026738202,0.00008777245,0.00006378786,0.00007310217,0.00029768932,0.0003566931,0.97141886,0.00055746915,0.00010070647,0.0002486429],"study_design_scores_gemma":[0.00014341029,0.000014580056,0.00020563584,0.000063359956,0.00005304426,0.000025760646,0.000660415,0.012359708,0.9836499,0.0019991838,0.00061814766,0.00020681579],"about_ca_topic_score_codex":0.00010850249,"about_ca_topic_score_gemma":0.0000074245318,"teacher_disagreement_score":0.026532566,"about_ca_system_score_codex":0.0001966277,"about_ca_system_score_gemma":0.000036337053,"threshold_uncertainty_score":0.99980754},"labels":[],"label_agreement":null},{"id":"W4401248648","doi":"10.1109/tpami.2024.3436860","title":"Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Defense Threat Reduction Agency; Canada Research Chairs","keywords":"Principal component analysis; Computer science; Artificial intelligence; Training (meteorology); Pattern recognition (psychology); Principal (computer security); Machine learning; Geography","score_opus":0.035126876729545155,"score_gpt":0.31721206572245936,"score_spread":0.2820851889929142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401248648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25012094,0.00020016196,0.7488711,0.00007775902,0.00002909211,0.000043875694,0.00047666702,0.000043015963,0.00013743255],"genre_scores_gemma":[0.99733824,0.00031916922,0.0011275735,0.000034081393,0.000031826978,0.000022778991,0.000042742744,0.000021053545,0.0010625545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984029,0.000014978304,0.00048636534,0.0005683026,0.00026356883,0.00026389622],"domain_scores_gemma":[0.9991542,0.00018646415,0.000096412296,0.00037209204,0.000058347036,0.0001324668],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001431763,0.00026244024,0.000632842,0.0007301455,0.00012710776,0.00010945571,0.00022720324,0.00010851841,0.0011543228],"category_scores_gemma":[0.0000072608545,0.00023571841,0.00069443975,0.0016744827,0.00013801082,0.00010877334,0.000005827291,0.0002521735,0.0000032143464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007362822,0.0021853687,0.03924233,0.0020825034,0.13079682,0.00006348574,0.00984536,0.39219746,0.09928524,0.001045278,0.000054908116,0.32246497],"study_design_scores_gemma":[0.00012553365,0.000027101509,0.00027165143,0.000016430746,0.015949156,0.0000060299053,0.00013256025,0.7625,0.22037166,0.00037583307,0.000008198438,0.00021584886],"about_ca_topic_score_codex":0.00095444696,"about_ca_topic_score_gemma":0.0009170474,"teacher_disagreement_score":0.7477435,"about_ca_system_score_codex":0.000033373,"about_ca_system_score_gemma":0.00002478037,"threshold_uncertainty_score":0.9997588},"labels":[],"label_agreement":null},{"id":"W4401465439","doi":"10.1016/b978-0-443-13293-3.00010-5","title":"Partial least squares regression to explore and predict environmental data","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Rimouski; Université de Montréal; Université Laval; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","funders":"","keywords":"Partial least squares regression; Statistics; Regression; Regression analysis; Total least squares; Econometrics; Mathematics","score_opus":0.047639540586802726,"score_gpt":0.28967032613419635,"score_spread":0.24203078554739363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401465439","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012711412,0.010782104,0.000002945892,0.00008551437,0.00014627758,0.00014251094,0.0010501404,0.00012556133,0.9863938],"genre_scores_gemma":[0.018954454,0.0005974072,0.000088023735,0.00009797416,0.0009309884,0.000027398455,0.00073756796,0.00012103634,0.9784452],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978525,0.0000049105706,0.00037925708,0.0010151512,0.00044165953,0.00030652646],"domain_scores_gemma":[0.99832463,0.000044775945,0.00012530234,0.0012399536,0.000007903279,0.0002574149],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000081232705,0.00050892803,0.0005249574,0.0001675541,0.00011236833,0.00009848309,0.0005740322,0.00037379476,0.003314032],"category_scores_gemma":[0.00001758546,0.00042646955,0.00012485601,0.000017967312,0.00016515034,0.00006041215,0.0011039235,0.0005768597,0.0003850382],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101200116,0.000032907796,0.000119072174,0.00062544533,0.00071067223,0.0002352032,0.0005570041,4.438422e-7,0.006792929,0.0005353482,0.006148362,0.9841414],"study_design_scores_gemma":[0.00017012603,0.000043615255,0.0000042736183,0.00064437167,0.0008206288,0.00003412679,0.00015872384,0.000024604522,0.008186317,0.0011155277,0.9883148,0.0004828696],"about_ca_topic_score_codex":8.984057e-7,"about_ca_topic_score_gemma":0.0000044561143,"teacher_disagreement_score":0.98365855,"about_ca_system_score_codex":0.000102264145,"about_ca_system_score_gemma":0.000037596204,"threshold_uncertainty_score":0.9998187},"labels":[],"label_agreement":null},{"id":"W4401474139","doi":"10.1016/j.jag.2024.104079","title":"Retrieving heavy metal concentrations in urban soil using satellite hyperspectral imagery","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperspectral imaging; Environmental science; Correlation coefficient; Pollution; Remote sensing; Soil science; Mathematics; Statistics; Geography","score_opus":0.022304816634703732,"score_gpt":0.2718812150708004,"score_spread":0.24957639843609664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401474139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98837316,0.0011443528,0.002738612,0.0005239668,0.0002638379,0.000043674514,0.000011512252,0.00003214944,0.0068687573],"genre_scores_gemma":[0.99591607,0.0006574224,0.0026975328,0.00021703076,0.00030536202,0.000001425365,0.000052047355,0.00000855324,0.00014457703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998708,0.0000058879473,0.00065583625,0.000103316415,0.00038953262,0.00013740777],"domain_scores_gemma":[0.99933803,0.00008500785,0.00025792787,0.000060323404,0.00020181292,0.000056906036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002684611,0.00011670366,0.00017267287,0.00040768494,0.00005067869,0.0002739566,0.00012225207,0.000075030184,0.00023778893],"category_scores_gemma":[0.0000634169,0.00011055651,0.000081712955,0.00039346734,0.000038128088,0.0011873507,0.000020281137,0.00025658467,0.000008297225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012424507,0.0003512573,0.0537578,0.0006186714,0.0016637329,0.00014807629,0.016145866,0.019392312,0.69462085,0.08746075,0.0006074869,0.12399077],"study_design_scores_gemma":[0.0034712849,0.00010355729,0.038202435,0.0006422208,0.00048254165,0.0005457278,0.007417952,0.25679362,0.6581116,0.006613685,0.026672324,0.000943075],"about_ca_topic_score_codex":0.000022080696,"about_ca_topic_score_gemma":0.0000092113805,"teacher_disagreement_score":0.23740132,"about_ca_system_score_codex":0.0001326302,"about_ca_system_score_gemma":0.000118437405,"threshold_uncertainty_score":0.45083636},"labels":[],"label_agreement":null},{"id":"W4401478523","doi":"10.1111/1750-3841.17252","title":"A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra","year":2024,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Calibration; Near-infrared spectroscopy; Sampling (signal processing); Computer science; Process analytical technology; Decision support system; Quality (philosophy); Process engineering; Environmental science; Biochemical engineering; Biotechnology; Data mining; Mathematics; Statistics; Engineering; Marketing; Business; Biology","score_opus":0.023079240481920886,"score_gpt":0.27900343548712825,"score_spread":0.25592419500520736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401478523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935113,0.00378627,0.0007290578,0.00080742687,0.000094393436,0.00003214661,0.000016444563,0.000009237709,0.0010137298],"genre_scores_gemma":[0.99861693,0.0001862868,0.00078507775,0.00007197062,0.00013770084,0.0000011249119,1.5805578e-7,0.00000604908,0.00019467622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986687,0.0000072910925,0.00037865527,0.0001928439,0.0005641322,0.00018841279],"domain_scores_gemma":[0.9992691,0.00013832812,0.00012844763,0.00021777571,0.00014441971,0.00010191342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000557595,0.00010846799,0.0002486436,0.0002574645,0.00012046466,0.0001986746,0.0005738378,0.000033179625,0.00024054028],"category_scores_gemma":[0.00025241272,0.000054472883,0.00010952746,0.0010593393,0.00029325252,0.0002955385,0.00009457413,0.0002116316,0.0000029088042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005089309,0.00003729107,0.0009287796,0.00002721119,0.00008707527,0.000011619834,0.0014995998,0.000014311252,0.99134725,0.00010238518,0.0007033467,0.0051902058],"study_design_scores_gemma":[0.00010342576,0.00022590079,0.0013736472,0.00016792992,0.00009915947,0.00008303322,0.0017448984,0.00020623166,0.9938336,0.0010563363,0.0010208464,0.000084988176],"about_ca_topic_score_codex":0.000023081324,"about_ca_topic_score_gemma":0.000018449664,"teacher_disagreement_score":0.0051056626,"about_ca_system_score_codex":0.00006434433,"about_ca_system_score_gemma":0.00026952845,"threshold_uncertainty_score":0.26337472},"labels":[],"label_agreement":null},{"id":"W4401644259","doi":"10.1007/s12293-024-00425-3","title":"Where does the crude oil originate? The role of near-infrared spectroscopy in accurate source detection","year":2024,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Xuzhou Science and Technology Program; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Tracing; Rendering (computer graphics); Computer science; Outlier; Near-infrared spectroscopy; Crude oil; Artificial intelligence; Pattern recognition (psychology); Data mining; Petroleum engineering; Geology; Optics","score_opus":0.006466877492983044,"score_gpt":0.2581186958881561,"score_spread":0.2516518183951731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401644259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9736855,0.00793488,0.0019318315,0.00041158963,0.0001800549,0.000043643384,0.00000284226,0.00014500707,0.015664646],"genre_scores_gemma":[0.99721706,0.00019748551,0.0002538639,0.000025646297,0.00023718069,0.000006546988,0.0000015147798,0.000024705141,0.0020359682],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872553,0.00004669608,0.00035121644,0.0002970149,0.00025387492,0.00032568423],"domain_scores_gemma":[0.9988934,0.000553745,0.000118595526,0.00036433426,0.00003907242,0.000030856812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037876706,0.00017960982,0.00023302568,0.00013731854,0.00022848407,0.00021355259,0.00040516228,0.00007682718,0.00054076174],"category_scores_gemma":[0.0001317959,0.00009681163,0.00012834455,0.0016125542,0.00012671364,0.00005581258,0.0001244198,0.00047699045,0.000019699168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051658586,0.00010474285,0.0066561266,0.0007177657,0.0003333828,0.000016792006,0.004439035,0.002191167,0.8462226,0.0003630763,0.00039562132,0.13850805],"study_design_scores_gemma":[0.00015741798,0.000014447946,0.0003331605,0.00015798966,0.0001221838,0.000012889445,0.002928013,0.097269356,0.87038714,0.00091342157,0.027549114,0.00015486297],"about_ca_topic_score_codex":0.00042422127,"about_ca_topic_score_gemma":0.0001173665,"teacher_disagreement_score":0.13835318,"about_ca_system_score_codex":0.000089009016,"about_ca_system_score_gemma":0.00005128775,"threshold_uncertainty_score":0.5920962},"labels":[],"label_agreement":null},{"id":"W4401730110","doi":"10.3390/foods13162633","title":"Recent Applications of Near-Infrared Spectroscopy in Food Quality Analysis","year":2024,"lang":"en","type":"editorial","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Quality (philosophy); Population; Global population; Near-infrared spectroscopy; Environmental science; Natural resource economics; Business; Nanotechnology; Materials science; Economics; Physics; Optics; Medicine; Environmental health","score_opus":0.018218314598582943,"score_gpt":0.3463715732473033,"score_spread":0.32815325864872036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401730110","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00807503,0.077223115,0.00474819,0.00046110674,0.4837376,0.0010455587,0.014851091,0.0010238244,0.40883452],"genre_scores_gemma":[0.021806702,0.010925951,0.005779516,0.000039782382,0.9113947,0.0010266611,0.010859467,0.0003189667,0.03784826],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968071,0.000042757107,0.0010284552,0.0008324971,0.00087590824,0.00041325446],"domain_scores_gemma":[0.99744993,0.00060145394,0.00047445713,0.0011400844,0.00022170236,0.000112392016],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035809638,0.00041520238,0.0012907193,0.0007558024,0.000059408092,0.000103331025,0.00068039726,0.0010317992,0.003153355],"category_scores_gemma":[0.0005113675,0.00042530408,0.0006271498,0.0057925563,0.00012989542,0.000046416484,0.00015999048,0.0012092176,0.000051770945],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008040559,0.000416518,0.0006506777,0.0013102158,0.0050188275,0.000003984355,0.00018485208,0.00002901408,0.0017662068,0.00020247001,0.98974514,0.0005917134],"study_design_scores_gemma":[0.00030147596,0.000072736744,0.000038148653,0.000044400225,0.0028942074,1.5369139e-7,0.00013133872,0.000048159785,0.03362403,0.0016712466,0.9607569,0.00041718225],"about_ca_topic_score_codex":0.0002514938,"about_ca_topic_score_gemma":0.00039033202,"teacher_disagreement_score":0.42765713,"about_ca_system_score_codex":0.00047442227,"about_ca_system_score_gemma":0.00051909464,"threshold_uncertainty_score":0.9998199},"labels":[],"label_agreement":null},{"id":"W4401732154","doi":"10.1016/j.foodcont.2024.110823","title":"A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis","year":2024,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Department of Science and Technology of Jilin Province; Education Department of Jilin Province","keywords":"Spectral analysis; Computer science; Physics; Spectroscopy","score_opus":0.026546881726689522,"score_gpt":0.3123240982791455,"score_spread":0.28577721655245597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401732154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11257682,0.001531247,0.882599,0.00037085093,0.000092125265,0.00021932075,0.0011803473,0.00038897223,0.0010413413],"genre_scores_gemma":[0.9953633,0.0000065136264,0.0034967246,0.00015556283,0.00017999839,0.00019852302,0.00010736291,0.000025901712,0.00046614552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983334,0.000022228445,0.00036489675,0.0005731689,0.00029068894,0.0004156356],"domain_scores_gemma":[0.9990392,0.00031247662,0.00010962331,0.0003025103,0.0001253377,0.00011086644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001595588,0.0002743813,0.00049102947,0.00029509608,0.0001551661,0.00016498407,0.00021605555,0.00018432396,0.00049564365],"category_scores_gemma":[0.0001592841,0.00025668682,0.00074417196,0.0014996665,0.00005429765,0.00015050184,0.000013893406,0.00018914137,0.000032325985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054854862,0.0012130523,0.009296185,0.0011892876,0.03908263,0.000041683707,0.0014251968,0.454902,0.44856945,0.028811384,0.0022882784,0.007695363],"study_design_scores_gemma":[0.0015051801,0.00020296729,0.00016358949,0.000012976531,0.004132824,0.0000011517584,0.00017045072,0.95552444,0.03481742,0.0030171648,0.0001744687,0.0002773752],"about_ca_topic_score_codex":0.000114002716,"about_ca_topic_score_gemma":0.0007334277,"teacher_disagreement_score":0.88278645,"about_ca_system_score_codex":0.00037383928,"about_ca_system_score_gemma":0.00012507655,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W4401732532","doi":"10.1021/acsfoodscitech.4c00331","title":"Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy","year":2024,"lang":"en","type":"article","venue":"ACS Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Innovate UK; National Oceanic and Atmospheric Administration; Directorate for Biological Sciences; Queen's University Belfast; Queen's University; Small Business Innovation Research","keywords":"Fillet (mechanics); Principal component analysis; Feature selection; Fluorescence spectroscopy; Linear discriminant analysis; Absorbance; Spectroscopy; Biological system; Pattern recognition (psychology); Analytical Chemistry (journal); Context (archaeology); Artificial intelligence; Mathematics; Chemistry; Computer science; Fluorescence; Materials science; Optics; Chromatography; Physics; Biology","score_opus":0.029488780441930592,"score_gpt":0.38451116995978246,"score_spread":0.35502238951785187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401732532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96653664,0.015423619,0.014258141,0.0026826905,0.000114074086,0.00017298423,0.00007784831,0.00025985108,0.00047412736],"genre_scores_gemma":[0.9850712,0.00023594797,0.014395875,0.000040121595,0.000031260584,0.000043746848,0.00003453532,0.000015028098,0.0001323003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99822587,0.000011646666,0.00020573543,0.0009192691,0.00016820994,0.00046926955],"domain_scores_gemma":[0.99923736,0.00014997872,0.000055716973,0.00047486305,0.000038794988,0.000043276676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006878377,0.00016749378,0.00025943195,0.0013282179,0.00017263483,0.00015186815,0.0009337451,0.00035092313,0.000024441393],"category_scores_gemma":[0.00044655317,0.00015160476,0.000018603985,0.0066049364,0.00043088966,0.00039739176,0.00037141357,0.0004838011,0.0000013666249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013552184,0.00004008111,0.0061758235,0.00007775953,0.000010303174,0.0000029843334,0.00005734016,0.0000018758575,0.98590136,0.0009947134,0.00042397602,0.006300228],"study_design_scores_gemma":[0.00022592118,0.00013804346,0.00011614808,0.00006506484,0.000032913893,0.0000311766,0.0005418308,0.0039644386,0.9845326,0.0035313987,0.0066516986,0.0001687804],"about_ca_topic_score_codex":0.00006378784,"about_ca_topic_score_gemma":0.0007354641,"teacher_disagreement_score":0.01853452,"about_ca_system_score_codex":0.00018766505,"about_ca_system_score_gemma":0.00014511115,"threshold_uncertainty_score":0.6182263},"labels":[],"label_agreement":null},{"id":"W4401811731","doi":"10.3390/s24165387","title":"An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Ontario Agri-Food Innovation Alliance; Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Fire blight; PEAR; Orchard; Computer science; Channel (broadcasting); Pipeline (software); Artificial intelligence; Blight; Machine learning; Agricultural engineering; Engineering; Horticulture; Biology","score_opus":0.03769860287125405,"score_gpt":0.3418619954199564,"score_spread":0.30416339254870234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401811731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981311,0.00071350054,0.0008322582,0.000045282824,0.00003229599,0.000110793415,0.000010581307,0.00007657884,0.00004762753],"genre_scores_gemma":[0.9990999,0.000024299296,0.0005718909,0.0000031203217,0.000054103675,0.000009115336,0.000033473145,0.000021077332,0.00018302059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990385,0.000051318748,0.00020413478,0.00029697004,0.00024587347,0.00016316245],"domain_scores_gemma":[0.99942774,0.00017402021,0.00006889008,0.0001272314,0.0001251816,0.00007691978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049510837,0.00012006932,0.00016597325,0.00017669439,0.00008649914,0.000038281825,0.000043442044,0.0000789301,0.00005305029],"category_scores_gemma":[0.0010297684,0.0001203627,0.00005921196,0.00033569342,0.000035464913,0.00010552212,0.000014983567,0.00015416935,0.0000023828247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025475366,0.00022711662,0.019711934,0.0020714144,0.00020211287,0.000030329154,0.0072438056,0.77078694,0.17510621,0.000042916723,0.000015546346,0.024306895],"study_design_scores_gemma":[0.00033262512,0.000018806357,0.0019571232,0.00007190621,0.00023454461,0.0000022251559,0.001241946,0.8390605,0.15684548,0.00012003907,0.000005649272,0.00010919384],"about_ca_topic_score_codex":0.0000825828,"about_ca_topic_score_gemma":0.000023868533,"teacher_disagreement_score":0.0682735,"about_ca_system_score_codex":0.00009655242,"about_ca_system_score_gemma":0.000048127324,"threshold_uncertainty_score":0.49082485},"labels":[],"label_agreement":null},{"id":"W4401865471","doi":"10.1016/j.infrared.2024.105520","title":"Classification of different wheat flour types using hyperspectral imaging and machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Lethbridge","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Pattern recognition (psychology); Remote sensing; Materials science; Geology","score_opus":0.018276849808430886,"score_gpt":0.28121658159396234,"score_spread":0.2629397317855315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401865471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97567016,0.007677748,0.01028903,0.00032795922,0.000040381186,0.000054982178,0.000016262575,0.0009170641,0.005006393],"genre_scores_gemma":[0.99755937,0.00023643982,0.0018478823,0.0000064537962,0.00007888151,0.000009252418,0.000016507594,0.000027071947,0.00021815822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924123,0.000007253637,0.00018949276,0.00027487578,0.00009751359,0.0001896611],"domain_scores_gemma":[0.99960124,0.000045336754,0.00007472272,0.00020820973,0.000046450627,0.00002405269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031255397,0.0001586556,0.0002466875,0.00026635482,0.000068703106,0.00003476605,0.00014265488,0.00013713601,0.00006540344],"category_scores_gemma":[0.000031309093,0.00014723535,0.00005894295,0.0005954239,0.0001712244,0.00009134678,0.00008369858,0.00041198012,0.0000019767397],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005420095,0.000036042675,0.017641243,0.00013293566,0.00008148403,0.0000045270076,0.00008144837,0.0000032789933,0.96031374,0.009235872,0.00001380051,0.012450197],"study_design_scores_gemma":[0.00007114161,0.000018877,0.00013735572,0.00007457397,0.00013717552,0.00002086777,0.00034729965,0.014732326,0.9600229,0.023973865,0.00031891128,0.00014468418],"about_ca_topic_score_codex":0.000027061522,"about_ca_topic_score_gemma":9.003932e-7,"teacher_disagreement_score":0.021889172,"about_ca_system_score_codex":0.00009580987,"about_ca_system_score_gemma":0.000025066372,"threshold_uncertainty_score":0.6004083},"labels":[],"label_agreement":null},{"id":"W4402269322","doi":"10.1364/cleo_at.2024.am4a.2","title":"Applications of Hadamard-Multiplexed Fluorescence Imaging using a Hyperspectral Camera","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Victoria","funders":"","keywords":"Hyperspectral imaging; Hadamard transform; Multiplexing; Full spectral imaging; Computer science; Fluorescence; Computer vision; Artificial intelligence; Optical imaging; Optics; Physics; Telecommunications","score_opus":0.014903824574718293,"score_gpt":0.2987516157205792,"score_spread":0.2838477911458609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402269322","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59104776,0.025670487,0.14631744,0.0014038879,0.00020474907,0.00039492812,0.00014588321,0.0017729725,0.2330419],"genre_scores_gemma":[0.9795803,0.000040251743,0.018350909,0.000026075466,0.00011457226,0.000013635075,0.00000761016,0.000018651303,0.0018480364],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917024,0.0000034785726,0.00020097262,0.00026646274,0.00015839346,0.0002004424],"domain_scores_gemma":[0.99953866,0.000077091354,0.000036371162,0.00024387924,0.000048984537,0.000055025903],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000045118457,0.00011567361,0.00016386446,0.00015371788,0.00005827325,0.00005073387,0.00017221941,0.00003935605,0.0020710966],"category_scores_gemma":[0.000024726432,0.000104698534,0.00011559709,0.00075192336,0.00008908428,0.00009524116,0.000037485293,0.00013694174,0.000021860113],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037059876,0.00003819049,0.002470774,0.00012400863,0.000066875386,0.0000049059286,0.00008258773,0.000013405906,0.9918948,0.0022151615,0.00024839994,0.0028372093],"study_design_scores_gemma":[0.000099388395,0.000002640121,0.000038821036,0.000031471758,0.00016306598,0.000021991731,0.00091829826,0.024477154,0.9719316,0.00030182558,0.0018627093,0.00015106415],"about_ca_topic_score_codex":0.00035717819,"about_ca_topic_score_gemma":0.0000035668704,"teacher_disagreement_score":0.38853252,"about_ca_system_score_codex":0.00009372833,"about_ca_system_score_gemma":0.00007435771,"threshold_uncertainty_score":0.99884117},"labels":[],"label_agreement":null},{"id":"W4402293612","doi":"10.1016/j.ocsci.2024.05.002","title":"Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging","year":2024,"lang":"en","type":"article","venue":"Oil Crop Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Hyperspectral imaging; Content (measure theory); Infrared; Remote sensing; Environmental science; Optics; Mathematics; Geology; Physics","score_opus":0.02318286683422939,"score_gpt":0.2703390970634583,"score_spread":0.24715623022922895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402293612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9201492,0.0043343883,0.0002800512,0.0001824601,0.00010316756,0.0000071789627,0.000026179327,0.00006536136,0.074851975],"genre_scores_gemma":[0.9881085,0.00008913159,0.001703948,0.000025485351,0.000028070277,0.000002327478,0.00000485669,0.000009421098,0.01002822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986741,0.0000068604095,0.0002598341,0.00033159024,0.00046455703,0.0002630607],"domain_scores_gemma":[0.99933344,0.00007369789,0.000109764784,0.00023734619,0.0001741139,0.00007163265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023599727,0.00011302532,0.0001866204,0.00016016058,0.00010762038,0.00013088986,0.00036858846,0.000042740958,0.0003886471],"category_scores_gemma":[0.00023718936,0.000102765145,0.00009086595,0.0011953105,0.00084462855,0.00030350927,0.000060680246,0.00012883334,0.000006692159],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007439729,0.000059859703,0.00046256528,0.00014930395,0.000007669958,0.0000048489483,0.0003669813,0.0000012611417,0.9746549,0.000058898106,0.000121824516,0.02410442],"study_design_scores_gemma":[0.00015031603,0.000024625706,0.00015489737,0.00007187962,0.000048040914,0.0000114341965,0.00072993245,0.004536613,0.99359125,0.00003975591,0.00052881363,0.000112434456],"about_ca_topic_score_codex":0.000090859045,"about_ca_topic_score_gemma":0.0000010968275,"teacher_disagreement_score":0.0679593,"about_ca_system_score_codex":0.00015050061,"about_ca_system_score_gemma":0.0001722578,"threshold_uncertainty_score":0.42554134},"labels":[],"label_agreement":null},{"id":"W4402438683","doi":"10.11159/icmie24.142","title":"Diagnosis and Improvement in a Fresh grape Agro-exporting Company Using Management Techniques","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Business; Agricultural engineering; Engineering","score_opus":0.009807807610576546,"score_gpt":0.242592173435409,"score_spread":0.23278436582483247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988733,0.00012180927,0.0000018751307,0.000101042955,0.0001724457,0.0001462188,0.000011677854,0.0001456833,0.00042596622],"genre_scores_gemma":[0.99733233,0.00034164928,0.0019551876,0.000031963256,0.00011219174,0.000093426876,0.00000154113,0.000034623583,0.00009708224],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998735,0.0000011980634,0.00040869607,0.00038301002,0.00019019863,0.00028187266],"domain_scores_gemma":[0.9996401,0.00005089799,0.00010791395,0.000102352664,0.000028207329,0.000070508635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014838744,0.00024078863,0.00034561878,0.00022598999,0.000046542213,0.00017087297,0.00022639285,0.00008544181,0.000071183924],"category_scores_gemma":[0.000041020485,0.00019459997,0.0000704735,0.00041908244,0.00005167696,0.00010444634,0.000314168,0.0002142709,1.9645977e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026429296,0.00004453537,0.00009830404,0.0016640524,0.00008270013,0.000003039803,0.000007347492,0.000003294714,0.9936902,0.0032407308,0.000104430044,0.0010349202],"study_design_scores_gemma":[0.0001729297,0.000016054059,0.0000070873702,0.0012041102,0.00013921803,0.000004920534,0.00009983826,0.0019434415,0.99527454,0.00044925534,0.00048056777,0.00020804915],"about_ca_topic_score_codex":0.000022051801,"about_ca_topic_score_gemma":0.0000010777958,"teacher_disagreement_score":0.0027914753,"about_ca_system_score_codex":0.000078713565,"about_ca_system_score_gemma":0.0000051052716,"threshold_uncertainty_score":0.7935556},"labels":[],"label_agreement":null},{"id":"W4402474933","doi":"10.1109/ccece59415.2024.10667130","title":"TAME-Faster R-CNN model for Image-based Tea Diseases Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Nanjing Agricultural University; National Research Council","keywords":"Computer science; Artificial intelligence; Image (mathematics); Convolutional neural network; Pattern recognition (psychology); Computer vision","score_opus":0.015357430764625882,"score_gpt":0.28769921418178157,"score_spread":0.2723417834171557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047774732,0.000990993,0.93065083,0.00039077073,0.000086165885,0.00008967318,0.0001235747,0.00071166,0.019181622],"genre_scores_gemma":[0.96424663,0.0000076220986,0.0024385299,0.00016926265,0.00018052383,0.000080879334,0.000036083715,0.000030982585,0.03280946],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992173,0.0000025233928,0.0001452483,0.00029783093,0.00012712718,0.00020996924],"domain_scores_gemma":[0.99954563,0.00011529406,0.000021194588,0.00019689281,0.000043013817,0.00007795076],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000030049396,0.00013922322,0.00013715122,0.00012285134,0.00007042299,0.00012617254,0.000100567675,0.000075354175,0.0021398407],"category_scores_gemma":[0.00005736266,0.00011427717,0.00019790603,0.0002643367,0.00003499953,0.00012569674,0.000016686678,0.00008953588,0.00007389301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001614606,0.00025462135,0.00038543862,0.0013225144,0.00029898062,0.000013521624,0.00006524982,0.0015576367,0.9660234,0.00039466243,0.01738129,0.012141238],"study_design_scores_gemma":[0.00015425116,0.000010248021,0.000004507621,0.000009555002,0.00019917564,9.606521e-7,0.000043766315,0.48781103,0.5099847,0.00063127646,0.001041763,0.00010880579],"about_ca_topic_score_codex":0.000014965772,"about_ca_topic_score_gemma":0.000007956784,"teacher_disagreement_score":0.9282123,"about_ca_system_score_codex":0.00008013666,"about_ca_system_score_gemma":0.0000590722,"threshold_uncertainty_score":0.9987723},"labels":[],"label_agreement":null},{"id":"W4402541667","doi":"10.1093/jas/skae234.774","title":"PSVII-23 Discriminating meat from different Canadian cattle feeding systems by visible and near-infrared spectroscopy","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Spectroscopy; Infrared; Infrared spectroscopy; Animal science; Biology; Chemistry; Optics; Physics","score_opus":0.015739263641870022,"score_gpt":0.28020607608102016,"score_spread":0.26446681243915016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402541667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983574,0.008031338,0.00014864159,0.00029589207,0.00025103358,0.000022569493,0.00004056672,0.000020073607,0.007615909],"genre_scores_gemma":[0.99866486,0.00015656378,0.0004087793,0.000023831293,0.0002435146,7.926442e-7,0.0000021879393,0.000012863504,0.00048662478],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99836916,0.000008401456,0.0003406886,0.00028640055,0.0005630495,0.00043230865],"domain_scores_gemma":[0.99906886,0.00011593689,0.00014749556,0.000114138595,0.000089748886,0.0004638393],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00026413705,0.00015870505,0.00028241394,0.00025117275,0.00036383362,0.0011135484,0.00040487095,0.00006126496,0.0002908558],"category_scores_gemma":[0.00015640222,0.00011942522,0.00007261333,0.0006829899,0.00024988057,0.0005252863,0.000061638704,0.00033360245,0.000006315186],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010342522,0.000015196626,0.006015399,0.00004609095,0.000035407924,0.000031724692,0.00034345503,0.0000035562784,0.9909091,0.00020514942,0.0022581422,0.00012646243],"study_design_scores_gemma":[0.0002081806,0.00021886546,0.0071160556,0.00038212267,0.0001998958,0.00010923802,0.0029264789,0.005975576,0.98091733,0.00023922641,0.0014312122,0.0002757935],"about_ca_topic_score_codex":0.012300684,"about_ca_topic_score_gemma":0.0006609555,"teacher_disagreement_score":0.015090867,"about_ca_system_score_codex":0.00049001514,"about_ca_system_score_gemma":0.00037816935,"threshold_uncertainty_score":0.9999234},"labels":[],"label_agreement":null},{"id":"W4402543604","doi":"10.1007/s11336-024-10001-w","title":"Correction to: Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes","year":2024,"lang":"en","type":"erratum","venue":"Psychometrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multivariate statistics; Component (thermodynamics); Multivariate analysis; Regular polygon; Mathematics; Statistics; Computer science; Econometrics; Applied mathematics; Geometry","score_opus":0.02159953879903501,"score_gpt":0.33543038699551503,"score_spread":0.31383084819648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402543604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046138614,0.038858958,0.6369508,0.0010422495,0.09460498,0.0026780488,0.0045550163,0.0042483765,0.170923],"genre_scores_gemma":[0.6690059,0.0002740706,0.061087836,0.00084450876,0.0032713523,0.0005760121,0.01563825,0.0007632973,0.24853878],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99382854,0.00027744254,0.0014186926,0.0022369474,0.0011501032,0.0010883086],"domain_scores_gemma":[0.99489564,0.0013786347,0.0009769677,0.0016468001,0.0005345536,0.000567393],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00057144003,0.0014970754,0.0031837053,0.00748279,0.0003830692,0.00078060105,0.0013775959,0.0009484834,0.003963428],"category_scores_gemma":[0.00042359144,0.0012938057,0.0011619877,0.017690128,0.00013235357,0.00016708,0.00033959243,0.0031475916,0.0001740182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023783192,0.0017818428,0.010573907,0.0028220895,0.045580797,0.00007250005,0.001338716,0.0068493416,0.09968228,0.000042570293,0.821495,0.0073826537],"study_design_scores_gemma":[0.008446612,0.0005981104,0.009634783,0.004189222,0.05734899,0.000078723606,0.00076475955,0.38764656,0.11292657,0.00015613671,0.41119283,0.007016701],"about_ca_topic_score_codex":0.002291645,"about_ca_topic_score_gemma":0.0004909041,"teacher_disagreement_score":0.6228673,"about_ca_system_score_codex":0.0010898578,"about_ca_system_score_gemma":0.00029230645,"threshold_uncertainty_score":0.99977785},"labels":[],"label_agreement":null},{"id":"W4403013615","doi":"10.1101/2024.09.30.614425","title":"Preserving Full Spectrum Information in Imaging Mass Spectrometry Data Reduction","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"","keywords":"Reduction (mathematics); Mass spectrometry; Computer science; Spectrum (functional analysis); Data reduction; Mass spectrometry imaging; Data mining; Chemistry; Physics; Mathematics; Chromatography","score_opus":0.015665720006176477,"score_gpt":0.2467531116156016,"score_spread":0.23108739160942512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403013615","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97564113,0.011030881,0.004339502,0.001647446,0.0023238608,0.00055870967,0.0013341984,0.0017441142,0.0013801645],"genre_scores_gemma":[0.9922039,0.0005728031,0.0058018574,0.000048234186,0.0011249799,0.000074977164,0.000013729279,0.00013057553,0.00002895025],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99610525,0.000045120225,0.001002076,0.0013584722,0.0006508843,0.0008382136],"domain_scores_gemma":[0.99587166,0.000060777296,0.00054192316,0.0031523595,0.0001603201,0.00021297408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073891395,0.0007114959,0.00072081917,0.0015561024,0.00012430162,0.0008340904,0.0018449475,0.0005512167,0.00086562446],"category_scores_gemma":[0.00041390245,0.0008184848,0.00016304871,0.0022349497,0.00009751844,0.0010171371,0.002607354,0.0022682215,0.00024555682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046123434,0.00010238347,0.004432701,0.002475638,0.00031311536,0.000078203906,0.00001992532,0.00015844758,0.9901379,0.00030825488,0.0019236895,0.000003647713],"study_design_scores_gemma":[0.00057178474,0.00001671937,0.0036512467,0.0009707551,0.0006903667,2.93887e-7,0.000078610894,0.01520911,0.97406906,0.00015382988,0.0030459797,0.0015422323],"about_ca_topic_score_codex":0.00030303528,"about_ca_topic_score_gemma":0.000005277612,"teacher_disagreement_score":0.016562771,"about_ca_system_score_codex":0.0011334087,"about_ca_system_score_gemma":0.0005735215,"threshold_uncertainty_score":0.9994266},"labels":[],"label_agreement":null},{"id":"W4403074117","doi":"10.1002/cjce.25496","title":"Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gelatin; Water content; Ibuprofen; Moisture; Spectroscopy; Infrared spectroscopy; Line (geometry); Production line; Materials science; Chemistry; Composite material; Organic chemistry; Medicine; Engineering; Mathematics; Mechanical engineering; Pharmacology; Physics","score_opus":0.00899894403418562,"score_gpt":0.21034700470532194,"score_spread":0.20134806067113634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403074117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889733,0.0061559654,0.0042425976,0.00034472102,0.00015933155,0.000041168034,0.0000062923204,0.000019619212,0.000056992536],"genre_scores_gemma":[0.9989674,0.000016200194,0.00053063064,0.000016626702,0.00032365034,0.0000019402387,0.000009343398,0.000024159668,0.00011002649],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911225,0.000006405704,0.00034659528,0.00013282994,0.00015052003,0.00025141297],"domain_scores_gemma":[0.9994303,0.00007029567,0.00011625387,0.00012089016,0.00006169408,0.00020055352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014960275,0.00015089184,0.00023475385,0.00010139492,0.000064688735,0.000094941446,0.00015912799,0.000116259325,0.00004980429],"category_scores_gemma":[0.00018307254,0.00012042069,0.00008190527,0.00017758436,0.00006962964,0.00013415421,0.000015548178,0.00057881384,3.5629623e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013053992,0.0000066692646,0.00020401466,0.00017333502,0.00011709541,0.000028534676,0.00031635546,0.020541474,0.9767592,0.000010016143,0.00023979344,0.0015904603],"study_design_scores_gemma":[0.000114698974,0.00001702049,0.00006849011,0.00008257597,0.00011645554,0.0001529016,0.000040775038,0.15492806,0.84424955,0.000037959755,0.000088214525,0.00010331387],"about_ca_topic_score_codex":0.00055837136,"about_ca_topic_score_gemma":0.00010611791,"teacher_disagreement_score":0.13438658,"about_ca_system_score_codex":0.00020902202,"about_ca_system_score_gemma":0.000071721806,"threshold_uncertainty_score":0.49106133},"labels":[],"label_agreement":null},{"id":"W4403289505","doi":"10.1016/j.saa.2024.125262","title":"Evaluation of phenolics in the analysis of virgin olive oil using near infrared spectroscopy","year":2024,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"China Scholarship Council; Horizon 2020; Spanish National Plan for Scientific and Technical Research and Innovation; Horizon 2020 Framework Programme; HORIZON EUROPE Food, Bioeconomy, Natural Resources, Agriculture and Environment; Ministerio de Ciencia e Innovación","keywords":"Chemistry; Olive oil; Infrared spectroscopy; Spectroscopy; Infrared; Food science; Analytical Chemistry (journal); Organic chemistry; Optics","score_opus":0.023190797532778768,"score_gpt":0.31807638870112265,"score_spread":0.2948855911683439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403289505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97317696,0.010693538,0.00256902,0.00019174958,0.00007080196,0.000184378,0.000107766304,0.00006357395,0.012942187],"genre_scores_gemma":[0.99479693,0.00094299525,0.0038289663,0.000097349264,0.00008195685,0.00003431624,0.00012068159,0.000062062085,0.00003471634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99541724,0.00029426214,0.0009693071,0.00092220603,0.0016726156,0.000724345],"domain_scores_gemma":[0.9981444,0.00014739204,0.00035436123,0.0009899236,0.00023631957,0.00012763968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013170412,0.0005579477,0.0010551119,0.0012659316,0.00013313159,0.0002262329,0.0006533208,0.00026556972,0.00068988017],"category_scores_gemma":[0.00020594732,0.00046581004,0.0006716227,0.005775587,0.00047774686,0.00016947414,0.00013224552,0.0005460066,0.0000034078912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084553714,0.00027138487,0.00033754215,0.00027267894,0.002905412,0.000054164844,0.00080356584,0.0001752836,0.9932934,0.0016049064,0.000060206025,0.00013691533],"study_design_scores_gemma":[0.0006820543,0.00012697633,0.00019735176,0.00014781678,0.012391008,0.000036239155,0.0005442798,0.043565825,0.93974483,0.001897428,0.0002296926,0.0004364807],"about_ca_topic_score_codex":0.00025992843,"about_ca_topic_score_gemma":0.000077579934,"teacher_disagreement_score":0.05354854,"about_ca_system_score_codex":0.0003362682,"about_ca_system_score_gemma":0.00048724035,"threshold_uncertainty_score":0.99977934},"labels":[],"label_agreement":null},{"id":"W4403364185","doi":"10.1016/j.lwt.2024.116899","title":"A ratiometric indicator pad utilizing Alz/BRI-PVA for visual assessment of fish freshness","year":2024,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Fish <Actinopterygii>; Food science; Chemistry; Business; Fishery; Biology","score_opus":0.028120535061326172,"score_gpt":0.3794183561562001,"score_spread":0.35129782109487395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403364185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9568998,0.0029771111,0.021814046,0.00045782625,0.00037858685,0.00018562812,0.00026755378,0.00027454493,0.016744856],"genre_scores_gemma":[0.9966679,0.00006629562,0.0012646181,0.00008037584,0.00023151095,0.00006499844,0.00008260717,0.000028982373,0.0015126893],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987845,0.000008914686,0.00032252123,0.00032799251,0.0002953731,0.0002607233],"domain_scores_gemma":[0.99907124,0.000489175,0.00010340861,0.00021072013,0.000048421436,0.00007702834],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009841978,0.00015451753,0.00030164135,0.00059089443,0.0000683909,0.00008195889,0.00021229571,0.00012772945,0.0017499677],"category_scores_gemma":[0.000177307,0.00014373129,0.00018169457,0.0018695234,0.000049496597,0.00010287259,0.000057156656,0.00016919617,0.00000972406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006742966,0.00095220073,0.04782664,0.004873448,0.0019532072,0.000052929736,0.00055880134,0.00001878777,0.8681431,0.009812105,0.04226081,0.023480544],"study_design_scores_gemma":[0.0004510621,0.000079113895,0.0018323669,0.00007662515,0.00037078798,0.000005570173,0.0006113308,0.0049079107,0.9643398,0.00024336389,0.026828337,0.0002537275],"about_ca_topic_score_codex":0.000025354393,"about_ca_topic_score_gemma":0.000004744226,"teacher_disagreement_score":0.09619671,"about_ca_system_score_codex":0.00010396508,"about_ca_system_score_gemma":0.000054912558,"threshold_uncertainty_score":0.99916255},"labels":[],"label_agreement":null},{"id":"W4403492960","doi":"10.3390/app14209313","title":"Multilayer Perception-Based Hybrid Spectral Band Selection Algorithm for Aflatoxin B1 Detection Using Hyperspectral Imaging","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Selection (genetic algorithm); Algorithm; Computer science; Artificial intelligence; Pattern recognition (psychology); Geography","score_opus":0.02122352694370194,"score_gpt":0.2992250415496399,"score_spread":0.27800151460593797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403492960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8044896,0.00027761242,0.19154663,0.00007641462,0.0001820479,0.00012327367,0.000017580747,0.00030453675,0.0029823082],"genre_scores_gemma":[0.9726963,0.000004917901,0.026523737,0.00005774434,0.00049523567,0.00003900456,0.000009441823,0.000022350036,0.00015128635],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983876,0.0000065106688,0.00021139433,0.0006333715,0.00030745615,0.00045369175],"domain_scores_gemma":[0.99958855,0.00013660332,0.000056781053,0.00010377474,0.000038101367,0.00007619916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026895598,0.00020435418,0.00018097309,0.00032456554,0.0005940168,0.00034166503,0.00018100916,0.000055143053,0.00052835373],"category_scores_gemma":[0.00001867394,0.00018770274,0.00014494696,0.0009885506,0.00022145048,0.00020642545,0.0000115248795,0.00018791076,0.000016903778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000102685335,0.000030830393,0.00022949371,0.000035944635,0.000025300797,0.0000018859756,0.00007271786,0.0015274349,0.979066,0.00007057702,0.000044236356,0.018885275],"study_design_scores_gemma":[0.00014036393,0.000013545089,0.00003688361,0.000008405592,0.00007770422,0.00001743895,0.00048758034,0.43516085,0.56345266,0.00032974873,0.000120629295,0.00015420669],"about_ca_topic_score_codex":0.00010584745,"about_ca_topic_score_gemma":0.00001471879,"teacher_disagreement_score":0.4336334,"about_ca_system_score_codex":0.0003086736,"about_ca_system_score_gemma":0.0001383336,"threshold_uncertainty_score":0.76542956},"labels":[],"label_agreement":null},{"id":"W4403659128","doi":"10.3389/fagro.2024.1435234","title":"Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches","year":2024,"lang":"en","type":"article","venue":"Frontiers in Agronomy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Zayed University","keywords":"Hyperspectral imaging; Computer science; Remote sensing; Environmental science; Artificial intelligence; Geology","score_opus":0.013675625095228721,"score_gpt":0.2345028385518185,"score_spread":0.2208272134565898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403659128","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871449,0.008656234,0.0035164375,0.000029316134,0.00006522576,0.00015979244,0.00001317065,0.00002056503,0.00039435137],"genre_scores_gemma":[0.99857444,0.000040528947,0.0012160473,0.0000042462534,0.000035800207,0.000012574872,0.000024268415,0.000016212007,0.000075891374],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985887,0.00005134034,0.00048584974,0.0003887251,0.00022679854,0.00025858555],"domain_scores_gemma":[0.9994806,0.00006656929,0.00012406611,0.00029014907,0.000008630272,0.000029979281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103131926,0.0001959318,0.000510601,0.00071588793,0.000036548645,0.000028943954,0.00030095322,0.00006126368,0.00012306045],"category_scores_gemma":[0.0000064634755,0.00016253053,0.00018632386,0.0013801861,0.00016000729,0.000101449274,0.00008596362,0.00029384877,8.3527243e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001369388,0.0015334813,0.8357055,0.00016213999,0.0048786327,0.00012335068,0.009769456,0.10817716,0.033680566,0.000030332272,0.00015241763,0.0056499774],"study_design_scores_gemma":[0.002853959,0.00009575188,0.08958632,0.00010143114,0.004614487,0.000009132645,0.32958686,0.5287242,0.04316379,0.00053222006,0.00007300265,0.00065882807],"about_ca_topic_score_codex":0.0003769311,"about_ca_topic_score_gemma":0.000011423844,"teacher_disagreement_score":0.7461192,"about_ca_system_score_codex":0.00023066206,"about_ca_system_score_gemma":0.000028121312,"threshold_uncertainty_score":0.6627803},"labels":[],"label_agreement":null},{"id":"W4403741372","doi":"10.18280/isi.290525","title":"A Hybrid Model for Palm Sugar Type Classification: Advancing Image-Based Analysis for Industry Applications","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Palm; Sugar; Image (mathematics); Type (biology); Artificial intelligence; Computer science; Pattern recognition (psychology); Biology; Food science","score_opus":0.027718606532997975,"score_gpt":0.2976012032564324,"score_spread":0.26988259672343445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403741372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017243475,0.00034846528,0.9772918,0.0001275809,0.000046646852,0.0003714068,0.00044073258,0.00032466085,0.0038052134],"genre_scores_gemma":[0.970399,0.00001885767,0.025364121,0.00012201611,0.00013862088,0.0012195093,0.0020780717,0.000024888683,0.0006349118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987066,0.0000056845956,0.0005657102,0.0002334284,0.00017912741,0.0003094922],"domain_scores_gemma":[0.99864966,0.0002287636,0.00021196803,0.00034139844,0.00047763204,0.00009055411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024365011,0.00019595567,0.00027615108,0.0005762404,0.0002907475,0.00033315856,0.00020649485,0.0002041609,0.0001833964],"category_scores_gemma":[0.00026574443,0.00019822588,0.00026168462,0.0014355255,0.00007464649,0.000994973,0.000018631174,0.00019563692,0.000031583142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014704721,0.00083244493,0.018420165,0.043949287,0.013720385,0.000006259735,0.008485598,0.42877007,0.1417449,0.08680589,0.04837931,0.20741525],"study_design_scores_gemma":[0.00025776314,0.000020933488,0.00007153063,0.00004509097,0.0010764186,0.0000026655914,0.0005395816,0.95358545,0.032287255,0.00345891,0.008407479,0.00024692607],"about_ca_topic_score_codex":0.000009141489,"about_ca_topic_score_gemma":0.0000038946127,"teacher_disagreement_score":0.9531555,"about_ca_system_score_codex":0.00044273213,"about_ca_system_score_gemma":0.00027611916,"threshold_uncertainty_score":0.8083417},"labels":[],"label_agreement":null},{"id":"W4403761212","doi":"10.7554/elife.95802.2.sa4","title":"eLife Assessment: A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Institute of Mental Health; National Institutes of Health","keywords":"Photometry (optics); Statistical analysis; Dynamics (music); Computer science; Statistics; Mathematics; Physics; Astrophysics; Acoustics","score_opus":0.09234830621448911,"score_gpt":0.46223350541353875,"score_spread":0.3698851991990496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403761212","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035111688,0.15466227,0.41451034,0.0108514745,0.004274894,0.005629352,0.1603594,0.00070419017,0.24549691],"genre_scores_gemma":[0.0103120655,0.004908296,0.1761559,0.0012787489,0.00061785255,0.0015776557,0.05304757,0.00026584353,0.75183606],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955227,0.00004421707,0.0017490401,0.0010523777,0.001080723,0.00055098726],"domain_scores_gemma":[0.9965059,0.0015264922,0.00063185324,0.0009168345,0.00024534503,0.00017356088],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005835741,0.00065944024,0.0030905835,0.0018720974,0.00004008807,0.00008433654,0.0006256944,0.0009707006,0.04805383],"category_scores_gemma":[0.0010550582,0.00058423175,0.001202313,0.00531513,0.00010391311,0.00004946015,0.00020014097,0.0010739195,0.00002711847],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016710651,0.002140585,0.0011376331,0.02504089,0.028469749,0.00005419089,0.000042937034,0.000011905029,0.00007085422,0.008818644,0.9301476,0.0023939444],"study_design_scores_gemma":[0.030234165,0.00093496416,0.00018934144,0.01440272,0.1894774,0.000007856251,0.0019732553,0.05370822,0.010772915,0.009796391,0.6819729,0.0065298243],"about_ca_topic_score_codex":0.0007966548,"about_ca_topic_score_gemma":0.0003541901,"teacher_disagreement_score":0.5063392,"about_ca_system_score_codex":0.0011534231,"about_ca_system_score_gemma":0.00050000194,"threshold_uncertainty_score":0.9996609},"labels":[],"label_agreement":null},{"id":"W4403820626","doi":"10.18280/ijdne.190533","title":"Non-Invasive NIR Spectroscopy for Precise Water Content Determination in Sumatran Coffee Beans","year":2024,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Andalas","keywords":"Spectroscopy; Content (measure theory); Near-infrared spectroscopy; Water content; Environmental science; Materials science; Food science; Chemistry; Physics; Mathematics; Engineering; Optics","score_opus":0.017355900661760047,"score_gpt":0.2899711826752106,"score_spread":0.27261528201345053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403820626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8589305,0.0034567586,0.13377707,0.0015518942,0.0011919988,0.00017419779,0.000089704336,0.000026981721,0.00080085004],"genre_scores_gemma":[0.9933124,0.00096688105,0.0046982975,0.00010104261,0.00040220591,0.000008716167,0.000042719883,0.000023001938,0.00044471704],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988305,0.000010435802,0.00046310778,0.00019696224,0.00029598532,0.00020299197],"domain_scores_gemma":[0.99909014,0.0003474303,0.00013373126,0.00007456049,0.0002874794,0.00006667257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028644325,0.00016848513,0.00026445006,0.0004914831,0.00003581277,0.00016837305,0.00029396874,0.0002255374,0.000113627415],"category_scores_gemma":[0.00010846036,0.00012578677,0.00017212307,0.00011674636,0.000037411766,0.0003158082,0.000026179277,0.00051620154,0.0000025746572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009443677,0.00024594628,0.0037871895,0.0002854358,0.0008839758,0.0004200304,0.0011655274,0.0004093037,0.9861322,0.000756044,0.0010668928,0.0039030532],"study_design_scores_gemma":[0.0016951059,0.00023853635,0.00043744798,0.00044020472,0.0003098664,0.00041554443,0.00045127363,0.04171343,0.94498676,0.008211656,0.000797903,0.0003022912],"about_ca_topic_score_codex":0.0000066795983,"about_ca_topic_score_gemma":0.00003681327,"teacher_disagreement_score":0.13438188,"about_ca_system_score_codex":0.00028858733,"about_ca_system_score_gemma":0.00009019099,"threshold_uncertainty_score":0.51294357},"labels":[],"label_agreement":null},{"id":"W4403858660","doi":"10.1130/abs/2024am-403228","title":"REFLECTIVE SPECTROSCOPY OF OTTAWA SAND, UREA, AND METHYLENE BLUE: A PROOF-OF-CONCEPT STUDY TOWARD DEVELOPING HYPERSPECTRAL REMOTE SENSING / IMAGE SPECTROSCOPY CAPABILITIES FOR HUMAN WASTE INVESTIGATIONS","year":2024,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Methylene blue; Spectroscopy; Proof of concept; Urea; Computer science; Remote sensing; Materials science; Environmental science; Chemistry; Artificial intelligence; Geology; Physics; Organic chemistry; Photocatalysis","score_opus":0.03594844768337539,"score_gpt":0.32447149883876686,"score_spread":0.28852305115539145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403858660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9695917,0.0013810246,0.026338825,0.0007530918,0.000023202707,0.00082363386,0.000032470365,0.00014862738,0.0009074347],"genre_scores_gemma":[0.6296087,0.000028640387,0.37013182,0.0000361788,0.00006874863,0.000017184213,0.000029267996,0.000027066128,0.00005244175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972166,0.00005279907,0.00088504987,0.00079022447,0.00043711963,0.00061821245],"domain_scores_gemma":[0.99808073,0.00051815965,0.00058683316,0.0003293418,0.00033898052,0.00014595308],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034766368,0.0004441923,0.0010531432,0.000095862015,0.00021144384,0.00007191152,0.00024460675,0.00021903064,0.000062570616],"category_scores_gemma":[0.00021033369,0.00034268,0.00035441085,0.0010121587,0.002416218,0.00017145908,0.00009292211,0.000519927,3.3774793e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026768836,0.0011812251,0.0031830515,0.00269797,0.003250726,0.00002109154,0.026293948,0.00018443735,0.9497387,0.00029346236,0.00026969204,0.012618028],"study_design_scores_gemma":[0.0007016686,0.0026595818,0.00037171665,0.00023087369,0.0006588694,0.000016084954,0.034826614,0.0004933204,0.9554495,0.0041068234,0.00009627033,0.0003886335],"about_ca_topic_score_codex":0.0011593549,"about_ca_topic_score_gemma":0.000031841297,"teacher_disagreement_score":0.34379297,"about_ca_system_score_codex":0.00017492501,"about_ca_system_score_gemma":0.00022968589,"threshold_uncertainty_score":0.99990255},"labels":[],"label_agreement":null},{"id":"W4404121238","doi":"10.26434/chemrxiv-2024-37v2j","title":"Spectro: A multi-modal approach for molecule elucidation using IR and NMR data","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Molecule; Modal; Computational chemistry; Chemistry; Organic chemistry; Polymer chemistry","score_opus":0.1031164035381926,"score_gpt":0.3551949137547089,"score_spread":0.2520785102165163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404121238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7490413,0.018514585,0.21387729,0.00024091438,0.00024219346,0.00070459524,0.0006002756,0.0005615304,0.016217282],"genre_scores_gemma":[0.8884805,0.00026822058,0.103736944,0.00005015168,0.0009295125,0.00010960239,0.0039848774,0.00013846326,0.0023017612],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99768853,0.000008029953,0.0003590864,0.0013567131,0.00021085361,0.00037679734],"domain_scores_gemma":[0.9982169,0.00005970147,0.00019665701,0.0013442708,0.00006803275,0.000114420094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019720232,0.00042345328,0.0005190619,0.00018387775,0.00010223545,0.00024035794,0.0007243037,0.0004887795,0.00015165869],"category_scores_gemma":[0.00017619932,0.00042767022,0.00016060522,0.00027241238,0.000097534255,0.000075288815,0.002011265,0.00073430967,0.0000065565764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009743073,0.00045413108,0.0008438251,0.0107507305,0.0017238627,0.00002003125,0.00033078092,0.0005021376,0.9797856,0.00040288304,0.0042901435,0.00079841475],"study_design_scores_gemma":[0.00051234936,0.000007865727,0.00001936432,0.000103791186,0.0013275907,0.00001924738,0.00022494113,0.37406552,0.6216368,0.0010914819,0.00040968403,0.00058133324],"about_ca_topic_score_codex":0.00007828237,"about_ca_topic_score_gemma":0.0000027836393,"teacher_disagreement_score":0.3735634,"about_ca_system_score_codex":0.00017887485,"about_ca_system_score_gemma":0.00016480294,"threshold_uncertainty_score":0.9998175},"labels":[],"label_agreement":null},{"id":"W4404180427","doi":"10.1109/bcd61269.2024.10743118","title":"Classification of Aflatoxin B1 Contamination Level Using Hyperspectral Images with Random Forest and QDA","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Random forest; Aflatoxin; Contamination; Artificial intelligence; Computer science; Environmental science; Remote sensing; Pattern recognition (psychology); Geography; Food science; Biology; Ecology","score_opus":0.03769040600671065,"score_gpt":0.2956566157355786,"score_spread":0.25796620972886797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404180427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9728893,0.0010415628,0.014804313,0.00009841323,0.000016347987,0.00003899241,0.000012291539,0.000060212933,0.011038568],"genre_scores_gemma":[0.995316,0.00005090832,0.0023705368,0.000006606313,0.000051413153,0.0000038166004,0.000013106182,0.000012179394,0.0021753886],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942905,0.0000052389337,0.00014429112,0.00018747886,0.00012854431,0.00010542312],"domain_scores_gemma":[0.999638,0.00010247942,0.000046962654,0.0001038707,0.00007827408,0.0000303829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006775848,0.00009475482,0.0001555352,0.000117754236,0.000037368096,0.000049218273,0.000048740145,0.000054153064,0.00025065592],"category_scores_gemma":[0.00004829114,0.000070869835,0.00003592888,0.0002894828,0.00008525503,0.00013908389,0.000010436201,0.000078241144,0.0000016516537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008053245,0.00004022218,0.024259914,0.00020528854,0.00014499595,0.0000042578695,0.000117967444,0.000026873548,0.97025603,0.0038745524,0.00017061955,0.00081873196],"study_design_scores_gemma":[0.000918324,0.000028070304,0.012295504,0.00005578324,0.00029274914,0.000019365212,0.0013994719,0.011738903,0.97271,0.00032056324,0.00007881729,0.0001424586],"about_ca_topic_score_codex":0.00011291603,"about_ca_topic_score_gemma":0.000035820478,"teacher_disagreement_score":0.022426747,"about_ca_system_score_codex":0.000056038403,"about_ca_system_score_gemma":0.000037223508,"threshold_uncertainty_score":0.2889988},"labels":[],"label_agreement":null},{"id":"W4404322558","doi":"10.1016/j.foodres.2024.115327","title":"Using portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattle","year":2024,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Gobierno de Navarra; Universidad Pública de Navarra","keywords":"Beef cattle; Agronomy; Chemistry; Biology; Animal science","score_opus":0.08575766070087705,"score_gpt":0.40198599793895495,"score_spread":0.3162283372380779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404322558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97553223,0.001924929,0.0009018867,0.0013196346,0.00020454924,0.00008918091,0.00036362774,0.000086120395,0.019577812],"genre_scores_gemma":[0.9862447,0.00007930937,0.0033798858,0.000042747008,0.00035741227,0.000020138948,0.000100836995,0.00003257693,0.009742432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981011,0.000023029776,0.00022492946,0.00053494377,0.0006936945,0.00042226395],"domain_scores_gemma":[0.9990818,0.00026202874,0.000028643395,0.00022224798,0.00015832322,0.0002469811],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029509488,0.00015175086,0.00018585112,0.0003487994,0.0002169842,0.0008173176,0.00030329268,0.000096227064,0.0036251396],"category_scores_gemma":[0.00027660714,0.00014580818,0.000050833354,0.0005619246,0.00017373917,0.0002015345,0.0003224497,0.00041627203,0.00008283253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023787252,0.00018800025,0.023891987,0.00017258112,0.0012164287,0.000090304755,0.00077182124,0.00001095049,0.9331058,0.008631167,0.031086626,0.00059644587],"study_design_scores_gemma":[0.00069355365,0.00025927232,0.004388458,0.00022321199,0.00012652027,0.000042708827,0.00063731003,0.017389486,0.90919167,0.025636883,0.040972266,0.00043863323],"about_ca_topic_score_codex":0.00069116254,"about_ca_topic_score_gemma":0.000052251402,"teacher_disagreement_score":0.023914117,"about_ca_system_score_codex":0.00020487412,"about_ca_system_score_gemma":0.00017547453,"threshold_uncertainty_score":0.99728566},"labels":[],"label_agreement":null},{"id":"W4404354692","doi":"10.1111/jfpe.14767","title":"Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of Tabriz","keywords":"Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Feature selection; Component (thermodynamics); Selection (genetic algorithm); Computer science; Feature (linguistics); Computer vision; Physics","score_opus":0.004609719736003634,"score_gpt":0.23868389258338388,"score_spread":0.23407417284738025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404354692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9578044,0.004852963,0.036928613,0.00007037242,0.00009693622,0.000053819498,0.0000036536533,0.00015101516,0.000038260547],"genre_scores_gemma":[0.9990744,0.00007243066,0.0005856517,0.0000022483173,0.00022414407,0.000009715072,0.000004910787,0.00001872057,0.000007783636],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990171,0.000007336501,0.0003993586,0.00020346948,0.0001554898,0.00021721296],"domain_scores_gemma":[0.9995755,0.000084740226,0.00017248862,0.00004394453,0.00007135792,0.000051982486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015239429,0.00017996735,0.0003559234,0.0010141162,0.00006871403,0.00029038248,0.000055054603,0.00013431997,0.0000031986194],"category_scores_gemma":[0.00006906125,0.00016719774,0.000065919776,0.0018898187,0.00002430713,0.000489677,0.000024350866,0.0006604144,9.012534e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074163276,0.0000809154,0.5034949,0.0017164245,0.0010117354,0.00006770449,0.0003649348,0.04774122,0.43549806,0.00036663195,0.0000353806,0.009547875],"study_design_scores_gemma":[0.00016482617,0.00016207242,0.0024858047,0.0006929924,0.0008360756,0.00046269316,0.00028129746,0.5743683,0.42020863,0.00006537972,0.000032012646,0.0002398952],"about_ca_topic_score_codex":0.000009506018,"about_ca_topic_score_gemma":0.000013159156,"teacher_disagreement_score":0.5266271,"about_ca_system_score_codex":0.00024329426,"about_ca_system_score_gemma":0.000026363732,"threshold_uncertainty_score":0.6818126},"labels":[],"label_agreement":null},{"id":"W4404396896","doi":"10.1016/j.jag.2024.104262","title":"Using UAV hyperspectral imagery and deep learning for Object-Based quantitative inversion of Zanthoxylum rust disease index","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Inversion (geology); Geography; Cartography; Remote sensing; Deep learning; Index (typography); Computer vision; Computer science; Geology; Seismology","score_opus":0.02963377540639802,"score_gpt":0.2994553630275054,"score_spread":0.2698215876211074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404396896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9255584,0.00052898633,0.07290256,0.00028506955,0.00011460917,0.00006473064,0.000016783635,0.000019705332,0.00050914154],"genre_scores_gemma":[0.989845,0.0001486629,0.009708344,0.000095021496,0.00008839509,0.0000021551466,0.00006352544,0.000007942373,0.00004095058],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990837,0.0000056134572,0.0004259275,0.000090456444,0.00030334733,0.00009095153],"domain_scores_gemma":[0.9990222,0.00017592052,0.00036612205,0.000039097686,0.00033075572,0.00006593696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019989845,0.000100275916,0.00015348352,0.00035691206,0.000068894406,0.00011485951,0.00007519881,0.000053894986,0.00007260459],"category_scores_gemma":[0.00013443473,0.00009321099,0.000077188204,0.00017901798,0.000054187673,0.00060732197,0.000017818438,0.0001518355,9.592552e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010835231,0.00047167664,0.075659156,0.004634761,0.0025602358,0.000046841604,0.012012725,0.1945667,0.48067358,0.06430194,0.00040455427,0.1538326],"study_design_scores_gemma":[0.0013587163,0.00007283301,0.010488989,0.00019336415,0.00019691199,0.000012669645,0.0026846055,0.9357831,0.046382017,0.0013087002,0.0013402416,0.0001778719],"about_ca_topic_score_codex":0.000009648463,"about_ca_topic_score_gemma":0.0000018478835,"teacher_disagreement_score":0.74121636,"about_ca_system_score_codex":0.00004808801,"about_ca_system_score_gemma":0.00009073849,"threshold_uncertainty_score":0.3801034},"labels":[],"label_agreement":null},{"id":"W4404471209","doi":"10.1088/1361-6501/ad93f1","title":"Calibration-free thickness and temperature measurement of oil films using broadband near-infrared absorption spectroscopy","year":2024,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Deutsche Forschungsgemeinschaft; Alexander von Humboldt-Stiftung","keywords":"Materials science; Calibration; Broadband; Absorption (acoustics); Spectroscopy; Infrared spectroscopy; Infrared; Near-infrared spectroscopy; Analytical Chemistry (journal); Optics; Optoelectronics; Composite material; Chemistry; Physics; Chromatography","score_opus":0.02519457929433299,"score_gpt":0.2631756467431976,"score_spread":0.2379810674488646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404471209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98144025,0.0128806485,0.0007244758,0.0012682322,0.00014989567,0.00007693112,0.000013890978,0.00024227079,0.003203404],"genre_scores_gemma":[0.9980988,0.00025758115,0.0014646243,0.00002358342,0.00003151688,0.000012291363,0.0000012280551,0.000012315177,0.000098074124],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973777,0.000009422144,0.00027773273,0.0005363764,0.0014739394,0.00032485093],"domain_scores_gemma":[0.9987312,0.000014995112,0.000084892556,0.00040912058,0.000685203,0.000074603246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011851914,0.00018853588,0.00025847394,0.0004917476,0.00034119477,0.00022964452,0.00034374063,0.00020386053,0.0001139732],"category_scores_gemma":[0.00064387,0.00015996776,0.00003093876,0.0021465,0.0010237428,0.00030634558,0.00014092596,0.00027233784,0.0000010869218],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009467007,0.000034214598,0.0021121872,0.00021776874,0.00004330388,0.0000035657406,0.000072814706,0.000005388893,0.99527246,0.0010453351,0.00028310096,0.0009004214],"study_design_scores_gemma":[0.0003009457,0.000051974068,0.00023259682,0.00018249365,0.00012552866,0.00003643742,0.0004729353,0.0035060763,0.99290144,0.0015959744,0.00040848317,0.00018511302],"about_ca_topic_score_codex":0.000048856666,"about_ca_topic_score_gemma":0.000023789955,"teacher_disagreement_score":0.016658535,"about_ca_system_score_codex":0.00027655865,"about_ca_system_score_gemma":0.0005696654,"threshold_uncertainty_score":0.6523296},"labels":[],"label_agreement":null},{"id":"W4404528472","doi":"10.1016/j.foodres.2024.115347","title":"Infrared spectroscopy and chemometrics for predicting commercial categories of virgin olive oils and supporting the panel test","year":2024,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Università di Bologna; Queen's University Belfast","keywords":"Chemometrics; Olive oil; Test (biology); Chemistry; Chromatography; Food science; Biology; Botany","score_opus":0.09347805357885469,"score_gpt":0.4047383863587023,"score_spread":0.3112603327798476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404528472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711519,0.0033091204,0.000957404,0.0022420487,0.0001801465,0.00018930233,0.00058212667,0.000064583175,0.021323353],"genre_scores_gemma":[0.99648523,0.00032618162,0.0005394795,0.0000191783,0.0005041995,0.00005293886,0.000053937514,0.00001912216,0.0019997207],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998523,0.000017679833,0.00029916663,0.0002771087,0.0005593492,0.0003237068],"domain_scores_gemma":[0.996569,0.0028353215,0.00007969866,0.00013385988,0.0003058859,0.00007624744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008375961,0.00011422008,0.0001673886,0.00044083133,0.00021398603,0.0002443121,0.00030111356,0.00008299415,0.00027643619],"category_scores_gemma":[0.003886243,0.00008747032,0.00005819962,0.0007242302,0.00033219144,0.00014035494,0.00022849509,0.00043246782,0.0000017359604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004459705,0.0005245441,0.19088623,0.0034684592,0.0023239905,0.0000367556,0.007269442,0.000008173512,0.7160815,0.041626032,0.023300996,0.01402793],"study_design_scores_gemma":[0.0005653236,0.00035173536,0.0022101072,0.00010952176,0.000081547834,0.000035714635,0.0030768877,0.0052824873,0.97059906,0.011860694,0.005661109,0.00016579997],"about_ca_topic_score_codex":0.000061049665,"about_ca_topic_score_gemma":0.00001366239,"teacher_disagreement_score":0.25451759,"about_ca_system_score_codex":0.0000933331,"about_ca_system_score_gemma":0.00012286004,"threshold_uncertainty_score":0.4652477},"labels":[],"label_agreement":null},{"id":"W4404590196","doi":"10.1079/cabireviews.2024.0054","title":"Tortuosity transformation of near-infrared diffuse reflectance spectra for the detection of enhanced and aggregate spectral changes with reference to dietary preferences of elephants","year":2024,"lang":"en","type":"article","venue":"CABI Reviews","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Tortuosity; Diffuse reflection; Reflectivity; Aggregate (composite); Diffuse reflectance infrared fourier transform; Remote sensing; Environmental science; Materials science; Spectral line; Biological system; Optics; Geography; Chemistry; Porosity; Physics; Nanotechnology; Biology; Composite material","score_opus":0.0493262455979475,"score_gpt":0.3132968532376202,"score_spread":0.26397060763967267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404590196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9408458,0.051618174,0.0050183325,0.00008843593,0.0000275683,0.00046990748,0.000060202554,0.000024714287,0.001846887],"genre_scores_gemma":[0.9729886,0.025922313,0.00068395643,0.000010659902,0.000028209644,0.0000834635,0.000008268963,0.000009579703,0.00026496482],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911404,0.000019652254,0.0003493017,0.0002205598,0.00014657517,0.00014987148],"domain_scores_gemma":[0.9992897,0.00013570163,0.00022441254,0.00024047999,0.000068972215,0.00004073387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018679361,0.00015250378,0.00046112412,0.00006314412,0.000050614173,0.000017858374,0.00015414834,0.000060694827,0.000050436836],"category_scores_gemma":[0.00007559114,0.000092832495,0.0000848821,0.00050727365,0.000099732904,0.000104094506,0.000012057573,0.00010783929,7.9478787e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018270862,0.00003440381,0.000059911803,0.002784439,0.0000880769,2.0203191e-7,0.0010134312,0.0000017305151,0.93980026,0.000052295953,0.000012460517,0.05597007],"study_design_scores_gemma":[0.00019252981,0.00030389085,0.0006809651,0.00063389546,0.00029785858,0.0000023938196,0.0001391484,0.0001352,0.99497575,0.00014499875,0.0023866277,0.00010673329],"about_ca_topic_score_codex":0.000033137167,"about_ca_topic_score_gemma":0.00023651279,"teacher_disagreement_score":0.055863336,"about_ca_system_score_codex":0.000031535084,"about_ca_system_score_gemma":0.00003287003,"threshold_uncertainty_score":0.37855992},"labels":[],"label_agreement":null},{"id":"W4404737771","doi":"10.1021/acsomega.4c05472","title":"Development and Application of a Quantitative Model for Proximate and Ultimate Analysis of Flue-Cured Tobacco Based on Near-Infrared Spectroscopy","year":2024,"lang":"en","type":"article","venue":"ACS Omega","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aroma; Near-infrared spectroscopy; Curing of tobacco; Reliability (semiconductor); Proximate; Food science; Environmental science; Chemistry; Botany; Power (physics); Psychology; Biology","score_opus":0.021567003017077743,"score_gpt":0.3153521520759682,"score_spread":0.2937851490588904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404737771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92482686,0.0005706622,0.073452935,0.00004919839,0.000005466704,0.00015113576,0.00015208471,0.000043679243,0.00074800226],"genre_scores_gemma":[0.91878366,0.000030211017,0.08077947,0.000021176867,0.000004898679,0.00008659376,0.0001075371,0.000015750442,0.00017068027],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990214,0.000004885273,0.0003199426,0.00032783148,0.00016810409,0.00015784834],"domain_scores_gemma":[0.9993395,0.00019691032,0.0001326512,0.00020380992,0.00008097819,0.000046115263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013121733,0.00015127452,0.0003901752,0.00031580063,0.000060809914,0.000042742973,0.00008901778,0.0000755237,0.000009510834],"category_scores_gemma":[0.000065183274,0.0001358905,0.00007800491,0.00094181317,0.00008207516,0.0000711194,0.000025104227,0.00006616226,6.813016e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059317145,0.0003025878,0.007099033,0.0017796289,0.0029109507,0.0000015784365,0.003079506,0.006808603,0.9683011,0.0061881347,0.00016071243,0.0027749648],"study_design_scores_gemma":[0.00021842257,0.000027159824,0.0003290151,0.0000251069,0.0006235348,1.2554372e-7,0.0001571817,0.55957586,0.43865696,0.0002509317,0.000049793805,0.000085927706],"about_ca_topic_score_codex":0.0000156118,"about_ca_topic_score_gemma":0.000010295464,"teacher_disagreement_score":0.5527672,"about_ca_system_score_codex":0.00005292201,"about_ca_system_score_gemma":0.00008034794,"threshold_uncertainty_score":0.55414534},"labels":[],"label_agreement":null},{"id":"W4404883626","doi":"10.1002/ppj2.70012","title":"Hyperspectral sensing for high‐throughput chloride detection in grapevines","year":2024,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Hyperspectral imaging; Throughput; Remote sensing; Environmental science; Computer science; Artificial intelligence; Geology; Telecommunications","score_opus":0.024048086883800363,"score_gpt":0.26017218272911696,"score_spread":0.2361240958453166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404883626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877354,0.0034408118,0.005176774,0.0011909382,0.0005705355,0.000057031,0.000042779327,0.00009280462,0.001692956],"genre_scores_gemma":[0.9965215,0.00042009115,0.00038144158,0.000055282115,0.0017502162,0.000003099288,0.000009165401,0.000023670453,0.00083555625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990282,0.000012639627,0.00027860605,0.00018211217,0.00016844491,0.00032998004],"domain_scores_gemma":[0.9994243,0.00028754378,0.000075841315,0.00012886658,0.000028391321,0.00005508383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002894789,0.0001525228,0.00020863311,0.00020713583,0.00021606476,0.00019861768,0.00017307661,0.00006595891,0.000256146],"category_scores_gemma":[0.00006479471,0.00010184694,0.00013375183,0.00043043788,0.00004001009,0.00014181601,0.00002060121,0.0004900364,0.000012830286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000500935,0.000084672836,0.00024466365,0.0002521751,0.0006078864,0.0001968753,0.0014579429,0.0003494448,0.98321956,0.0023024525,0.00093987794,0.00984351],"study_design_scores_gemma":[0.00078694127,0.000086913555,0.0003247317,0.00016703161,0.00051487697,0.002792754,0.0021676987,0.0059258984,0.9578909,0.023051407,0.0058970777,0.00039375687],"about_ca_topic_score_codex":0.00006672279,"about_ca_topic_score_gemma":0.000058286783,"teacher_disagreement_score":0.025328651,"about_ca_system_score_codex":0.00020851132,"about_ca_system_score_gemma":0.00004826702,"threshold_uncertainty_score":0.41531977},"labels":[],"label_agreement":null},{"id":"W4404992555","doi":"10.1016/j.asoc.2024.112576","title":"Dandelion center detection in perennial ryegrass with Heat maps using Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Perennial plant; Dandelion; Computer science; Center (category theory); Artificial intelligence; Agronomy; Medicine; Biology; Chemistry","score_opus":0.013360194344961412,"score_gpt":0.25304238674482693,"score_spread":0.23968219239986552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404992555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7933499,0.0005886319,0.2040324,0.000021257938,0.00013500337,0.000051035247,0.0000033461215,0.00020409041,0.0016143207],"genre_scores_gemma":[0.9984551,0.0000037278585,0.00089151855,0.000054370692,0.0004917122,0.000004057676,0.00004400006,0.000029306198,0.000026201918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878275,0.000009916138,0.00024819636,0.000398227,0.00019431862,0.00036658937],"domain_scores_gemma":[0.99963254,0.00012856154,0.000038897513,0.00012024137,0.000024394583,0.000055382392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011037677,0.00019421935,0.00020877997,0.00016820736,0.00016225004,0.00011513944,0.0001047715,0.00012512873,0.000080080004],"category_scores_gemma":[0.00000690395,0.00017619517,0.000060998766,0.0006822098,0.000055280227,0.00006552657,0.000065110355,0.0004511721,0.000004778013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005955209,0.0003511883,0.062477138,0.00058450113,0.0005166159,0.00016989396,0.0008179246,0.58328295,0.30053854,0.0010910482,0.00023813349,0.04933656],"study_design_scores_gemma":[0.00054852804,0.000013878102,0.00035499604,0.00006801018,0.00006249898,0.00007297714,0.00023551026,0.98265505,0.015576796,0.00008373178,0.000091914975,0.00023608687],"about_ca_topic_score_codex":0.000100117795,"about_ca_topic_score_gemma":0.000031825984,"teacher_disagreement_score":0.39937213,"about_ca_system_score_codex":0.00023766386,"about_ca_system_score_gemma":0.000029713145,"threshold_uncertainty_score":0.71850306},"labels":[],"label_agreement":null},{"id":"W4405010024","doi":"10.1016/j.foodcont.2024.111071","title":"Pixel-level spectral aflatoxin B1 content intelligent prediction via fine-tuning large language model (LLM)","year":2024,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Pixel; Aflatoxin; Content (measure theory); Computer science; Fine-tuning; Artificial intelligence; Biological system; Chemistry; Food science; Mathematics; Physics; Biology","score_opus":0.038395903833196604,"score_gpt":0.2694659419521947,"score_spread":0.2310700381189981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405010024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40582302,0.012883524,0.568748,0.000508758,0.00034638194,0.00022389027,0.001090074,0.00079805375,0.009578323],"genre_scores_gemma":[0.992505,0.000042792926,0.0003134667,0.00016716945,0.0005276008,0.0000603533,0.00010210129,0.000054138596,0.0062273648],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981455,0.000017747576,0.0004232577,0.0004978782,0.00034836988,0.000567227],"domain_scores_gemma":[0.99921256,0.00011374136,0.00008017926,0.0003735391,0.00005729846,0.00016268002],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018180278,0.00030720673,0.00039159696,0.00021051544,0.00011586726,0.00012481106,0.00025671226,0.00018506215,0.0012929464],"category_scores_gemma":[0.00008423465,0.00027982125,0.00030497913,0.00036919472,0.000037684844,0.00015438278,0.000052539242,0.00048522218,0.00012436359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020029354,0.0004029058,0.0011100033,0.00026646958,0.0015802379,0.00006839255,0.001236421,0.0012978397,0.9844904,0.0040728147,0.0014722901,0.0038019326],"study_design_scores_gemma":[0.0018552666,0.00026660354,0.00012977366,0.000112484966,0.00079190056,0.000037221125,0.0010509352,0.5552103,0.43696886,0.00095035165,0.002129933,0.0004963796],"about_ca_topic_score_codex":0.00003834077,"about_ca_topic_score_gemma":0.00007064054,"teacher_disagreement_score":0.58668196,"about_ca_system_score_codex":0.00024854194,"about_ca_system_score_gemma":0.000085892796,"threshold_uncertainty_score":0.99996537},"labels":[],"label_agreement":null},{"id":"W4405092768","doi":"10.1109/tafe.2024.3504956","title":"TranSEF: Transformer Enhanced Self-Ensemble Framework for Damage Assessment in Canola Crops","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on AgriFood Electronics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Agriculture - Saskatchewan","keywords":"Canola; Environmental science; Agronomy; Biology","score_opus":0.011510756906144943,"score_gpt":0.30050447835592475,"score_spread":0.2889937214497798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405092768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092732735,0.0014197985,0.8994027,0.0006225206,0.00025630553,0.00031341388,0.00012125322,0.0004853659,0.0046459567],"genre_scores_gemma":[0.9904467,0.0014792337,0.0060480493,0.00013257645,0.00009935766,0.00039836395,0.00002230669,0.00009320085,0.0012802235],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99754584,0.000023353541,0.0004950668,0.0006920314,0.000322637,0.0009210458],"domain_scores_gemma":[0.9989597,0.0004250261,0.00004849549,0.00037296384,0.000056141173,0.00013762772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001800778,0.00042198104,0.00045636928,0.00031005955,0.00021548042,0.00014468475,0.00030101102,0.00044777224,0.00085322774],"category_scores_gemma":[0.0000051212337,0.00041230483,0.00042400468,0.0013693745,0.000040370065,0.00020637349,4.6180216e-7,0.0016070874,0.000025538606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031086963,0.001506003,0.000008903665,0.0009250897,0.0012563856,0.000018828276,0.001214944,0.004201551,0.96219295,0.008378886,0.00017817662,0.019807413],"study_design_scores_gemma":[0.0008151832,0.0003708154,0.000008764943,0.00012523537,0.000564614,0.000010236302,0.00026757832,0.0029204895,0.9875745,0.0035239141,0.003324269,0.0004943592],"about_ca_topic_score_codex":0.0000487888,"about_ca_topic_score_gemma":0.0006790068,"teacher_disagreement_score":0.89771396,"about_ca_system_score_codex":0.00090303825,"about_ca_system_score_gemma":0.00059724244,"threshold_uncertainty_score":0.99983287},"labels":[],"label_agreement":null},{"id":"W4405124246","doi":"10.1007/s00226-024-01615-5","title":"Transfer learning for predicting wood density of different tree species: calibration transfer from portable NIR spectrometer to hyperspectral imaging","year":2024,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities","keywords":"Hyperspectral imaging; Transfer of learning; Artificial intelligence; Calibration; Computer science; Spectrometer; Artificial neural network; Tree (set theory); Near-infrared spectroscopy; Remote sensing; Overfitting; Biological system; Machine learning; Pattern recognition (psychology); Environmental science; Mathematics; Optics; Statistics; Geology; Physics","score_opus":0.010863258549208563,"score_gpt":0.2430200457199615,"score_spread":0.23215678717075294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405124246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9806253,0.00047607865,0.015981002,0.0013538081,0.00006448713,0.000103153754,0.000022712527,0.0002412262,0.0011322841],"genre_scores_gemma":[0.9988954,0.000041840412,0.0006051407,0.000032828164,0.00007955076,0.000022864087,0.000008665388,0.000016186712,0.0002975124],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984208,0.0000047631447,0.00025985896,0.000598963,0.0002817532,0.00043386468],"domain_scores_gemma":[0.9995125,0.000095587406,0.000018235998,0.00020257082,0.00008701378,0.00008409363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015965954,0.00017629207,0.00031161957,0.00066944904,0.00021363056,0.00010520646,0.00028241618,0.000113691276,0.00017060674],"category_scores_gemma":[0.00013208741,0.00015217294,0.00007489232,0.0016671596,0.00035194022,0.00028954452,0.00004539027,0.00028208867,0.0000012348296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015258558,0.000038901053,0.008488253,0.000046131347,0.00004627656,0.000005259842,0.0004073192,0.0000042987876,0.98471737,0.0035433443,0.000018343764,0.0026692634],"study_design_scores_gemma":[0.00023633784,0.00011564193,0.0004332657,0.000043639775,0.00014546819,0.000012108124,0.0027105212,0.0042583407,0.9900229,0.0016826125,0.00017310692,0.00016607232],"about_ca_topic_score_codex":0.000045230783,"about_ca_topic_score_gemma":0.00004389911,"teacher_disagreement_score":0.018270165,"about_ca_system_score_codex":0.000104056424,"about_ca_system_score_gemma":0.00009073061,"threshold_uncertainty_score":0.62054324},"labels":[],"label_agreement":null},{"id":"W4405484627","doi":"10.1016/j.jfca.2024.107130","title":"Feasibility of near-infrared spectroscopy as a tool to estimate carotenoid content in ‘IAC Rurik’ specialty potato cultivar","year":2024,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cultivar; Carotenoid; Near-infrared spectroscopy; Horticulture; Chemistry; Food science; Biology","score_opus":0.02445330090848015,"score_gpt":0.32738607148692866,"score_spread":0.30293277057844853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405484627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99401265,0.0014781391,0.002328231,0.00044850298,0.000040303803,0.00005949862,0.00005279959,0.000018699395,0.0015611493],"genre_scores_gemma":[0.99615866,0.00009175207,0.0033674657,0.00007393113,0.00009065308,0.0000026480357,0.000014441449,0.000011143091,0.00018932139],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99825835,0.000040908082,0.00081952906,0.00026638363,0.00040278805,0.00021204172],"domain_scores_gemma":[0.99905866,0.00008649007,0.00027590513,0.0002042087,0.00020139183,0.00017334183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029153912,0.00019228716,0.0007702909,0.0006731613,0.000068719775,0.00015441208,0.00016830582,0.000084352505,0.0006118496],"category_scores_gemma":[0.0000745592,0.00016316194,0.0005232454,0.0017913124,0.00006477549,0.00013943737,0.000043635497,0.00029371522,0.000005470646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089364254,0.00080695027,0.054766644,0.0003395442,0.0046157874,0.00019833414,0.0010497641,0.0011009632,0.93450826,0.00066289585,0.00037858883,0.0006786271],"study_design_scores_gemma":[0.0016678676,0.0007978645,0.033985686,0.00034411665,0.0050905077,0.000147361,0.00090377167,0.004493183,0.9498205,0.001979275,0.00030621322,0.00046365106],"about_ca_topic_score_codex":0.000089559646,"about_ca_topic_score_gemma":0.00003269529,"teacher_disagreement_score":0.020780956,"about_ca_system_score_codex":0.00022771192,"about_ca_system_score_gemma":0.00009903431,"threshold_uncertainty_score":0.66993237},"labels":[],"label_agreement":null},{"id":"W4405602010","doi":"10.1109/compas60761.2024.10796775","title":"Single-Level Fusion for Enhancing Meat Quality Classification with Explainable AI","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"","keywords":"Computer science; Fusion; Quality (philosophy)","score_opus":0.0982285965020443,"score_gpt":0.3494011769776943,"score_spread":0.25117258047565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405602010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2136856,0.0009211408,0.66944915,0.0014662362,0.000090506575,0.00015142707,0.00003450091,0.0005997627,0.11360171],"genre_scores_gemma":[0.9533715,0.000014279186,0.0038259407,0.0001177051,0.00013708885,0.000043975153,0.00005694619,0.000022380387,0.04241016],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990058,0.0000065654435,0.00022319258,0.00034751857,0.00018944175,0.00022748913],"domain_scores_gemma":[0.9993585,0.00021169531,0.000044170836,0.00023544436,0.00009436423,0.00005584881],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014981159,0.00013342097,0.00016927707,0.00009467399,0.00012379583,0.00012499618,0.000103527665,0.00009635375,0.0014718295],"category_scores_gemma":[0.000091963026,0.000099208235,0.00007588737,0.00042289725,0.00002808068,0.00022945127,0.000020465865,0.00011081738,0.000027341985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048372825,0.00007587259,0.00029637545,0.00045677472,0.00006821804,0.0000017218285,0.00012715468,0.0000035195671,0.98505396,0.009379211,0.0018274705,0.0026613688],"study_design_scores_gemma":[0.00019718175,0.0000379098,0.0000674862,0.000050046066,0.000107423366,0.0000040867235,0.0018040816,0.0013669903,0.9853828,0.0006994376,0.010106668,0.00017590151],"about_ca_topic_score_codex":0.000094215655,"about_ca_topic_score_gemma":0.00018519537,"teacher_disagreement_score":0.73968595,"about_ca_system_score_codex":0.00014631837,"about_ca_system_score_gemma":0.00006241157,"threshold_uncertainty_score":0.99944097},"labels":[],"label_agreement":null},{"id":"W4405733893","doi":"10.18280/i2m.230604","title":"Enhancing Ship Coating Quality Detection via Machine Learning-Optimized Visible Near-Infrared Spectroscopy","year":2024,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","keywords":"Infrared; Coating; Spectroscopy; Infrared spectroscopy; Quality (philosophy); Materials science; Computer science; Optics; Chemistry; Nanotechnology; Physics","score_opus":0.027126635069764465,"score_gpt":0.3331259743461424,"score_spread":0.3059993392763779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405733893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88049215,0.0012044612,0.108533524,0.00032915242,0.0004024076,0.00014207144,0.000026108277,0.0012757909,0.007594342],"genre_scores_gemma":[0.9865706,0.000113426366,0.011116639,0.00013657266,0.00023154407,0.000056077533,0.00017883843,0.000052090283,0.0015442086],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99739677,0.00017710279,0.0007347187,0.00068037095,0.00047625444,0.0005347571],"domain_scores_gemma":[0.9986914,0.00052088354,0.00027436472,0.00031498913,0.000081293234,0.00011711309],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000854252,0.0003575466,0.0004636748,0.0002393198,0.00044644586,0.00043177698,0.0002416786,0.00027584174,0.0032591838],"category_scores_gemma":[0.00059357565,0.0003461183,0.00023953176,0.0009691305,0.000109021574,0.00041294395,0.00007292353,0.0008321197,0.00014217185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022497197,0.000053327385,0.014535257,0.00023172122,0.00024895868,0.000015024993,0.00035258854,0.00053842436,0.9783349,0.000061592735,0.00005410834,0.0053491076],"study_design_scores_gemma":[0.0009674678,0.00013074197,0.0012001472,0.00003392529,0.00023492212,0.000018195522,0.00046951036,0.015555754,0.97920984,0.0012063261,0.0006186032,0.00035454374],"about_ca_topic_score_codex":0.00030971575,"about_ca_topic_score_gemma":0.000078339646,"teacher_disagreement_score":0.10607846,"about_ca_system_score_codex":0.00043879735,"about_ca_system_score_gemma":0.00012001133,"threshold_uncertainty_score":0.9998991},"labels":[],"label_agreement":null},{"id":"W4405808886","doi":"10.3390/app15010105","title":"IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Blockchain; Big data; Computer science; Internet of Things; Computer security; Data science; Data mining","score_opus":0.10543127783018913,"score_gpt":0.347726484808404,"score_spread":0.24229520697821488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405808886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88947034,0.0054974714,0.06354358,0.0011944395,0.00096141244,0.00031723283,0.00038683668,0.0007172667,0.037911437],"genre_scores_gemma":[0.9858593,0.00021487691,0.012598571,0.000018482075,0.0010556359,0.000019939607,0.000011590425,0.000014212356,0.00020743531],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982843,0.000004625383,0.0002727692,0.00075925904,0.00031411045,0.0003649785],"domain_scores_gemma":[0.99865276,0.00075556524,0.000057834237,0.00041923713,0.000020854035,0.00009372648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053008675,0.00016775048,0.00020694699,0.00014461527,0.0003929097,0.000305587,0.00076613086,0.00012233986,0.00015172966],"category_scores_gemma":[0.00016622755,0.00014292603,0.00003795077,0.0010793732,0.00039745454,0.00004854743,0.00026173773,0.00019267942,0.00002854166],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081611746,0.00012347431,0.0005574512,0.00042000404,0.00027093207,0.0000077327195,0.0011107955,0.00037883513,0.54053444,0.17909585,0.00041792996,0.27700093],"study_design_scores_gemma":[0.000037574697,0.0000663249,0.000020747322,0.000094052084,0.00015872058,0.0000065320337,0.0031643123,0.018449077,0.88024133,0.09544989,0.0019154566,0.00039595665],"about_ca_topic_score_codex":0.000021205164,"about_ca_topic_score_gemma":0.000005544558,"teacher_disagreement_score":0.33970693,"about_ca_system_score_codex":0.00003460024,"about_ca_system_score_gemma":0.000091333306,"threshold_uncertainty_score":0.58283544},"labels":[],"label_agreement":null},{"id":"W4405926818","doi":"10.1080/07373937.2024.2445769","title":"A Two-pass deep learning system for monitoring visual attributes of food in real-time during fluidized bed drying","year":2024,"lang":"en","type":"article","venue":"Drying Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Tertiary Education Trust Fund","keywords":"Fluidized bed; Artificial intelligence; Process engineering; Environmental science; Computer science; Engineering; Waste management","score_opus":0.014157489338902434,"score_gpt":0.2944918008389786,"score_spread":0.28033431150007615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405926818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939655,0.0034268864,0.00045574026,0.000051864743,0.000103130405,0.00012519279,0.00000874027,0.0015028063,0.00036015784],"genre_scores_gemma":[0.99786514,0.000094628485,0.0015680298,5.130175e-7,0.00014594688,0.00014855215,0.00001485383,0.00006631178,0.000096036485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811494,0.00001892373,0.00057087775,0.0005323852,0.00017960239,0.00058328034],"domain_scores_gemma":[0.99904805,0.0003864266,0.00016460357,0.00027808448,0.00007795799,0.00004487797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024442645,0.00026100103,0.0006366018,0.001087877,0.00015937626,0.000049340702,0.00034348996,0.0003907784,0.00003487915],"category_scores_gemma":[0.000402338,0.00028311624,0.00016764445,0.0015936687,0.0000802356,0.000101376354,0.00018719405,0.0005018422,0.000011639782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004651344,0.000034168806,0.016761953,0.0012854388,0.0002231298,0.00003638918,0.00015265429,0.000057164565,0.97978425,0.00068805664,0.0000010983645,0.00092915836],"study_design_scores_gemma":[0.0009471106,0.00007676897,0.000047308968,0.0006148466,0.00015340738,0.000030366982,0.0022109754,0.003272267,0.99218327,0.00019975302,0.000016572665,0.00024736172],"about_ca_topic_score_codex":0.000046071746,"about_ca_topic_score_gemma":0.0000031090676,"teacher_disagreement_score":0.016714644,"about_ca_system_score_codex":0.00042001813,"about_ca_system_score_gemma":0.000042818105,"threshold_uncertainty_score":0.9999621},"labels":[],"label_agreement":null},{"id":"W4405969775","doi":"10.1016/j.jmva.2024.105402","title":"Semi-functional varying coefficient mode-based regression","year":2025,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Statistics; Regression; Regression analysis; Applied mathematics; Econometrics","score_opus":0.01748829317425127,"score_gpt":0.3158091702372329,"score_spread":0.2983208770629816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405969775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3658079,0.0013867706,0.62008417,0.00086447486,0.0002280253,0.000035383768,0.000018536399,0.000051251493,0.011523461],"genre_scores_gemma":[0.9948436,0.000038597238,0.0020718477,0.00018690019,0.0001237298,0.000001913236,0.000022593034,0.000010175248,0.0027006303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982031,0.00004399987,0.00073920697,0.0002429844,0.0005339807,0.0002367347],"domain_scores_gemma":[0.9981069,0.00034634388,0.0006831773,0.00029856502,0.00045202058,0.000113013215],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033533617,0.0002051489,0.0006517311,0.001439388,0.00018549582,0.0000651041,0.00028953407,0.0001513306,0.0020138477],"category_scores_gemma":[0.00034582661,0.00015321099,0.00086530467,0.0031671405,0.0000439626,0.00011355883,0.000048052727,0.00039358245,0.0000055373407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063708343,0.0009875776,0.033659935,0.00010527564,0.01141165,0.00006415964,0.00011490231,0.60009223,0.34883443,0.00028815103,0.002234123,0.001570464],"study_design_scores_gemma":[0.0027868806,0.000040623287,0.004283511,0.00018242783,0.01731623,0.000008991287,0.00024942143,0.47473502,0.4972949,0.0003185723,0.0024336884,0.0003497261],"about_ca_topic_score_codex":0.00009494438,"about_ca_topic_score_gemma":0.0000047995454,"teacher_disagreement_score":0.6290357,"about_ca_system_score_codex":0.00026173575,"about_ca_system_score_gemma":0.00020201539,"threshold_uncertainty_score":0.99889845},"labels":[],"label_agreement":null},{"id":"W4405994049","doi":"10.2139/ssrn.5079955","title":"Using Synchrotron-Based X-Ray Fluorescence Imaging to Study Oat Variety Impact on Mineral Distribution and Relative Concentration","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Synchrotron; X-ray fluorescence; Mineral; Distribution (mathematics); Fluorescence; Synchrotron radiation; Mineralogy; Materials science; Chemistry; Optics; Physics; Mathematics; Metallurgy","score_opus":0.014243450152784403,"score_gpt":0.3206013445172936,"score_spread":0.3063578943645092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405994049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87214875,0.0013075981,0.12543915,0.00023920386,0.00012066085,0.0003213484,0.00013312824,0.000055564997,0.00023457718],"genre_scores_gemma":[0.9986293,0.00020973818,0.00046944997,0.00004877994,0.00028800106,0.000017811664,0.00013608667,0.000025456087,0.0001753912],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964613,0.00012950321,0.0005358292,0.000688796,0.0004899082,0.00169467],"domain_scores_gemma":[0.9986167,0.00016523818,0.00042980758,0.00037859342,0.00022769962,0.00018196755],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008305677,0.0005332592,0.000575294,0.0001775826,0.00043183603,0.0002523153,0.000363798,0.00021908965,0.00008731438],"category_scores_gemma":[0.00025453506,0.00046821,0.00027286672,0.0004827596,0.00006965875,0.00015366515,0.000160684,0.0040984675,0.0000021803673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048006484,0.0048826323,0.62874573,0.00059747434,0.011990756,0.00015755979,0.002972135,0.14238387,0.16036394,0.012632379,0.0004769901,0.02999587],"study_design_scores_gemma":[0.020656552,0.004777228,0.05755687,0.0039481805,0.016986523,0.00058546924,0.023252096,0.6461554,0.12512217,0.09260074,0.00013946922,0.008219253],"about_ca_topic_score_codex":0.00044470254,"about_ca_topic_score_gemma":0.000031297535,"teacher_disagreement_score":0.57118887,"about_ca_system_score_codex":0.006928316,"about_ca_system_score_gemma":0.003544246,"threshold_uncertainty_score":0.99977696},"labels":[],"label_agreement":null},{"id":"W4406038710","doi":"10.1007/978-3-031-68915-4_8","title":"The Rank-Deficient Least Squares Problem","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Rank (graph theory); Least-squares function approximation; Computer science; Mathematics; Statistics; Combinatorics","score_opus":0.014322135144406162,"score_gpt":0.24773402270763337,"score_spread":0.2334118875632272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406038710","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013420332,0.017298426,0.000034132067,0.0006443373,0.000115691764,0.00006889199,0.00004254676,0.00025979264,0.98152274],"genre_scores_gemma":[0.004986679,0.0010957017,0.000048813446,0.00007289632,0.00035926377,0.000018211733,0.000038533126,0.00008315983,0.99329674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838287,0.0000015669978,0.00036338484,0.00048798186,0.00043999145,0.0003242071],"domain_scores_gemma":[0.9989977,0.00015046951,0.00011881907,0.0005828166,0.00007005148,0.000080128324],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009144711,0.00039277622,0.0003428668,0.00012020168,0.00021353702,0.00022372032,0.00045238403,0.00033987453,0.018446503],"category_scores_gemma":[0.000013951453,0.00023422591,0.00038033046,0.00008685936,0.00016495628,0.00002219765,0.00016198224,0.0006509471,0.0021563615],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020836193,0.000022249298,0.0000053227745,0.00037954072,0.00067486445,0.00003270061,0.000048809674,0.0000049322966,0.0008434337,0.9236515,0.07156834,0.0027474593],"study_design_scores_gemma":[0.00009024674,0.000015630134,2.1479157e-7,0.000102246595,0.0005397367,0.000016456823,0.0001430861,0.000024560408,0.0075772554,0.04527654,0.94586855,0.00034550324],"about_ca_topic_score_codex":0.000027118844,"about_ca_topic_score_gemma":0.000033412853,"teacher_disagreement_score":0.878375,"about_ca_system_score_codex":0.00014045305,"about_ca_system_score_gemma":0.000071204566,"threshold_uncertainty_score":0.99862057},"labels":[],"label_agreement":null},{"id":"W4406142201","doi":"10.1016/j.afres.2025.100701","title":"Monitoring of milk rennet coagulation: Chemical and physical perspective using Raman spectroscopy","year":2025,"lang":"en","type":"article","venue":"Applied Food Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"Syddansk Universitet","keywords":"Rennet; Coagulation; Raman spectroscopy; Perspective (graphical); Chemistry; Food science; Medicine; Computer science; Internal medicine; Physics; Artificial intelligence; Optics","score_opus":0.07002648559885939,"score_gpt":0.4135532702054484,"score_spread":0.34352678460658903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406142201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9547066,0.00061063963,0.00015844335,0.00010962507,0.000015724243,0.00009287092,0.0000058139494,0.000034379762,0.044265866],"genre_scores_gemma":[0.9980101,0.000049450264,0.0014828359,0.000004379642,0.00026378958,0.000020402285,0.0000032171133,0.000016128191,0.00014971857],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99849427,0.000018865203,0.0001930099,0.0004299921,0.0004627621,0.00040111988],"domain_scores_gemma":[0.99905366,0.00029933333,0.000050594103,0.00032421187,0.00019518242,0.000077040466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002032545,0.00014765924,0.0003247905,0.00027521138,0.00016787088,0.000045133747,0.00023379036,0.00013586298,0.000058990347],"category_scores_gemma":[0.0001059436,0.0001465955,0.000064189524,0.0012104277,0.00036929178,0.000040679217,0.00021033053,0.0005532841,0.0000029171185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011089386,0.0001400116,0.0021106948,0.000111124275,0.00017020345,0.0000010683867,0.00034921255,0.000005777035,0.98220193,0.01456897,0.000058315232,0.00017178808],"study_design_scores_gemma":[0.00047271032,0.000037239064,0.00029735104,0.000038401624,0.00006936868,0.0000013395036,0.003857658,0.00031056185,0.98457974,0.010200037,0.000024257468,0.00011132162],"about_ca_topic_score_codex":0.00008038217,"about_ca_topic_score_gemma":9.561176e-7,"teacher_disagreement_score":0.044116147,"about_ca_system_score_codex":0.00026436828,"about_ca_system_score_gemma":0.00010004294,"threshold_uncertainty_score":0.59779906},"labels":[],"label_agreement":null},{"id":"W4406250205","doi":"10.18280/ts.410639","title":"Integrated Approach for Defect Detection and Grading of Apples: Thermal Imaging and Adaptive Neuro-Fuzzy Inference System for Enhanced Quality Assessment","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptive neuro fuzzy inference system; Computer science; Fuzzy inference system; Artificial intelligence; Neuro-fuzzy; Inference; Grading (engineering); Fuzzy inference; Machine learning; Fuzzy logic; Fuzzy control system; Engineering","score_opus":0.03828471977048323,"score_gpt":0.3140743244311692,"score_spread":0.275789604660686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406250205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46634868,0.00032820588,0.5324003,0.000007072282,0.000020383333,0.00023966345,0.00007001491,0.00005728361,0.00052839954],"genre_scores_gemma":[0.9952374,0.000010617803,0.0042964458,0.000008360337,0.00007078509,0.0003015624,0.000038883125,0.000019408759,0.000016518796],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890417,0.000030920102,0.0003375889,0.0003801332,0.00014716711,0.00020003643],"domain_scores_gemma":[0.99909925,0.0005736211,0.000120909914,0.00008434455,0.000070277616,0.00005159634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035525495,0.00018466836,0.0002972902,0.00011752863,0.000107814296,0.00008069762,0.00007430854,0.000052087944,0.000017310356],"category_scores_gemma":[0.000033271335,0.00015807663,0.00011706253,0.0001805984,0.00006334756,0.00011711503,0.000023709632,0.000118884614,8.68226e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013301999,0.000055109842,0.00052800326,0.001909885,0.00024326981,4.9752333e-7,0.00033960585,0.00007940833,0.98594946,0.0017095237,0.0000055150504,0.009046694],"study_design_scores_gemma":[0.0007367691,0.00012997146,0.00038430767,0.000107792424,0.00038000912,0.0000038783787,0.0048823734,0.10200044,0.89100885,0.00016166644,0.000018249615,0.00018565977],"about_ca_topic_score_codex":0.000053340576,"about_ca_topic_score_gemma":0.000003902283,"teacher_disagreement_score":0.5288887,"about_ca_system_score_codex":0.00011261542,"about_ca_system_score_gemma":0.000040518644,"threshold_uncertainty_score":0.64461774},"labels":[],"label_agreement":null},{"id":"W4406389358","doi":"10.48130/fia-0025-0004","title":"Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness","year":2025,"lang":"en","type":"article","venue":"Food Innovation and Advances","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Tianjin Science and Technology Program; Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Artificial neural network; Artificial intelligence; Computer science; Computer vision; Remote sensing; Geology","score_opus":0.0218208013463175,"score_gpt":0.30905166518962796,"score_spread":0.2872308638433105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406389358","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7148203,0.0013003469,0.27842396,0.0006615473,0.00005490646,0.0002138334,0.00022040102,0.0000454938,0.004259194],"genre_scores_gemma":[0.9910925,0.00003200524,0.0067489753,0.00017327727,0.00005104357,0.000047007587,0.00018091669,0.0000053110875,0.0016689893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928,0.0000033695856,0.00028099513,0.0002105229,0.00008780182,0.00013735254],"domain_scores_gemma":[0.99949193,0.000049429458,0.000125392,0.000113440925,0.00020225461,0.000017557346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000069069174,0.000096644566,0.00023779536,0.0003472923,0.00007603536,0.00002289086,0.00008337856,0.00004271449,0.000069778944],"category_scores_gemma":[0.00006861088,0.00008864596,0.000051561885,0.0038072446,0.000034737168,0.00014703324,0.000021004707,0.00004783074,2.943694e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007386464,0.00038660294,0.06990119,0.0014590203,0.0033172236,2.363681e-7,0.00050743634,0.5668481,0.23404446,0.09109183,0.007903106,0.023802156],"study_design_scores_gemma":[0.0008848229,0.00017518894,0.0017321259,0.00004696227,0.0013284789,3.4073227e-7,0.0007752979,0.532989,0.4534852,0.0056875595,0.0025780639,0.00031695538],"about_ca_topic_score_codex":0.0000023910004,"about_ca_topic_score_gemma":0.000009048645,"teacher_disagreement_score":0.27627215,"about_ca_system_score_codex":0.000020226566,"about_ca_system_score_gemma":0.00002210502,"threshold_uncertainty_score":0.36148772},"labels":[],"label_agreement":null},{"id":"W4406445752","doi":"10.1016/s0967-0653(97)85840-1","title":"10.1016/s0967-0653(97)85840-1","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Environmental science","score_opus":0.008376621265747427,"score_gpt":0.20243589624264965,"score_spread":0.19405927497690223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406445752","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005631094,0.00027706483,0.0000011407529,0.00014004759,6.678164e-7,0.000032794665,0.000021507472,0.00020795007,0.9987557],"genre_scores_gemma":[0.00026471092,0.0000011598444,0.000108445274,0.00003821815,0.00024908644,0.000015031456,0.00003239704,0.000030932053,0.99926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998882,0.000008386762,0.00020652916,0.00031610497,0.0002249048,0.00036205185],"domain_scores_gemma":[0.99923515,0.000079327314,0.000037440343,0.00042304109,0.000036529986,0.00018853691],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006003572,0.00019296225,0.00024807005,0.000096089825,0.00009114619,0.000050503997,0.0003321362,0.00011876641,0.9999094],"category_scores_gemma":[0.00007060363,0.00018921494,0.000115937095,0.0005350518,0.00003874497,0.00007542566,0.00004096089,0.0001691702,0.9994809],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010703975,0.0001481521,4.978967e-7,0.00002717158,0.000118897064,0.000014801901,0.00001949561,0.000031587962,0.0013784626,0.0000012458191,0.19333558,0.8048171],"study_design_scores_gemma":[0.00022925623,0.000033737353,8.341956e-7,0.000008657365,0.00008846233,0.000008327604,0.000004021691,0.00004522896,0.04676826,0.000009018149,0.95257026,0.00023392453],"about_ca_topic_score_codex":0.00003541848,"about_ca_topic_score_gemma":1.4979776e-7,"teacher_disagreement_score":0.80458313,"about_ca_system_score_codex":0.00006992662,"about_ca_system_score_gemma":0.000025743144,"threshold_uncertainty_score":0.7715962},"labels":[],"label_agreement":null},{"id":"W4406610674","doi":"10.1016/j.compag.2025.109980","title":"Identification of maize seed vigor under different accelerated aging times using hyperspectral imaging and spectral deep features","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Major Scientific and Technological Innovation Project of Shandong Province; Taishan Scholar Project of Shandong Province; National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Identification (biology); Accelerated aging; Artificial intelligence; Remote sensing; Spectral analysis; Pattern recognition (psychology); Environmental science; Engineering; Computer science; Biology; Botany; Geology; Physics; Spectroscopy","score_opus":0.006790410047759099,"score_gpt":0.25322830701037685,"score_spread":0.24643789696261775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406610674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762181,0.018976454,0.003752638,0.0004066674,0.00005512136,0.0000697819,0.000003311498,0.00004174888,0.0004761613],"genre_scores_gemma":[0.99811184,0.00080785074,0.000499575,0.00007346164,0.00005833749,0.0000028458446,0.000021622733,0.000008592331,0.0004158615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989021,0.000017010185,0.0002732632,0.00035915506,0.00011245927,0.0003360251],"domain_scores_gemma":[0.9995989,0.000058450416,0.00011782753,0.00012942214,0.000050408744,0.000044984292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059299742,0.00021677444,0.00031458246,0.0001678915,0.00011981823,0.00013019542,0.00014899284,0.00010678822,0.000015312802],"category_scores_gemma":[0.0000100628595,0.00016733201,0.00006078668,0.0005458025,0.00006517443,0.000097761535,0.00007210964,0.0003450223,1.2162133e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026704925,0.00010457565,0.01727632,0.00014386475,0.00018339427,0.0000040460322,0.00027552582,0.0006769611,0.974955,0.0054174834,0.00025100156,0.00068510324],"study_design_scores_gemma":[0.0012323135,0.00002407057,0.06975799,0.00015042046,0.00035795424,0.00006303558,0.0018841261,0.02736537,0.8936995,0.004951215,0.000051359544,0.00046260704],"about_ca_topic_score_codex":0.000042838896,"about_ca_topic_score_gemma":0.000041161784,"teacher_disagreement_score":0.08125548,"about_ca_system_score_codex":0.0002013099,"about_ca_system_score_gemma":0.000029348463,"threshold_uncertainty_score":0.6823601},"labels":[],"label_agreement":null},{"id":"W4406614037","doi":"10.1016/j.saa.2025.125770","title":"High-throughput precision assessment of pea-derived protein products using near infrared hyperspectral imaging","year":2025,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada; Saskatchewan Research Council (Canada)","funders":"National Research Council Canada","keywords":"Chemistry; Hyperspectral imaging; Throughput; Chemical imaging; Infrared; Remote sensing; Near-infrared spectroscopy; Biological system; Optics","score_opus":0.008807861073051374,"score_gpt":0.2862824491901534,"score_spread":0.277474588117102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406614037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96652484,0.0022381875,0.022613864,0.0011058869,0.00014280072,0.00059250626,0.000047616093,0.00020117115,0.0065331226],"genre_scores_gemma":[0.90367055,0.00014815877,0.09540249,0.00018192612,0.00012371814,0.00004748097,0.00008928592,0.0000851467,0.0002512399],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99565667,0.000118180506,0.0009447289,0.0014805184,0.0007309773,0.0010689404],"domain_scores_gemma":[0.99774235,0.00005938157,0.00047972758,0.0012458537,0.00025291267,0.00021978098],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002726312,0.0007922855,0.0011096755,0.00053583237,0.00041085065,0.00030522357,0.0006618237,0.00025235314,0.00035657687],"category_scores_gemma":[0.00025128698,0.00078769826,0.00037528598,0.0018215185,0.00056616357,0.0002495594,0.0003980466,0.0006432971,0.0000032072678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017606273,0.00040101717,0.00068155956,0.00029746298,0.0005400579,0.000086500804,0.000067162655,0.000011656269,0.99356073,0.0039389464,0.00017484896,0.00006401835],"study_design_scores_gemma":[0.0015848718,0.0001581503,0.00032035264,0.000238293,0.00072567404,0.000056183413,0.00018731979,0.0014307031,0.99142706,0.0027094653,0.0004658558,0.0006960706],"about_ca_topic_score_codex":0.00014486644,"about_ca_topic_score_gemma":0.000004349156,"teacher_disagreement_score":0.072788626,"about_ca_system_score_codex":0.00042470163,"about_ca_system_score_gemma":0.00065176917,"threshold_uncertainty_score":0.9994574},"labels":[],"label_agreement":null},{"id":"W4406616645","doi":"10.3390/s25020587","title":"Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Quantum cascade laser; Spectrometer; Benchmark (surveying); Detector; Computer science; Fourier transform infrared spectroscopy; Laser; Data processing; Optics; Materials science; Physics; Telecommunications","score_opus":0.030327138035132564,"score_gpt":0.2979880228833293,"score_spread":0.2676608848481968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406616645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96299124,0.00041491186,0.00007216019,0.00068705995,0.00007174135,0.000096445,0.00027172375,0.00030329454,0.03509145],"genre_scores_gemma":[0.9925488,0.00003994967,0.00073165476,0.00035378436,0.000094346935,0.000004809778,0.0003633336,0.00004639903,0.0058168783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790704,0.000055344728,0.0003578308,0.00074420916,0.00036525851,0.0005703242],"domain_scores_gemma":[0.9974526,0.0008061922,0.00019708095,0.0012808751,0.0000964347,0.00016682838],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014095134,0.00040052563,0.000640705,0.00030227337,0.00024480608,0.00009527622,0.000576167,0.00018091183,0.00050006615],"category_scores_gemma":[0.00033739113,0.00033626845,0.00012248961,0.0012272072,0.00020363448,0.00009488667,0.00008576466,0.0003869418,0.000049257338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005166174,0.004793562,0.2840107,0.0037637956,0.012511974,0.005731363,0.0027862347,0.030255517,0.5157583,0.001115468,0.13101126,0.0030956657],"study_design_scores_gemma":[0.0016296449,0.00006265975,0.0006816341,0.0001589425,0.0007079073,0.00002727773,0.0023328888,0.0140010975,0.97450095,0.000015484553,0.0053986306,0.00048288345],"about_ca_topic_score_codex":0.00043798375,"about_ca_topic_score_gemma":0.00011477443,"teacher_disagreement_score":0.45874268,"about_ca_system_score_codex":0.00014567246,"about_ca_system_score_gemma":0.00022655376,"threshold_uncertainty_score":0.9999089},"labels":[],"label_agreement":null},{"id":"W4406707037","doi":"10.3390/app15031061","title":"The Differentiation of Extra Virgin Olive Oil from Other Olive Oil Categories Based on FTIR Spectroscopy and Random Forest","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Olive oil; Fourier transform infrared spectroscopy; Chemistry; Environmental science; Mathematics; Food science; Chemical engineering; Engineering","score_opus":0.009853015720343198,"score_gpt":0.2547102111654282,"score_spread":0.244857195445085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406707037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88131267,0.001060122,0.00062896154,0.00021152302,0.00006327471,0.000027641072,0.000023481853,0.00003222119,0.11664013],"genre_scores_gemma":[0.9975795,0.00040503277,0.000314685,0.00013342433,0.000056899935,0.00003414614,0.000008702142,0.0000063005195,0.0014612715],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988458,0.00001712425,0.00022744792,0.00034846642,0.0003210354,0.00024013918],"domain_scores_gemma":[0.99865204,0.0008851518,0.00015935274,0.00023462043,0.000030423507,0.000038434813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019738464,0.0001639731,0.00024699394,0.00011634929,0.00045172154,0.00013351292,0.0003511139,0.00007346725,0.00021888598],"category_scores_gemma":[0.00007121169,0.000105478146,0.000057995556,0.00058792706,0.00072307314,0.000050437684,0.000040949406,0.00012438392,0.0000049625132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069783645,0.00022700932,0.020136103,0.00023502077,0.00020345721,9.191127e-7,0.0007833699,0.00040103073,0.92598563,0.046411145,0.0005882705,0.0043302276],"study_design_scores_gemma":[0.0012571055,0.00003050014,0.0013320376,0.00005660066,0.00013208747,1.1855355e-7,0.0018871791,0.0029173812,0.98349303,0.008353705,0.0003799734,0.00016030087],"about_ca_topic_score_codex":0.0004358409,"about_ca_topic_score_gemma":0.00030146146,"teacher_disagreement_score":0.11626689,"about_ca_system_score_codex":0.000037743343,"about_ca_system_score_gemma":0.00008784072,"threshold_uncertainty_score":0.4301274},"labels":[],"label_agreement":null},{"id":"W4406768277","doi":"10.1177/09603360241313184","title":"A critique of multiplicative scatter correction pre-treatment of NIR spectra","year":2024,"lang":"en","type":"article","venue":"NIR news","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Multiplicative function; Spectral line; Mathematics; Physics; Mathematical analysis; Astronomy","score_opus":0.014660593556328878,"score_gpt":0.31958181908262523,"score_spread":0.30492122552629636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406768277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9296335,0.0037395568,0.0021878239,0.00041752425,0.00022226146,0.00015306947,0.00006783933,0.00015806631,0.063420385],"genre_scores_gemma":[0.9880301,0.00033467563,0.00042322258,0.00002470872,0.00015118413,0.000038871065,0.00001844563,0.000017374854,0.010961443],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922955,0.000009930143,0.0002460227,0.00025583067,0.000121825244,0.00013686094],"domain_scores_gemma":[0.9994299,0.00013667133,0.00007574923,0.0002669185,0.00004996856,0.000040814426],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000025481739,0.00013507603,0.00025820028,0.000119402335,0.000020719615,0.00001311328,0.000089482084,0.00009322022,0.0014871175],"category_scores_gemma":[0.000030868974,0.00011169728,0.00016570979,0.00038672218,0.00006540671,0.000055443026,0.000014051101,0.00008225051,0.000027357175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075750824,0.0006044603,0.012416268,0.00032361964,0.0004235151,0.00000842745,0.0017856479,0.000014833964,0.96367776,0.00018855755,0.0076462575,0.012834923],"study_design_scores_gemma":[0.00021001825,0.00011218488,0.00090958097,0.000058658577,0.0001652767,0.000008840154,0.0004508639,0.0004043586,0.99288476,0.000182496,0.004517203,0.00009577875],"about_ca_topic_score_codex":0.0007824377,"about_ca_topic_score_gemma":0.000061177576,"teacher_disagreement_score":0.058396604,"about_ca_system_score_codex":0.00014988422,"about_ca_system_score_gemma":0.000049462487,"threshold_uncertainty_score":0.99942565},"labels":[],"label_agreement":null},{"id":"W4407085439","doi":"10.1002/cjce.25621","title":"Research on non‐contact infrared imaging technique for multilayer storage identification of oil tanks based on an improved edge‐detection algorithm","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sobel operator; Computer science; Algorithm; Edge detection; Artificial intelligence; Image processing; Image (mathematics)","score_opus":0.012422459624903516,"score_gpt":0.29153115262330753,"score_spread":0.279108692998404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407085439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6101647,0.00058740703,0.38411155,0.0010926727,0.0007254948,0.00045759164,0.00014781779,0.00008074067,0.0026320596],"genre_scores_gemma":[0.9984501,0.0000014386736,0.0011157971,0.00003482355,0.00014572543,0.00003171378,0.0000059271547,0.000022014487,0.00019245865],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895644,0.000014888236,0.00039188133,0.00015091315,0.00020372927,0.00028213183],"domain_scores_gemma":[0.9987363,0.00029985004,0.00015344415,0.00028928785,0.0003403571,0.00018074029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007536128,0.00012968648,0.00021518514,0.00067251315,0.000104239545,0.00005881429,0.0003653264,0.0001261833,0.000033077456],"category_scores_gemma":[0.0005440302,0.00011227208,0.00012390921,0.0005810736,0.000045418237,0.000083939536,0.000008690971,0.00068543566,4.0076748e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052542047,0.000024856812,0.000007517689,0.00008988772,0.000038910293,0.0000032429018,0.000032849428,0.0025412694,0.99214923,0.000018930446,0.00006227722,0.0049784817],"study_design_scores_gemma":[0.00032321978,0.000035571215,0.000028643492,0.00011517318,0.00004052984,0.0000037504785,0.000048295085,0.20275858,0.7964085,0.00005055766,0.00011143276,0.000075791504],"about_ca_topic_score_codex":0.00046474658,"about_ca_topic_score_gemma":0.000031096868,"teacher_disagreement_score":0.38828543,"about_ca_system_score_codex":0.00068897154,"about_ca_system_score_gemma":0.00036132647,"threshold_uncertainty_score":0.45783225},"labels":[],"label_agreement":null},{"id":"W4407238630","doi":"10.1007/s11222-025-10580-3","title":"Robust Bayesian functional principal component analysis","year":2025,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Bayesian probability; Mathematics; Artificial intelligence; Pattern recognition (psychology); Component (thermodynamics); Computer science; Statistics","score_opus":0.018728099276970587,"score_gpt":0.26839750405507845,"score_spread":0.24966940477810787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407238630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06365456,0.00033026494,0.9226338,0.00005996537,0.000054281907,0.000016244016,0.00006117215,0.000044685614,0.013145017],"genre_scores_gemma":[0.97842026,0.000015917305,0.019653441,0.0000922518,0.00006112956,0.0000011838405,0.000112546004,0.0000053974286,0.0016378603],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991933,0.000008483101,0.00022354591,0.0002535198,0.00013332572,0.00018779353],"domain_scores_gemma":[0.9994709,0.0002001623,0.000074717755,0.00013517022,0.000060950068,0.000058120055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000788471,0.00011632741,0.0002334772,0.00019716902,0.00023270729,0.00007072211,0.00007790861,0.000046643003,0.00061225606],"category_scores_gemma":[0.000045853183,0.000116101226,0.000057184683,0.0006736391,0.00005377068,0.000014796948,0.000090856425,0.00013413526,0.0000024387098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009620216,0.00049036834,0.4619164,0.00069900043,0.008775513,0.0000620972,0.00035753835,0.042462815,0.0077360477,0.43326998,0.0061553144,0.037978742],"study_design_scores_gemma":[0.0006681909,0.00001822928,0.0967625,0.000029733268,0.0030643425,0.0000052508067,0.00050259626,0.8891438,0.0031296925,0.004093433,0.002185824,0.00039635424],"about_ca_topic_score_codex":0.00006712307,"about_ca_topic_score_gemma":0.00001934729,"teacher_disagreement_score":0.9147657,"about_ca_system_score_codex":0.000042534884,"about_ca_system_score_gemma":0.000032939915,"threshold_uncertainty_score":0.67037743},"labels":[],"label_agreement":null},{"id":"W4407364934","doi":"10.1007/978-3-031-80760-2_6","title":"Machine Learning Tool for Yield Maximization in Cream Cheese Production","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Polytechnique Montréal; École Nationale d'Administration Publique","funders":"","keywords":"Maximization; Production (economics); Yield (engineering); Computer science; Food science; Mathematics; Chemistry; Mathematical optimization; Materials science; Economics; Microeconomics","score_opus":0.035344790678278046,"score_gpt":0.303104705498744,"score_spread":0.26775991482046596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407364934","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014399376,0.0022099628,0.08193006,0.0018241905,0.00027767633,0.00079771836,0.000060015474,0.00018271897,0.9112777],"genre_scores_gemma":[0.67363733,0.040487304,0.12785994,0.0014724577,0.0003180737,0.00048862107,0.00260141,0.00005858863,0.15307629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989662,0.0000055363876,0.0005090409,0.00019903695,0.00017732357,0.00014281795],"domain_scores_gemma":[0.99864906,0.00015486046,0.00023674224,0.0006986195,0.00023460951,0.000026122802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050803914,0.00014532516,0.00020479177,0.000996931,0.0002857704,0.00015806775,0.0006858213,0.00013187801,0.000056932702],"category_scores_gemma":[0.00041815577,0.00015415587,0.00003673901,0.00053139176,0.00025023083,0.0015673973,0.00040445846,0.0004026834,0.0000040089626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009865877,0.00030378232,0.007043201,0.001427724,0.00007810294,4.5161275e-7,0.004147827,0.010014451,0.0011334107,0.31587052,0.0016521548,0.6582297],"study_design_scores_gemma":[0.000793072,0.0000735765,0.0009312824,0.00081372674,0.000074196956,0.000011325811,0.00013535483,0.7604422,0.0020381524,0.0065784394,0.22739333,0.00071535335],"about_ca_topic_score_codex":0.00001968629,"about_ca_topic_score_gemma":0.000017651142,"teacher_disagreement_score":0.7582014,"about_ca_system_score_codex":0.00018715345,"about_ca_system_score_gemma":0.00014626163,"threshold_uncertainty_score":0.62862945},"labels":[],"label_agreement":null},{"id":"W4407424984","doi":"10.1016/j.compag.2025.110071","title":"Pixel-level spectral reconstruction and compressed projection based on deep learning in detecting aflatoxin B1","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Pixel; Artificial intelligence; Projection (relational algebra); Aflatoxin; Compressed sensing; Computer vision; Pattern recognition (psychology); Computer science; Deep learning; Environmental science; Remote sensing; Algorithm; Biology; Geology; Food science","score_opus":0.007652635304264446,"score_gpt":0.22971499639987028,"score_spread":0.22206236109560584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407424984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988955,0.0018623746,0.0057195774,0.00019128104,0.000075544354,0.00009968045,9.694927e-7,0.000075486045,0.003020055],"genre_scores_gemma":[0.998069,0.00030385933,0.0012299299,0.0000739442,0.000056811496,0.000012119564,0.00001184126,0.000007308137,0.00023521522],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893457,0.00003264172,0.00022066453,0.00039263372,0.00009389385,0.00032557748],"domain_scores_gemma":[0.99964035,0.00013044887,0.00008176843,0.00008623842,0.000024880876,0.0000363102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116294825,0.0001919659,0.00025252745,0.0002712591,0.00014545511,0.0000812938,0.000083676,0.00018651393,0.000010334003],"category_scores_gemma":[0.00004196131,0.00016301051,0.00004077173,0.0008057483,0.000034772434,0.000072226874,0.000032229134,0.00080732675,2.8761409e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000550678,0.0005890102,0.24448669,0.00085053407,0.0002520327,0.000030161962,0.0009744049,0.031725064,0.42862174,0.0027253246,0.00041226033,0.28878212],"study_design_scores_gemma":[0.005593549,0.0004467796,0.07164066,0.00094175263,0.00017719621,0.00013043452,0.0020423443,0.60075164,0.31207386,0.0026681158,0.0023227124,0.001210988],"about_ca_topic_score_codex":0.00005849679,"about_ca_topic_score_gemma":0.00038793476,"teacher_disagreement_score":0.5690265,"about_ca_system_score_codex":0.0002682848,"about_ca_system_score_gemma":0.00003537871,"threshold_uncertainty_score":0.6647375},"labels":[],"label_agreement":null},{"id":"W4407486471","doi":"10.1016/j.foodchem.2025.143381","title":"Correlation awareness evolutionary sparse hybrid spectral band selection algorithm to detect aflatoxin B1 contaminated almonds using hyperspectral images","year":2025,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Aflatoxin; Correlation; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Computer science; Algorithm; Mathematics; Biology; Food science","score_opus":0.012226796107741758,"score_gpt":0.26523094173248135,"score_spread":0.2530041456247396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407486471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9600996,0.0012808286,0.026557906,0.00007863255,0.0001605874,0.000117955016,0.00016261791,0.00034164242,0.011200272],"genre_scores_gemma":[0.98871654,0.000023542465,0.004681654,0.00004907345,0.00033961466,0.00003229863,0.00020806001,0.00004572137,0.0059035206],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977441,0.000019647456,0.00047242324,0.00077315554,0.00034929477,0.00064136815],"domain_scores_gemma":[0.99893564,0.00009432964,0.00017298381,0.00039673163,0.00020426935,0.0001960564],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000101025325,0.00044339392,0.00047412253,0.00021624252,0.00035994058,0.00009410547,0.00032735182,0.00028305812,0.0011311704],"category_scores_gemma":[0.00015027194,0.00051405275,0.00022086044,0.0013301785,0.000101812766,0.00018590067,0.00008008129,0.00051676127,0.000016940465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009866723,0.00012261543,0.003779452,0.00014936805,0.00031858616,0.0000216669,0.000027073125,0.0011702768,0.99236655,0.000008648401,0.0011521118,0.0007849907],"study_design_scores_gemma":[0.00081562536,0.0000537753,0.00075225584,0.00008896624,0.0004021014,0.00014263878,0.00025854795,0.010667859,0.9855827,0.00036232028,0.00036582502,0.00050736713],"about_ca_topic_score_codex":0.0001246676,"about_ca_topic_score_gemma":0.0000057912016,"teacher_disagreement_score":0.028616956,"about_ca_system_score_codex":0.0010055284,"about_ca_system_score_gemma":0.00031695288,"threshold_uncertainty_score":0.9997819},"labels":[],"label_agreement":null},{"id":"W4407737422","doi":"10.1109/whispers65427.2024.10876484","title":"Multispectral Drone Imaging for Non-Destructive Estimation of Nitrogen Content in Canola and Wheat","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"University of Lethbridge","keywords":"Canola; Multispectral image; Drone; Nitrogen; Environmental science; Remote sensing; Computer science; Agronomy; Artificial intelligence; Chemistry; Biology; Botany; Geography","score_opus":0.016332760950453645,"score_gpt":0.28680327101541087,"score_spread":0.2704705100649572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407737422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894073,0.0010435361,0.005642331,0.00013041741,0.000022310316,0.00007603222,0.000021662649,0.000034432043,0.0036219673],"genre_scores_gemma":[0.99465346,0.00002255246,0.0047512944,0.000013460471,0.00002355164,0.00002164905,0.00001312898,0.000010462526,0.0004904175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999449,0.0000021057813,0.00016689712,0.0001792774,0.000066579225,0.00013612365],"domain_scores_gemma":[0.9997529,0.00009542496,0.000024951685,0.00007090526,0.000025572359,0.00003025352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004458244,0.00008641164,0.00015947325,0.00012389787,0.000019257772,0.000025917654,0.00004346688,0.000034388744,0.00016915175],"category_scores_gemma":[0.00003752447,0.00007700678,0.000044414184,0.00016595228,0.000043113738,0.00008243459,0.00001546559,0.00006718884,9.264905e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006876692,0.00006667705,0.048720904,0.0006133441,0.00017317507,0.000010938666,0.0005911442,0.00005090699,0.93782824,0.0032396528,0.00021884209,0.008417429],"study_design_scores_gemma":[0.00048448952,0.000014346152,0.001075511,0.000055909855,0.0000861018,0.000011369855,0.000969124,0.070634365,0.9236128,0.0029485761,0.000010700414,0.00009672605],"about_ca_topic_score_codex":0.0010601567,"about_ca_topic_score_gemma":0.00006881654,"teacher_disagreement_score":0.070583455,"about_ca_system_score_codex":0.000093092705,"about_ca_system_score_gemma":0.0000288371,"threshold_uncertainty_score":0.31402454},"labels":[],"label_agreement":null},{"id":"W4407737480","doi":"10.1109/whispers65427.2024.10876526","title":"UAV-Based Multispectral and RGB Imaging Techniques for Dry Bean Phenotyping","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Multispectral image; RGB color model; Computer science; Remote sensing; Artificial intelligence; Optical imaging; Computer vision; Geology; Optics; Physics","score_opus":0.011331211020301494,"score_gpt":0.29554006838986485,"score_spread":0.28420885736956336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407737480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041035965,0.01161446,0.836938,0.00215246,0.00013211407,0.00026357317,0.00007825603,0.0030227823,0.10476236],"genre_scores_gemma":[0.9683562,0.000026663278,0.027062818,0.00022997218,0.00021424412,0.000039993687,0.000020669466,0.000032496384,0.00401695],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921113,0.0000026960831,0.00015094782,0.0003145428,0.000072757495,0.0002479377],"domain_scores_gemma":[0.99958086,0.0001709836,0.000021538224,0.00014298446,0.000024796951,0.000058807957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008051109,0.0001499502,0.00015944117,0.00014327043,0.00009130543,0.00013542506,0.00009394614,0.000056736375,0.0008080759],"category_scores_gemma":[0.000045373545,0.00012782316,0.00009773617,0.00021144777,0.00005446037,0.00012981547,0.000024309558,0.00012499599,0.0000050191497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042433938,0.0000838178,0.005794063,0.0012622427,0.0001868995,0.000018989214,0.00018492038,0.000003036025,0.8967046,0.0077183545,0.004075155,0.08392548],"study_design_scores_gemma":[0.0001583463,0.00000881452,0.000023266677,0.00005288683,0.00008359167,0.0000033120727,0.00023152304,0.035689734,0.95261735,0.0013442773,0.009591993,0.00019489296],"about_ca_topic_score_codex":0.00008773763,"about_ca_topic_score_gemma":0.000010981846,"teacher_disagreement_score":0.92732024,"about_ca_system_score_codex":0.00006513151,"about_ca_system_score_gemma":0.00003321527,"threshold_uncertainty_score":0.8847864},"labels":[],"label_agreement":null},{"id":"W4407864565","doi":"10.1016/j.foodres.2025.116045","title":"WITHDRAWN: A novel calibration transfer algorithm for near-infrared spectroscopy of thin-skinned fruits based on KPCA, weighted DELM and TrAdaBoost","year":2025,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Calibration; Algorithm; Spectroscopy; Infrared spectroscopy; Infrared; Near-infrared spectroscopy; Artificial intelligence; Computer science; Chemistry; Pattern recognition (psychology); Mathematics; Biological system; Analytical Chemistry (journal); Optics; Chromatography; Physics; Biology; Statistics; Organic chemistry","score_opus":0.03707025007252092,"score_gpt":0.3485269574962285,"score_spread":0.31145670742370757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407864565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29744074,0.0005253308,0.64150554,0.0051818355,0.0003503505,0.00086569384,0.0023331167,0.00014776655,0.05164964],"genre_scores_gemma":[0.97562313,0.000025957139,0.01945082,0.00020357424,0.00018105248,0.00014625449,0.00032722976,0.000028439026,0.0040135575],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998017,0.00003078429,0.000359032,0.00043387717,0.0008130856,0.0003461816],"domain_scores_gemma":[0.9985413,0.00062269747,0.000051463638,0.00024612914,0.000441945,0.00009647892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033796186,0.00018114512,0.00026374328,0.00052790926,0.00020952703,0.0001489112,0.00040121036,0.00016870227,0.000772785],"category_scores_gemma":[0.00023930921,0.00016641505,0.0001229052,0.0006955711,0.00022285692,0.000140105,0.00004380665,0.0003764107,0.0000017892237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003384831,0.0031210093,0.0025395104,0.00067892286,0.0022626824,0.000011235077,0.00081528205,0.00022969038,0.9320202,0.03246151,0.013874616,0.008600528],"study_design_scores_gemma":[0.0024112305,0.00034652397,0.0003039015,0.00008902569,0.000041841995,0.00000124532,0.00013640455,0.25946692,0.73243356,0.0020437145,0.0025944964,0.00013117256],"about_ca_topic_score_codex":0.00006230371,"about_ca_topic_score_gemma":0.000026991449,"teacher_disagreement_score":0.67818236,"about_ca_system_score_codex":0.00017017614,"about_ca_system_score_gemma":0.00036809102,"threshold_uncertainty_score":0.84614533},"labels":[],"label_agreement":null},{"id":"W4407914115","doi":"10.1007/978-3-031-84356-3_16","title":"Eigenmaps - Modified PCA-Based Approach for Dimensionality Reduction of Multivariate Time Series of Geospatial Data","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Dimensionality reduction; Geospatial analysis; Computer science; Multivariate statistics; Curse of dimensionality; Principal component analysis; Artificial intelligence; Time series; Series (stratigraphy); Pattern recognition (psychology); Data mining; Machine learning; Cartography; Geography","score_opus":0.036776874747852274,"score_gpt":0.28756765306749205,"score_spread":0.25079077831963975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407914115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003502671,0.00022153923,0.99607253,0.000068247944,0.00015226139,0.00019087543,0.00039080434,0.000030047695,0.0025233997],"genre_scores_gemma":[0.3863703,0.0000147161,0.6095013,0.000059011003,0.00033599293,0.00001371143,0.0012005696,0.000032003492,0.002472377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978013,0.000011052744,0.00052558025,0.00092848873,0.000486232,0.00024733375],"domain_scores_gemma":[0.99764735,0.0003454423,0.00045182434,0.0012159285,0.00029314173,0.000046307374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000417515,0.00029720186,0.00063481246,0.00036384934,0.000106597785,0.000028412049,0.0012383747,0.00030802996,0.00007583603],"category_scores_gemma":[0.00024098864,0.000270684,0.000120388126,0.0004097153,0.0006441966,0.00014340838,0.00048329792,0.0002881048,4.3973725e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013338758,0.0011462816,0.00012100888,0.0058560176,0.0007995815,0.000007961188,0.0006517168,0.476553,0.3847091,0.013771946,0.00036012358,0.11468939],"study_design_scores_gemma":[0.00046595887,0.00006389979,0.000011074863,0.00018818137,0.00016126041,0.0000032251419,6.44547e-7,0.5565022,0.43128014,0.010922381,0.000086899345,0.00031414436],"about_ca_topic_score_codex":0.00010196166,"about_ca_topic_score_gemma":0.000005405428,"teacher_disagreement_score":0.38657123,"about_ca_system_score_codex":0.0001071042,"about_ca_system_score_gemma":0.00053250627,"threshold_uncertainty_score":0.99997455},"labels":[],"label_agreement":null},{"id":"W4408033976","doi":"10.18280/ts.420119","title":"Enhanced Detection of Benign and Malignant Breast Cancer Cells via DEA-ELM Hybrid Approach","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Breast cancer; Cancer; Computer science; Artificial intelligence; Pattern recognition (psychology); Medicine; Internal medicine","score_opus":0.007144356517876107,"score_gpt":0.23290908482785522,"score_spread":0.22576472830997912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408033976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88707453,0.0007545123,0.107235685,0.000045617893,0.00004750234,0.00010773286,0.00008649056,0.000044427605,0.004603495],"genre_scores_gemma":[0.99881226,0.00011513021,0.0002293432,0.000057450263,0.000079895464,0.00005391699,0.00001271112,0.000012298935,0.0006270195],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988724,0.000014585305,0.00032211313,0.00034425154,0.00020655601,0.00024011054],"domain_scores_gemma":[0.9995361,0.000042800606,0.00013157162,0.00015757926,0.000069386966,0.00006258462],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009686691,0.00018585403,0.00028879882,0.00014824353,0.00008880198,0.000023371691,0.00013825764,0.00006784579,0.0013056865],"category_scores_gemma":[0.0000033973115,0.00017132254,0.00009089012,0.00033506297,0.00007466579,0.00006741556,0.000040688472,0.000118703334,0.0000020377356],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102842365,0.00018955028,0.0004828415,0.0002721769,0.00023063447,0.0000010858647,0.00006740785,0.00011130153,0.9906216,0.000049860028,0.00009070747,0.007779956],"study_design_scores_gemma":[0.0006586084,0.000025673326,0.0008236295,0.000038425373,0.0002960154,0.0000063259026,0.00017715064,0.0020945473,0.9955533,0.000086407446,0.00008132431,0.00015856676],"about_ca_topic_score_codex":0.00022469631,"about_ca_topic_score_gemma":0.000010906377,"teacher_disagreement_score":0.1117377,"about_ca_system_score_codex":0.00009602866,"about_ca_system_score_gemma":0.00004387186,"threshold_uncertainty_score":0.99960726},"labels":[],"label_agreement":null},{"id":"W4408039585","doi":"10.1016/j.atech.2025.100860","title":"Accurate estimation of tuber size in large potato throughput at potato storage using machine vision and machine learning techniques","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Throughput; Computer science; Artificial intelligence; Machine learning; Agricultural engineering; Computer vision; Engineering; Operating system","score_opus":0.008537113858245132,"score_gpt":0.28347231628889397,"score_spread":0.27493520243064884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408039585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99488765,0.0022287057,0.0007539928,0.000632955,0.000025661027,0.000116980504,0.00001884669,0.00029980773,0.0010354233],"genre_scores_gemma":[0.9953119,0.00020023226,0.0032840807,0.000027848595,0.000011349343,0.000015743235,0.00005643824,0.000011548156,0.0010808203],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873817,0.000025472153,0.00040593478,0.00038043113,0.00012870139,0.00032127596],"domain_scores_gemma":[0.9993722,0.00012185281,0.00020394598,0.00019970744,0.00007230003,0.000029985176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012452321,0.00025514254,0.00046461256,0.0003324039,0.0001641965,0.000021140477,0.00019151442,0.00036617063,0.00014905118],"category_scores_gemma":[0.0003231908,0.0001813517,0.00006905839,0.0015420329,0.00011913351,0.00015820669,0.0003531062,0.0004774703,0.0000027496856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049395352,0.000126061,0.11912487,0.0001830414,0.00008628627,0.000014700202,0.000093252434,0.00015749338,0.8777889,0.0007279067,0.00006421964,0.0015838984],"study_design_scores_gemma":[0.00063165143,0.00006353808,0.014405924,0.0001441207,0.000120872486,0.00005947709,0.00045641843,0.007935105,0.97473615,0.0006326225,0.0005433999,0.00027069403],"about_ca_topic_score_codex":0.00023966265,"about_ca_topic_score_gemma":0.0002512591,"teacher_disagreement_score":0.104718946,"about_ca_system_score_codex":0.00021455644,"about_ca_system_score_gemma":0.00001619289,"threshold_uncertainty_score":0.7395308},"labels":[],"label_agreement":null},{"id":"W4408040119","doi":"10.18280/ts.420117","title":"LwSANet: Light Weight Self-Attention Network Model to Recognize Fruits from Images","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.010699498897898457,"score_gpt":0.2505491642506421,"score_spread":0.23984966535274366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408040119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7373425,0.003141611,0.10672406,0.0048966166,0.00043128402,0.00048391154,0.00026847122,0.0010713476,0.14564024],"genre_scores_gemma":[0.9837513,0.000095646195,0.007128641,0.0011038566,0.0006836467,0.000082940736,0.00015929635,0.000031019976,0.0069636256],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99816835,0.000021685593,0.00044876846,0.0005581908,0.00032963348,0.0004733927],"domain_scores_gemma":[0.9992501,0.00010162171,0.00011062986,0.00030328156,0.00008834395,0.00014599873],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001386133,0.00029549567,0.00035842494,0.00016159478,0.00019043738,0.000096702504,0.00036122647,0.00015082271,0.005252699],"category_scores_gemma":[0.000018367733,0.00028579036,0.00017716334,0.0007005984,0.00001898001,0.00012649116,0.00009620333,0.00020520693,0.00015425951],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023683746,0.000776995,0.014510302,0.00014953094,0.001506049,0.00002269179,0.00031928482,0.0036390766,0.65394205,0.00070127356,0.31949192,0.004703983],"study_design_scores_gemma":[0.002440612,0.000083193154,0.003906261,0.00024451577,0.0016799426,0.0000020683972,0.00022239734,0.027675007,0.92807037,0.005313819,0.029388163,0.0009736779],"about_ca_topic_score_codex":0.00004739181,"about_ca_topic_score_gemma":0.000013040277,"teacher_disagreement_score":0.29010376,"about_ca_system_score_codex":0.00015569982,"about_ca_system_score_gemma":0.00006152606,"threshold_uncertainty_score":0.9999594},"labels":[],"label_agreement":null},{"id":"W4408059061","doi":"10.1016/j.saa.2025.125975","title":"Leveraging infrared spectroscopy for cocoa content prediction: A dual approach with Kohonen neural network and multivariate modeling","year":2025,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Canadian Association of Gastroenterology","keywords":"Chemistry; Multivariate statistics; Self-organizing map; Artificial neural network; Dual (grammatical number); Spectroscopy; Multivariate analysis; Content (measure theory); Chemometrics; Infrared spectroscopy; Biological system; Artificial intelligence; Pattern recognition (psychology); Machine learning; Chromatography; Organic chemistry","score_opus":0.021024884847907016,"score_gpt":0.25835307985018857,"score_spread":0.23732819500228156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408059061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6134782,0.003792784,0.37070924,0.0009243852,0.00014619438,0.00095086376,0.00009035164,0.00037241227,0.0095355725],"genre_scores_gemma":[0.9489667,0.0002986305,0.048402548,0.00071432284,0.00035414746,0.00030021605,0.00028159388,0.000120441815,0.00056140963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99565065,0.00007371352,0.00075542193,0.0016449093,0.00047788996,0.0013974274],"domain_scores_gemma":[0.99834675,0.00009271555,0.00026187702,0.0007754405,0.00016644894,0.0003567908],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002626224,0.00091399404,0.0010695406,0.00038538594,0.00069354684,0.00046065892,0.0003717884,0.00032827724,0.00006125986],"category_scores_gemma":[0.00008047484,0.00082818937,0.00030554523,0.0009906719,0.00033510415,0.0002221518,0.00027050768,0.0006567951,0.0000012394562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00110733,0.000242064,0.0009083641,0.0002723953,0.0011743869,0.000051226973,0.00015547691,0.00041986708,0.99228865,0.0027851935,0.00056709273,0.000027928412],"study_design_scores_gemma":[0.0052314517,0.000470184,0.00013944448,0.00018975695,0.001622184,0.00022541593,0.0006264691,0.088987425,0.89779234,0.002710595,0.00093549705,0.0010692222],"about_ca_topic_score_codex":0.00007979599,"about_ca_topic_score_gemma":0.000011522643,"teacher_disagreement_score":0.3354885,"about_ca_system_score_codex":0.00021704847,"about_ca_system_score_gemma":0.0001838077,"threshold_uncertainty_score":0.9994169},"labels":[],"label_agreement":null},{"id":"W4408116567","doi":"10.1080/07060661.2025.2465996","title":"Potential in detection of cereal yellow dwarf virus in cereals through VisNIR spectroscopy","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Plant Pathology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Barley yellow dwarf; Agronomy; Spectroscopy; Biology; Plant virus; Virus; Physics; Virology; Astronomy","score_opus":0.008576354152155989,"score_gpt":0.24986513748451447,"score_spread":0.24128878333235848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408116567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911978,0.0013346466,0.00084713683,0.00015919977,0.0002802897,0.000035879177,0.00013032655,0.000004211859,0.006010493],"genre_scores_gemma":[0.9991089,0.0002607938,0.00023629876,0.00009652993,0.00009142958,0.000001329209,0.000011087926,0.000009312148,0.00018428116],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986354,0.000055088898,0.00063920754,0.00017710526,0.000102228645,0.00039093726],"domain_scores_gemma":[0.9992779,0.000077822,0.00029960176,0.00015467752,0.00005918489,0.00013079663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021001254,0.00014732372,0.0005061802,0.0009992634,0.000040142208,0.000013842272,0.00025981615,0.00024904462,0.00040432613],"category_scores_gemma":[0.00013532984,0.00014889636,0.00011469738,0.00066303386,0.000120273726,0.000093527175,0.00001327307,0.00050828356,0.0000031179306],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017049948,0.0000681161,0.03378722,0.00006814614,0.00006614531,0.0026227776,0.00036592595,0.0001651629,0.9616648,0.00016355087,0.0004974489,0.00036019812],"study_design_scores_gemma":[0.0012540997,0.00012058972,0.02494612,0.00010531571,0.00011248213,0.0006929146,0.0007330325,0.000059332622,0.96895844,0.0018577002,0.0009976815,0.00016227925],"about_ca_topic_score_codex":0.015763538,"about_ca_topic_score_gemma":0.15326594,"teacher_disagreement_score":0.1375024,"about_ca_system_score_codex":0.00038822065,"about_ca_system_score_gemma":0.000691072,"threshold_uncertainty_score":0.9907906},"labels":[],"label_agreement":null},{"id":"W4408266472","doi":"10.2118/223992-ms","title":"Real-Time Monitoring of One-Dimensional Oil-In-Water Emulsion Composition Profiles Using Electromagnetic Frequency Sweeps","year":2025,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Emulsion; Composition (language); Environmental science; Petroleum engineering; Geology; Engineering; Chemical engineering","score_opus":0.013816918585888342,"score_gpt":0.2774344932514733,"score_spread":0.26361757466558494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408266472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9740041,0.00021002667,0.000032033906,0.00006418237,0.000031959105,0.000029401197,0.0000024828953,0.0000613761,0.02556444],"genre_scores_gemma":[0.9858504,0.000047486024,0.010923407,0.00000885954,0.000054038417,0.000007069982,0.00003158722,0.000012888161,0.003064257],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881566,0.00001879125,0.0003738792,0.0002757314,0.00022017608,0.00029576733],"domain_scores_gemma":[0.9995283,0.00007064172,0.00006812061,0.0002138794,0.00008226505,0.000036785037],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000065142,0.00015584897,0.00029652801,0.00030329882,0.0000753869,0.000017667748,0.00013052794,0.00012892224,0.0017701497],"category_scores_gemma":[0.000014678947,0.00013364854,0.00007966424,0.00045788658,0.000043832322,0.00009116352,0.00005486253,0.00014922075,0.000015024599],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003345896,0.00016605128,0.011609192,0.00013794204,0.00004974878,0.000003878899,0.000024383537,0.000057216726,0.9876809,0.00012136758,0.00001601009,0.00009982136],"study_design_scores_gemma":[0.00035555192,0.000029194003,0.0017562356,0.00017694492,0.00011541635,0.0000025624818,0.000047450616,0.0009889791,0.99588066,0.0005084266,0.0000012648314,0.00013731311],"about_ca_topic_score_codex":0.00056418125,"about_ca_topic_score_gemma":0.0000020015489,"teacher_disagreement_score":0.022500183,"about_ca_system_score_codex":0.00019075717,"about_ca_system_score_gemma":0.000035386234,"threshold_uncertainty_score":0.99914235},"labels":[],"label_agreement":null},{"id":"W4408346145","doi":"10.1093/jaoacint/qsaf022","title":"Rapid Authentication of Plant-Based Milk Alternatives by Coupling Portable Raman Spectroscopy With Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Raman spectroscopy; Artificial intelligence; Overfitting; Support vector machine; Machine learning; Random forest; Computer science; Sample (material); Pattern recognition (psychology); Analytical Chemistry (journal); Materials science; Chemistry; Chromatography; Physics; Optics","score_opus":0.007703345110546335,"score_gpt":0.26530068031064724,"score_spread":0.2575973352001009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408346145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9672935,0.0017711961,0.021488857,0.00074296736,0.00019563719,0.000036927493,0.000090627764,0.000021090964,0.008359195],"genre_scores_gemma":[0.9948004,0.00019147656,0.002451846,0.000054724922,0.000110871944,0.0000018660023,0.00009834779,0.000012204586,0.0022782611],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987158,0.000010231988,0.000517527,0.00014255763,0.00047295817,0.00014092059],"domain_scores_gemma":[0.9985576,0.000159201,0.000813745,0.00010282212,0.00031882402,0.000047776943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002178893,0.00013719757,0.000284976,0.0003728214,0.00006142645,0.00004469088,0.00034759525,0.000056695942,0.0015600366],"category_scores_gemma":[0.00011571266,0.000112243746,0.00012036795,0.0002454265,0.00006728574,0.0001466152,0.000022689159,0.00034563086,0.0000015320453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010288972,0.000579996,0.12198854,0.000115288465,0.0016261115,0.000023631868,0.00012897696,0.004033318,0.8668142,0.0008980661,0.002563141,0.00019984967],"study_design_scores_gemma":[0.0014529568,0.0001217132,0.00068457855,0.00017937367,0.00021111062,0.000020278183,0.00028318696,0.018398747,0.975456,0.00013289716,0.0029502504,0.000108884306],"about_ca_topic_score_codex":0.000039521132,"about_ca_topic_score_gemma":0.0000037754728,"teacher_disagreement_score":0.12130396,"about_ca_system_score_codex":0.00014402726,"about_ca_system_score_gemma":0.00013314173,"threshold_uncertainty_score":0.9993527},"labels":[],"label_agreement":null},{"id":"W4408613291","doi":"10.3390/f16030538","title":"Monitoring and Discrimination of Salt Stress in Salix matsudana × alba Using Vis/NIR-HSI Technology","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stress (linguistics); Salt (chemistry); Horticulture; Botany; Biology; Chemistry","score_opus":0.015027435726185699,"score_gpt":0.3177939312549606,"score_spread":0.3027664955287749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408613291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963998,0.0017579169,0.00036030277,0.000070409165,0.0000404507,0.000029502275,0.0000066924176,0.000031892865,0.0013030122],"genre_scores_gemma":[0.999093,0.00008607027,0.00056840904,0.000002420353,0.00002634018,0.000005544363,0.0000046834934,0.000007825467,0.00020568939],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993876,0.000004582073,0.0001935585,0.00016995842,0.00008777657,0.00015648108],"domain_scores_gemma":[0.99966717,0.000043496166,0.0000746487,0.00016382622,0.000032009128,0.000018856363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038466787,0.00009200638,0.00018244817,0.00042025463,0.000035743476,0.000012546057,0.00011630707,0.00012806192,0.00002584747],"category_scores_gemma":[0.000093070696,0.0000929385,0.00002457776,0.00069889217,0.0000698439,0.000063868785,0.000076571756,0.000115894945,5.055663e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001110258,0.000051914052,0.8266288,0.00030179892,0.00003083623,0.0000042210954,0.00007880395,0.000041716856,0.16990416,0.0012640404,0.000010729629,0.0016718495],"study_design_scores_gemma":[0.0002567501,0.000009077726,0.05190429,0.00030473174,0.000063973726,0.0000018332173,0.0007069764,0.000638133,0.94352967,0.002483635,0.000019581643,0.00008136941],"about_ca_topic_score_codex":0.00029037416,"about_ca_topic_score_gemma":0.00036562321,"teacher_disagreement_score":0.77472454,"about_ca_system_score_codex":0.00008640762,"about_ca_system_score_gemma":0.000026258771,"threshold_uncertainty_score":0.3789922},"labels":[],"label_agreement":null},{"id":"W4408659441","doi":"10.1093/mnras/staf448","title":"Synthetic Spectroscopy for White Dwarf Classification: Addressing Label Uncertainty and Class Imbalance","year":2025,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Physics; White dwarf; Spectroscopy; Astrophysics; Class (philosophy); Astronomy; Stars; Artificial intelligence","score_opus":0.018787536628463875,"score_gpt":0.2724723229827581,"score_spread":0.2536847863542942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408659441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917378,0.0007769434,0.00055714976,0.0022017048,0.00009771043,0.00014335282,0.0001066518,0.00004296969,0.00433574],"genre_scores_gemma":[0.9914965,0.0000013758059,0.0061630304,0.000083389765,0.00010621971,0.000035940833,0.000013546618,0.000014812259,0.0020851756],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987431,0.00001994004,0.00035324588,0.00040930393,0.00013707162,0.00033733493],"domain_scores_gemma":[0.99887806,0.00031868805,0.00025686715,0.00041807964,0.00006269,0.00006560626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015987421,0.00020873053,0.00037908735,0.000019901636,0.00026783848,0.000070895345,0.0004676631,0.00016082654,0.00009707087],"category_scores_gemma":[0.00010431801,0.00016147702,0.00030751032,0.00018687829,0.00037670907,0.000054109565,0.00016658743,0.00025437004,0.0000012720473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012154608,0.0014319445,0.484358,0.0023615228,0.0026416925,2.335503e-7,0.0008154849,0.3885909,0.08878131,0.002594265,0.023533309,0.0036758673],"study_design_scores_gemma":[0.0013231783,0.00003913501,0.043930493,0.00015704935,0.0006917039,1.7763675e-8,0.0011712458,0.8788675,0.071498975,0.00018161604,0.0018539598,0.0002851073],"about_ca_topic_score_codex":0.000046777557,"about_ca_topic_score_gemma":0.000011033492,"teacher_disagreement_score":0.4902766,"about_ca_system_score_codex":0.00020150974,"about_ca_system_score_gemma":0.000074636824,"threshold_uncertainty_score":0.65848416},"labels":[],"label_agreement":null},{"id":"W4408939634","doi":"10.1016/j.aac.2025.03.006","title":"Aflatoxin B1 contamination level detection in almond kernels through short wave infrared hyperspectral image analysis","year":2025,"lang":"en","type":"article","venue":"Advanced Agrochem","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Aflatoxin; Contamination; Infrared; Environmental science; Remote sensing; Environmental chemistry; Chemistry; Biology; Food science; Optics; Geology; Physics; Ecology","score_opus":0.01827575948593212,"score_gpt":0.29281115897013443,"score_spread":0.2745353994842023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408939634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91731143,0.00067008,0.037812218,0.00009010661,0.000052280608,0.00010215961,0.000023582223,0.00013212243,0.043806035],"genre_scores_gemma":[0.98806036,0.00014686635,0.0041425345,0.000095743206,0.00004579869,0.000056294135,0.00010555556,0.000018263323,0.007328568],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982895,0.000017211978,0.000451111,0.00058954355,0.0002462498,0.00040635528],"domain_scores_gemma":[0.99915075,0.00014498878,0.000114073315,0.00041100936,0.00012430134,0.0000548896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009095703,0.00028497173,0.00050788926,0.00047054962,0.000105547275,0.00004854862,0.0002016537,0.00020640799,0.00035842267],"category_scores_gemma":[0.00021470294,0.0003001655,0.00025813328,0.0033330158,0.00007603088,0.00044459596,0.000060945567,0.0003383724,0.000009023315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007876559,0.00014788283,0.003878554,0.0000550289,0.0005864768,0.000020490636,0.000306661,0.00016216176,0.98892504,0.0002658796,0.000041322168,0.0055317273],"study_design_scores_gemma":[0.00069274224,0.000015830781,0.012468624,0.000023906803,0.0006674037,0.0000030918734,0.001656143,0.0010132797,0.9802534,0.0026528107,0.00025541327,0.00029736012],"about_ca_topic_score_codex":0.00010087557,"about_ca_topic_score_gemma":0.0002554605,"teacher_disagreement_score":0.07074896,"about_ca_system_score_codex":0.00053651724,"about_ca_system_score_gemma":0.000043525386,"threshold_uncertainty_score":0.99994504},"labels":[],"label_agreement":null},{"id":"W4409049548","doi":"10.3390/spectroscj3020012","title":"Spectroscopic Advances in Real Time Monitoring of Pharmaceutical Bioprocesses: A Review of Vibrational and Fluorescence Techniques","year":2025,"lang":"en","type":"review","venue":"Spectroscopy Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sanofi (Canada); University of Waterloo","funders":"Mitacs","keywords":"Fluorescence; Biochemical engineering; Nanotechnology; Chemistry; Computer science; Materials science; Engineering; Physics; Optics","score_opus":0.02385653497956359,"score_gpt":0.4046556081803609,"score_spread":0.3807990732007973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409049548","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023003078,0.9923639,0.00015171058,0.000031091913,0.00007514802,0.00023094795,0.00005309561,0.000039585964,0.007031458],"genre_scores_gemma":[0.000006971759,0.98350203,0.015855549,0.000009122266,0.0003497407,0.00003920611,0.000022671222,0.00003633583,0.00017834408],"study_design_codex":"systematic_review","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99647886,0.00008770885,0.0018340176,0.00051774667,0.00059874577,0.00048294643],"domain_scores_gemma":[0.9975505,0.00040122136,0.0013612795,0.00034120135,0.00019429781,0.00015153259],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005063447,0.00057355594,0.0029285788,0.00081134855,0.00008355809,0.000046573823,0.0006619166,0.00031542842,0.0014716719],"category_scores_gemma":[0.00038854807,0.0004832625,0.00045502925,0.0016405598,0.0002686186,0.00031050263,0.00014035765,0.0012035879,0.000003723568],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006537142,0.00039618215,0.0008888194,0.68615884,0.0004298633,0.000057773137,0.000028266251,1.1783722e-7,0.046853304,0.0003646702,0.00022451142,0.26453233],"study_design_scores_gemma":[0.00035935323,0.00015897906,0.0000072002836,0.42148778,0.0024772477,0.00024974777,0.000037858495,0.000002949595,0.516147,0.00054558134,0.05790057,0.0006257099],"about_ca_topic_score_codex":0.000006674827,"about_ca_topic_score_gemma":4.8896527e-7,"teacher_disagreement_score":0.4692937,"about_ca_system_score_codex":0.00033238012,"about_ca_system_score_gemma":0.001044508,"threshold_uncertainty_score":0.9997619},"labels":[],"label_agreement":null},{"id":"W4409364279","doi":"10.1609/aaai.v39i16.33917","title":"Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transformer; Infrared spectroscopy; Infrared; Spectral line; Analytical Chemistry (journal); Chemistry; Computer science; Engineering; Environmental chemistry; Physics; Electrical engineering; Organic chemistry; Optics; Voltage; Astronomy","score_opus":0.07794249129309727,"score_gpt":0.3422450982234941,"score_spread":0.2643026069303968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409364279","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3250795,0.00009478805,0.030461725,0.0027998302,0.00013286673,0.0004219733,0.000047588524,0.00014743018,0.6408143],"genre_scores_gemma":[0.9902788,0.0000724826,0.000634556,0.00012950653,0.00006548174,0.000056127676,0.0000036334989,0.000014873437,0.008744561],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821454,0.0000014367921,0.000670909,0.00040169692,0.00030047868,0.00041091052],"domain_scores_gemma":[0.9988214,0.00013584939,0.0001958577,0.00022755272,0.0005415624,0.00007782117],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016915865,0.00026314388,0.0003891698,0.000127786,0.0001994342,0.00012153039,0.0008740757,0.00018093365,0.0010200109],"category_scores_gemma":[0.00080119196,0.00020517527,0.00029697455,0.0008442932,0.00021668509,0.00014596643,0.000053546562,0.00031574335,0.000011624496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066657056,0.00035715243,0.00030785787,0.0011090889,0.00030928804,2.2521479e-7,0.0006747644,0.00043300638,0.55338943,0.43106675,0.0008912993,0.010794531],"study_design_scores_gemma":[0.00005484432,0.000023800128,0.0000021280393,0.00015717752,0.00015860237,6.727998e-7,0.001543934,0.11266533,0.7998734,0.08509802,0.00025000787,0.00017206921],"about_ca_topic_score_codex":0.000023751472,"about_ca_topic_score_gemma":0.0000047569247,"teacher_disagreement_score":0.6651993,"about_ca_system_score_codex":0.00012005963,"about_ca_system_score_gemma":0.00021524701,"threshold_uncertainty_score":0.9998932},"labels":[],"label_agreement":null},{"id":"W4409416296","doi":"10.1016/j.jfca.2025.107630","title":"Food quality assessment and quantification using multispectral images from screen and smartphone for kiwifruit ripeness and rice discrimination","year":2025,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Ripeness; Kiwi; Multispectral image; Quality assessment; Food science; Biology; Geography; Remote sensing; Business; Ripening","score_opus":0.05328819642378099,"score_gpt":0.37598054648212337,"score_spread":0.3226923500583424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409416296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83434844,0.0024268148,0.16262923,0.0003742437,0.00001428691,0.000036885303,0.000112340385,0.0000063437396,0.0000514487],"genre_scores_gemma":[0.9903955,0.00043592334,0.008999369,0.000032357606,0.00004221541,0.0000024072665,0.0000700786,0.000004793523,0.000017339822],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990456,0.000047505313,0.00042982385,0.00022024811,0.00015573682,0.000101102545],"domain_scores_gemma":[0.99901897,0.00023651251,0.0004024271,0.000096808064,0.00017098765,0.00007429156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026312374,0.00012805704,0.00042394496,0.00038684477,0.00022341897,0.00019100869,0.000055653178,0.00006850814,0.000013389137],"category_scores_gemma":[0.000028348803,0.00011552569,0.00011011966,0.0004261138,0.000071407536,0.00023015552,0.000030545096,0.000112112655,1.9429002e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020618667,0.00020450525,0.053948678,0.00020267967,0.0031005999,9.903846e-7,0.00017189732,0.000051976633,0.9371307,0.0009440559,0.000010763184,0.004026994],"study_design_scores_gemma":[0.002264553,0.00025209275,0.61005634,0.000105791754,0.010359908,0.000014032049,0.0019193726,0.01842247,0.353726,0.0025951609,0.000008906251,0.00027537174],"about_ca_topic_score_codex":0.00012380342,"about_ca_topic_score_gemma":0.000038978713,"teacher_disagreement_score":0.58340466,"about_ca_system_score_codex":0.000058697555,"about_ca_system_score_gemma":0.000023820716,"threshold_uncertainty_score":0.4711001},"labels":[],"label_agreement":null},{"id":"W4409787740","doi":"10.61091/jcmcc127a-495","title":"Design of a real-time monitoring system for blending ratio and purity of tobacco for hyperspectral imaging","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Process engineering; Environmental science; Artificial intelligence; Computer science; Computer vision; Remote sensing; Engineering; Geology","score_opus":0.01691234473883051,"score_gpt":0.285645946678575,"score_spread":0.2687336019397445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.931867,0.001113807,0.06299311,0.000044207285,0.0029868912,0.0003815536,0.000009326583,0.000033061853,0.0005710165],"genre_scores_gemma":[0.9752116,0.000058153386,0.02410552,9.647765e-7,0.00058216864,0.0000055567657,8.758253e-7,0.000023279661,0.000011839692],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997998,0.000032536795,0.0012058757,0.000198446,0.00029389933,0.0002712359],"domain_scores_gemma":[0.9956666,0.0020335927,0.0013613685,0.000173688,0.000681441,0.00008330925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012529966,0.00023549955,0.0010662932,0.0002836537,0.00020610615,0.00009148899,0.00026761854,0.00012818421,0.000002323326],"category_scores_gemma":[0.0006397325,0.00022525869,0.00020874487,0.0003089679,0.000080775106,0.00013239436,0.000098101664,0.00018511794,3.988557e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070970354,0.0005796215,0.0022659095,0.005228706,0.00081258814,0.0000042252514,0.0010420978,0.00012712182,0.50848025,0.4797814,0.00008339823,0.00088499114],"study_design_scores_gemma":[0.008526966,0.0004977734,0.00003958554,0.0020165397,0.0013622431,0.00003907601,0.0035700856,0.022731656,0.8140489,0.14680389,0.000017319293,0.00034597702],"about_ca_topic_score_codex":0.000009541839,"about_ca_topic_score_gemma":1.9796822e-8,"teacher_disagreement_score":0.3329775,"about_ca_system_score_codex":0.00014247709,"about_ca_system_score_gemma":0.0001619599,"threshold_uncertainty_score":0.91857827},"labels":[],"label_agreement":null},{"id":"W4409791058","doi":"10.61091/jcmcc127a-440","title":"Deep Learning Based Peach Quality and Disease Detection Methods for Research in Intelligent Food Safety Detection","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Computer science; Food safety; Deep learning; Artificial intelligence; Food science; Biology","score_opus":0.06529168983682028,"score_gpt":0.42125030703974514,"score_spread":0.3559586172029249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41024974,0.0019553741,0.5844364,0.00016155862,0.0024967873,0.00025662087,0.0000011779298,0.000033805693,0.0004085267],"genre_scores_gemma":[0.993536,0.000075610704,0.005932015,0.000010722157,0.00040508175,0.00000846434,0.0000013639036,0.00002186003,0.0000089146915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99739075,0.00035992498,0.0011811374,0.000293561,0.00040968318,0.00036497568],"domain_scores_gemma":[0.99412847,0.0042213076,0.0006244363,0.00018292514,0.00062747474,0.00021537096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006121384,0.00022647384,0.0006968561,0.0006391255,0.0004959538,0.00019239867,0.00022639945,0.00020256134,0.0000064087476],"category_scores_gemma":[0.005297563,0.00021958783,0.00017640738,0.00087498216,0.000101608195,0.00011049311,0.00014920453,0.0009752166,1.4819804e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00694822,0.004173872,0.005731426,0.01038871,0.001214709,0.000019868554,0.0025405446,0.0018249154,0.072474934,0.5583997,0.000020029289,0.33626306],"study_design_scores_gemma":[0.0063542607,0.00081279466,0.0004445677,0.00055243965,0.0003364284,0.000008936412,0.0018942436,0.12605688,0.09111763,0.7713302,0.0007222694,0.00036940665],"about_ca_topic_score_codex":0.000016441589,"about_ca_topic_score_gemma":0.0000026934704,"teacher_disagreement_score":0.5832862,"about_ca_system_score_codex":0.00033955526,"about_ca_system_score_gemma":0.00016586648,"threshold_uncertainty_score":0.8954532},"labels":[],"label_agreement":null},{"id":"W4409795197","doi":"10.61091/jcmcc127b-512","title":"Application of Least Squares Support Vector Regression Analysis to Green Concrete with Oil Palm Husk Aggregate","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Husk; Aggregate (composite); Palm oil; Palm; Partial least squares regression; Mathematics; Regression analysis; Support vector machine; Statistics; Materials science; Computer science; Composite material; Chemistry; Botany; Food science; Artificial intelligence; Biology","score_opus":0.00823321450374949,"score_gpt":0.27116493464815333,"score_spread":0.26293172014440386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409795197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98364013,0.00039570857,0.010840805,0.00022818329,0.0010754695,0.00009814009,0.000009700073,0.00003898801,0.0036728575],"genre_scores_gemma":[0.9972834,0.00003463702,0.0021177079,0.000022008047,0.000400177,0.000003647895,0.00000602869,0.00002384657,0.00010857211],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974358,0.000042862586,0.0012167424,0.00029520155,0.00070486136,0.00030450805],"domain_scores_gemma":[0.9964816,0.00052240974,0.0015832572,0.00039558395,0.00081803725,0.00019913742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006881854,0.00032525384,0.001301958,0.0006530891,0.00019534034,0.000101953185,0.0004911701,0.00019036267,0.000039318387],"category_scores_gemma":[0.00029988552,0.0002555783,0.0002948306,0.001975135,0.000104033476,0.0001092294,0.00018328078,0.00038806163,0.0000012566384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033616752,0.0017654044,0.037312284,0.005305768,0.014688951,0.00010515822,0.004612747,0.0005699229,0.15872271,0.755526,0.00092379923,0.017105572],"study_design_scores_gemma":[0.02909259,0.0045946823,0.002477238,0.0044209156,0.02635525,0.00018205534,0.0058963336,0.020567745,0.72519827,0.17348796,0.004858124,0.0028688414],"about_ca_topic_score_codex":0.00007384594,"about_ca_topic_score_gemma":0.0000018729613,"teacher_disagreement_score":0.58203804,"about_ca_system_score_codex":0.00011409884,"about_ca_system_score_gemma":0.00020545226,"threshold_uncertainty_score":0.9999896},"labels":[],"label_agreement":null},{"id":"W4409846755","doi":"10.1016/j.jfca.2025.107700","title":"Exploring natural variation in seed oil traits across the global germplasm of the oilseed crop, Carthamus tinctorius L. (Safflower) using near-infrared reflectance spectroscopy and gas chromatography","year":2025,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Science and Engineering Research Board; University of Delhi; Ministry of Science and Technology of the People's Republic of China; Council of Scientific and Industrial Research, India; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Carthamus; Germplasm; Crop; Near infrared reflectance spectroscopy; Near-infrared spectroscopy; Agronomy; Environmental science; Biology; Traditional medicine","score_opus":0.018770436679107824,"score_gpt":0.2844207361850154,"score_spread":0.26565029950590757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409846755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99673307,0.0019098491,0.0005030458,0.00026718082,0.00008584716,0.000020327976,0.000020805115,0.000008382172,0.0004514788],"genre_scores_gemma":[0.9989517,0.0002938638,0.0006275832,0.000050090213,0.00005273801,0.0000017976962,0.0000017794116,0.000005017702,0.00001547157],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986886,0.000065943306,0.0005574282,0.00018496202,0.0002994871,0.00020357277],"domain_scores_gemma":[0.9990108,0.00008948255,0.0005252674,0.00016301527,0.00015989046,0.00005153874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021575847,0.00016606179,0.00047294042,0.0002231193,0.0002996465,0.00015028326,0.0002025663,0.000069562244,0.000012112176],"category_scores_gemma":[0.000038659848,0.00011303564,0.00033328723,0.0031859505,0.0001319497,0.0002156363,0.00005255592,0.0002934772,4.7828806e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030753572,0.0001375102,0.03686776,0.000109783345,0.0024653752,0.000008567068,0.0010060488,0.0010451941,0.95732147,0.00016177955,0.0000050593662,0.0005639464],"study_design_scores_gemma":[0.003319686,0.000169242,0.48546985,0.00048448343,0.006066357,0.00017412334,0.0017456113,0.021220688,0.4796969,0.0011718222,0.000040589795,0.0004406736],"about_ca_topic_score_codex":0.000053412317,"about_ca_topic_score_gemma":0.00008349619,"teacher_disagreement_score":0.47762457,"about_ca_system_score_codex":0.00014007326,"about_ca_system_score_gemma":0.000064260916,"threshold_uncertainty_score":0.46094596},"labels":[],"label_agreement":null},{"id":"W4409880084","doi":"10.1016/j.ifset.2025.104033","title":"Experimental and computational study of synchrotron X-ray micro-computed tomography imaging in peas and pinto beans after radiofrequency heating","year":2025,"lang":"en","type":"article","venue":"Innovative Food Science & Emerging Technologies","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Saskatchewan Pulse Growers","keywords":"Synchrotron; Computed tomography; Tomography; X-ray; Nuclear medicine; Medicine; Optics; Physics; Radiology","score_opus":0.009524296171165372,"score_gpt":0.29342049759010286,"score_spread":0.2838962014189375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409880084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99355793,0.003928058,0.0016932966,0.0002096644,0.000022899325,0.00014266217,0.000004879747,0.00019889961,0.00024168268],"genre_scores_gemma":[0.99445236,0.000005752538,0.005455756,0.000019917152,0.000003967904,0.00005136449,0.000001393593,0.0000061577707,0.0000033269444],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985668,0.000011109595,0.00037526034,0.0005142592,0.00024362054,0.0002889151],"domain_scores_gemma":[0.9993792,0.000071022856,0.0001377889,0.00020058091,0.00019620119,0.000015189617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023011165,0.0001878638,0.00027349638,0.0016850135,0.00020585394,0.00006736024,0.0003498796,0.000048389174,0.000006372736],"category_scores_gemma":[0.00014897903,0.00018044958,0.000020130494,0.0064299363,0.0011962368,0.000285521,0.00037902856,0.000219801,7.326158e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017251237,0.0002397324,0.45976454,0.00004275282,0.000045277153,0.00000473222,0.0018054094,0.000090945345,0.5329879,0.0010872778,0.000004419601,0.0039097564],"study_design_scores_gemma":[0.0008736178,0.0002368813,0.0582454,0.00018650392,0.000022947539,0.000004843245,0.097317114,0.0023360106,0.8379195,0.0025719388,0.0000054855022,0.00027977995],"about_ca_topic_score_codex":0.000083340514,"about_ca_topic_score_gemma":0.000004116809,"teacher_disagreement_score":0.40151915,"about_ca_system_score_codex":0.00011454189,"about_ca_system_score_gemma":0.00006677907,"threshold_uncertainty_score":0.73585206},"labels":[],"label_agreement":null},{"id":"W4409893036","doi":"10.3390/toxins17050219","title":"Detection of Mycotoxins in Cereal Grains and Nuts Using Machine Learning Integrated Hyperspectral Imaging: A Review","year":2025,"lang":"en","type":"review","venue":"Toxins","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College","funders":"","keywords":"Hyperspectral imaging; Mycotoxin; Biology; Artificial intelligence; Environmental science; Botany; Computer science","score_opus":0.030153818617118928,"score_gpt":0.3360237056595825,"score_spread":0.3058698870424636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409893036","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002704341,0.9964033,0.00008601306,0.00001682031,0.000035886555,0.00022827405,0.000076191485,0.00007538948,0.0028077133],"genre_scores_gemma":[0.0010131542,0.9974158,0.00021519753,0.000026657504,0.000053368443,0.00002796714,0.0001236741,0.00004579961,0.0010783833],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979805,0.00010440418,0.0008260292,0.0005400234,0.00019194098,0.00035711034],"domain_scores_gemma":[0.9988264,0.00019820406,0.00050369935,0.00032739158,0.000060670463,0.00008363503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002226404,0.0004993215,0.002140071,0.00067995174,0.0000768324,0.000027998683,0.00025502234,0.00025775874,0.0003162987],"category_scores_gemma":[0.00050301163,0.00039568692,0.00042848007,0.00200341,0.00009302393,0.00007400612,0.00010968761,0.0010633774,0.000002882467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001757401,0.00023951399,0.0005156754,0.21430936,0.00049979705,0.00005765477,0.00010402302,0.0000018833646,0.006595167,0.000056136636,0.000036832666,0.7775664],"study_design_scores_gemma":[0.001032684,0.00008021682,0.000011583828,0.19308789,0.009928297,0.00040602867,0.0003369566,0.0011208558,0.012845236,0.00006026055,0.77941966,0.0016703111],"about_ca_topic_score_codex":0.0009172613,"about_ca_topic_score_gemma":0.00017492603,"teacher_disagreement_score":0.7793828,"about_ca_system_score_codex":0.00045308637,"about_ca_system_score_gemma":0.00029518877,"threshold_uncertainty_score":0.9998495},"labels":[],"label_agreement":null},{"id":"W4409949481","doi":"10.3390/rs17091574","title":"Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Environmental science; Computer science; Geology","score_opus":0.031140962394024415,"score_gpt":0.31619812178098805,"score_spread":0.28505715938696363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409949481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6870235,0.12792502,0.0771474,0.015503341,0.00017585306,0.0003231621,0.000025602183,0.00050116185,0.091374904],"genre_scores_gemma":[0.9538438,0.0054925373,0.03945333,0.000052414485,0.00008304896,4.848704e-8,0.0000104933415,0.000013834816,0.0010505184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940896,0.000007988156,0.00015073575,0.0002085031,0.00006267145,0.0001611219],"domain_scores_gemma":[0.99961174,0.00007586515,0.00006248934,0.00012642024,0.00009308351,0.000030428922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006715172,0.00010534348,0.00019169916,0.00023256974,0.000059872524,0.000030074849,0.000030049836,0.000056910696,0.000006028342],"category_scores_gemma":[0.000092539296,0.00009948661,0.000034689274,0.00023242598,0.00006654207,0.000052149237,0.000030279161,0.000091851136,5.822301e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025064099,0.000007881343,0.000047835973,0.00020074098,0.000045933182,0.000001084909,0.00023637904,4.6838755e-7,0.5146158,0.00025897543,0.00012536546,0.48443452],"study_design_scores_gemma":[0.0002304493,0.000019850653,0.00021959457,0.00018858169,0.00012293053,0.000012193119,0.0017924515,0.023853922,0.96337175,0.0018248174,0.008233412,0.000130022],"about_ca_topic_score_codex":0.00011154534,"about_ca_topic_score_gemma":0.000010181187,"teacher_disagreement_score":0.4843045,"about_ca_system_score_codex":0.000045606856,"about_ca_system_score_gemma":0.000022906783,"threshold_uncertainty_score":0.40569463},"labels":[],"label_agreement":null},{"id":"W4409993904","doi":"10.1016/j.afres.2025.100941","title":"Authentication of rapeseed variety based on hyperspectral imaging and chemometrics","year":2025,"lang":"en","type":"article","venue":"Applied Food Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Ministry of Agriculture and Rural Affairs of the People's Republic of China; National Key Research and Development Program of China; Agriculture Research System of China; Chinese Academy of Agricultural Sciences; Ministry of Science and Technology of the People's Republic of China","keywords":"Hyperspectral imaging; Chemometrics; Rapeseed; Variety (cybernetics); Computer science; Authentication (law); Artificial intelligence; Chemistry; Machine learning; Food science; Computer security","score_opus":0.03276292684613892,"score_gpt":0.34720331121139203,"score_spread":0.3144403843652531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409993904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5740336,0.0010017987,0.002563151,0.0016015662,0.000028628168,0.00024304341,0.00001907218,0.00009917108,0.42040995],"genre_scores_gemma":[0.9984889,0.00003641189,0.0007191577,0.00006882578,0.0000286134,0.000034902798,0.000013155408,0.000012935123,0.0005971121],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985434,0.000017743783,0.00020434987,0.00036068825,0.0005239374,0.00034989344],"domain_scores_gemma":[0.9986555,0.0006554485,0.000048592752,0.00041114507,0.0001587173,0.000070626986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005573981,0.00011723262,0.00021031495,0.0009527053,0.00015645805,0.00004890233,0.00026864588,0.000099395365,0.00025412513],"category_scores_gemma":[0.00032787753,0.000111698886,0.00005548654,0.0031759557,0.00024152116,0.000025651074,0.00009157542,0.00043916987,0.000007008328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018042339,0.0003829459,0.0032481311,0.0003246771,0.00012794673,0.0000010298556,0.00012686636,0.000012108032,0.9480612,0.043562394,0.00045154375,0.003520723],"study_design_scores_gemma":[0.00074534083,0.000041040705,0.0011286635,0.000017816134,0.000054745233,3.6568173e-7,0.0008323951,0.0022610764,0.99214584,0.002343581,0.00032936942,0.00009978817],"about_ca_topic_score_codex":0.000015051744,"about_ca_topic_score_gemma":6.972849e-7,"teacher_disagreement_score":0.42445526,"about_ca_system_score_codex":0.00011506575,"about_ca_system_score_gemma":0.00011706618,"threshold_uncertainty_score":0.45549482},"labels":[],"label_agreement":null},{"id":"W4410132851","doi":"10.1016/j.fochx.2025.102528","title":"Construction and optimization of quantitative analysis models for pigments in broccoli (Brassica oleracea L. var. italica) based on near-infrared spectroscopy technology","year":2025,"lang":"en","type":"article","venue":"Food Chemistry X","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"National Natural Science Foundation of China","keywords":"Brassica oleracea; Brassica; Pigment; Spectroscopy; Chemistry; Botany; Materials science; Biology; Physics","score_opus":0.012296931693098104,"score_gpt":0.2795992637891064,"score_spread":0.2673023320960083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410132851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6148838,0.0004792386,0.35718322,0.00042647886,0.000032769924,0.00025873331,0.0003295292,0.00013835216,0.026267877],"genre_scores_gemma":[0.94104826,0.000028661172,0.05823522,0.000029163843,0.000010957254,0.00008711564,0.00015692868,0.000014854938,0.00038884283],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870455,0.000008370643,0.0004087942,0.00047189207,0.00015933889,0.0002470353],"domain_scores_gemma":[0.99906665,0.00015977124,0.00024406248,0.0003529957,0.00013013351,0.00004636519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063184474,0.0002187677,0.00050563423,0.00038692958,0.000090388094,0.000035885794,0.00017925716,0.0002869205,0.00019630694],"category_scores_gemma":[0.00017614943,0.00024212193,0.00013503178,0.0022863827,0.0002117063,0.00007928775,0.0000392652,0.00017936554,2.5823283e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010775654,0.0007288702,0.010642651,0.0010347032,0.0020087524,0.000001993765,0.00009139467,0.3749812,0.6068061,0.0021924742,0.00022138492,0.00021294554],"study_design_scores_gemma":[0.0009608248,0.000083877814,0.000021686947,0.00004843478,0.00048143347,4.3997701e-7,0.0003406231,0.32535863,0.67085254,0.0016891547,0.00002639055,0.0001359724],"about_ca_topic_score_codex":0.000008238546,"about_ca_topic_score_gemma":0.0000029866685,"teacher_disagreement_score":0.32616445,"about_ca_system_score_codex":0.000158592,"about_ca_system_score_gemma":0.00011299535,"threshold_uncertainty_score":0.98734456},"labels":[],"label_agreement":null},{"id":"W4410176203","doi":"10.21203/rs.3.rs-6465545/v1","title":"Application of Non-destructive and Chemical-free Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) coupled with Machine Learning Regression for Rapid Quantification of Deoxynivalenol (DON) in Individual Corn Kernels","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College; University of Prince Edward Island; University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Hyperspectral imaging; Infrared; Chemical imaging; Regression; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Computer science; Environmental science; Materials science; Optics; Mathematics; Statistics; Physics; Geology","score_opus":0.05206017702523209,"score_gpt":0.3721327210577388,"score_spread":0.32007254403250673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410176203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881431,0.0024764352,0.0062704002,0.000113920214,0.000014615874,0.0011121517,0.0006332633,0.00004903694,0.0011870721],"genre_scores_gemma":[0.9934647,0.0005224015,0.0041457065,0.0000022076365,0.000058914127,0.0003898239,0.001180051,0.000041624,0.00019453927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969632,0.00009356261,0.00066514785,0.00090709585,0.00088855054,0.00048241677],"domain_scores_gemma":[0.9968198,0.0008943282,0.00044734168,0.0008716101,0.0008591949,0.000107716914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090727914,0.0003484353,0.00079021096,0.00092805264,0.00012177329,0.000057459296,0.00061543036,0.0003906241,0.00006651174],"category_scores_gemma":[0.000986822,0.0003196884,0.00013892837,0.0010850332,0.00043086885,0.000102333564,0.0007086461,0.0015041032,2.6854158e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019101782,0.00049896224,0.15904835,0.0091275135,0.0004976278,0.0000076053593,0.0019200204,0.0004567149,0.81737936,0.0003510396,0.0000653504,0.008737303],"study_design_scores_gemma":[0.0019117338,0.00012871555,0.011761679,0.0017332842,0.00022906053,0.0000064120554,0.0036060812,0.07681874,0.9012932,0.0021069215,0.000016860382,0.0003873093],"about_ca_topic_score_codex":0.00047696708,"about_ca_topic_score_gemma":0.000022310702,"teacher_disagreement_score":0.14728667,"about_ca_system_score_codex":0.00032292225,"about_ca_system_score_gemma":0.00038745507,"threshold_uncertainty_score":0.9999255},"labels":[],"label_agreement":null},{"id":"W4410197753","doi":"10.1016/j.idairyj.2025.106291","title":"Application of multivariate adaptive regression splines (MARS) to study the colorization occurring in the process of lactulose production following lactose electro-activation","year":2025,"lang":"en","type":"article","venue":"International Dairy Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université Laval; Agriculture and Agri-Food Canada","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Lactulose; Multivariate adaptive regression splines; Lactose; Multivariate statistics; Mars Exploration Program; Process (computing); Regression; Bayesian multivariate linear regression; Regression analysis; Environmental science; Computer science; Statistics; Chemistry; Food science; Mathematics; Biology; Biochemistry","score_opus":0.017327770545241688,"score_gpt":0.355011610401524,"score_spread":0.3376838398562823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410197753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98070794,0.00004449291,0.017340206,0.00079455716,0.00014852674,0.00025026282,0.0000030019694,0.000009834673,0.000701147],"genre_scores_gemma":[0.9994384,0.000008031543,0.00018100231,0.000057093657,0.00014588535,0.000053982643,0.000009687068,0.0000067210294,0.000099183664],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987072,0.000064737884,0.000476656,0.00018244187,0.00046201324,0.00010698225],"domain_scores_gemma":[0.9988318,0.00018389375,0.0004530937,0.0001621603,0.00035281666,0.000016216973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868353,0.00011219938,0.0001605021,0.000303808,0.00013274219,0.000035159366,0.00042718666,0.000048098555,0.000023600456],"category_scores_gemma":[0.0006690203,0.000070256305,0.00007470491,0.0009254452,0.000020739384,0.00023527392,0.000040423998,0.00028051098,5.085029e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014212553,0.0019640403,0.16040888,0.00006367774,0.00081872626,0.0000041147846,0.0079944115,0.009028509,0.8013054,0.0012983179,0.00015590299,0.015536719],"study_design_scores_gemma":[0.001121339,0.00011697564,0.05093421,0.00024549043,0.00019657376,0.000011639678,0.011063829,0.0048487973,0.9290419,0.0020501788,0.0002320069,0.00013702323],"about_ca_topic_score_codex":0.00007624467,"about_ca_topic_score_gemma":0.000012981127,"teacher_disagreement_score":0.1277365,"about_ca_system_score_codex":0.00014229942,"about_ca_system_score_gemma":0.00006905569,"threshold_uncertainty_score":0.2864969},"labels":[],"label_agreement":null},{"id":"W4410203813","doi":"10.1016/j.foodres.2025.116561","title":"Fusion of near-infrared and Raman spectroscopy with machine learning strategies: Non-destructive rapid assessment of freshness and TVB-N value prediction in Pacific white shrimp (Litopenaeus vannamei)","year":2025,"lang":"en","type":"article","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Litopenaeus; Shrimp; Penaeidae; Food science; Fishery; Chemistry; Artificial intelligence; Biology; Computer science; Decapoda; Crustacean","score_opus":0.016388402662961436,"score_gpt":0.3474411194358266,"score_spread":0.3310527167728652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410203813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9469284,0.00038720862,0.0014421216,0.00016149202,0.000047045978,0.0001484872,0.00010422298,0.0000150061915,0.050766043],"genre_scores_gemma":[0.9970282,0.0002600411,0.0016872968,0.0000028464779,0.00003852799,0.000029542476,0.00008377023,0.000012284914,0.0008575201],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99827623,0.000084439314,0.00035072572,0.00036682235,0.00067482,0.00024697493],"domain_scores_gemma":[0.9990587,0.00021927831,0.00014340391,0.00017934227,0.00033543125,0.000063801686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053017813,0.00014740108,0.00028612016,0.00057531515,0.00012267455,0.00011920116,0.00023465462,0.000105157356,0.00046573475],"category_scores_gemma":[0.00014590418,0.00012885964,0.000035961737,0.0007096143,0.00035956927,0.00023387642,0.00019606248,0.00063721964,2.9340043e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010339731,0.0004094542,0.7400315,0.0005739983,0.0007327972,0.000012375918,0.0011319037,0.0009931853,0.24871999,0.0054700687,0.00015969852,0.00073109067],"study_design_scores_gemma":[0.006864189,0.0026478895,0.39383206,0.001043229,0.00013601045,0.00002488103,0.018651275,0.10852183,0.45888498,0.008548159,0.000435237,0.0004102498],"about_ca_topic_score_codex":0.00025645483,"about_ca_topic_score_gemma":0.000057676367,"teacher_disagreement_score":0.3461994,"about_ca_system_score_codex":0.0001761468,"about_ca_system_score_gemma":0.00033109786,"threshold_uncertainty_score":0.52547437},"labels":[],"label_agreement":null},{"id":"W4410217998","doi":"10.1016/j.aca.2025.344167","title":"Locally-weighted-RoBoost-PLS: a multivariate calibration approach to simultaneously cope with non-linearities and outliers","year":2025,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Chemistry; Outlier; Multivariate statistics; Calibration; Multivariate analysis; Statistics; Pattern recognition (psychology); Artificial intelligence; Mathematics; Computer science","score_opus":0.00950516635224138,"score_gpt":0.2535632086887039,"score_spread":0.24405804233646253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410217998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7173788,0.0001273679,0.023237282,0.003361467,0.000062631116,0.00064908294,0.00012278759,0.00048632082,0.25457427],"genre_scores_gemma":[0.98498416,0.00005023875,0.005140321,0.0009121486,0.00007162253,0.000029926035,0.00008464722,0.000034595134,0.008692327],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826044,0.000018657334,0.00036559545,0.0006539727,0.00026826252,0.0004330879],"domain_scores_gemma":[0.99880534,0.0002633759,0.000100542034,0.0004856657,0.0001282774,0.00021677878],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008903068,0.00034618852,0.0005167516,0.00032406813,0.00021145403,0.0002059153,0.00032353812,0.00019298948,0.00016381472],"category_scores_gemma":[0.00018945556,0.0002836518,0.000084040956,0.0010622614,0.00018265292,0.00015408422,0.00013202358,0.0003236709,0.00000978395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007491891,0.007743378,0.050376132,0.0049546445,0.017317642,0.00025125916,0.011231251,0.0028748305,0.8033522,0.023876125,0.06570192,0.004828731],"study_design_scores_gemma":[0.004762541,0.000366866,0.00094381423,0.00039226652,0.0034994609,0.00007923016,0.005945264,0.74591506,0.2275008,0.00090423535,0.007647565,0.0020428884],"about_ca_topic_score_codex":0.00015212661,"about_ca_topic_score_gemma":0.000031655036,"teacher_disagreement_score":0.74304026,"about_ca_system_score_codex":0.000106550375,"about_ca_system_score_gemma":0.00014692439,"threshold_uncertainty_score":0.99996156},"labels":[],"label_agreement":null},{"id":"W4410241975","doi":"10.18280/mmep.120431","title":"Performance Evaluation and Optimization of a Palm Kernel Cracker – A Taguchi-Grey Relational Analysis Approach","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Grey relational analysis; Taguchi methods; Palm; Kernel (algebra); Palm kernel; Computer science; Artificial intelligence; Mathematics; Statistics; Palm oil; Machine learning; Chemistry; Food science; Combinatorics","score_opus":0.025319886706297194,"score_gpt":0.2438229630686238,"score_spread":0.2185030763623266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410241975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24155359,0.00049022364,0.7554272,0.000015161753,0.000004160344,0.000053227046,0.0000018620242,0.000032767046,0.0024218059],"genre_scores_gemma":[0.8958201,0.000111253124,0.10375874,0.0000027287065,0.000006889369,0.00003056869,0.000023858494,0.000008242022,0.0002376446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919087,0.0000054398647,0.00028986737,0.00020254825,0.00018885043,0.00012245028],"domain_scores_gemma":[0.9995901,0.000094360956,0.00006258678,0.00013000457,0.00008472196,0.000038216946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027915466,0.00012014439,0.00026416284,0.00023679073,0.000051144314,0.000026712769,0.000047893143,0.000093815266,0.00006708047],"category_scores_gemma":[0.00007069458,0.00010888079,0.000050929088,0.0005882638,0.000030145064,0.00007386391,0.000024563835,0.00011063371,4.0079541e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044388507,0.00004751662,0.0012742544,0.0008227317,0.00028193096,3.4274308e-8,0.00016769649,0.99395776,0.00030411038,0.0030069305,0.000001387139,0.00013119953],"study_design_scores_gemma":[0.00019844825,0.000005869093,0.00011338265,0.00009042472,0.0009874484,0.0000012998759,0.000030524985,0.99654955,0.000654833,0.0012652614,0.0000029203252,0.00010001209],"about_ca_topic_score_codex":0.0000042482266,"about_ca_topic_score_gemma":6.0556225e-8,"teacher_disagreement_score":0.6542665,"about_ca_system_score_codex":0.000024661758,"about_ca_system_score_gemma":0.0000140196435,"threshold_uncertainty_score":0.444003},"labels":[],"label_agreement":null},{"id":"W4410345906","doi":"10.1016/j.foodres.2025.116646","title":"Miniaturized spectroscopy and AI-driven probes in food industry automation","year":2025,"lang":"en","type":"review","venue":"Food Research International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Automation; Food industry; Spectroscopy; Nanotechnology; Engineering; Biochemical engineering; Business; Computer science; Process engineering; Chemistry; Materials science; Food science; Mechanical engineering; Physics","score_opus":0.10146369043589601,"score_gpt":0.4668027930141658,"score_spread":0.36533910257826974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410345906","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083447475,0.9708252,0.000019748155,0.00048000386,0.00019557081,0.00042693267,0.00040503562,0.00008796462,0.026725102],"genre_scores_gemma":[0.0016219594,0.98997205,0.00075074995,0.000025494815,0.00052948704,0.00044356976,0.00051563623,0.00004299549,0.006098074],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968463,0.00015163569,0.0006920531,0.0007388776,0.0010159888,0.00055515586],"domain_scores_gemma":[0.9982983,0.0007258572,0.0002088865,0.0003765658,0.00025558923,0.00013483535],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050065696,0.0003800057,0.0009900137,0.0020843598,0.00011536819,0.00026287706,0.0009290286,0.0010438688,0.0016352976],"category_scores_gemma":[0.0011738604,0.00034280607,0.00024198722,0.0015336506,0.00017124835,0.0001696567,0.0004953514,0.0032246958,0.00003369666],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005070331,0.0046763914,0.0025418138,0.19635879,0.013907614,0.00031649185,0.0010055654,0.00001550407,0.0025278353,0.033229537,0.08160819,0.6633052],"study_design_scores_gemma":[0.0010992934,0.00027619308,0.00003381446,0.013989261,0.0002748416,0.000040582152,0.00012681245,0.000129745,0.005510567,0.0020655305,0.97582513,0.00062822347],"about_ca_topic_score_codex":0.000024045175,"about_ca_topic_score_gemma":0.000040726452,"teacher_disagreement_score":0.89421695,"about_ca_system_score_codex":0.00077599817,"about_ca_system_score_gemma":0.0009343308,"threshold_uncertainty_score":0.99990237},"labels":[],"label_agreement":null},{"id":"W4410461379","doi":"10.1016/j.jpba.2025.116959","title":"Addressing temperature variations of miniaturized NIR spectrometers: Advancing quantitative models for pharmaceutical analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Pharmaceutical and Biomedical Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Chemistry; Quantitative analysis (chemistry); Spectrometer; Chromatography; Analytical Chemistry (journal)","score_opus":0.06243865051937513,"score_gpt":0.4205207072933873,"score_spread":0.35808205677401217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410461379","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.511761,0.012393523,0.46662438,0.0068626306,0.00019308856,0.00018964887,0.00045860806,0.000049185895,0.0014679264],"genre_scores_gemma":[0.9678498,0.0009369662,0.030283572,0.00033774774,0.00015268152,0.000007072454,0.00005664844,0.000011050938,0.0003644886],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971285,0.00009032076,0.0013063769,0.00038670082,0.0006391645,0.00044895714],"domain_scores_gemma":[0.9967271,0.0015196506,0.0005880028,0.00018669727,0.0005299097,0.00044861648],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008614127,0.00028682232,0.0015376883,0.0025205032,0.00017575096,0.000077287325,0.00034376135,0.00025697442,0.001002465],"category_scores_gemma":[0.0006924487,0.00020984866,0.0014249294,0.007864273,0.00040060573,0.00024013165,0.0000887924,0.00061043043,3.8495963e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021136235,0.0021169072,0.0041063437,0.0010093176,0.111301295,0.00004878579,0.00044190697,0.0010939003,0.86722296,0.0046214713,0.0015529582,0.004370521],"study_design_scores_gemma":[0.006041052,0.00031144943,0.000709044,0.00019315742,0.23665565,0.000023091376,0.0016168207,0.43465617,0.30951735,0.006212152,0.003434056,0.0006300031],"about_ca_topic_score_codex":0.000011612585,"about_ca_topic_score_gemma":0.000004700051,"teacher_disagreement_score":0.55770564,"about_ca_system_score_codex":0.00011267424,"about_ca_system_score_gemma":0.00016975444,"threshold_uncertainty_score":0.9999108},"labels":[],"label_agreement":null},{"id":"W4410495179","doi":"10.3390/agriculture15101100","title":"Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms","year":2025,"lang":"en","type":"article","venue":"Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Education Department of Guangxi Zhuang Autonomous Region; Guangxi Normal University; National Natural Science Foundation of China; British Columbia Innovation Council","keywords":"Hyperspectral imaging; Yield (engineering); Algorithm; Machine learning; Artificial intelligence; Agronomy; Computer science; Environmental science; Remote sensing; Biology; Materials science; Geology","score_opus":0.00845778844168797,"score_gpt":0.22285839729429,"score_spread":0.21440060885260204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410495179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869845,0.0013044558,0.0033928938,0.00037388803,0.000056547327,0.00008137587,0.00003528322,0.0002043756,0.007566675],"genre_scores_gemma":[0.99594826,0.000016309756,0.0018871693,0.000057566136,0.00007817745,0.0000063790844,0.000081348066,0.000012365671,0.0019124403],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990115,0.000013753729,0.0002208094,0.00028652046,0.00022423634,0.00024317656],"domain_scores_gemma":[0.99940294,0.00007619973,0.0001508294,0.00016309854,0.00015395907,0.00005297632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005379238,0.00020732926,0.0002917391,0.00012908994,0.00015867528,0.00004215771,0.00015553723,0.00011788431,0.00021060175],"category_scores_gemma":[0.00006252295,0.00014062661,0.00011230831,0.0010413898,0.0000581916,0.00010240643,0.000025656233,0.0004112861,0.0000012888363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014680257,0.0001701612,0.0924727,0.0001308506,0.00024035168,0.000012960109,0.0001357022,0.00096555054,0.90505415,0.00008162707,0.00025209115,0.00033707294],"study_design_scores_gemma":[0.0014357299,0.000107025684,0.011444265,0.00021983402,0.00064012123,0.000012118097,0.00100059,0.011836159,0.9728678,0.000028430917,0.00018235865,0.00022559146],"about_ca_topic_score_codex":0.00042678183,"about_ca_topic_score_gemma":0.000010445738,"teacher_disagreement_score":0.08102844,"about_ca_system_score_codex":0.000091574206,"about_ca_system_score_gemma":0.000050869206,"threshold_uncertainty_score":0.5734587},"labels":[],"label_agreement":null},{"id":"W4410560763","doi":"10.18280/isi.300410","title":"Evaluation of Wavelet and Gray Level Co-Occurrence Matrix Combination Model for Texture Image Feature Extraction of Various Types of Meat","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Diponegoro","keywords":"Co-occurrence matrix; Pattern recognition (psychology); Gray level; Artificial intelligence; Texture (cosmology); Gray (unit); Wavelet; Feature extraction; Image texture; Computer science; Computer vision; Image (mathematics); Mathematics; Image processing; Medicine; Radiology","score_opus":0.028992853340787432,"score_gpt":0.33046024871889057,"score_spread":0.30146739537810313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410560763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5574273,0.0009862038,0.43013602,0.000048851656,0.0000859526,0.0005227495,0.0012331052,0.000045097277,0.009514745],"genre_scores_gemma":[0.9962315,0.000057883128,0.0029823466,0.000006907663,0.0000068173013,0.000029660188,0.0005291217,0.0000040617747,0.00015170386],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989087,0.000020884909,0.00049131026,0.00009717414,0.000375565,0.0001063427],"domain_scores_gemma":[0.9977832,0.00011511059,0.0006198672,0.00015815483,0.0013042786,0.00001940279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005319965,0.00012363077,0.00027366562,0.0003249016,0.00007259622,0.00002677378,0.00009823737,0.00017744307,0.0000329955],"category_scores_gemma":[0.000676926,0.00011879152,0.000068186106,0.00039329278,0.000106657775,0.00081183173,0.000018334984,0.00009374997,5.176404e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096959167,0.00068514183,0.009522502,0.024478912,0.0013941766,2.7265517e-7,0.014364221,0.014807599,0.7116363,0.024708314,0.006348939,0.19108403],"study_design_scores_gemma":[0.0011145016,0.000046558376,0.0022171244,0.00019111761,0.00065049226,0.0000036661197,0.0008721052,0.4807688,0.50547427,0.008486218,0.000048409256,0.000126722],"about_ca_topic_score_codex":0.000030171292,"about_ca_topic_score_gemma":0.0000062615204,"teacher_disagreement_score":0.4659612,"about_ca_system_score_codex":0.00014444735,"about_ca_system_score_gemma":0.00017082384,"threshold_uncertainty_score":0.48441777},"labels":[],"label_agreement":null},{"id":"W4410715560","doi":"10.3899/jrheum.2025-0390.pv027","title":"PREDICTION OF SLE FLARES BY MEASURING AUTOANTIBODY DYNAMICS: A NOVEL APPROACH FOR EARLY DETECTION AND MONITORING","year":2025,"lang":"en","type":"article","venue":"The Journal of Rheumatology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Autoantibody; Immunopathology; Internal medicine; Immunology; Antibody","score_opus":0.019043453149713153,"score_gpt":0.2550968120135357,"score_spread":0.23605335886382253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410715560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8168032,0.0011636529,0.18157366,0.00003759185,0.00011491178,0.000030255136,0.0000104116925,0.000009645552,0.00025663484],"genre_scores_gemma":[0.9985578,0.00041803275,0.0008706914,0.0000017659014,0.000010494755,0.0000027135259,0.00000141378,0.0000067880164,0.0001303034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993365,0.000016497634,0.0003524635,0.00007489781,0.000100576006,0.000119085125],"domain_scores_gemma":[0.99920934,0.00021191056,0.00033862292,0.00009666334,0.00012063456,0.000022843742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029441586,0.00008060666,0.00027336853,0.00014713185,0.00009663784,0.000011072758,0.00014707155,0.000097008175,0.0000033838935],"category_scores_gemma":[0.00017183168,0.000059822272,0.000065577966,0.00017672397,0.000064888714,0.00007179134,0.000026577214,0.00018363712,5.2824273e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034625852,0.00011914426,0.28509423,0.0004538821,0.0006835277,1.3514041e-7,0.00034398207,0.00014065992,0.71094954,0.00014426457,0.00007607055,0.0016482881],"study_design_scores_gemma":[0.0018621682,0.00016699998,0.005903064,0.00019073747,0.0007798429,0.000540471,0.0031868503,0.022333713,0.96438,0.0004897444,0.000052400806,0.00011400837],"about_ca_topic_score_codex":0.0001306981,"about_ca_topic_score_gemma":0.0000037382176,"teacher_disagreement_score":0.27919117,"about_ca_system_score_codex":0.00006150019,"about_ca_system_score_gemma":0.00002678503,"threshold_uncertainty_score":0.24394815},"labels":[],"label_agreement":null},{"id":"W4410736073","doi":"10.1016/j.tifs.2025.105108","title":"Modern smart sensing technologies in the fruit supply chain: contributions, challenges, and future prospects","year":2025,"lang":"en","type":"article","venue":"Trends in Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"National Natural Science Foundation of China","keywords":"Supply chain; Computer science; Business; Marketing","score_opus":0.018380183111957177,"score_gpt":0.29275430969390753,"score_spread":0.2743741265819504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410736073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80116504,0.07639739,0.00013546289,0.089788884,0.00012396838,0.00018272271,0.000022030667,0.0009461936,0.031238288],"genre_scores_gemma":[0.9976284,0.0019328289,0.00020101911,0.000051268657,0.000019025623,0.000035507874,0.0000027934636,0.000005699718,0.00012347387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998288,0.000012580495,0.00025861018,0.0006145767,0.00022654286,0.0005997219],"domain_scores_gemma":[0.99912524,0.00006899275,0.00007701802,0.0006511385,0.000058444253,0.00001916104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043375752,0.00019806644,0.00031626117,0.0031097254,0.00024010583,0.000050165774,0.00090122875,0.00041431808,0.000011028553],"category_scores_gemma":[0.00029261748,0.00015235749,0.00003416913,0.007871457,0.0014496818,0.000121009034,0.00030221447,0.00071942917,9.944171e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025640573,0.00023320287,0.01711456,0.00006678576,0.00004061633,0.000054027347,0.0010656983,0.000005507311,0.042219438,0.28234556,0.00008373622,0.65674525],"study_design_scores_gemma":[0.002137469,0.00031731025,0.012515163,0.00020422111,0.00008644804,0.00020405848,0.04682658,0.0018646838,0.6939032,0.23175944,0.009425049,0.00075639144],"about_ca_topic_score_codex":0.000012943464,"about_ca_topic_score_gemma":0.00039129233,"teacher_disagreement_score":0.6559888,"about_ca_system_score_codex":0.000231615,"about_ca_system_score_gemma":0.000087045635,"threshold_uncertainty_score":0.6212958},"labels":[],"label_agreement":null},{"id":"W4410835549","doi":"10.1016/j.jfca.2025.107839","title":"Advancements in rapid and non-destructive approaches for quality assessment of fried foods and frying oil","year":2025,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Edible oil; Food science; Food quality; Food composition data; Quality (philosophy); Quality assessment; Environmental science; Business; Chemistry; Marketing; Philosophy","score_opus":0.0408365186506058,"score_gpt":0.3522275288984915,"score_spread":0.31139101024788574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410835549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97950655,0.0014176128,0.017907495,0.00012991235,0.000010010859,0.00001919335,0.000018953278,0.0000020013263,0.0009882544],"genre_scores_gemma":[0.99431,0.000462975,0.0051490497,0.000023669352,0.000013905324,0.0000038539174,0.000009696387,0.0000027941298,0.000024060762],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999146,0.000027162736,0.00046785682,0.00014199392,0.0001283787,0.00008865161],"domain_scores_gemma":[0.99926406,0.0001318992,0.00039575272,0.00007389667,0.000088734974,0.000045653076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002599786,0.00009727915,0.0005056297,0.00048815514,0.00006637236,0.000031551062,0.000054866632,0.000052232586,0.000013950064],"category_scores_gemma":[0.000020064486,0.00008668314,0.00012969793,0.00058027735,0.000055753884,0.00010813543,0.000027920392,0.00011200535,4.563647e-9],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007894215,0.0009412961,0.49315393,0.0017698322,0.01384049,0.0000034398051,0.0007772777,0.00045750893,0.44341698,0.00398554,0.0000140255415,0.040850252],"study_design_scores_gemma":[0.0122881625,0.0010957364,0.39224344,0.00057345605,0.014956771,0.000021064649,0.012399521,0.023423336,0.5317616,0.010467357,0.00005866494,0.0007108872],"about_ca_topic_score_codex":0.000008582745,"about_ca_topic_score_gemma":0.0000063730618,"teacher_disagreement_score":0.10091051,"about_ca_system_score_codex":0.000044494464,"about_ca_system_score_gemma":0.000027189226,"threshold_uncertainty_score":0.3534836},"labels":[],"label_agreement":null},{"id":"W4411140005","doi":"10.3168/jds.2024-25970","title":"Predicting reticuloruminal pH and subacute ruminal acidosis of individual cows using machine learning and Fourier-transform infrared spectroscopy milk analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ste. Anne's Hospital; Agriculture and Agri-Food Canada; Université Laval","funders":"","keywords":"Acidosis; Fourier transform infrared spectroscopy; Chemistry; Fourier transform; Animal science; Food science; Internal medicine; Medicine; Mathematics; Biology; Physics","score_opus":0.0122908031773548,"score_gpt":0.29119867299305624,"score_spread":0.2789078698157014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411140005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99263924,0.0026997307,0.0027897318,0.00013909073,0.00004633955,0.000029797959,0.000021876394,0.000010347866,0.0016238199],"genre_scores_gemma":[0.9866832,0.00038764937,0.012591773,0.000024667423,0.00005312756,5.0144746e-7,0.0000024264834,0.000006513376,0.00025016547],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783653,0.000026063844,0.00067873026,0.00029522952,0.0008058323,0.0003576412],"domain_scores_gemma":[0.99861705,0.00018070736,0.000646849,0.0001499795,0.00022726922,0.00017813784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001144431,0.00019103383,0.00057012605,0.0012289996,0.0004072658,0.00015875752,0.00041940386,0.00008771144,0.00011445074],"category_scores_gemma":[0.0004589887,0.00016491745,0.00016548221,0.0033628081,0.00075733155,0.0005245456,0.00014934233,0.00051500427,9.722928e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012344519,0.000052324584,0.6162929,0.00010265027,0.000619043,0.000015490166,0.00062570645,0.00007939344,0.38118786,0.000022943272,0.000011896435,0.00086636096],"study_design_scores_gemma":[0.0010701924,0.00026282624,0.03415263,0.00019955353,0.0050591477,0.00019058866,0.003401342,0.02228917,0.93272907,0.00025707032,0.00014825937,0.00024016004],"about_ca_topic_score_codex":0.000044984998,"about_ca_topic_score_gemma":0.000012075906,"teacher_disagreement_score":0.58214027,"about_ca_system_score_codex":0.000106493564,"about_ca_system_score_gemma":0.0002877271,"threshold_uncertainty_score":0.67251384},"labels":[],"label_agreement":null},{"id":"W4411153878","doi":"10.1364/jocn.561437","title":"Supervised and unsupervised learning for classifying changes in optical time domain reflectometer traces","year":2025,"lang":"en","type":"article","venue":"Journal of Optical Communications and Networking","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Optical time-domain reflectometer; Computer science; Artificial intelligence; Pattern recognition (psychology); Domain (mathematical analysis); Unsupervised learning; Time domain; Supervised learning; Artificial neural network; Optical fiber; Computer vision; Telecommunications; Mathematics; Fiber optic sensor; Fiber optic splitter","score_opus":0.042711253210193895,"score_gpt":0.3342893213858931,"score_spread":0.2915780681756992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411153878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9505373,0.026559357,0.0041200686,0.0062320153,0.00003920811,0.00009774536,0.0000012806571,0.000023598543,0.01238938],"genre_scores_gemma":[0.97060525,0.0051529147,0.023853008,0.00009615769,0.0000934001,0.000009108644,0.0000037134107,0.000010554817,0.00017589591],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991194,0.00004618809,0.0003920642,0.00012608343,0.00009844727,0.00021781241],"domain_scores_gemma":[0.99801,0.0014971,0.00011456509,0.00021968306,0.00008118758,0.00007749793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055157876,0.000116815354,0.00035634372,0.0002631764,0.0002489017,0.00010517068,0.0002858403,0.00012186656,0.00003322289],"category_scores_gemma":[0.00012627347,0.00010188366,0.0000678831,0.00044710987,0.00016219525,0.00008879191,0.0001554729,0.0005311193,2.4108678e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011548116,0.0010370688,0.09384111,0.0008248775,0.001275705,0.000028307379,0.002170812,0.0001736347,0.49473998,0.012716705,0.00031678705,0.3917202],"study_design_scores_gemma":[0.030675873,0.0029543631,0.026841339,0.007857585,0.005215859,0.0006553579,0.023342514,0.5834445,0.08189339,0.036918648,0.19659102,0.003609582],"about_ca_topic_score_codex":0.0000018883381,"about_ca_topic_score_gemma":0.000011106718,"teacher_disagreement_score":0.58327085,"about_ca_system_score_codex":0.000052055668,"about_ca_system_score_gemma":0.000031773798,"threshold_uncertainty_score":0.41546953},"labels":[],"label_agreement":null},{"id":"W4411202435","doi":"10.1063/5.0280801","title":"Tomato ripeness detection method based on FasterNet block and attention mechanism","year":2025,"lang":"en","type":"article","venue":"AIP Advances","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Ripeness; Mechanism (biology); Block (permutation group theory); Chemistry; Computer science; Biochemical engineering; Mathematics; Food science; Engineering; Ripening; Philosophy; Epistemology","score_opus":0.006929780301879661,"score_gpt":0.29621878886259084,"score_spread":0.28928900856071116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411202435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73814595,0.001694238,0.24049678,0.00029390748,0.00022803141,0.00009140009,0.000010375199,0.00022053334,0.018818792],"genre_scores_gemma":[0.9952475,0.00006675399,0.0024065329,0.00021103735,0.00003199922,0.000024187106,0.000004392923,0.000007978022,0.0019996248],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929476,0.000018803988,0.00013723294,0.00028521178,0.00012345958,0.00014055343],"domain_scores_gemma":[0.9995613,0.0001401648,0.00006840379,0.00016815739,0.000029854475,0.00003209156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084452855,0.00012489031,0.00016379033,0.00013962007,0.00009732537,0.0000355042,0.00008746048,0.00007489965,0.00011539003],"category_scores_gemma":[0.0000409204,0.000115301926,0.000048589704,0.00034004866,0.000020050253,0.00010255432,0.000022786127,0.00009391139,0.0000071309387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000272252,0.0002954264,0.010230993,0.0007025497,0.0002089615,0.000015287224,0.00004793964,0.0010153879,0.8684404,0.0034521858,0.00009862197,0.11522002],"study_design_scores_gemma":[0.0004393913,0.00003870559,0.0004396131,0.00005115949,0.00011503902,0.000004867703,0.00020570421,0.015139667,0.9782051,0.003087982,0.0021338374,0.00013896593],"about_ca_topic_score_codex":0.000023958699,"about_ca_topic_score_gemma":0.000026996242,"teacher_disagreement_score":0.25710157,"about_ca_system_score_codex":0.000044664223,"about_ca_system_score_gemma":0.000012360768,"threshold_uncertainty_score":0.4701876},"labels":[],"label_agreement":null},{"id":"W4411329762","doi":"10.3390/agriengineering7060193","title":"Jasmine Flower Color Degradation User-Coded Computer Vision Image Analysis Tool and Kinetics Modeling","year":2025,"lang":"en","type":"article","venue":"AgriEngineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Agricultural Research Service; National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Degradation (telecommunications); Computer science; Image (mathematics); Computer vision; Artificial intelligence; Computer graphics (images); Telecommunications","score_opus":0.004558395875064083,"score_gpt":0.2373173424232166,"score_spread":0.23275894654815252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411329762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5575295,0.00020333633,0.4417428,0.000042620144,0.000031301744,0.000021442507,0.0000036152924,0.00007325945,0.00035212957],"genre_scores_gemma":[0.97684693,0.000059657577,0.022193518,0.00003339487,0.00005980505,0.0000072629045,0.000057114747,0.000012078478,0.0007302648],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914145,0.0000035138696,0.0002499142,0.00027660796,0.00012773549,0.00020078295],"domain_scores_gemma":[0.99957204,0.00006289345,0.000029984712,0.00022178738,0.000068155074,0.000045112985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005753851,0.00016504401,0.00026703265,0.00032848163,0.000053884483,0.00006838004,0.00011023887,0.00009259975,0.000104860614],"category_scores_gemma":[0.000042592794,0.000158226,0.00009216184,0.0011649859,0.000015709036,0.00013802038,0.00008080635,0.00012542719,0.0000025959305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027627251,0.00009072446,0.0019456552,0.0001780407,0.00092533237,0.000009206014,0.00006676507,0.40097904,0.5935959,0.00034464625,0.0002589653,0.0015780758],"study_design_scores_gemma":[0.00023348551,0.000011463228,0.00055890734,0.000020173835,0.00071763864,0.0000013397886,0.000033976405,0.88559324,0.112455234,0.00001211525,0.00020412111,0.00015828785],"about_ca_topic_score_codex":0.000028101646,"about_ca_topic_score_gemma":0.000012029848,"teacher_disagreement_score":0.4846142,"about_ca_system_score_codex":0.00006424888,"about_ca_system_score_gemma":0.000008244866,"threshold_uncertainty_score":0.6452269},"labels":[],"label_agreement":null},{"id":"W4411439615","doi":"10.1016/j.meatsci.2025.109893","title":"Non-destructive spectroscopy-based technologies for meat and meat product discrimination - A review","year":2025,"lang":"en","type":"review","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Gobierno de Navarra; Universidad Pública de Navarra","keywords":"Food science; Meat packing industry; Product (mathematics); Business; Environmental science; Chemistry; Mathematics","score_opus":0.0357146620051207,"score_gpt":0.36589821962684727,"score_spread":0.3301835576217266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411439615","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000137524985,0.9924671,0.00076695974,0.00029667278,0.000113388676,0.00107614,0.00008833613,0.00021770883,0.0049599526],"genre_scores_gemma":[0.0001071179,0.9933125,0.0043276567,0.00004731892,0.0000651092,0.00045398905,0.000064472195,0.000029229826,0.0015926332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966606,0.000018223776,0.0005916768,0.0015123581,0.0005928999,0.00062422844],"domain_scores_gemma":[0.9978499,0.00030459464,0.00044619988,0.001038911,0.00027563737,0.00008474883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004425867,0.00062181946,0.0018761972,0.00078308437,0.00046583143,0.00017545538,0.0012899757,0.00022348991,0.00005947613],"category_scores_gemma":[0.002466242,0.00046119833,0.00036854425,0.0038968604,0.001119004,0.00027021684,0.0002717049,0.0004095418,0.0000068454815],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012977183,0.00013558358,0.000055476376,0.39317054,0.00016645515,0.0000071105387,0.000037288115,3.7547107e-7,0.0053889737,0.0018853608,0.0019646976,0.5971752],"study_design_scores_gemma":[0.00049424934,0.00013817124,0.0000056878935,0.12671468,0.009702329,0.000060769777,0.00025863815,0.000050524617,0.45579994,0.0015809239,0.40339276,0.0018013167],"about_ca_topic_score_codex":0.000013957751,"about_ca_topic_score_gemma":0.000008031976,"teacher_disagreement_score":0.59537387,"about_ca_system_score_codex":0.00033496704,"about_ca_system_score_gemma":0.001046362,"threshold_uncertainty_score":0.999784},"labels":[],"label_agreement":null},{"id":"W4411540250","doi":"10.1080/87559129.2025.2520456","title":"Smart Quality Control in Logistics for Fresh Edible Nuts/Fruits with Rich Lipid: Research Progress, Challenges and Potential Applications ","year":2025,"lang":"en","type":"article","venue":"Food Reviews International","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Quality (philosophy); Biotechnology; Food science; Lipid oxidation; Business; Biology; Biochemistry","score_opus":0.11509301050613466,"score_gpt":0.42910002924781643,"score_spread":0.3140070187416818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411540250","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015582156,0.6608141,0.15002398,0.03486256,0.00074866926,0.005710483,0.0011233669,0.00025281202,0.13088188],"genre_scores_gemma":[0.9667715,0.024340563,0.003519257,0.00018749537,0.0005500015,0.0026074424,0.00013589453,0.000017406242,0.0018704569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985795,0.00005788543,0.00042783775,0.00038923824,0.000304447,0.00024108932],"domain_scores_gemma":[0.9988726,0.0003480337,0.00013895266,0.00026049942,0.00033113363,0.000048783837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008407029,0.00013779997,0.00034428458,0.00023522315,0.000100600424,0.00005643415,0.0003498414,0.00010545686,0.00014974287],"category_scores_gemma":[0.00040856685,0.00011164735,0.00006383746,0.00034691457,0.00017473285,0.00006126014,0.00007795824,0.00026482242,0.0000069486628],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016117574,0.0046881144,0.08086225,0.011240592,0.003159057,0.0000112654,0.00061337615,0.000050178303,0.00954299,0.5652315,0.052074518,0.2709144],"study_design_scores_gemma":[0.005102975,0.00032529572,0.0056360066,0.00083164027,0.00035111507,0.000012158796,0.000769969,0.00070526014,0.010170286,0.021320399,0.95419925,0.000575623],"about_ca_topic_score_codex":0.000011306354,"about_ca_topic_score_gemma":0.00014462814,"teacher_disagreement_score":0.95118934,"about_ca_system_score_codex":0.00010714577,"about_ca_system_score_gemma":0.00008198339,"threshold_uncertainty_score":0.45528468},"labels":[],"label_agreement":null},{"id":"W4411570645","doi":"10.3390/rs17132127","title":"Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Canola; Computer science; Algorithm; Estimation; Yield (engineering); Remote sensing; Tree (set theory); Mathematics; Geography; Engineering; Agronomy","score_opus":0.02472607940727438,"score_gpt":0.2931665726320719,"score_spread":0.2684404932247975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411570645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28278127,0.0006646524,0.6979142,0.00021814578,0.000048009813,0.000044326258,0.0000065429294,0.00014493856,0.018177915],"genre_scores_gemma":[0.54387236,0.000076760225,0.45301893,0.00016764634,0.000057505036,6.344844e-9,0.000009196537,0.000025069461,0.0027725478],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998873,0.000012790989,0.0002669443,0.00037443065,0.0001729868,0.00029982606],"domain_scores_gemma":[0.999285,0.00014982969,0.00010568975,0.00030353505,0.0000860342,0.000069905385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086047854,0.00021513992,0.0003042277,0.00023450515,0.00022564534,0.00010039573,0.00006781847,0.00017340621,0.000016988732],"category_scores_gemma":[0.00025967901,0.00022810703,0.00007677815,0.0005006078,0.000075339645,0.0001088104,0.000065322056,0.00023687731,0.0000021223889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012184246,0.000008064675,0.000008875391,0.000084263294,0.00006790584,0.000025370837,0.00007127092,0.003975476,0.61968666,0.000008420074,0.0001551822,0.3758963],"study_design_scores_gemma":[0.00016267606,0.0000025156162,0.000009209551,0.00011475767,0.0001680978,0.00004442939,0.00010413246,0.64539784,0.35269472,0.0011482716,0.000016248665,0.00013707421],"about_ca_topic_score_codex":0.0006457681,"about_ca_topic_score_gemma":0.000024498926,"teacher_disagreement_score":0.6414224,"about_ca_system_score_codex":0.00016978684,"about_ca_system_score_gemma":0.00010201555,"threshold_uncertainty_score":0.9301935},"labels":[],"label_agreement":null},{"id":"W4411597529","doi":"10.1016/j.foodcont.2025.111516","title":"Corrigendum to “A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis” [Food Control, 167, January (2025), 110823]","year":2025,"lang":"en","type":"erratum","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","score_opus":0.03030420742276725,"score_gpt":0.3031157174752228,"score_spread":0.2728115100524556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411597529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011511053,0.0053894506,0.94250053,0.0009824885,0.0069343485,0.0022217152,0.02499613,0.0006821097,0.015142121],"genre_scores_gemma":[0.9101132,0.00004956342,0.002714277,0.001961808,0.0017694567,0.0015269833,0.003346731,0.00012816985,0.07838982],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933046,0.00015717298,0.0016049175,0.0021897317,0.0011448436,0.0015987532],"domain_scores_gemma":[0.9953835,0.0005721829,0.0011375892,0.0014158705,0.00095488696,0.0005359822],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00045851513,0.0015052254,0.0031319503,0.0013884236,0.0006018215,0.00033920503,0.0013380481,0.0017561862,0.00074563816],"category_scores_gemma":[0.0010475412,0.0015632856,0.0025749127,0.0033106836,0.00015413392,0.00023081881,0.0001018225,0.001506483,0.000067159184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013097005,0.001873765,0.0012355042,0.0016652379,0.052947246,0.00003606632,0.0004088536,0.094521604,0.010988074,0.0039396114,0.81797916,0.0013078433],"study_design_scores_gemma":[0.009913501,0.0017775634,0.00028433802,0.00015782048,0.022019468,0.0000021222256,0.000381214,0.9410846,0.0045782584,0.0013282732,0.016648341,0.0018244807],"about_ca_topic_score_codex":0.00037427532,"about_ca_topic_score_gemma":0.005865106,"teacher_disagreement_score":0.93978626,"about_ca_system_score_codex":0.0017639552,"about_ca_system_score_gemma":0.0014033059,"threshold_uncertainty_score":0.9997697},"labels":[],"label_agreement":null},{"id":"W4411657331","doi":"10.51847/bdjpzq9","title":"10.51847/bDJPzq9","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artemether; Curcumin; High-performance liquid chromatography; Chromatography; Chemistry; Pharmacology; Medicine; Biochemistry; Artemisinin; Immunology; Malaria; Plasmodium falciparum","score_opus":0.006855489364939866,"score_gpt":0.20706594753637306,"score_spread":0.20021045817143318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411657331","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068947724,0.0001510893,6.3838587e-7,0.0001277996,6.27534e-7,0.000020987398,0.000011529705,0.0001544749,0.99263805],"genre_scores_gemma":[0.003268036,9.672457e-7,0.00009598244,0.000064966654,0.00014154695,0.000008799992,0.000016769462,0.000019491788,0.9963834],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992494,0.000004357891,0.00013392998,0.00021420435,0.00014980302,0.00024833475],"domain_scores_gemma":[0.99949,0.000037274287,0.000020910913,0.00029973465,0.000022635857,0.00012940071],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003710878,0.00012382772,0.00016525647,0.00006569715,0.0000610818,0.00003098737,0.00022080305,0.000073120544,0.9995542],"category_scores_gemma":[0.000032251435,0.000120977325,0.00007294584,0.00039718454,0.000023379567,0.000047076275,0.00002404361,0.00009425609,0.97864604],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009943047,0.00015096008,0.000004848757,0.00002352943,0.00011354987,0.000014956232,0.000018270162,0.00004202102,0.013477118,0.000002431438,0.09540005,0.89065284],"study_design_scores_gemma":[0.00014545719,0.000021913509,0.000009734798,0.0000048055654,0.000056519253,0.000005738875,0.000001993758,0.000046557117,0.020918367,0.0000030503,0.97862923,0.00015665541],"about_ca_topic_score_codex":0.00002105501,"about_ca_topic_score_gemma":5.1817562e-8,"teacher_disagreement_score":0.8904962,"about_ca_system_score_codex":0.00004373613,"about_ca_system_score_gemma":0.00001588274,"threshold_uncertainty_score":0.49333122},"labels":[],"label_agreement":null},{"id":"W4411658338","doi":"10.51847/eesxg7t","title":"10.51847/EEsxg7T","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"All India Council for Technical Education","keywords":"Fourier transform infrared spectroscopy; Fourier transform; Infrared; Fourier transform spectroscopy; Infrared spectroscopy; Spectroscopy; Analytical Chemistry (journal); Traditional medicine; Materials science; Environmental science; Mathematics; Chemistry; Physics; Optics; Environmental chemistry; Medicine; Mathematical analysis; Organic chemistry; Astronomy","score_opus":0.006781459113169984,"score_gpt":0.20703849502917918,"score_spread":0.20025703591600919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411658338","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066056335,0.0001443618,6.582042e-7,0.0001309812,5.9950173e-7,0.000020806288,0.0000117504405,0.00015445537,0.99293077],"genre_scores_gemma":[0.0029546984,9.538153e-7,0.00009748351,0.00006587189,0.00014372247,0.000008652105,0.000017093203,0.000019455636,0.99669206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924594,0.000004340751,0.00013399523,0.00021434961,0.00015072073,0.00025067638],"domain_scores_gemma":[0.9994898,0.00003629971,0.00002092189,0.00030022094,0.000022318865,0.00013047435],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003734086,0.00012386615,0.00016535234,0.000065656444,0.000060396458,0.000030960055,0.00022131624,0.00007319257,0.9995709],"category_scores_gemma":[0.000031844043,0.00012101098,0.00007317019,0.00039857687,0.000022865983,0.0000467869,0.000024414321,0.00009422713,0.9782542],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009577863,0.00014111969,0.000003998177,0.000021506563,0.000107659216,0.000013830352,0.000018043318,0.000040689945,0.01206109,0.0000024462136,0.09059309,0.8969007],"study_design_scores_gemma":[0.00014250692,0.00002341464,0.000008704838,0.000004802182,0.000056528024,0.000005717826,0.000002213009,0.00004473914,0.018522572,0.0000031567274,0.9810287,0.00015695827],"about_ca_topic_score_codex":0.000020387835,"about_ca_topic_score_gemma":5.2401465e-8,"teacher_disagreement_score":0.8967438,"about_ca_system_score_codex":0.00004284274,"about_ca_system_score_gemma":0.000016159027,"threshold_uncertainty_score":0.49346843},"labels":[],"label_agreement":null},{"id":"W4411658706","doi":"10.51847/lfrftwx","title":"10.51847/LFrfTWX","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Catharanthus roseus; In silico; Annotation; Computational biology; Biology; Expressed sequence tag; Botany; Bioinformatics; Genetics; Genome; Gene","score_opus":0.006840839556941677,"score_gpt":0.20685349331209824,"score_spread":0.20001265375515656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411658706","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064998385,0.0001464992,7.0089993e-7,0.00012851313,6.222513e-7,0.000020687196,0.000011811281,0.00015782971,0.99303347],"genre_scores_gemma":[0.0030377419,9.595925e-7,0.000097091,0.00006826603,0.00014428944,0.000008710159,0.000017530547,0.000019487614,0.99660593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924845,0.000004425879,0.00013395688,0.00021389629,0.00015067487,0.00024863076],"domain_scores_gemma":[0.99948764,0.00003875027,0.000020915859,0.00030084563,0.00002234633,0.00012953086],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000037945898,0.00012381897,0.00016483408,0.00006562842,0.000060436454,0.000031170064,0.00022127878,0.0000732369,0.99956065],"category_scores_gemma":[0.000033704502,0.00012099282,0.00007298144,0.0003982142,0.000023292594,0.000047010766,0.000024453973,0.000094606345,0.9784725],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098077726,0.0001513681,0.0000050693766,0.00002377366,0.00011815083,0.000015386866,0.000018346911,0.000040751795,0.013089226,0.000002750512,0.10599925,0.88043785],"study_design_scores_gemma":[0.00014151432,0.000021687154,0.000008836689,0.0000047863487,0.000056561534,0.0000058780256,0.0000019149086,0.0000391308,0.019229088,0.0000035240366,0.98032373,0.00016331888],"about_ca_topic_score_codex":0.000020932233,"about_ca_topic_score_gemma":5.0393325e-8,"teacher_disagreement_score":0.88027453,"about_ca_system_score_codex":0.000042929132,"about_ca_system_score_gemma":0.000016312311,"threshold_uncertainty_score":0.4933944},"labels":[],"label_agreement":null},{"id":"W4411848285","doi":"10.1016/j.rechem.2025.102419","title":"Raman spectroscopy for determination of compositions in liquid–liquid dispersions","year":2025,"lang":"en","type":"article","venue":"Results in Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research, Innovation and Science; Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen","keywords":"Raman spectroscopy; Materials science; Spectroscopy; Analytical Chemistry (journal); Nanotechnology; Chromatography; Optics; Chemistry; Physics","score_opus":0.011904620343082406,"score_gpt":0.3192163063295424,"score_spread":0.30731168598645997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411848285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9230806,0.00031506224,0.001747701,0.00057661056,0.000036030713,0.000116246905,0.0002537663,0.000042552158,0.07383138],"genre_scores_gemma":[0.9953823,0.000085898566,0.0025441274,0.000028679737,0.000049385915,0.000060328304,0.00023156809,0.000012281355,0.0016054207],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986209,0.000008240382,0.00058402645,0.0003646453,0.00013506967,0.00028710993],"domain_scores_gemma":[0.999005,0.0003568354,0.00015138724,0.00037328582,0.0000675932,0.00004589317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014926287,0.00016814053,0.00033266883,0.00018222973,0.00005776177,0.000014249314,0.0002877936,0.00021874772,0.0001594763],"category_scores_gemma":[0.00042898397,0.00018577497,0.00012552441,0.00079333835,0.00009988235,0.000058373327,0.00006332749,0.00024167167,0.0000012876634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009812362,0.00033482964,0.00037626197,0.00044339674,0.000020634763,0.0000037951813,0.000094914634,0.000031881988,0.9972179,0.00013840085,0.00026736624,0.000089414476],"study_design_scores_gemma":[0.0017759176,0.0000398037,0.00009137908,0.00024854302,0.000055189572,0.0000017508677,0.00042665168,0.0005803186,0.9959368,0.0002469132,0.0004434649,0.00015325366],"about_ca_topic_score_codex":0.00005205007,"about_ca_topic_score_gemma":0.000032121145,"teacher_disagreement_score":0.072301656,"about_ca_system_score_codex":0.00030277664,"about_ca_system_score_gemma":0.00010526871,"threshold_uncertainty_score":0.75756836},"labels":[],"label_agreement":null},{"id":"W4412305106","doi":"","title":"Prediction of the variance of stereological volume estimates in systematic sampling with errors in sampling locations","year":2016,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Statistics; Sampling (signal processing); Systematic sampling; Variance (accounting); Mathematics; Volume (thermodynamics); Stereology; Systematic error; Sampling design; Computer science; Biology; Demography; Physics","score_opus":0.049828627540024276,"score_gpt":0.2824010131925008,"score_spread":0.23257238565247657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412305106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9255071,0.0001227758,0.07310733,0.00010438012,0.000011893413,0.000096697935,0.000010272441,0.000019570285,0.0010199562],"genre_scores_gemma":[0.9956748,0.0000076155793,0.0039465744,0.000005192297,0.0000064158503,0.000022166952,0.0000012435145,0.0000051233797,0.00033086137],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991867,0.000014971783,0.00040625854,0.00014000751,0.00013349864,0.00011859236],"domain_scores_gemma":[0.9991703,0.00034101587,0.0001871252,0.00024019807,0.000045705645,0.000015632271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015106541,0.00008002508,0.0002719626,0.000090920614,0.00001999916,0.0000041742296,0.00016393176,0.000064841886,0.00024205029],"category_scores_gemma":[0.00038917907,0.000038120997,0.000033929085,0.0005107253,0.000091487804,0.00006069588,0.000031078187,0.000068400026,0.0000013827228],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019601695,0.00011839949,0.8921068,0.0026884512,0.00004238678,2.8428474e-7,0.00013906545,0.0012499179,0.10285488,0.00075737294,0.0000026064722,0.000020249716],"study_design_scores_gemma":[0.0018055965,0.00010673377,0.48353574,0.01464837,0.0003744013,0.000022385973,0.0036871266,0.0125083355,0.48039842,0.0025923522,0.000003400683,0.00031714232],"about_ca_topic_score_codex":0.00013860865,"about_ca_topic_score_gemma":0.00008779775,"teacher_disagreement_score":0.40857103,"about_ca_system_score_codex":0.00006943824,"about_ca_system_score_gemma":0.000031886786,"threshold_uncertainty_score":0.2650281},"labels":[],"label_agreement":null},{"id":"W4412507965","doi":"10.1002/fsn3.70643","title":"Exploring the Frontiers of Food Science: A Comprehensive Review of Advanced Magnetic Resonance Applications in Food Analysis, Quality Analysis, and Safety Assessment","year":2025,"lang":"en","type":"review","venue":"Food Science & Nutrition","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Food safety; Quality (philosophy); Food quality; Quality assessment; Engineering ethics; Engineering; Medicine; Engineering management; Physics; Pathology","score_opus":0.06824606444318054,"score_gpt":0.38777682283187687,"score_spread":0.3195307583886963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412507965","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016750286,0.9955494,0.0010177891,0.000039157923,0.000032904558,0.0008676073,0.00046628533,0.00001778941,0.00033406663],"genre_scores_gemma":[0.023968585,0.9733157,0.0017309367,0.000016073078,0.00001721773,0.00080252416,0.00013118346,0.000007850007,0.000009910071],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956019,0.0001409784,0.0015496017,0.0010739332,0.001198414,0.000435204],"domain_scores_gemma":[0.9964479,0.0004977868,0.0011231087,0.0011791998,0.00063957466,0.00011241886],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0015342246,0.00037087064,0.0026516232,0.0026599462,0.00037729886,0.000060089307,0.0013403055,0.0000940493,0.000021778367],"category_scores_gemma":[0.0003753472,0.0002908206,0.0007586537,0.044707157,0.0015635556,0.00038172354,0.000278422,0.0003928611,1.7336649e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000511431,0.0015786589,0.001964054,0.35816884,0.0031136288,7.588295e-7,0.00027126787,0.000043523018,0.001507795,0.0062708203,0.000043847922,0.62698567],"study_design_scores_gemma":[0.0047297715,0.0036327175,0.040706076,0.2502062,0.16313434,0.000014417425,0.014686271,0.00042207004,0.039012082,0.0032227028,0.47472927,0.00550408],"about_ca_topic_score_codex":0.000025690144,"about_ca_topic_score_gemma":0.000054020526,"teacher_disagreement_score":0.6214816,"about_ca_system_score_codex":0.00049689563,"about_ca_system_score_gemma":0.0009218182,"threshold_uncertainty_score":0.9999544},"labels":[],"label_agreement":null},{"id":"W4412589521","doi":"10.1038/s41592-025-02760-w","title":"Symmetric alternatives to the ordinary least squares regression","year":2025,"lang":"en","type":"article","venue":"Nature Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Ordinary least squares; Regression; Least-squares function approximation; Regression analysis; Statistics; Mathematics; Computational biology; Computer science; Biology","score_opus":0.015495228796666375,"score_gpt":0.43926706288117334,"score_spread":0.42377183408450697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412589521","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07207869,0.1009338,0.1409909,0.01873825,0.002008009,0.00033195608,0.000044661545,0.0004888273,0.6643849],"genre_scores_gemma":[0.77695996,0.0002933286,0.15538539,0.0028219058,0.0006400864,0.00005977529,0.000015142336,0.000034816883,0.06378959],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867463,0.0001594711,0.00021350567,0.00040752045,0.00026451246,0.0002803385],"domain_scores_gemma":[0.9980676,0.0010633967,0.00008858301,0.00059449195,0.00011136162,0.0000745329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006068241,0.00021568827,0.0003021287,0.0005036378,0.00023103064,0.000064492444,0.0006999759,0.00033767728,0.0006642496],"category_scores_gemma":[0.002410583,0.00012683314,0.0001597795,0.0034844838,0.000052475978,0.00005883088,0.00023398227,0.0010289277,0.000019978663],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027728322,0.0002796088,0.006555481,0.00028902874,0.00072713563,0.00002885444,0.00040569203,0.000055736815,0.22100236,0.02080566,0.11174954,0.63782364],"study_design_scores_gemma":[0.00021498663,0.000022606335,0.0018715884,0.000078708625,0.0001699517,0.0000057264206,0.00045876874,0.00015694318,0.73519236,0.0024408917,0.2592001,0.00018738497],"about_ca_topic_score_codex":0.000029334798,"about_ca_topic_score_gemma":0.0000034730815,"teacher_disagreement_score":0.70488125,"about_ca_system_score_codex":0.00010681008,"about_ca_system_score_gemma":0.000047524078,"threshold_uncertainty_score":0.7273067},"labels":[],"label_agreement":null},{"id":"W4412627723","doi":"10.1016/j.infrared.2025.106034","title":"Determination of glyphosate residues in lentils using near-infrared hyperspectral imaging coupled with chemometric regression techniques","year":2025,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College; University of Prince Edward Island; University of Lethbridge; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Hyperspectral imaging; Chemical imaging; Remote sensing; Near-infrared spectroscopy; Infrared; Environmental science; Optics; Geology; Physics","score_opus":0.010396849755290585,"score_gpt":0.2882068151434972,"score_spread":0.2778099653882066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412627723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97512275,0.0013099414,0.015014238,0.00014655919,0.000029870418,0.00016991554,0.000011095536,0.00045603738,0.0077396063],"genre_scores_gemma":[0.9720243,0.00007076215,0.027388768,0.000027026475,0.000026478498,0.000038033846,0.00001921429,0.000033091826,0.00037228764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983372,0.000014690883,0.0004875815,0.00048361806,0.00024829688,0.00042860268],"domain_scores_gemma":[0.99862033,0.00009838316,0.00038140157,0.0006362506,0.0002319689,0.000031672935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010444818,0.00030572584,0.00059569464,0.0014357044,0.000118929966,0.00004403919,0.00047297898,0.00030967675,0.00005064348],"category_scores_gemma":[0.00016453845,0.00028803034,0.00009502077,0.0062392573,0.00040397973,0.00024355992,0.00016640662,0.00048177803,0.0000011645903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001381618,0.0002546343,0.06735278,0.00025305405,0.00008326227,0.000034880584,0.00013633615,0.000021824757,0.9127336,0.0009531219,0.000041512754,0.017996801],"study_design_scores_gemma":[0.00069308607,0.000038033533,0.00086912,0.0003766989,0.00013952545,0.00001118028,0.0005259948,0.00885517,0.96973556,0.018438157,0.00005140361,0.0002660713],"about_ca_topic_score_codex":0.00018706365,"about_ca_topic_score_gemma":0.00000714573,"teacher_disagreement_score":0.066483654,"about_ca_system_score_codex":0.0003595995,"about_ca_system_score_gemma":0.0002015013,"threshold_uncertainty_score":0.9999572},"labels":[],"label_agreement":null},{"id":"W4412776016","doi":"10.1016/j.measurement.2025.118620","title":"Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; McGill University","keywords":"Hyperspectral imaging; Contamination; Fermentation; Liquid culture; Artificial intelligence; Computer science; Microbiology; Biology; Food science; Ecology; Botany","score_opus":0.02023781586701609,"score_gpt":0.2737008303831277,"score_spread":0.25346301451611164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412776016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903562,0.0005702329,0.008345693,0.0002849182,0.000038183567,0.00014135703,0.0000031823292,0.000023129605,0.00023708174],"genre_scores_gemma":[0.99796474,0.00001040249,0.0017877542,0.00007004249,0.000024821751,0.00003768138,0.00000845491,0.000008256829,0.00008785325],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925125,0.000018776636,0.00024922675,0.00020707064,0.0001258214,0.0001478368],"domain_scores_gemma":[0.99968255,0.000018580986,0.00008968769,0.000058342153,0.00012286346,0.000027951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027352947,0.00011148824,0.00016039136,0.0002319818,0.00008730806,0.00003924161,0.000057590638,0.00003544881,0.000009055188],"category_scores_gemma":[0.00021718309,0.000113215065,0.000038218925,0.00026317977,0.00002523251,0.000070500086,0.00002317444,0.00011519568,2.9310962e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113131144,0.000025730202,0.023544801,0.00014244157,0.00003291932,2.4301113e-7,0.0010144841,0.00003774183,0.97294813,0.00001749548,0.000006429524,0.0021164285],"study_design_scores_gemma":[0.00065529515,0.000053927764,0.007506473,0.00016884097,0.00006659598,9.872258e-7,0.0013148115,0.0003845864,0.9896616,0.000048712223,0.000042693548,0.00009548041],"about_ca_topic_score_codex":0.0004640634,"about_ca_topic_score_gemma":0.00030277902,"teacher_disagreement_score":0.016713442,"about_ca_system_score_codex":0.0003542461,"about_ca_system_score_gemma":0.000022271615,"threshold_uncertainty_score":0.46167764},"labels":[],"label_agreement":null},{"id":"W4412806427","doi":"10.1002/app.57698","title":"Method for Rapid Determination of Hexeneuronic Acid in Non‐Wood Pulp by Multivariate Analysis of <scp>FT</scp>‐<scp>NIR</scp> Spectroscopic Data","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Bangladesh Council of Scientific and Industrial Research","keywords":"Pulp (tooth); Multivariate analysis; Chemistry; Composite material; Pulp and paper industry; Materials science; Chromatography; Mathematics; Engineering; Statistics; Dentistry; Medicine","score_opus":0.016571109909340652,"score_gpt":0.33120004112329227,"score_spread":0.31462893121395163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412806427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8706661,0.0042406293,0.116219394,0.000083194114,0.00015231795,0.0001961715,0.00017634498,0.000016069971,0.008249814],"genre_scores_gemma":[0.9812518,0.00017051784,0.01785639,0.00007966341,0.000047241865,0.000009934677,0.000024286594,0.000015126004,0.0005450766],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968626,0.0000305816,0.0012016052,0.00059189927,0.00077502814,0.0005383035],"domain_scores_gemma":[0.9960553,0.0012441447,0.0014834115,0.0008366897,0.00023903167,0.00014139271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016153653,0.00026565217,0.0010005494,0.0020430507,0.00012319494,0.00007357887,0.0021183456,0.00014557302,0.000042337426],"category_scores_gemma":[0.00063894736,0.0002410041,0.0002547956,0.0067568556,0.00031162114,0.0004225032,0.00030876964,0.00035855308,8.088037e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021548374,0.00025900552,0.0009076866,0.00010372096,0.0003518913,0.0000018119215,0.0003131316,0.00004302075,0.99422973,0.00020129343,0.00025012286,0.0033170306],"study_design_scores_gemma":[0.001002273,0.00008138334,0.000901913,0.000040503215,0.0015756681,0.0000029072892,0.0009065744,0.015114034,0.97991437,0.0002208985,0.00018536496,0.000054105123],"about_ca_topic_score_codex":0.000073459676,"about_ca_topic_score_gemma":0.00001255591,"teacher_disagreement_score":0.1105857,"about_ca_system_score_codex":0.0001692062,"about_ca_system_score_gemma":0.0005208184,"threshold_uncertainty_score":0.98278624},"labels":[],"label_agreement":null},{"id":"W4412981307","doi":"10.1016/j.foodcont.2025.111619","title":"Effect of machine learning techniques to detect Listeria monocytogenes in Queso fresco using shortwave-infrared imaging","year":2025,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund","keywords":"Listeria monocytogenes; Fresco; Shortwave; Listeria; Food science; Computer science; Microbiology; Chemistry; Biology; Bacteria; Physics; Optics; Art; Visual arts","score_opus":0.009416053892653695,"score_gpt":0.3000518927493352,"score_spread":0.2906358388566815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412981307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97837305,0.004571385,0.012187679,0.00005459106,0.000042461448,0.0002520752,0.000031951156,0.00018695416,0.004299863],"genre_scores_gemma":[0.9981886,0.000016900929,0.0013121266,0.00008566533,0.00005095519,0.000057080997,0.0000070070364,0.00002480246,0.00025683825],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871755,0.00008602618,0.0004046848,0.0003305731,0.0001433726,0.00031779823],"domain_scores_gemma":[0.9991789,0.00029083525,0.000121955345,0.00030092098,0.000050888826,0.000056511242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003026023,0.00024166025,0.0005991654,0.00049126695,0.00006862107,0.000045278946,0.00026001793,0.00010395584,0.00012653958],"category_scores_gemma":[0.0004140748,0.00023043626,0.00015005804,0.00070882513,0.0000336951,0.000069878835,0.000093103554,0.00024269485,0.0000014703488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043766538,0.000019992825,0.090837926,0.00020370871,0.0001447223,0.000009275898,0.000039723665,0.00011280759,0.895529,0.000011469752,0.000013519006,0.01264021],"study_design_scores_gemma":[0.0010089859,0.0002229782,0.00028266397,0.00015979348,0.0002225965,0.0000032673627,0.00003558593,0.004551668,0.9928651,0.00009719238,0.0003654655,0.00018471862],"about_ca_topic_score_codex":0.00042822133,"about_ca_topic_score_gemma":0.000071639006,"teacher_disagreement_score":0.097336106,"about_ca_system_score_codex":0.00017461166,"about_ca_system_score_gemma":0.00005026683,"threshold_uncertainty_score":0.9396918},"labels":[],"label_agreement":null},{"id":"W4413003132","doi":"10.1021/acsfoodscitech.5c00268","title":"Automatic NMR Spectral Profiling of Commercial Cow’s Milk","year":2025,"lang":"en","type":"article","venue":"ACS Food Science & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"Alberta Innovates; Canada Foundation for Innovation; National Center for Complementary and Integrative Health; Genome Canada","keywords":"Profiling (computer programming); Chemistry; Computer science; Operating system","score_opus":0.01492553742292313,"score_gpt":0.2967459914382398,"score_spread":0.2818204540153167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413003132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796657,0.00047656766,0.000473544,0.0010161281,0.00006785785,0.000062306266,0.0000061529054,0.00029611023,0.017935637],"genre_scores_gemma":[0.997862,0.000015363179,0.0018127728,0.00006407997,0.000017240174,0.00001552586,0.0000016146412,0.0000062973054,0.00020515292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855554,0.0000046324885,0.00032326183,0.00038279677,0.00026764258,0.0004661171],"domain_scores_gemma":[0.9990772,0.000060595128,0.0001389442,0.0005643059,0.00011856545,0.000040405062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020179107,0.00014372256,0.00032628453,0.0013626999,0.00022636176,0.000024548983,0.0011919255,0.00020618972,0.00014681651],"category_scores_gemma":[0.0005428509,0.00013648454,0.000060789625,0.0067672157,0.0016047375,0.00010886807,0.00027353634,0.0003039426,0.0000091891625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005940868,0.0001213431,0.04067466,0.00008164277,0.000051841467,0.0000025487468,0.000049767103,0.0000062541167,0.9125188,0.041817736,0.00007812502,0.004591354],"study_design_scores_gemma":[0.00022582208,0.000092385286,0.00094252866,0.000027451477,0.00006892999,0.000008100756,0.0006869433,0.00041637363,0.98988736,0.0074143317,0.000112096146,0.00011766983],"about_ca_topic_score_codex":0.000013804831,"about_ca_topic_score_gemma":0.000015956442,"teacher_disagreement_score":0.07736858,"about_ca_system_score_codex":0.00014065918,"about_ca_system_score_gemma":0.00032984346,"threshold_uncertainty_score":0.59127223},"labels":[],"label_agreement":null},{"id":"W4413457113","doi":"10.1109/cleo/europe-eqec65582.2025.11111265","title":"Multidimensional Fluorescence Spectroscopy for Chemical Chromatography","year":2025,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Victoria","funders":"","keywords":"Fluorescence; Fluorescence spectroscopy; Spectroscopy; Chemistry; Computer science; Analytical Chemistry (journal); Materials science; Chromatography; Physics; Optics","score_opus":0.008950051776096184,"score_gpt":0.28537792672123635,"score_spread":0.27642787494514015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413457113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71181273,0.002071238,0.04519021,0.0013750549,0.00024627123,0.00028716875,0.000059743743,0.000784331,0.23817325],"genre_scores_gemma":[0.9096617,0.00003353645,0.082407415,0.0004287501,0.000117959426,0.00007385158,0.00004989067,0.000017346665,0.0072095506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989628,0.00000255067,0.0002154489,0.00036031342,0.00014053758,0.00031832492],"domain_scores_gemma":[0.99936396,0.00018302376,0.00004177967,0.0002734116,0.00006796902,0.000069855916],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004857513,0.0001676221,0.00023689467,0.00015772054,0.00009731572,0.000023303719,0.00020791018,0.000121903744,0.0023119962],"category_scores_gemma":[0.0001035476,0.00014814462,0.00021203376,0.00059307355,0.00009688083,0.000046537076,0.000055597273,0.00012141181,0.000022333687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048027505,0.00012179496,0.0024284408,0.00007687893,0.00011117545,8.6193114e-7,0.0000071206614,5.497024e-7,0.97113687,0.010909867,0.014899778,0.00025863256],"study_design_scores_gemma":[0.0005859242,0.000009088678,0.000099067736,0.000017987071,0.00010475109,0.0000016186763,0.00005898554,0.00059378496,0.9918947,0.001986015,0.0044923536,0.00015572083],"about_ca_topic_score_codex":0.0000142740755,"about_ca_topic_score_gemma":0.0000010961097,"teacher_disagreement_score":0.2309637,"about_ca_system_score_codex":0.000054476863,"about_ca_system_score_gemma":0.000059267022,"threshold_uncertainty_score":0.9986},"labels":[],"label_agreement":null},{"id":"W4413458465","doi":"10.1109/cleo/europe-eqec65582.2025.11110096","title":"Hadamard-Multiplexed Multispectral Fluorescence Microscopy","year":2025,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Multispectral image; Microscopy; Fluorescence microscope; Hadamard transform; Fluorescence; Multiplexing; Computer science; Optics; Materials science; Artificial intelligence; Physics; Telecommunications","score_opus":0.008206296609889994,"score_gpt":0.29330776295607675,"score_spread":0.28510146634618677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413458465","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43380216,0.0014847375,0.0038465362,0.0011256386,0.00026861517,0.00012024525,0.000027042983,0.00080900453,0.558516],"genre_scores_gemma":[0.91130024,0.00006171357,0.012448451,0.00035588935,0.00006459472,0.000015174276,0.000014805391,0.000013742288,0.07572536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880457,0.0000067741285,0.000254862,0.00039148537,0.00015052805,0.00039179897],"domain_scores_gemma":[0.99931085,0.00008541002,0.00004869236,0.0004195705,0.000055671997,0.00007978221],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000052076753,0.00020052992,0.00026214472,0.00015828805,0.00014089186,0.00006986813,0.0003670816,0.0001365984,0.005981197],"category_scores_gemma":[0.00014161502,0.00018112789,0.00013856891,0.0006878531,0.000103422324,0.000080367055,0.00008281007,0.00022216751,0.00012653219],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033755092,0.00010208393,0.021582644,0.00005264948,0.000095398944,0.000005440195,0.000021459524,0.0000026980206,0.9655716,0.0020127767,0.009659009,0.0008605103],"study_design_scores_gemma":[0.00058470975,0.000006904128,0.0016122491,0.000019658944,0.00008394195,0.0000021768728,0.00018297101,0.0004355083,0.99024385,0.0002772003,0.006357744,0.00019309478],"about_ca_topic_score_codex":0.00025439402,"about_ca_topic_score_gemma":0.000037597354,"teacher_disagreement_score":0.48279068,"about_ca_system_score_codex":0.000116456125,"about_ca_system_score_gemma":0.00007256331,"threshold_uncertainty_score":0.99492747},"labels":[],"label_agreement":null},{"id":"W4413561488","doi":"10.1139/cjps-2025-0084","title":"UAV-based hyperspectral imaging to evaluate plant moisture and desiccant response in lentil (<i>Lens culinaris</i>)","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; Lethbridge College; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canada First Research Excellence Fund; Western Grains Research Foundation","keywords":"Hyperspectral imaging; Desiccant; Lens (geology); Environmental science; Moisture; Remote sensing; Biology; Geography; Meteorology","score_opus":0.014453536881762211,"score_gpt":0.2718807933190331,"score_spread":0.2574272564372709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413561488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993642,0.00117277,0.00021441052,0.0033652901,0.00014393438,0.0000408614,0.00015935855,0.000005738104,0.0012556395],"genre_scores_gemma":[0.998189,0.000026476315,0.0007554851,0.0007890487,0.000033687902,0.000001390102,0.0000020905056,0.000005414648,0.00019745166],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985422,0.00003095832,0.00031895077,0.0002695847,0.00031729034,0.0005210051],"domain_scores_gemma":[0.9988723,0.00016023617,0.000118334996,0.00017792013,0.00013081744,0.0005404186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012326862,0.00014386054,0.00025377874,0.0014253184,0.00025111355,0.0001544245,0.00050923054,0.000044770306,0.00007245219],"category_scores_gemma":[0.0007883069,0.00012765154,0.000052583113,0.0014354511,0.0002960016,0.00018589816,0.000022429767,0.00032867887,0.00000212404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011863044,0.00006304484,0.25705987,0.000073976604,0.000051794545,0.003094522,0.0013722281,0.001844232,0.7273195,0.0005402989,0.0069026914,0.0004915707],"study_design_scores_gemma":[0.00318603,0.00026820414,0.1979477,0.0012664348,0.00037918388,0.0015935372,0.0065132068,0.007932269,0.7654498,0.000723309,0.013763127,0.0009772031],"about_ca_topic_score_codex":0.004557975,"about_ca_topic_score_gemma":0.0101664625,"teacher_disagreement_score":0.059112184,"about_ca_system_score_codex":0.0006816574,"about_ca_system_score_gemma":0.00438462,"threshold_uncertainty_score":0.7778128},"labels":[],"label_agreement":null},{"id":"W4413877241","doi":"10.70121/001c.143827","title":"Application of Machine Learning to Classify Wheat Seeds","year":2025,"lang":"en","type":"article","venue":"Scholarly review .","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science","score_opus":0.015486628450707344,"score_gpt":0.3163631317166681,"score_spread":0.3008765032659607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413877241","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0317864,0.73817205,0.017096933,0.0056384425,0.0000784271,0.00047634213,0.000027075052,0.00026255625,0.20646176],"genre_scores_gemma":[0.93079734,0.038551018,0.0018473098,0.002667129,0.000076138545,0.00012763854,0.00007584435,0.000025922685,0.025831658],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990946,0.000024357945,0.00032590266,0.00024095419,0.00017176125,0.0001423956],"domain_scores_gemma":[0.99926233,0.00005381146,0.00010888445,0.00037842477,0.00013544015,0.00006111736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029750695,0.00011763826,0.00035787345,0.00012376183,0.000071316696,0.000045005057,0.0002813682,0.000075828175,0.00068277214],"category_scores_gemma":[0.0005187621,0.0001053638,0.00011619454,0.0013504415,0.000018396016,0.0001728249,0.00007562145,0.0003945734,0.00010119437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030245827,0.00019523936,0.14249183,0.012318898,0.00031803828,0.000001753254,0.000060616618,0.000014688388,0.66988266,0.0033891571,0.0074850465,0.16381182],"study_design_scores_gemma":[0.00031246047,0.000034866433,0.0021010472,0.002035673,0.0006884623,0.0000031265815,0.00006277754,0.00017816278,0.2030074,0.00027853416,0.79102874,0.0002687791],"about_ca_topic_score_codex":0.00004361115,"about_ca_topic_score_gemma":0.0000032928276,"teacher_disagreement_score":0.89901096,"about_ca_system_score_codex":0.000067148016,"about_ca_system_score_gemma":0.000036771256,"threshold_uncertainty_score":0.7475876},"labels":[],"label_agreement":null},{"id":"W4413886330","doi":"10.1109/agro-geoinformatics66479.2025.11136239","title":"High-Throughput Imaging System for Early Detection of Root Rot Disease in Field Peas (Pisum sativum L.)","year":2025,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Sativum; Pisum; Throughput; Plant disease; Root (linguistics); Biology; Horticulture; Field pea; Agronomy; Computer science; Biotechnology; Operating system","score_opus":0.006116746372064855,"score_gpt":0.2627735603158945,"score_spread":0.25665681394382966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413886330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92450535,0.00055243936,0.064695545,0.0003340879,0.00013133467,0.00013557088,0.000015927446,0.000111266636,0.009518453],"genre_scores_gemma":[0.99678415,0.0000034834145,0.00024690045,0.000038002618,0.00006108917,0.00006078548,0.000006074502,0.000009710596,0.0027898313],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991329,0.000009439993,0.000299625,0.00025043593,0.000112057896,0.00019559026],"domain_scores_gemma":[0.9993536,0.00020328377,0.00008534185,0.00024841764,0.00006242447,0.0000469359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000751381,0.00012290318,0.00024246656,0.0001948703,0.000052447544,0.000025419324,0.00013444573,0.000060448958,0.00015872445],"category_scores_gemma":[0.00016987619,0.00011637122,0.000114977345,0.00048666008,0.00002146192,0.00008830478,0.00003969264,0.000099549936,0.0000036785475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093668787,0.00038111646,0.7216412,0.0044148993,0.0003356205,0.000016210151,0.00016645936,0.00011301668,0.2533622,0.010516292,0.0009520529,0.0071642515],"study_design_scores_gemma":[0.0007348048,0.00001740351,0.008814108,0.00011925575,0.00015792745,3.5882937e-7,0.00036117644,0.0015741931,0.9873576,0.00058455986,0.00014731506,0.00013127204],"about_ca_topic_score_codex":0.002130375,"about_ca_topic_score_gemma":0.00016082035,"teacher_disagreement_score":0.7339954,"about_ca_system_score_codex":0.00014770494,"about_ca_system_score_gemma":0.000047954072,"threshold_uncertainty_score":0.47454807},"labels":[],"label_agreement":null},{"id":"W4414126853","doi":"10.1007/s00107-025-02320-1","title":"Use of NIR spectroscopy and partial least squares regression for prediction of chemical properties of Salix clones","year":2025,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Resources Canada; Canada Research Chairs","keywords":"Willow; Partial least squares regression; Chemical composition; Raw material; Twig; Biomass (ecology); Salix viminalis; Hardwood","score_opus":0.0370821729408746,"score_gpt":0.25509952736548336,"score_spread":0.21801735442460876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414126853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993475,0.005589966,0.000074916716,0.00036891588,0.00008518385,0.00007075774,0.000039983377,0.000008279504,0.00028699345],"genre_scores_gemma":[0.9978055,0.000456078,0.0012474818,0.00000836087,0.00025448238,8.3509843e-7,0.000004259576,0.000015365356,0.00020764538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987461,0.000057562138,0.00066616444,0.00019719722,0.00019285036,0.0001401473],"domain_scores_gemma":[0.99875546,0.00005494341,0.00061618385,0.00018030743,0.00033795927,0.000055157474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031993218,0.00014940188,0.00044946064,0.00016921807,0.00004055575,0.00002037788,0.00012958009,0.00004054741,0.000012119787],"category_scores_gemma":[0.00052764674,0.000106099294,0.00008666199,0.00025704762,0.00020677564,0.00015826526,0.000063614825,0.00015482979,9.021539e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008319227,0.00020467267,0.0030577146,0.0011050841,0.00021057566,0.0000029597218,0.00037523702,0.0000027857252,0.99245137,0.000055600103,0.0008468002,0.00085528346],"study_design_scores_gemma":[0.0009094149,0.00036932423,0.00064888457,0.0007052359,0.00034478548,0.000028016684,0.00028643053,0.000016766298,0.9959695,0.000032777083,0.0006129625,0.00007590833],"about_ca_topic_score_codex":0.0000036297868,"about_ca_topic_score_gemma":1.1325154e-7,"teacher_disagreement_score":0.005133888,"about_ca_system_score_codex":0.000012459793,"about_ca_system_score_gemma":0.00006971614,"threshold_uncertainty_score":0.43266037},"labels":[],"label_agreement":null},{"id":"W4414210377","doi":"10.1364/boe.571299","title":"Noninvasive estimation of superficial layer thickness using multi-channel diffuse correlation spectroscopy","year":2025,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Imaging phantom; Diffuse optical imaging; Blood flow; Photon diffusion; Sensitivity (control systems); Cerebral blood flow; Perfusion; Diffuse reflectance infrared fourier transform","score_opus":0.028459740264899423,"score_gpt":0.31523413406114337,"score_spread":0.2867743937962439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414210377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47382674,0.00021921676,0.5245847,0.00007829209,0.00026524675,0.00007407078,0.000045100125,0.00006098428,0.0008456358],"genre_scores_gemma":[0.9719362,0.00004474523,0.027237082,0.000048881862,0.00014273217,0.00001075623,0.000095927346,0.000020657519,0.00046296424],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983987,0.00002262813,0.0005109877,0.00033697145,0.00042187655,0.0003088259],"domain_scores_gemma":[0.99897105,0.00026188753,0.00019255692,0.00032660193,0.00013107335,0.00011683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012713073,0.00021900555,0.00040138589,0.0003022163,0.00012981663,0.000037335725,0.00028990512,0.0003661077,0.00024409096],"category_scores_gemma":[0.00050399627,0.00020800458,0.00012380267,0.0007518598,0.00028875648,0.00011314886,0.00012030016,0.00028587278,0.000007579453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007407308,0.0011642406,0.0010431957,0.00039467818,0.000209214,0.000016908389,0.00039870056,0.0035500627,0.9917379,0.0010439688,0.00019390338,0.00017313624],"study_design_scores_gemma":[0.0012330139,0.000026373309,0.00013098905,0.00014825826,0.00030300152,0.0000060696566,0.00036562243,0.30740696,0.6897864,0.00035704114,0.000048095328,0.00018819541],"about_ca_topic_score_codex":0.00010401163,"about_ca_topic_score_gemma":0.000001946837,"teacher_disagreement_score":0.49810952,"about_ca_system_score_codex":0.00013704621,"about_ca_system_score_gemma":0.00014649927,"threshold_uncertainty_score":0.8482181},"labels":[],"label_agreement":null},{"id":"W4414300001","doi":"10.1016/j.chemolab.2025.105532","title":"Simple methods for uncertainty estimation in neural networks applied to spectral data processing: A case study on mango dry matter prediction","year":2025,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche","keywords":"Interpretability; Uncertainty quantification; Chemometrics; Artificial neural network; Sensitivity analysis; Uncertainty analysis; Key (lock); Dropout (neural networks); Calibration; Parametric statistics","score_opus":0.04829269171040289,"score_gpt":0.3828707936075379,"score_spread":0.334578101897135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414300001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.602304,0.003648555,0.39075354,0.00005811523,0.00035821533,0.0014325364,0.00022955687,0.00012286597,0.0010926415],"genre_scores_gemma":[0.99777275,0.00002098502,0.0012016958,0.00018839212,0.00013946791,0.00025095692,0.00017295085,0.000031989992,0.0002208178],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978449,0.000051444425,0.0006983376,0.0008174517,0.00020007648,0.00038782303],"domain_scores_gemma":[0.99836236,0.0004158157,0.00019207782,0.00070653606,0.00018352046,0.00013966457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097350776,0.00031227677,0.0005313431,0.0012082634,0.00016239025,0.00027983868,0.00036766444,0.00019204184,0.00003056356],"category_scores_gemma":[0.00036768054,0.00029241026,0.00004001229,0.005103555,0.000034815643,0.00014299164,0.00016061268,0.00029082946,0.0000031393693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015515203,0.006317425,0.5262499,0.007545389,0.0018044723,0.0003423624,0.0043345885,0.23683286,0.011326731,0.00093863736,0.024696648,0.17805949],"study_design_scores_gemma":[0.0008553069,0.00020821378,0.0008897857,0.00007257549,0.000289748,0.000014257292,0.012469122,0.97121483,0.010588096,0.000039154227,0.0029433956,0.00041552997],"about_ca_topic_score_codex":0.00018875622,"about_ca_topic_score_gemma":0.00002265311,"teacher_disagreement_score":0.734382,"about_ca_system_score_codex":0.00031703908,"about_ca_system_score_gemma":0.00006561172,"threshold_uncertainty_score":0.9999528},"labels":[],"label_agreement":null},{"id":"W4414561867","doi":"10.5772/intechopen.1010036","title":"Synergistic Effects of Molecular Structure and Nutritional Propertiesin Enzymatically and Thermally Treated Oat Tissues Revealed Using Synchrotron and Globar Molecular Spectroscopic Techniques","year":2025,"lang":"en","type":"book-chapter","venue":"Agricultural sciences.","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Prairie Oat Growers Association","keywords":"Molecular spectroscopy; Endosperm; Synchrotron; Molecule; Synchrotron radiation; Spectroscopy; Molecular model","score_opus":0.006619652892851842,"score_gpt":0.23468652893039305,"score_spread":0.2280668760375412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414561867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9550685,0.020189824,0.00006775841,0.0001101041,0.00001677643,0.000381382,0.000051210725,0.00006828552,0.0240461],"genre_scores_gemma":[0.98705524,0.0009920448,0.004304012,0.00005525405,0.000057010726,0.000011707436,0.000040923613,0.000016000182,0.007467791],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983619,0.00002075726,0.000339119,0.0006005228,0.0004013175,0.0002763827],"domain_scores_gemma":[0.9993009,0.000098667995,0.00022367964,0.00015243074,0.00011837804,0.00010597068],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005752227,0.0004340902,0.0006345683,0.00013179993,0.00017159262,0.00011881851,0.00021867188,0.00032818512,0.00010624042],"category_scores_gemma":[0.00007098497,0.00027449257,0.00007447037,0.00021098478,0.00077157,0.00010503727,0.00014913766,0.00023186454,3.3162e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008731385,0.000015949128,0.00016981733,0.0010898982,0.00013245968,0.00003754921,0.000018229755,0.0000024431702,0.99074966,0.0073117083,0.00005287534,0.00041070272],"study_design_scores_gemma":[0.00027546275,0.00015302296,0.0007502486,0.0015882609,0.00082813704,0.00005056135,0.000030494955,0.0000672942,0.9912803,0.004431656,0.00007917576,0.00046537654],"about_ca_topic_score_codex":0.000098001794,"about_ca_topic_score_gemma":0.000003502224,"teacher_disagreement_score":0.0319867,"about_ca_system_score_codex":0.00012056995,"about_ca_system_score_gemma":0.00006570504,"threshold_uncertainty_score":0.99997073},"labels":[],"label_agreement":null},{"id":"W4414569139","doi":"10.1016/j.measurement.2025.119052","title":"Corrigendum to “Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures” [Measurement 257(Part A) (2026) 118620]","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Contamination; Fermentation; Mie scattering; Dynamic light scattering","score_opus":0.0355143556860052,"score_gpt":0.27267003086670183,"score_spread":0.23715567518069663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414569139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9819198,0.0008916281,0.015283971,0.0007048819,0.00026130027,0.00048909883,0.000012102927,0.000050322466,0.0003868624],"genre_scores_gemma":[0.9973876,0.000017323257,0.0018496149,0.00021915193,0.0000665884,0.00012812344,0.000014360555,0.000020035744,0.0002971846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982824,0.00004298335,0.0004878837,0.00043675912,0.0004203025,0.00032968208],"domain_scores_gemma":[0.99919486,0.000013854337,0.00015802783,0.00013511474,0.0004033314,0.0000948027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007813473,0.00023057962,0.0003027518,0.00035047444,0.00017625511,0.00007727465,0.00012327284,0.000060481718,0.000026367456],"category_scores_gemma":[0.00043326017,0.00024258277,0.000076018994,0.000521055,0.000030771902,0.00009710641,0.00005011904,0.0001952339,0.0000018674895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002561135,0.00006725984,0.013546556,0.00019599596,0.000073776275,7.3575745e-7,0.0010905823,0.00008151071,0.9820143,0.000013672359,0.00014138948,0.0025180979],"study_design_scores_gemma":[0.0009975528,0.00014184177,0.005617128,0.00042453947,0.00013040172,0.0000017539147,0.001423985,0.00030401253,0.9900592,0.000022913928,0.00066684757,0.0002098386],"about_ca_topic_score_codex":0.00066645554,"about_ca_topic_score_gemma":0.0008953084,"teacher_disagreement_score":0.015467785,"about_ca_system_score_codex":0.0011873539,"about_ca_system_score_gemma":0.00007133185,"threshold_uncertainty_score":0.98922384},"labels":[],"label_agreement":null},{"id":"W4414603553","doi":"10.1109/tcomm.2025.3615770","title":"Tracking and Positioning Dynamic Targets in VLC: Signal Dependent Noises and Bayesian Bound Analysis","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Tracking (education); Position (finance); Bayesian probability; Noise (video); Visible light communication; Upper and lower bounds; Particle filter; Noise measurement","score_opus":0.012754772127856062,"score_gpt":0.3002715988149857,"score_spread":0.28751682668712963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414603553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23963031,0.0053924466,0.74309886,0.0019403504,0.00003211189,0.00012971091,0.000101443984,0.00013027292,0.009544469],"genre_scores_gemma":[0.99579716,0.0014632547,0.0019010323,0.00007558004,0.0000029759767,0.00005010071,0.00002403928,0.0000096041795,0.0006762267],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991472,0.000045030985,0.000281482,0.00025318988,0.000102601625,0.0001705147],"domain_scores_gemma":[0.9988344,0.00038203056,0.000060470687,0.00062225736,0.00004444918,0.000056430497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102290134,0.00014326158,0.00024192956,0.00075093826,0.0005962822,0.00011590168,0.00027141147,0.00009757353,0.00024433344],"category_scores_gemma":[0.000007896334,0.00016271089,0.00008969417,0.0013280927,0.00015628968,0.00014286417,0.000007910024,0.00038745665,0.0000015961756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005876347,0.009792186,0.083785184,0.0010711554,0.02128459,0.000048235168,0.011377161,0.062305477,0.6133044,0.0069919745,0.00012439083,0.18932764],"study_design_scores_gemma":[0.0043928255,0.00014067444,0.04401767,0.0005481349,0.015506089,0.00005524337,0.011528447,0.42134684,0.49153465,0.0084090335,0.0005508932,0.0019694795],"about_ca_topic_score_codex":0.00037407872,"about_ca_topic_score_gemma":0.0039648153,"teacher_disagreement_score":0.7561669,"about_ca_system_score_codex":0.00013845792,"about_ca_system_score_gemma":0.000041073457,"threshold_uncertainty_score":0.66351575},"labels":[],"label_agreement":null},{"id":"W4414656585","doi":"10.1093/mnras/staf1659","title":"Testing the performance of cross-correlation techniques to search for molecular features in <i>JWST</i> NIRSpec G395H observations of transiting exoplanets","year":2025,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; Science Mission Directorate; European Social Fund; Agencia Nacional de Investigación y Desarrollo; Agencia Estatal de Investigación; Fondation Sanofi Espoir; KU Leuven; European Commission; European Space Agency; Imperial College London; National Aeronautics and Space Administration; Deutsche Forschungsgemeinschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Space Telescope Science Institute; Ministerio de Ciencia e Innovación; California Department of Fish and Game","keywords":"Exoplanet; James Webb Space Telescope; Normalization (sociology); Gaussian; Spectral line; Wavelength","score_opus":0.016560405974857445,"score_gpt":0.27361151113248,"score_spread":0.25705110515762253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414656585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982566,0.00012754026,0.00031960369,0.00034943648,0.000016076428,0.00018998829,0.00005187649,0.000013290116,0.0006755618],"genre_scores_gemma":[0.98809224,1.8330755e-7,0.011688043,0.000036014964,0.000024188212,0.000023495684,0.000010074976,0.000007879806,0.00011787904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910134,0.000016737642,0.00038311566,0.00017054104,0.00014055666,0.00018769727],"domain_scores_gemma":[0.9989238,0.0005257655,0.0001773756,0.00023316375,0.00011976987,0.000020142052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028467356,0.00010987601,0.00023949097,0.000027492499,0.00010776538,0.000017723878,0.00041977694,0.00009922793,0.000008395197],"category_scores_gemma":[0.00014654745,0.00007859573,0.0002087165,0.00038310754,0.00016510635,0.000050949762,0.000092272516,0.00022092748,9.668597e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004925081,0.000060787537,0.2626163,0.00021121663,0.00006388422,1.245559e-8,0.00026387998,0.6592647,0.076924086,0.00005625156,0.000053274573,0.00043638324],"study_design_scores_gemma":[0.00017922513,0.00002745973,0.21269254,0.000102578015,0.00007537286,5.7619234e-9,0.00034645293,0.14300083,0.6434949,0.00000816569,0.000016152351,0.000056314384],"about_ca_topic_score_codex":0.00035163492,"about_ca_topic_score_gemma":0.000013808334,"teacher_disagreement_score":0.5665708,"about_ca_system_score_codex":0.00007670444,"about_ca_system_score_gemma":0.00005803425,"threshold_uncertainty_score":0.32050407},"labels":[],"label_agreement":null},{"id":"W4414730995","doi":"10.1007/978-1-0716-4787-5_18","title":"Artificial Intelligence and Machine Learning in Food Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Methods and protocols in food science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"","keywords":"Convolutional neural network; Hyperspectral imaging; Artificial neural network; Support vector machine; Deep learning; Anomaly detection; Standardization; Supervised learning","score_opus":0.13009518385396165,"score_gpt":0.43613635155838054,"score_spread":0.3060411677044189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414730995","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025377667,0.0080261165,0.15571463,0.0003744143,0.00009485271,0.025157768,0.00012421934,0.0002046397,0.8077656],"genre_scores_gemma":[0.2600771,0.0015820012,0.5000601,0.00040503475,0.00037958843,0.048246056,0.000055215693,0.00017646994,0.18901849],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99765384,0.0000624371,0.00061427697,0.0009531422,0.00034380882,0.0003725176],"domain_scores_gemma":[0.99874675,0.00047953718,0.00024012744,0.0003606784,0.00006530241,0.00010761018],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002465118,0.00032906976,0.0007668706,0.0016083502,0.00019322378,0.00018916158,0.00047506302,0.00027708057,0.00037324455],"category_scores_gemma":[0.00072384404,0.00030549627,0.00010822123,0.001959366,0.00060099433,0.00011342407,0.00036312474,0.00091812253,8.182541e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017049817,0.0001362234,0.0095387185,0.0010458239,0.00045894488,0.000021025404,0.00068254786,0.00024265754,0.012187593,0.12710164,0.0000038742996,0.8484104],"study_design_scores_gemma":[0.00040376585,0.0012483437,0.0004181291,0.001150921,0.0007629281,0.000015161143,0.0003739803,0.021518258,0.57269365,0.3770791,0.022368245,0.0019675402],"about_ca_topic_score_codex":0.000050558825,"about_ca_topic_score_gemma":0.00027721733,"teacher_disagreement_score":0.8464429,"about_ca_system_score_codex":0.00010873617,"about_ca_system_score_gemma":0.000175884,"threshold_uncertainty_score":0.99993974},"labels":[],"label_agreement":null},{"id":"W4414742902","doi":"10.48550/arxiv.2509.16120","title":"Euclid preparation. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"National Astronomical Observatory of Japan; Norsk Romsenter; Agenția Spațială Română; Ministerio de Ciencia e Innovación; European Space Agency; Agenzia Spaziale Italiana; Narodowa Agencja Wymiany Akademickiej; Fundação para a Ciência e a Tecnologia; Magyar Tudományos Akadémia; European Commission; Ministerio de Ciencia, Innovación y Universidades; National Aeronautics and Space Administration","keywords":"Galaxy; Redshift; Spurious relationship; Stacking; Residual; Spectral line; Noise (video); Milky Way; Scaling","score_opus":0.03277126076398596,"score_gpt":0.30396569966996806,"score_spread":0.2711944389059821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414742902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94775975,0.0010312696,0.0041428152,0.0009453117,0.0001763574,0.0002948995,0.0001382104,0.00045352874,0.045057856],"genre_scores_gemma":[0.98562413,0.00019482631,0.0020671682,0.00011627686,0.0007935802,0.000098664466,0.0002186722,0.00006233345,0.010824361],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969857,0.00004893436,0.00072183483,0.0009913023,0.000561468,0.0006907889],"domain_scores_gemma":[0.99739486,0.00047034427,0.0006507136,0.001161233,0.00019832201,0.0001245531],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039128057,0.00057611766,0.0006102141,0.0002633846,0.0008865302,0.00024888467,0.00079381577,0.00042230653,0.0011411913],"category_scores_gemma":[0.00026210558,0.00045426656,0.00030329172,0.0008743853,0.00017776815,0.0002399096,0.00045602417,0.0020016495,0.00003568126],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015739522,0.0001376113,0.97374946,0.00049016345,0.0013926408,0.000026936526,0.0029502695,0.0116757,0.007299641,0.00038558006,0.0016341502,0.00010042769],"study_design_scores_gemma":[0.0025865964,0.00021088969,0.11833391,0.002839635,0.006362731,0.00008231972,0.0146336,0.025969986,0.8141451,0.0023179033,0.008976931,0.0035404188],"about_ca_topic_score_codex":0.00033101463,"about_ca_topic_score_gemma":0.00041949374,"teacher_disagreement_score":0.8554156,"about_ca_system_score_codex":0.0005417163,"about_ca_system_score_gemma":0.00051967497,"threshold_uncertainty_score":0.9997909},"labels":[],"label_agreement":null},{"id":"W4414805025","doi":"10.1016/j.aiia.2025.09.001","title":"Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Pipeline (software); Nutrient; Univariate; Multivariate statistics; Linear regression; Partial least squares regression; Reflectivity; Regression; Lasso (programming language)","score_opus":0.02385205654331827,"score_gpt":0.3534822536942532,"score_spread":0.3296301971509349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414805025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942828,0.001193824,0.0031508713,0.00024708072,0.000088222325,0.000187286,0.000024269611,0.000049324426,0.0007763195],"genre_scores_gemma":[0.99428946,0.00021813245,0.0044392124,0.000115951676,0.0000326712,0.000011580356,0.000029235971,0.000012446572,0.00085130177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982786,0.000030585656,0.00054268015,0.00057285366,0.00015377457,0.00042146907],"domain_scores_gemma":[0.9993941,0.00012468241,0.000118522665,0.00018588331,0.000068202164,0.000108619795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013588136,0.00027599628,0.00038059222,0.00023178615,0.00015178094,0.00014181079,0.00017936468,0.0002482085,0.00008547663],"category_scores_gemma":[0.00024191049,0.00021589902,0.00003334086,0.0016629497,0.00007893926,0.00013527257,0.000118129945,0.0004235927,0.000008154668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004533624,0.00023564027,0.014580229,0.0000878072,0.000029342955,0.00008486682,0.0015433271,0.001516795,0.97724193,0.00030362754,0.00035955833,0.003563526],"study_design_scores_gemma":[0.00022175582,0.000026627533,0.0027303512,0.0006115929,0.000042334916,0.000016535634,0.0013233455,0.030887334,0.9605187,0.0030522777,0.00022264807,0.0003464512],"about_ca_topic_score_codex":0.0005723268,"about_ca_topic_score_gemma":0.00060538267,"teacher_disagreement_score":0.029370539,"about_ca_system_score_codex":0.00016985969,"about_ca_system_score_gemma":0.000041821997,"threshold_uncertainty_score":0.8804107},"labels":[],"label_agreement":null},{"id":"W4414830604","doi":"10.1093/jas/skaf300.624","title":"PSVII-3 Japanese quality and yield grades predictions using Computer Vision Systems at the Canadian beef grading site.","year":2025,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Marbled meat; Grading (engineering); Beef industry; Beef cattle; Quality assurance; Carcass weight","score_opus":0.04980474860522046,"score_gpt":0.352798269222311,"score_spread":0.30299352061709056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414830604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99631107,0.0010252495,0.00018489099,0.00090306456,0.0001515006,0.000023188124,0.0000072646767,0.000006564661,0.0013872181],"genre_scores_gemma":[0.9993541,0.00003719688,0.00016325816,0.00011287156,0.00014801134,3.008267e-7,2.1982653e-7,0.0000026253033,0.00018141654],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988963,0.000017908998,0.0003124401,0.00015812028,0.00038041777,0.00023483243],"domain_scores_gemma":[0.99913573,0.00017522986,0.00021310001,0.0001240578,0.00016633564,0.000185538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007791056,0.00008457598,0.00017417813,0.00026642726,0.0010620358,0.0002910732,0.00029256262,0.000053163505,0.000048134334],"category_scores_gemma":[0.00018601764,0.00005593914,0.000059863505,0.0009982222,0.0003853828,0.00028269924,0.00008570592,0.00022001006,9.0183556e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003435321,0.000027508022,0.052991185,0.000050399864,0.00004111486,0.000014125326,0.0004060253,0.0002965941,0.9447677,0.0004501233,0.00081021275,0.000110620334],"study_design_scores_gemma":[0.0012037173,0.00089236314,0.599108,0.0013779505,0.0010126793,0.0025673178,0.008976299,0.10324088,0.27731213,0.00044575447,0.0029477326,0.0009152225],"about_ca_topic_score_codex":0.029231394,"about_ca_topic_score_gemma":0.007034669,"teacher_disagreement_score":0.6674556,"about_ca_system_score_codex":0.00053391187,"about_ca_system_score_gemma":0.00031169268,"threshold_uncertainty_score":0.97723305},"labels":[],"label_agreement":null},{"id":"W4414885497","doi":"10.2139/ssrn.5574464","title":"Classification of Cannabinoid Spectra Using Machine Learning","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Pattern recognition (psychology); Noise (video); Raman spectroscopy; Spectral line; Random forest; Process (computing); Gaussian process; Class (philosophy)","score_opus":0.02092278680674618,"score_gpt":0.2997921179443099,"score_spread":0.2788693311375637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414885497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89722013,0.050504193,0.027090615,0.00055716303,0.00036784122,0.00014623454,0.00007703095,0.00015303167,0.023883784],"genre_scores_gemma":[0.9730343,0.009805929,0.00049587747,0.000012990487,0.00043374373,0.000005055414,0.00007339415,0.000037854894,0.016100818],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966874,0.00006339901,0.0007183314,0.0004438998,0.00041832522,0.001668653],"domain_scores_gemma":[0.9981992,0.00005693269,0.0010290646,0.0004105441,0.00022121695,0.000083007755],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00082078547,0.00037653485,0.00065021386,0.00052110763,0.00022115871,0.000066195345,0.000678917,0.0004405722,0.0005275861],"category_scores_gemma":[0.00021485801,0.00037805506,0.0004232123,0.00050634565,0.000076995595,0.00006735246,0.00022784682,0.008115081,0.0000030288725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005518639,0.0010348072,0.13949573,0.002620599,0.009347301,0.00002496647,0.0007483565,0.015268884,0.7311786,0.08833625,0.00022198395,0.011170667],"study_design_scores_gemma":[0.003245942,0.00038535654,0.0012404132,0.0011785829,0.008161898,0.0010495809,0.009096977,0.062153634,0.5670118,0.33948615,0.004052301,0.0029373972],"about_ca_topic_score_codex":0.001025647,"about_ca_topic_score_gemma":0.00046915622,"teacher_disagreement_score":0.2511499,"about_ca_system_score_codex":0.0029996112,"about_ca_system_score_gemma":0.0056201285,"threshold_uncertainty_score":0.99986714},"labels":[],"label_agreement":null},{"id":"W4415186821","doi":"10.1016/j.ejpb.2025.114902","title":"Effects of Co-Processed and conventional excipients on content uniformity","year":2025,"lang":"en","type":"article","venue":"European Journal of Pharmaceutics and Biopharmaceutics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Purdue Pharma (Canada)","funders":"National Institute of Radiological Sciences; Stiftelsen för Strategisk Forskning; Stiftelsen för Strategisk Forskning","keywords":"Microcrystalline cellulose; Excipient; Particle size; Scanning electron microscope; Cellulose; Active ingredient; Colloid; Silicon dioxide","score_opus":0.047252048633645,"score_gpt":0.3447332232724058,"score_spread":0.2974811746387608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415186821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852284,0.008444915,0.00033981906,0.00022472668,0.00022516851,0.00006829027,0.000024637808,0.000013271019,0.0054307515],"genre_scores_gemma":[0.99140656,0.00716108,0.0001761895,0.00056591013,0.0000975077,5.255831e-7,0.0000029576893,0.000016505977,0.0005727779],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986203,0.00008232368,0.0006091923,0.00017614455,0.00029746868,0.00021459899],"domain_scores_gemma":[0.99866855,0.00028946454,0.000495213,0.00009366123,0.00025044702,0.00020267618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005116795,0.00021443158,0.00036982467,0.0002814427,0.00010789035,0.00005176075,0.00023041251,0.000044716893,0.00009837945],"category_scores_gemma":[0.0001458221,0.00017775399,0.00011886597,0.00028625265,0.0002462091,0.00009706454,0.000082519065,0.0004420554,0.0000024807678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072736724,0.000630415,0.014805969,0.0018890323,0.0009123259,0.00015740514,0.00011212454,0.0000094500865,0.9695758,0.00063011766,0.00041477694,0.01013519],"study_design_scores_gemma":[0.0040988205,0.00013953919,0.0037663393,0.00025608978,0.0008771413,0.000042953387,0.00015783758,0.00027243196,0.9755196,0.000050342725,0.014658159,0.00016075655],"about_ca_topic_score_codex":6.8775887e-7,"about_ca_topic_score_gemma":4.846204e-8,"teacher_disagreement_score":0.014243382,"about_ca_system_score_codex":0.00004059577,"about_ca_system_score_gemma":0.000056360866,"threshold_uncertainty_score":0.7248597},"labels":[],"label_agreement":null},{"id":"W4415261409","doi":"10.1093/jrsssc/qlaf052","title":"Gaussian process with dissolution spline kernel for in vitro dissolution testing","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"European Regional Development Fund","keywords":"Dissolution; Kernel (algebra); Piecewise; Spline (mechanical); Gaussian process; Kernel principal component analysis; Parametric statistics; Gaussian","score_opus":0.010304921948276785,"score_gpt":0.2762404070129939,"score_spread":0.2659354850647171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415261409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014603484,0.00013269077,0.98000294,0.0007352642,0.00011669402,0.00022928361,0.00109569,0.000030399688,0.0030535774],"genre_scores_gemma":[0.8568916,0.00001109469,0.14169487,0.000116891424,0.00013527177,0.00003284716,0.000055827153,0.000026248856,0.0010353524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981002,0.000019612444,0.00073283433,0.00026536413,0.0004253611,0.00045662798],"domain_scores_gemma":[0.9979779,0.000900927,0.0005305947,0.00018733984,0.00029073792,0.000112505535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029771886,0.00026352078,0.00052180554,0.000049178074,0.0003451057,0.00008884636,0.00035151115,0.0001825023,0.00010262107],"category_scores_gemma":[0.0008664673,0.00017902166,0.000119855096,0.0006212194,0.00033086442,0.000078590325,0.00006528055,0.0007095254,8.9997485e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03118849,0.005888115,0.033257972,0.013979043,0.005920805,0.00013600607,0.00462075,0.043124408,0.2552861,0.48174813,0.0748189,0.050031267],"study_design_scores_gemma":[0.019707559,0.001470821,0.06403022,0.0015837231,0.0077635944,0.00013808045,0.022595553,0.22273935,0.300221,0.35193107,0.004600325,0.0032186809],"about_ca_topic_score_codex":0.0000307647,"about_ca_topic_score_gemma":0.000021931704,"teacher_disagreement_score":0.8422881,"about_ca_system_score_codex":0.00039906686,"about_ca_system_score_gemma":0.00027765913,"threshold_uncertainty_score":0.7300291},"labels":[],"label_agreement":null},{"id":"W4415666476","doi":"10.14738/aivp.1305.19516","title":"Early Experiments on Meat in Near Infrared (NIR) Light","year":2025,"lang":"","type":"article","venue":"European journal of applied sciences","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Near-infrared spectroscopy; Transmittance; Wavelength; Muscle fibre; Sarcomere; Optical fiber","score_opus":0.021822701016323904,"score_gpt":0.2899644667723591,"score_spread":0.2681417657560352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415666476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50320077,0.0013285956,0.000042630272,0.0002618004,0.00024953418,0.000049026774,0.0000015745363,0.000008659195,0.4948574],"genre_scores_gemma":[0.9944047,0.00018403593,0.0008153037,0.00045659693,0.00022986376,0.0000010622373,3.8796406e-7,0.000020621263,0.0038874655],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964342,0.00014821989,0.0013153679,0.0005386168,0.0009664312,0.0005971533],"domain_scores_gemma":[0.9982972,0.0001787229,0.0008335683,0.00036241836,0.00011022645,0.00021783877],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016758012,0.00038126003,0.0006480402,0.0009317665,0.00048553012,0.0006315758,0.0017824828,0.000078162084,0.0009205119],"category_scores_gemma":[0.00016288411,0.00031759392,0.00023558356,0.0033009679,0.0006551557,0.00025334116,0.00021798773,0.0008409773,0.00015190433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002864942,0.0030053544,0.01038869,0.00025838954,0.0010524824,0.0013187788,0.016869318,0.0047497433,0.91433203,0.00910504,0.019669173,0.016386043],"study_design_scores_gemma":[0.0028715248,0.00086276303,0.0041309567,0.00055035687,0.00021323665,0.000014402251,0.0060731764,0.000112524365,0.96702194,0.0006987748,0.016937314,0.0005130448],"about_ca_topic_score_codex":0.0000037083446,"about_ca_topic_score_gemma":5.946614e-7,"teacher_disagreement_score":0.4912039,"about_ca_system_score_codex":0.00016653928,"about_ca_system_score_gemma":0.0004056738,"threshold_uncertainty_score":0.9999928},"labels":[],"label_agreement":null},{"id":"W4415718662","doi":"10.1039/d5ay01538c","title":"Construction of prediction models for phenolic compounds in Cabernet Sauvignon grapes based on visible/near-infrared spectroscopy","year":2025,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Key Technology Research and Development Program of Shandong","keywords":"Partial least squares regression; Smoothing; Principal component analysis; Calibration; Wine; Preprocessor; Chemometrics; Convolution (computer science); Pattern recognition (psychology); Set (abstract data type)","score_opus":0.02902712923255163,"score_gpt":0.3732038458147598,"score_spread":0.34417671658220816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415718662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06344999,0.00025204374,0.824152,0.00030720586,0.00013965712,0.00022154713,0.00011572211,0.00011607816,0.111245744],"genre_scores_gemma":[0.6484784,0.000039794446,0.3501008,0.000192438,0.0000693238,0.00005728566,0.00007432444,0.000024209441,0.00096343854],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821174,0.00010394675,0.00061686564,0.00047760407,0.00023758407,0.00035226],"domain_scores_gemma":[0.9979789,0.0012458881,0.00015403173,0.00041967153,0.00011987141,0.00008158461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055447867,0.00023971494,0.0006427918,0.0005378811,0.000085243824,0.00004494367,0.00021501724,0.0003395533,0.00028148136],"category_scores_gemma":[0.0004928122,0.00023359951,0.000247285,0.0015609844,0.00023143376,0.00009778458,0.000029687615,0.00037725185,0.0000012581455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005812413,0.0035933317,0.065229304,0.00300304,0.0022861473,0.000012385477,0.000345657,0.03759935,0.5407619,0.30861834,0.0069373744,0.025800759],"study_design_scores_gemma":[0.0010585467,0.00013829525,0.0006287187,0.00008120169,0.0003652819,9.861753e-7,0.00010398897,0.63875896,0.30941838,0.048986748,0.00031373065,0.00014519473],"about_ca_topic_score_codex":0.000092256014,"about_ca_topic_score_gemma":0.0000049475307,"teacher_disagreement_score":0.6011596,"about_ca_system_score_codex":0.00022305686,"about_ca_system_score_gemma":0.00015439947,"threshold_uncertainty_score":0.9525912},"labels":[],"label_agreement":null},{"id":"W4415989203","doi":"10.3390/foods14223805","title":"A Comprehensive Review on Minimally Destructive Quality and Safety Assessment of Agri-Food Products: Chemometrics-Coupled Mid-Infrared Spectroscopy","year":2025,"lang":"en","type":"review","venue":"Foods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; Canadian Light Source (Canada); Lethbridge College; University of Manitoba","funders":"","keywords":"Quality (philosophy); Food safety; Quality assurance; Attenuated total reflection; Food quality; Food products; Agriculture; Infrared Spectrophotometry","score_opus":0.07075958387463743,"score_gpt":0.40887822989802736,"score_spread":0.3381186460233899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415989203","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017430674,0.9784598,0.000056916302,0.000088629306,0.0001523794,0.0010868065,0.00071981363,0.00010349282,0.019157885],"genre_scores_gemma":[0.000055814864,0.99290055,0.004789878,0.00014763174,0.00019341706,0.00020767683,0.0005631059,0.00007574681,0.0010662031],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99453336,0.00023461948,0.0022200407,0.0015267265,0.00085613015,0.00062914006],"domain_scores_gemma":[0.9938493,0.0017604587,0.0020675838,0.0015419674,0.00058361545,0.00019708704],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053887966,0.0011550825,0.005953364,0.00089509913,0.00014889806,0.000059939928,0.0008349959,0.00061290077,0.0005358721],"category_scores_gemma":[0.0018869062,0.0009826942,0.0009338467,0.0051763365,0.0002700109,0.000090165966,0.00036612168,0.0011534769,0.0000072699736],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118564865,0.0007627616,0.00007344263,0.91921204,0.004777621,0.000014010253,0.00003741582,3.9198764e-7,0.0010115579,0.0005734383,0.0016617636,0.07175698],"study_design_scores_gemma":[0.0019227203,0.0012323295,0.00041249217,0.12976427,0.015117514,0.000056871628,0.00018629253,0.0000042925226,0.014428527,0.00040207664,0.83400685,0.0024657443],"about_ca_topic_score_codex":0.000020456919,"about_ca_topic_score_gemma":0.0000010759226,"teacher_disagreement_score":0.83234507,"about_ca_system_score_codex":0.0007688044,"about_ca_system_score_gemma":0.0013255966,"threshold_uncertainty_score":0.99926233},"labels":[],"label_agreement":null},{"id":"W4416193857","doi":"10.26434/chemrxiv-2025-409m2","title":"Faster, Simpler, and More Precise Calibration Curves: Expanding the Scope of Continuous Calibration","year":2025,"lang":"","type":"article","venue":"ChemRxiv","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Range (aeronautics); Analyte; Calibration curve; Scope (computer science); Noise (video); Matrix (chemical analysis)","score_opus":0.017146883673233394,"score_gpt":0.29950580004727984,"score_spread":0.2823589163740464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416193857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83842564,0.13174811,0.008788868,0.0033677558,0.00031955526,0.0007319388,0.000042301723,0.00010651,0.016469296],"genre_scores_gemma":[0.979716,0.012468473,0.00008489628,0.0004983203,0.00015207037,0.000048384376,0.00007195643,0.000031543157,0.006928361],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976519,0.000067049696,0.0009040819,0.00061531196,0.00034868607,0.00041299762],"domain_scores_gemma":[0.9979729,0.0005112938,0.00051598734,0.00072785275,0.00015941368,0.00011254788],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030800008,0.00041261615,0.0007193832,0.0001925214,0.0003052699,0.0001841201,0.0004346572,0.0003208998,0.0009393433],"category_scores_gemma":[0.000540309,0.00033848014,0.00019901236,0.0014314621,0.00047803542,0.0004055261,0.00023422058,0.0003835056,0.0000021479125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031414162,0.0006265356,0.026598712,0.008476635,0.0012858905,0.000009229354,0.0020935365,0.000060772763,0.92116064,0.00089336484,0.031455908,0.007024621],"study_design_scores_gemma":[0.0009116248,0.000040042225,0.00045317193,0.0011256768,0.0009336666,0.0000047420936,0.0022244793,0.0027940664,0.99029696,0.00029684522,0.0006145989,0.0003041286],"about_ca_topic_score_codex":0.000087313354,"about_ca_topic_score_gemma":0.0000092446535,"teacher_disagreement_score":0.14129034,"about_ca_system_score_codex":0.00008661767,"about_ca_system_score_gemma":0.00020909947,"threshold_uncertainty_score":0.99997395},"labels":[],"label_agreement":null},{"id":"W4416347458","doi":"10.1016/j.fbp.2025.11.011","title":"Enhanced mask-random forest RCNN and 3D point cloud assisted shape and defect recognition for protein and aflatoxin prediction in nut processing","year":2025,"lang":"en","type":"article","venue":"Food and Bioproducts Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Guangzhou Science and Technology Program key projects","keywords":"Point cloud; Aflatoxin; Sphericity; Nut; Brazil nut; Cloud computing; Food processing; Quality (philosophy); Random forest","score_opus":0.016245168213687634,"score_gpt":0.2544107519732545,"score_spread":0.2381655837595669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416347458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9836852,0.014049786,0.000834669,0.00033025813,0.000028378417,0.00043965803,0.00002726991,0.000089511814,0.00051524845],"genre_scores_gemma":[0.9971832,0.00017131191,0.0019138247,0.000039046816,0.00016741465,0.00013402109,0.00005127765,0.000021801496,0.0003180735],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99845964,0.000016187067,0.0003606416,0.00073788373,0.00011928075,0.0003063676],"domain_scores_gemma":[0.9994632,0.00004533707,0.00017394578,0.00010861325,0.00013552542,0.00007338487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029412264,0.00026297415,0.00037101848,0.0002567591,0.0003331057,0.000268668,0.00005287129,0.00019039215,0.000008716501],"category_scores_gemma":[0.00040161592,0.00023396831,0.000027679833,0.0006852151,0.00015640323,0.00035353316,0.000059670467,0.00019781764,1.6109445e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007153329,0.00013132332,0.0057609505,0.0061310753,0.000079168676,0.0000016529344,0.00054476346,5.797104e-7,0.7774867,0.000017734399,0.00001740403,0.20911334],"study_design_scores_gemma":[0.0042045894,0.00026130298,0.0045364983,0.0018140667,0.00036708912,0.000039574854,0.0010786096,0.0031809288,0.9808944,0.0030909667,0.00010906827,0.00042289766],"about_ca_topic_score_codex":0.000013182865,"about_ca_topic_score_gemma":0.000046989706,"teacher_disagreement_score":0.20869043,"about_ca_system_score_codex":0.000036803456,"about_ca_system_score_gemma":0.000089079585,"threshold_uncertainty_score":0.9540951},"labels":[],"label_agreement":null},{"id":"W4416397324","doi":"10.1016/j.jpba.2025.117255","title":"Strategic use of calibration transfer to minimize experimental runs for multivariate calibrations within the QbD design space","year":2025,"lang":"en","type":"article","venue":"Journal of Pharmaceutical and Biomedical Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Calibration; Partial least squares regression; Factorial experiment; Design of experiments; Factorial; Multivariate statistics; Regression; Chemometrics; Quality by Design; Fractional factorial design","score_opus":0.09917798255260855,"score_gpt":0.3807292547500394,"score_spread":0.2815512721974308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416397324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30318898,0.00060767814,0.6897737,0.006124356,0.00006415477,0.00011155606,0.0000657766,0.000009938953,0.00005392112],"genre_scores_gemma":[0.9898047,0.000049644827,0.009304514,0.00042804386,0.000093264,0.000010014537,0.000010924415,0.0000060466605,0.00029283695],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985569,0.00007309929,0.0006897726,0.0001765654,0.00032850064,0.00017517898],"domain_scores_gemma":[0.9986405,0.0007719757,0.00010453871,0.00010799154,0.00012517573,0.00024986445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035843937,0.00014826705,0.0004662346,0.00038428503,0.00012119331,0.000077098644,0.00018559703,0.00011387897,0.00052304124],"category_scores_gemma":[0.00017772938,0.00008649667,0.0003462792,0.0016309184,0.00021882345,0.00013158594,0.000029510906,0.00021086675,2.9252593e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008185623,0.0007111214,0.0006349594,0.00007407904,0.0038994777,0.0000068294426,0.00034054983,0.0010780958,0.9887659,0.002880396,0.0005869689,0.00020304223],"study_design_scores_gemma":[0.0012218604,0.00012652732,0.00010045403,0.000027141965,0.0062849224,0.0000056382187,0.001117737,0.11241783,0.8773862,0.00039150583,0.0007938208,0.0001263732],"about_ca_topic_score_codex":0.00003808845,"about_ca_topic_score_gemma":0.0000032023268,"teacher_disagreement_score":0.68661577,"about_ca_system_score_codex":0.000035276815,"about_ca_system_score_gemma":0.00012012798,"threshold_uncertainty_score":0.57269347},"labels":[],"label_agreement":null},{"id":"W4416411238","doi":"10.1016/j.fochx.2025.103300","title":"A homogeneity evaluation method of food soft matter based on moisture content test by casting-near-infrared spectroscopy","year":2025,"lang":"en","type":"article","venue":"Food Chemistry X","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Science Foundation of Hubei Province; Natural Science Foundation of Xiaogan City; National Natural Science Foundation of China","keywords":"Homogeneity (statistics); Partial least squares regression; Principal component analysis; Analytical Chemistry (journal); Spectroscopy; Soft matter; Compatibility (geochemistry); Reflectivity","score_opus":0.02618919815121732,"score_gpt":0.30128910568046513,"score_spread":0.2750999075292478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416411238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77746236,0.0017140369,0.010224403,0.0006580251,0.000101682395,0.00033025618,0.0012886525,0.00020828487,0.20801231],"genre_scores_gemma":[0.9898795,0.00000448177,0.0036409558,0.00034448944,0.000087523,0.00009434577,0.00031765038,0.000037906902,0.00559314],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99762875,0.000033356842,0.0005563845,0.0007078303,0.0006371671,0.0004365024],"domain_scores_gemma":[0.99785334,0.0004884908,0.0003539857,0.00086816394,0.00031183867,0.00012418446],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003032102,0.00041613288,0.0005753052,0.000068199515,0.000157699,0.00006956685,0.00044805667,0.00035634224,0.0049033444],"category_scores_gemma":[0.0008701055,0.00041837597,0.0002826865,0.0006542439,0.000118451666,0.000039798026,0.00007782706,0.00044372774,0.00001643333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013862026,0.00056575594,0.009784951,0.000640691,0.00031572985,0.0000014107953,0.000028074792,0.00013495302,0.97597015,0.0000048688325,0.012232072,0.00018273918],"study_design_scores_gemma":[0.0015507821,0.00020544212,0.0003729249,0.00013038854,0.0006063942,0.000005063333,0.00015685985,0.0032553144,0.9921801,0.000283075,0.0009158068,0.00033783817],"about_ca_topic_score_codex":0.000038858085,"about_ca_topic_score_gemma":0.0000050108783,"teacher_disagreement_score":0.21241717,"about_ca_system_score_codex":0.00036257133,"about_ca_system_score_gemma":0.00029681684,"threshold_uncertainty_score":0.9998268},"labels":[],"label_agreement":null},{"id":"W4416717720","doi":"10.1177/27551857251398877","title":"Determination of Isophthalic Acid Comonomer Content in Poly(Ethylene Terephthalate) (PET) Using Raman Spectroscopy","year":2025,"lang":"en","type":"article","venue":"Applied Spectroscopy Practica","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Comonomer; Isophthalic acid; Raman spectroscopy; Bottle; Spectroscopy; Analytical Chemistry (journal); Calibration; Amorphous solid","score_opus":0.03577854428520779,"score_gpt":0.3271806530742054,"score_spread":0.29140210878899764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416717720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8157755,0.0011454208,0.010090188,0.00049249904,0.00014888063,0.00037839508,0.000023409815,0.00013666856,0.17180903],"genre_scores_gemma":[0.96420914,0.00018297342,0.034218706,0.00022694508,0.00010011695,0.0000753533,0.000035455367,0.000061807266,0.0008895185],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996579,0.000057641482,0.00113408,0.0009137942,0.00046596164,0.00084952044],"domain_scores_gemma":[0.9976928,0.0003777132,0.00071776245,0.00095854816,0.00010894731,0.00014420174],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040537785,0.0005576141,0.0010308818,0.000819112,0.00017451606,0.00011408252,0.00056384015,0.00026807655,0.002004427],"category_scores_gemma":[0.00021708693,0.0005918076,0.0002218231,0.0015240673,0.00025593457,0.00026046476,0.00018997707,0.00081729837,0.000042126947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043755228,0.0006698004,0.007468239,0.00018189792,0.00017331475,0.000049929942,0.00010367836,0.0000043873492,0.9851812,0.0053530536,0.000073307914,0.00030363505],"study_design_scores_gemma":[0.0015741611,0.00007887993,0.0012065016,0.00008619272,0.00041236987,0.00004802285,0.00068668067,0.0005412792,0.99170154,0.002908123,0.00029748707,0.00045876805],"about_ca_topic_score_codex":0.0004907041,"about_ca_topic_score_gemma":0.000032850505,"teacher_disagreement_score":0.17091952,"about_ca_system_score_codex":0.0007974107,"about_ca_system_score_gemma":0.000256697,"threshold_uncertainty_score":0.99965334},"labels":[],"label_agreement":null},{"id":"W4416926420","doi":"10.1145/3770678","title":"MobiLyzer: Fine-grained Mobile Liquid Analyzer","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RGB color model; Robustness (evolution); Mobile device; Interference (communication); Spectral imaging; Hyperspectral imaging; Spectral signature","score_opus":0.007438857499284834,"score_gpt":0.2713830344703098,"score_spread":0.26394417697102496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416926420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9724543,0.0026400723,0.0000043465407,0.0008855194,0.000087925204,0.00030728214,0.000019141598,0.00052783196,0.023073576],"genre_scores_gemma":[0.99300677,0.0008284763,0.00022189056,0.00007955015,0.000030210098,0.00062781293,0.0000013758523,0.000024057004,0.005179846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830616,0.00000458039,0.00042777407,0.0005987121,0.0002460283,0.0004167739],"domain_scores_gemma":[0.99819356,0.00032335136,0.0003584064,0.00078260066,0.00031015996,0.00003192698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014419595,0.00035960396,0.0005735733,0.0004848451,0.00025391654,0.00008615296,0.0019427143,0.0003098782,0.00016053874],"category_scores_gemma":[0.0018402118,0.0002511877,0.00022902037,0.001306447,0.0004695975,0.00023007294,0.0015941542,0.00067433366,0.000007564445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007696922,0.00051515823,0.005704803,0.0005012272,0.00064304273,0.000001712545,0.0003447468,0.000032481334,0.9695863,0.0017148476,0.00835234,0.011833634],"study_design_scores_gemma":[0.00035182966,0.00045159078,0.00010194015,0.00035498085,0.00020456083,0.0000070434244,0.010043134,0.000040535597,0.97603446,0.0075107124,0.004664179,0.00023502667],"about_ca_topic_score_codex":0.000050303926,"about_ca_topic_score_gemma":0.000004111529,"teacher_disagreement_score":0.020552477,"about_ca_system_score_codex":0.0001432525,"about_ca_system_score_gemma":0.00004040725,"threshold_uncertainty_score":0.99999404},"labels":[],"label_agreement":null},{"id":"W4417290452","doi":"10.1016/b978-0-443-22163-7.00014-0","title":"Variable selection for analytical chemistry data","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; The Metabolomics Innovation Centre; University of Victoria","funders":"","keywords":"Analyte; Feature selection; Instrumentation (computer programming); Selection (genetic algorithm); Chemometrics; Gas chromatography; Table (database); Feature (linguistics)","score_opus":0.02955691923508154,"score_gpt":0.2958508523400166,"score_spread":0.2662939331049351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417290452","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000021374258,0.0010537594,0.0006124913,0.00004122056,0.000075647346,0.00014076996,0.0007027933,0.00014523159,0.99722594],"genre_scores_gemma":[0.000040224433,0.00004678808,0.004167107,0.00013763995,0.00084224506,0.000038695234,0.001378446,0.000075292795,0.99327356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782026,0.000003109832,0.0005078536,0.0009855637,0.0002856069,0.00039758833],"domain_scores_gemma":[0.9978939,0.00021767379,0.00024398361,0.0013441822,0.00017039783,0.00012980975],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017877722,0.00048691247,0.00075064367,0.00012008932,0.00016264344,0.00008396818,0.0009375097,0.00081009074,0.0072191516],"category_scores_gemma":[0.00018847766,0.0005057358,0.00024546066,0.00006198454,0.00009783179,0.00004740329,0.00035523763,0.00065730035,0.00003286717],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021551346,0.00011725411,0.000043976845,0.00580267,0.0049126693,0.000013221739,0.000022067623,0.000004148236,0.0077170725,0.06419521,0.0784619,0.8384943],"study_design_scores_gemma":[0.0003152852,0.000009973165,1.00206144e-7,0.00019698356,0.0022141088,0.000010298373,0.0000051958023,0.0009426985,0.008582236,0.012413263,0.97484183,0.00046801887],"about_ca_topic_score_codex":0.0000014748179,"about_ca_topic_score_gemma":0.0000035562148,"teacher_disagreement_score":0.89637995,"about_ca_system_score_codex":0.00023721516,"about_ca_system_score_gemma":0.00044292316,"threshold_uncertainty_score":0.9997394},"labels":[],"label_agreement":null},{"id":"W4417362134","doi":"10.1111/1750-3841.70769","title":"Prediction of Apple Quality Indicators Under Different Bagging Treatments Using Hyperspectral Imaging Integrated With a Stacking SDAE‐PLSR‐RR Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Guangxi Normal University; National Natural Science Foundation of China; British Columbia Innovation Council","keywords":"Hyperspectral imaging; Partial least squares regression; Autoencoder; Pattern recognition (psychology); Deep learning; Artificial neural network; Sorting; Quality (philosophy); Regression","score_opus":0.03650919593983142,"score_gpt":0.3197072824222248,"score_spread":0.28319808648239336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417362134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8911298,0.00038593018,0.10772287,0.000034124114,0.00003517085,0.000028245264,0.0000054138636,0.000018327377,0.0006401295],"genre_scores_gemma":[0.99665004,0.000025067351,0.0032271154,0.000014319858,0.000022884222,9.6119e-7,0.0000014179003,0.000008387813,0.000049832008],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99827796,0.000026126823,0.0005032653,0.00024174168,0.00064868014,0.00030222978],"domain_scores_gemma":[0.998771,0.00007249803,0.00067510083,0.00015183758,0.00022564823,0.00010387694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004315685,0.00016514151,0.00035167567,0.00080193975,0.0002880087,0.00008790256,0.0003317427,0.00004282965,0.00002766824],"category_scores_gemma":[0.00014957813,0.00011935279,0.000107437634,0.0018480963,0.00027601997,0.00040059251,0.00005417432,0.00043556333,8.549686e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010122721,0.00021825671,0.17735867,0.00005558511,0.00026735984,0.0000034790714,0.0008393563,0.03516221,0.78442985,0.0003109911,0.000001755737,0.0012512412],"study_design_scores_gemma":[0.001122242,0.0001603049,0.008063555,0.00029127244,0.00047815294,0.00003864939,0.011259658,0.1629216,0.8149528,0.0005482379,0.0000019282097,0.00016160375],"about_ca_topic_score_codex":0.00003429779,"about_ca_topic_score_gemma":0.000005202515,"teacher_disagreement_score":0.16929512,"about_ca_system_score_codex":0.0007634467,"about_ca_system_score_gemma":0.00044410495,"threshold_uncertainty_score":0.48670658},"labels":[],"label_agreement":null},{"id":"W48821728","doi":"10.53846/goediss-1745","title":"Methodische Untersuchungen zum Einsatz der Nahinfrarot-Spektroskopie (NIRS) zur Qualitätsbeurteilung von High-Oleic-Sonnenblumen","year":2007,"lang":"de","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universidade Federal de Ouro Preto; Rheinische Friedrich-Wilhelms-Universität Bonn; Killam Trusts","keywords":"Achene; Sunflower; Sunflower oil; Yield (engineering); Chemistry; Food science; Oleic acid; Mathematics; Environmental science; Horticulture; Materials science; Biology; Biochemistry","score_opus":0.019236919718044244,"score_gpt":0.3530897246977426,"score_spread":0.3338528049796983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W48821728","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43342826,0.0664447,0.075648315,0.0016391285,0.0081211645,0.0027445653,0.0012113951,0.0022265757,0.4085359],"genre_scores_gemma":[0.40052998,0.0040488117,0.05377222,0.0018561652,0.006421231,0.0003554561,0.0196433,0.0010494173,0.51232344],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9869636,0.00042206753,0.0034760803,0.0034589842,0.002601687,0.0030775322],"domain_scores_gemma":[0.990903,0.0018075091,0.0022649223,0.0028786757,0.000937264,0.001208632],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0020012653,0.0029870945,0.0035456128,0.0017921053,0.0009581292,0.00083755486,0.002617144,0.0040867324,0.047007896],"category_scores_gemma":[0.0012428403,0.002959144,0.0019308113,0.0033868582,0.0003561712,0.00077422,0.0003796443,0.0035169048,0.004006945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005759503,0.0062746704,0.01618532,0.014521864,0.044807967,0.0011851002,0.020376252,0.00013469723,0.7430599,0.026635457,0.09960162,0.021457685],"study_design_scores_gemma":[0.0034768179,0.0003167128,0.0014007923,0.0005223323,0.013783931,0.000029999062,0.024877204,0.00045502375,0.91821873,0.0012686853,0.031032683,0.00461707],"about_ca_topic_score_codex":0.003918365,"about_ca_topic_score_gemma":0.000795822,"teacher_disagreement_score":0.17515889,"about_ca_system_score_codex":0.001574883,"about_ca_system_score_gemma":0.00084216933,"threshold_uncertainty_score":0.99878204},"labels":[],"label_agreement":null},{"id":"W571326948","doi":"10.1016/b978-0-444-63578-5.50072-4","title":"An Approximate Modelling Method for Industrial l-lysine Fermentation Process","year":2015,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Process (computing); Bayesian network; Fermentation; Multivariate statistics; Imputation (statistics); Computer science; Conditional independence; Process engineering; Missing data; Machine learning; Chemistry; Artificial intelligence; Engineering; Food science","score_opus":0.04465391413712568,"score_gpt":0.28520640839441197,"score_spread":0.24055249425728628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571326948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004789137,0.00047892463,0.99016994,0.000054724347,0.00075599836,0.00061605655,0.00021504764,0.0019669945,0.00095314614],"genre_scores_gemma":[0.03402964,0.000043126798,0.9379821,0.0001657148,0.017266586,0.00045750488,0.005157694,0.0015262547,0.0033714075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99274856,0.000011117552,0.0020244808,0.0024257742,0.001153467,0.0016366071],"domain_scores_gemma":[0.99569106,0.00049488293,0.0006222835,0.001384288,0.0005820476,0.0012254332],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.00043581298,0.002250708,0.002651251,0.00073202635,0.0001003396,0.00035484112,0.0017568833,0.0023642709,0.00024434717],"category_scores_gemma":[0.000107658765,0.0025631133,0.00094721327,0.00048171825,0.00009190562,0.00049295323,0.00048097677,0.0024993669,0.000021132073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014617888,0.0002111518,8.692846e-7,0.0018988997,0.0010456083,0.000034330566,0.00019062794,0.72492373,0.26352713,0.002658294,0.0009358037,0.004427402],"study_design_scores_gemma":[0.0020040518,0.000096565964,4.3461846e-8,0.0004435069,0.00067806494,0.00007855667,0.000003925787,0.70176643,0.2896972,0.0009864619,0.0023667456,0.001878426],"about_ca_topic_score_codex":0.0000087816,"about_ca_topic_score_gemma":6.668703e-8,"teacher_disagreement_score":0.0521879,"about_ca_system_score_codex":0.0011734928,"about_ca_system_score_gemma":0.00019084422,"threshold_uncertainty_score":0.99980193},"labels":[],"label_agreement":null},{"id":"W574148716","doi":"10.71781/11732","title":"Mise en place et application d'un spectromètre de dichroïsme linéaire infrarouge avec modulation de la polarisation pour l'étude de l'orientation des mélanges polymères","year":2008,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.0041449383386514445,"score_gpt":0.20858504508901393,"score_spread":0.20444010675036248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W574148716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9549517,0.013573369,0.018777383,0.00037568103,0.00012430034,0.0003166807,0.00013854775,0.00023399331,0.011508349],"genre_scores_gemma":[0.97052383,0.0039255815,0.007832318,0.000121830526,0.00048709565,0.00007718867,0.0025114352,0.00007981498,0.014440897],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9966006,0.00047878682,0.0005927705,0.00085142575,0.0007336117,0.0007427503],"domain_scores_gemma":[0.9974091,0.0005663277,0.00080434245,0.00047556165,0.00031224222,0.00043241045],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00039576428,0.0006818486,0.0005282491,0.00054570247,0.0043162713,0.00014387358,0.0005053279,0.0012995687,0.00013248865],"category_scores_gemma":[0.00032808539,0.0008836962,0.0003995866,0.000776306,0.0003943762,0.00067401526,0.00010201424,0.0009187553,0.000033936267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010272657,0.0007959717,0.23248054,0.0005468045,0.0009245992,0.00068951375,0.12841469,0.021211317,0.601288,0.007416549,0.00025307245,0.0049516703],"study_design_scores_gemma":[0.0017905841,0.00009114964,0.49479628,0.00025650632,0.0014106083,0.0016454451,0.040816437,0.019048166,0.43527395,0.002255527,0.0016874905,0.00092787866],"about_ca_topic_score_codex":0.03988588,"about_ca_topic_score_gemma":0.003932905,"teacher_disagreement_score":0.26231572,"about_ca_system_score_codex":0.017066302,"about_ca_system_score_gemma":0.0032795158,"threshold_uncertainty_score":0.99999696},"labels":[],"label_agreement":null},{"id":"W622906426","doi":"10.71781/9924","title":"Recherche d'éléments répétés par analyse des distributions de fréquences d'oligonucléotides","year":2009,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy; Psychology; Physics","score_opus":0.1667893234626097,"score_gpt":0.43954526936635846,"score_spread":0.27275594590374874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W622906426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85671973,0.006209285,0.00051631936,0.00032073614,0.00013817563,0.00031877865,0.00063197967,0.000011048239,0.13513397],"genre_scores_gemma":[0.7542413,0.0040590786,0.15076639,0.00010044345,0.00040342828,0.000113103335,0.008146268,0.00008965634,0.082080364],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960539,0.0003316526,0.0009954552,0.0011579557,0.00039849404,0.0010625568],"domain_scores_gemma":[0.9970429,0.00057553797,0.00073088147,0.0008085673,0.00035934054,0.00048279008],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010352076,0.00081230834,0.0010980488,0.00028042874,0.00092779065,0.0009673776,0.0019964185,0.0014869754,0.030130025],"category_scores_gemma":[0.0017221939,0.00084684114,0.00049853,0.0022789105,0.00037404915,0.0005609136,0.0001616835,0.0013883067,0.0010640795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005532753,0.0033148385,0.06983497,0.000608397,0.00485516,0.00032459045,0.008383929,0.0001697571,0.3563397,0.00019415973,0.0044240686,0.55099714],"study_design_scores_gemma":[0.0005351307,0.000111181886,0.008489529,0.0004995833,0.0035737117,0.000029490822,0.011929599,0.00012532466,0.9641655,0.003627323,0.0059908824,0.0009227253],"about_ca_topic_score_codex":0.0037913464,"about_ca_topic_score_gemma":0.0023585572,"teacher_disagreement_score":0.6078258,"about_ca_system_score_codex":0.0015724106,"about_ca_system_score_gemma":0.0011808112,"threshold_uncertainty_score":0.9998093},"labels":[],"label_agreement":null},{"id":"W6903322985","doi":"10.1021/jf401279w.s001","title":"Authentication of the Botanical and Geographical Origin\\nof Distillers Dried Grains and Solubles (DDGS) by FT-IR Spectroscopy","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Principal component analysis; Linear discriminant analysis; Authentication (law); Spectroscopy; Characterization (materials science); Chemometrics; Reflection (computer programming)","score_opus":0.015498252238181574,"score_gpt":0.2541259096680103,"score_spread":0.23862765742982872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903322985","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9118939,0.0020359093,0.00006627983,0.0029683893,0.000048511698,0.00021778261,0.07839534,0.00014875623,0.0042251097],"genre_scores_gemma":[0.99763995,0.000084147476,0.00007842565,0.00004642668,0.0000484113,0.00002737909,0.0011892534,0.00001598283,0.0008700079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905175,0.000014279495,0.00020102243,0.00029873353,0.00020850752,0.00022570015],"domain_scores_gemma":[0.99918777,0.00022194519,0.00012997762,0.00030885468,0.000046836143,0.000104625215],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000032922628,0.00014947282,0.0002039605,0.000038807906,0.000108503395,0.00003453893,0.00018755063,0.00013336963,0.028169667],"category_scores_gemma":[0.00056790235,0.000090087386,0.00008463567,0.00025317047,0.000114576236,0.00007012826,0.00012531293,0.0001201228,0.000016952838],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006122753,0.00018978234,0.028693536,0.00034230488,0.00023972397,0.0000025287839,0.00012129727,8.2238074e-8,0.70070046,0.000975417,0.2679095,0.00076415093],"study_design_scores_gemma":[0.0011460078,0.00005618969,0.020586547,0.0006286783,0.00021368283,0.000016820093,0.00016059753,0.000069177695,0.9286585,0.0010429079,0.047011208,0.00040971133],"about_ca_topic_score_codex":0.000010108924,"about_ca_topic_score_gemma":0.000009689334,"teacher_disagreement_score":0.22795801,"about_ca_system_score_codex":0.000029056806,"about_ca_system_score_gemma":0.00003163339,"threshold_uncertainty_score":0.9727187},"labels":[],"label_agreement":null},{"id":"W6910741221","doi":"10.5061/dryad.d28080r","title":"Data from: A note on measuring natural selection on principal component scores","year":2018,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multicollinearity; Selection (genetic algorithm); Principal component analysis; Regression; Principal component regression; Regression analysis; Feature selection; Interpretation (philosophy)","score_opus":0.04856258292910402,"score_gpt":0.3063255079014235,"score_spread":0.25776292497231945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6910741221","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060322054,0.00073594117,0.000042341442,0.000029948296,0.00041038624,0.00011373811,0.9378467,0.00014795737,0.0003508928],"genre_scores_gemma":[0.025303524,0.0009931119,0.00034107539,0.00026861206,0.0028758126,0.000011203235,0.97001314,0.00006240699,0.0001311042],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957207,0.00012282602,0.0005698374,0.0020930697,0.0007758778,0.0007177016],"domain_scores_gemma":[0.9941682,0.0007565393,0.00049746933,0.0043047857,0.000039653154,0.00023332993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042189195,0.0007827748,0.0007935629,0.00027798093,0.0005916812,0.00035197937,0.0049087554,0.00032590318,0.0003165105],"category_scores_gemma":[0.00008175168,0.0006777891,0.000077733515,0.00042103458,0.00015577117,0.00032545833,0.0033016284,0.0014999603,0.00010203509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075949077,0.00031420158,0.00087991427,0.0009908301,0.0008844712,0.000038772483,0.000109833425,0.00012238837,0.0023143508,0.0000019172433,0.99269015,0.00089370087],"study_design_scores_gemma":[0.0010192414,0.00016000723,0.0021367169,0.0031673443,0.0017712476,0.00003226857,0.000108488435,0.11998264,0.002613236,0.00005132029,0.8673906,0.0015668926],"about_ca_topic_score_codex":0.0047972286,"about_ca_topic_score_gemma":0.011548346,"teacher_disagreement_score":0.12529953,"about_ca_system_score_codex":0.00011827826,"about_ca_system_score_gemma":0.000076581215,"threshold_uncertainty_score":0.99956733},"labels":[],"label_agreement":null},{"id":"W6912688990","doi":"10.5281/zenodo.5186740","title":"Atheta (Dimetrota) strigosula Casey 1910","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; Yukon University; Natural Resources Canada","funders":"","keywords":"Balsam; Nearctic ecozone; Habitat; Deciduous; Plant litter; Table (database)","score_opus":0.03549927949250312,"score_gpt":0.27125757313531856,"score_spread":0.23575829364281545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912688990","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14229886,0.00020574863,0.002121389,0.00035535867,0.000075068834,0.00012492694,0.00024189186,0.0012141013,0.8533627],"genre_scores_gemma":[0.9895355,0.00004827686,0.00025401657,0.00013605725,0.0004921223,2.2938453e-8,0.0006504229,0.0009743962,0.007909183],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984234,0.00006706175,0.00024025116,0.00045146825,0.00037820355,0.0004396127],"domain_scores_gemma":[0.9985596,0.000027184153,0.00010735934,0.000614272,0.00048446748,0.00020713774],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00030103253,0.0001689805,0.00018283298,0.00025329812,0.0018678129,0.00048414146,0.0009634319,0.0000965591,0.14604516],"category_scores_gemma":[0.0006507989,0.0001725726,0.000083754705,0.0010519125,0.00029006612,0.00016407113,0.00069519825,0.00027310592,0.014521634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023193777,0.00067707285,0.00010274607,0.00019056707,0.00049503904,0.00007630753,0.0013304151,0.000010426298,0.31824708,0.0116918655,0.5996555,0.06729104],"study_design_scores_gemma":[0.00040689463,0.00013741184,0.00008107439,0.000010731486,0.00005855732,0.00011467621,0.00031120254,0.00015320802,0.09615796,0.0001997878,0.90216166,0.00020686476],"about_ca_topic_score_codex":0.000035068915,"about_ca_topic_score_gemma":3.6333748e-7,"teacher_disagreement_score":0.84723663,"about_ca_system_score_codex":0.00016974038,"about_ca_system_score_gemma":0.000004168857,"threshold_uncertainty_score":0.9994316},"labels":[],"label_agreement":null},{"id":"W6912796475","doi":"10.5683/sp3/iarhpy","title":"Replication data for: 1986 Census of Canada: Basic summary tabulations","year":2016,"lang":"en","type":"dataset","venue":"Borealis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Replication (statistics); Statistical analysis; Population; Comparability","score_opus":0.04463945726349234,"score_gpt":0.3114490888608326,"score_spread":0.2668096315973403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912796475","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000062882746,0.00060897757,0.000034223565,0.00031485118,0.000042983524,0.00008954065,0.9969175,0.000020345049,0.0019653337],"genre_scores_gemma":[0.000024556288,0.0007734644,0.0000764174,0.000052260726,0.00043663883,0.00004149032,0.9970753,0.000021993952,0.0014978624],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998453,0.000008835312,0.0004175836,0.00060098805,0.00026310826,0.00025646735],"domain_scores_gemma":[0.9940724,0.00030960926,0.0004440246,0.0049379934,0.00014510223,0.00009092185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011627607,0.00022371103,0.00041140273,0.000105120664,0.00009333787,0.000013060686,0.0010668152,0.00024482032,0.00038432004],"category_scores_gemma":[0.00085516187,0.00018631178,0.00007690462,0.00019524274,0.000053616077,0.000057537094,0.00014646632,0.000102728845,5.374575e-7],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021546237,0.0000369478,0.000018454857,0.00021834864,0.00015955298,0.0000014292659,9.877e-7,4.978337e-7,0.00022941016,0.00003484131,0.9988957,0.0003822527],"study_design_scores_gemma":[0.00020593348,0.000011547475,0.000045515782,0.000042266296,0.0005284897,0.000002261122,0.000011779597,0.000013930816,0.0058859335,0.000065202854,0.9929827,0.00020446247],"about_ca_topic_score_codex":0.868837,"about_ca_topic_score_gemma":0.89070725,"teacher_disagreement_score":0.0218703,"about_ca_system_score_codex":0.00030985917,"about_ca_system_score_gemma":0.0009615948,"threshold_uncertainty_score":0.7597574},"labels":[],"label_agreement":null},{"id":"W6923791702","doi":"10.14288/cl.vi260.197879","title":"The Next Day","year":2023,"lang":"en","type":"article","venue":"Open Collections","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Poetry; Period (music); Work (physics)","score_opus":0.048843640107191624,"score_gpt":0.32256442704069166,"score_spread":0.27372078693350005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923791702","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009182313,0.00008702506,0.00003908532,0.0007507636,0.00010651933,0.000052740033,0.000011104913,0.00012091421,0.9979136],"genre_scores_gemma":[0.032291207,0.00021359912,0.000034127417,0.000048495258,0.00008943453,0.000108283435,0.000009462881,0.00001008735,0.9671953],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994876,0.000009320886,0.00009754481,0.00013538812,0.000089935995,0.0001802262],"domain_scores_gemma":[0.9993532,0.00028371028,0.000029523817,0.00026183942,0.00003105612,0.00004068212],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012336303,0.000059229023,0.00007817835,0.00003499398,0.004010602,0.0014621243,0.00042470553,0.000037034126,0.0061423634],"category_scores_gemma":[0.00017308272,0.00004303219,0.00004826391,0.003218024,0.000039439852,0.00008985132,0.00015991945,0.00010647098,0.00025391666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004140044,0.000017187816,0.00004657896,0.0000017640855,0.00006004591,0.0000017193709,0.000028587043,0.0000114583545,0.002190208,0.00018895447,0.99694574,0.0005036133],"study_design_scores_gemma":[0.00018261047,0.000008955008,0.00006282812,0.000002970104,0.000055406483,0.0000048876886,0.0015347133,0.00038737818,0.033298247,0.0023318897,0.96203095,0.00009915499],"about_ca_topic_score_codex":0.0012720443,"about_ca_topic_score_gemma":0.0005469615,"teacher_disagreement_score":0.034914784,"about_ca_system_score_codex":0.000051225194,"about_ca_system_score_gemma":0.000063774336,"threshold_uncertainty_score":0.9995745},"labels":[],"label_agreement":null},{"id":"W6927831497","doi":"10.3389/fcvm.2022.971848.s004","title":"Image_3_Left atrial appendage flow velocity predicts recurrence of atrial fibrillation after catheter ablation: A systematic review and meta-analysis.tif","year":2022,"lang":"en","type":"other","venue":"Figshare","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atrial fibrillation; Catheter ablation; Meta-analysis; Catheter; Grading (engineering); Statistical analysis","score_opus":0.04132517903804071,"score_gpt":0.2937350534175283,"score_spread":0.2524098743794876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6927831497","genre_codex":"review","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000021727512,0.53266025,0.000011636984,0.000042566535,0.000037388676,0.0010869319,0.4392977,0.0001316207,0.026729716],"genre_scores_gemma":[0.0032915745,0.0063370448,0.0011625227,0.00030639235,0.002662979,0.0005790858,0.5616985,0.0008097657,0.42315215],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.997393,0.0001587285,0.0008535472,0.00067728007,0.00067427626,0.00024315868],"domain_scores_gemma":[0.9972201,0.0002485975,0.0014257984,0.00089160405,0.00011720121,0.000096724354],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001970301,0.00047942513,0.0029811696,0.00037776757,0.000066462875,0.00006272239,0.00035924627,0.00032298165,0.8965126],"category_scores_gemma":[0.0015821347,0.00039831147,0.0026305285,0.0010755048,0.000020408574,0.00009111435,0.00022679158,0.00032672586,0.00019759373],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035126217,0.00002018107,0.000049924187,0.43812183,0.124182865,0.000020439244,0.00009153151,0.00004730742,0.0000021679757,6.809635e-7,0.43740597,0.000021991174],"study_design_scores_gemma":[0.00040425372,0.000038181464,0.000009986232,0.0077583417,0.9291883,0.000013615412,0.000018329616,0.00033767484,0.00008261313,0.000007820677,0.06140984,0.00073106145],"about_ca_topic_score_codex":0.000025707834,"about_ca_topic_score_gemma":0.000022561835,"teacher_disagreement_score":0.89631504,"about_ca_system_score_codex":0.00009832184,"about_ca_system_score_gemma":0.00008854511,"threshold_uncertainty_score":0.9998469},"labels":[],"label_agreement":null},{"id":"W6930227706","doi":"10.5281/zenodo.12128727","title":"MANUP Gummies Canada For Sex?","year":2024,"lang":"sl","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Product (mathematics); Work (physics); Limiting","score_opus":0.026039575302852964,"score_gpt":0.2564423839160616,"score_spread":0.23040280861320864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930227706","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004138404,0.0027626795,0.0007073029,0.00089615845,0.00033742358,0.00048851024,0.0033514225,0.00048470648,0.99055797],"genre_scores_gemma":[0.022605946,0.0004896903,0.00015959091,0.00025965748,0.001184874,8.6714486e-8,0.0045265877,0.0071363845,0.9636372],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967064,0.00010351117,0.00051674095,0.0011151695,0.0007017921,0.0008563738],"domain_scores_gemma":[0.99791324,0.00006753946,0.00027571528,0.00093749916,0.0004693319,0.00033670128],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00036478072,0.00054160564,0.00057892466,0.00048728602,0.0015058371,0.0013558851,0.0018465356,0.00035987637,0.5310468],"category_scores_gemma":[0.000753084,0.00058399077,0.00022156235,0.0009852456,0.00023279,0.0000854136,0.0012201885,0.00074120966,0.07956771],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043000862,0.00009440748,9.1718124e-7,0.0015722829,0.0007434859,0.00004305662,0.00039382215,0.0000052011255,0.0018826614,0.000879209,0.9857181,0.008623893],"study_design_scores_gemma":[0.00045207952,0.00010773109,0.0000041088647,0.00017018555,0.00046909385,0.000071254704,0.0031384923,0.000084305764,0.007627522,0.00024976156,0.9870286,0.00059688004],"about_ca_topic_score_codex":0.0050068176,"about_ca_topic_score_gemma":0.0005249997,"teacher_disagreement_score":0.4514791,"about_ca_system_score_codex":0.0009866537,"about_ca_system_score_gemma":0.000019552725,"threshold_uncertainty_score":0.99979407},"labels":[],"label_agreement":null},{"id":"W6930499316","doi":"10.5281/zenodo.12068824","title":"Du großer gott wenn ich die welt betrachte pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thou; 2019-20 coronavirus outbreak; Social life","score_opus":0.021625867997206182,"score_gpt":0.2524521783215836,"score_spread":0.23082631032437745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930499316","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043111067,0.0076298,0.0005469512,0.0013238933,0.00050242484,0.00037008853,0.0010613028,0.0015013978,0.986633],"genre_scores_gemma":[0.031106543,0.0038629314,0.00015497814,0.0003013696,0.0033354908,1.5905603e-7,0.0066277646,0.014817204,0.9397936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952631,0.0002663563,0.00074615434,0.0016019256,0.0010547598,0.0010677146],"domain_scores_gemma":[0.9969882,0.0000625846,0.000411629,0.0014027967,0.0005947764,0.0005400322],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00067335216,0.0007808156,0.000760752,0.0010363908,0.0017713107,0.002415442,0.0026291749,0.0007044492,0.7416587],"category_scores_gemma":[0.0010643943,0.0008299976,0.0003948657,0.0019715787,0.00049150974,0.00020229934,0.0024826017,0.0017449199,0.6782089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006788445,0.00037791397,0.0000065267723,0.0011178184,0.0013693536,0.00023755892,0.00046191068,0.0000040219466,0.0046672383,0.0014967407,0.9838048,0.006388195],"study_design_scores_gemma":[0.0006008834,0.00016935334,0.000023652674,0.000258661,0.00091584335,0.00013079871,0.00048107808,0.000105174244,0.004011544,0.00026690372,0.9922091,0.00082704367],"about_ca_topic_score_codex":0.000060091177,"about_ca_topic_score_gemma":0.0000012704477,"teacher_disagreement_score":0.06344976,"about_ca_system_score_codex":0.00065701734,"about_ca_system_score_gemma":0.000016304171,"threshold_uncertainty_score":0.9995282},"labels":[],"label_agreement":null},{"id":"W6931153878","doi":"10.5281/zenodo.3793285","title":"Carpophilus brachypterus","year":2008,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; Nova Scotia Hospital","funders":"","keywords":"Nova scotia; Willow; White (mutation); Christmas tree; New england; Ribes","score_opus":0.04416795889806541,"score_gpt":0.2512357228699066,"score_spread":0.20706776397184118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931153878","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2222328,0.00033420592,0.0009332439,0.0003471479,0.000045023444,0.00008042624,0.0001236247,0.0012290365,0.7746745],"genre_scores_gemma":[0.9921022,0.00016308748,0.00015992527,0.00013721293,0.00020663641,1.606967e-8,0.0006280582,0.00083411083,0.0057687648],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877983,0.0000421125,0.000192524,0.00034341755,0.00030805817,0.00033403878],"domain_scores_gemma":[0.99906486,0.000019346231,0.00007679563,0.00043411172,0.00024122206,0.0001636575],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013017046,0.0001329135,0.0001561357,0.00018371842,0.0020481476,0.00018633319,0.0007617195,0.000074418596,0.06536859],"category_scores_gemma":[0.00035461393,0.00014051671,0.00007253565,0.00062163355,0.0001768614,0.00015036068,0.0004971945,0.00025684037,0.0072565777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021800656,0.00086927356,0.00028186603,0.00027639733,0.0004674136,0.0003486475,0.0032231992,0.00008586648,0.25651234,0.0059256507,0.6652718,0.06651954],"study_design_scores_gemma":[0.00044188782,0.00006246398,0.00040407802,0.000007241857,0.00003565691,0.00050944206,0.00023054268,0.00008117088,0.0354878,0.00011448169,0.9624102,0.000215028],"about_ca_topic_score_codex":0.000028823077,"about_ca_topic_score_gemma":9.264806e-8,"teacher_disagreement_score":0.7698694,"about_ca_system_score_codex":0.00013542201,"about_ca_system_score_gemma":0.0000030943925,"threshold_uncertainty_score":0.99925107},"labels":[],"label_agreement":null},{"id":"W6931614532","doi":"10.5281/zenodo.6873638","title":"CPP SPM","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Triacetin; Emperipolesis; Fusible alloy","score_opus":0.027703573648757796,"score_gpt":0.2551107994040472,"score_spread":0.22740722575528938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931614532","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000464549,0.0007551955,0.00015031398,0.0001330738,0.000059781574,0.00010146323,0.000710419,0.0019127517,0.9961305],"genre_scores_gemma":[0.0025131921,0.00042862367,0.00012939291,0.00015777057,0.0005377476,5.9221623e-8,0.007472244,0.01331248,0.9754485],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983171,0.00007658779,0.00020232325,0.0005443166,0.00047616122,0.00038346945],"domain_scores_gemma":[0.9988277,0.000015545995,0.00018798342,0.0007218437,0.000094207324,0.00015273268],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001821971,0.00023475425,0.00026598808,0.00048806908,0.0011885469,0.0003676037,0.0014431046,0.00016291611,0.90819365],"category_scores_gemma":[0.00030686805,0.00026230377,0.00010762989,0.0008093552,0.00012431394,0.000047447713,0.001446117,0.0005671403,0.017237805],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012425793,0.00009773144,0.0000016037046,0.00013591177,0.00015550852,0.000021435335,0.00007871675,0.0000016873653,0.0014551317,0.00070901733,0.990578,0.006752811],"study_design_scores_gemma":[0.0002687827,0.000041596977,0.0000032361359,0.0000171049,0.00008387257,0.000055753895,0.00027836236,0.000008743603,0.0008297364,0.00004229643,0.99809694,0.00027356378],"about_ca_topic_score_codex":0.00004530018,"about_ca_topic_score_gemma":3.6845867e-7,"teacher_disagreement_score":0.89095587,"about_ca_system_score_codex":0.000275138,"about_ca_system_score_gemma":0.000004214273,"threshold_uncertainty_score":0.9999829},"labels":[],"label_agreement":null},{"id":"W6931670346","doi":"10.5281/zenodo.7399201","title":"Haemogamasus pontiger","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Czech; Debris; Transmission (telecommunications)","score_opus":0.03326187934770005,"score_gpt":0.2530524265352396,"score_spread":0.21979054718753957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931670346","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07516591,0.0003331559,0.0008749753,0.0008836342,0.00008024087,0.00011005906,0.00033800845,0.0012918916,0.9209221],"genre_scores_gemma":[0.9840224,0.000022770448,0.00014460682,0.00022038289,0.00010102908,5.9556264e-8,0.0011747015,0.00092413416,0.013389925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986703,0.00007920768,0.00017402126,0.00033901524,0.00041333726,0.00032410983],"domain_scores_gemma":[0.99921185,0.000018293165,0.00008258715,0.00040978444,0.00015714015,0.00012036896],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00028653856,0.00011161692,0.00013023189,0.00021222992,0.0032345033,0.0002950843,0.0009662947,0.000033760003,0.31747288],"category_scores_gemma":[0.00026507422,0.00012801276,0.000070411625,0.0008478797,0.00008118216,0.000101065096,0.0013737596,0.0003660797,0.0031347214],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017221758,0.0005759828,0.000039400922,0.00010953408,0.000208121,0.0000955911,0.0013022501,0.0002622945,0.12532942,0.0067951707,0.82933295,0.03577706],"study_design_scores_gemma":[0.00033623184,0.000064130356,0.000056500172,0.0000017798073,0.00003143435,0.00013270514,0.0009628426,0.00013502716,0.010477874,0.00014602613,0.9875032,0.00015229062],"about_ca_topic_score_codex":0.000016211558,"about_ca_topic_score_gemma":5.7596115e-8,"teacher_disagreement_score":0.9088565,"about_ca_system_score_codex":0.00025208658,"about_ca_system_score_gemma":0.0000030901947,"threshold_uncertainty_score":0.99806315},"labels":[],"label_agreement":null},{"id":"W6931805443","doi":"10.5683/sp2/watjgh","title":"Central Experimental Farm Building 1 (Dominion Observatory) (Includes Building 8 and 9)","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Control (management); Sustainability; Key (lock); Architecture; Data collection","score_opus":0.03044738147226524,"score_gpt":0.3034999276742178,"score_spread":0.2730525462019526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931805443","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039480915,0.002765642,0.000026697897,0.000078691955,0.000094159695,0.000073458286,0.9925538,0.000112504975,0.00034699705],"genre_scores_gemma":[0.004728176,0.0007914365,0.00069432077,0.0002917264,0.0009663303,0.000032416083,0.99239415,0.000054015687,0.00004743539],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978371,0.000020689344,0.0004255896,0.00074577396,0.0004098513,0.0005609829],"domain_scores_gemma":[0.99873567,0.00011046357,0.0002801851,0.0005125074,0.000030040923,0.00033112423],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007004931,0.0005258372,0.0006806147,0.00014200089,0.0002192755,0.00017614274,0.0005443774,0.00045622687,0.00052259426],"category_scores_gemma":[0.00016571692,0.00053406926,0.00019676726,0.0002793607,0.00012726597,0.00009534022,0.00039872504,0.00052796403,0.00000439151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044203873,0.00008119325,0.00010725207,0.0002794387,0.00021292211,0.000059931986,0.00004979685,0.0000011253238,0.050950248,0.000044636974,0.94808537,0.000083884064],"study_design_scores_gemma":[0.00037261142,0.000027865295,0.000051368133,0.000067625406,0.00030151446,0.000014073187,0.00019355756,0.000017315022,0.25641957,0.00002273915,0.7420826,0.00042914037],"about_ca_topic_score_codex":0.007813217,"about_ca_topic_score_gemma":0.0002056523,"teacher_disagreement_score":0.20600276,"about_ca_system_score_codex":0.00027540335,"about_ca_system_score_gemma":0.00008086469,"threshold_uncertainty_score":0.9997111},"labels":[],"label_agreement":null},{"id":"W6939149304","doi":"10.60692/cr6be-7w904","title":"XRF online analyzer for measurements of P2O5 content in phosphate slurry","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Slurry; Phosphate; Analytical Chemistry (journal); Calibration; Range (aeronautics); Flow (mathematics); Accuracy and precision; Filter (signal processing); Sample (material)","score_opus":0.15410332118878817,"score_gpt":0.2828757003949609,"score_spread":0.12877237920617274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939149304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99569416,0.000014336574,0.0010213683,0.00003264865,0.00009315516,0.0001638418,0.00028833596,0.00013480871,0.0025573364],"genre_scores_gemma":[0.99885213,4.296558e-7,0.00014372989,0.000044220014,0.000034104814,0.0000636104,0.00014788205,0.000009096498,0.00070481584],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998604,0.000008565894,0.00073504215,0.00011854639,0.00029919823,0.00023465142],"domain_scores_gemma":[0.9991174,0.000012563995,0.00033872935,0.00024003656,0.00023963483,0.00005165995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028415705,0.00013788613,0.0003183156,0.00041447868,0.000047201876,0.00003225309,0.00015859926,0.00009555556,0.00009065469],"category_scores_gemma":[0.00007052573,0.00011778696,0.000116312134,0.0006959037,0.000017672775,0.00020008457,0.000033019285,0.00006044515,0.00010836461],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002169909,0.000017021492,0.98055416,0.0021157605,0.0003341731,0.0000019850008,0.014363998,0.00043231217,0.001068418,0.000074794305,0.0004974916,0.00032290304],"study_design_scores_gemma":[0.008455684,0.000093667375,0.13484167,0.0006917881,0.0004354108,0.000008892175,0.08070846,0.011736919,0.7614369,0.0000059400936,0.0008351661,0.0007495571],"about_ca_topic_score_codex":0.00003556886,"about_ca_topic_score_gemma":0.0000010673416,"teacher_disagreement_score":0.8457125,"about_ca_system_score_codex":0.00013089729,"about_ca_system_score_gemma":0.000026353311,"threshold_uncertainty_score":0.48032132},"labels":[],"label_agreement":null},{"id":"W6949721880","doi":"10.5281/zenodo.4065768","title":"Comunidad indígena de San Rafael de Chuquipogio, Chimborazo: transformaciones agrarias y cambio climático","year":2020,"lang":"es","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"State (computer science); Context (archaeology); Latin Americans; Work (physics); Product (mathematics)","score_opus":0.03933623326756532,"score_gpt":0.26941268890260195,"score_spread":0.23007645563503662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949721880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5414573,0.0034506824,0.0126799615,0.015787316,0.00016005857,0.0009808302,0.00224684,0.0033478073,0.41988918],"genre_scores_gemma":[0.9921427,0.0014358112,0.0002922965,0.0014724584,0.0005703713,1.02407064e-7,0.0016780351,0.0016871551,0.0007210732],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99665934,0.0003661091,0.00058929616,0.000734042,0.0005874405,0.0010637739],"domain_scores_gemma":[0.9979594,0.00005867362,0.00024123222,0.00061962253,0.0004048584,0.00071621797],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00061433157,0.00043577593,0.00049893453,0.00029484986,0.002517467,0.0015794524,0.0019387787,0.00032855076,0.042808063],"category_scores_gemma":[0.0009875208,0.0004891996,0.0002444832,0.0018914187,0.00036576606,0.00032825198,0.0008384308,0.0010315415,0.005097304],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011892675,0.0015311853,0.00089044584,0.0025573932,0.0016887871,0.00025118035,0.019997226,0.00023846788,0.51595265,0.007985721,0.39932752,0.048390146],"study_design_scores_gemma":[0.0017259341,0.00044447888,0.0011237022,0.00008538781,0.000455208,0.000194156,0.002883639,0.0016704631,0.12731142,0.00010915709,0.86329967,0.0006967725],"about_ca_topic_score_codex":0.00006973506,"about_ca_topic_score_gemma":5.0476945e-7,"teacher_disagreement_score":0.46397215,"about_ca_system_score_codex":0.00045575324,"about_ca_system_score_gemma":0.000038187976,"threshold_uncertainty_score":0.999756},"labels":[],"label_agreement":null},{"id":"W6962194517","doi":"10.17605/osf.io/4rpx8","title":"A Systematic Review on Calibration Transfer and Maintenance by Université de Sherbrooke, Faculty of Engineering, Department of Chemical and Biotechnological Engineering","year":2024,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Workflow; Calibration; Table (database); Process (computing); Quality (philosophy); Systematic review; Data extraction","score_opus":0.006568285345456283,"score_gpt":0.2179026413574274,"score_spread":0.21133435601197112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6962194517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853388,0.001252146,0.0063512865,0.00019648188,0.000014956414,0.00046812542,0.00009452787,0.00018426689,0.006099408],"genre_scores_gemma":[0.99270725,0.0027966204,0.00043850913,0.000018278435,0.0000049627624,0.000049964907,0.000021033016,0.000017215885,0.003946139],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876255,0.000017457414,0.00034671035,0.00055604475,0.00015658865,0.0001606769],"domain_scores_gemma":[0.99918497,0.00016901916,0.000050590934,0.00049673143,0.000021872644,0.000076802186],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040272885,0.00016792986,0.0004356551,0.00007083486,0.000015415515,0.000014505489,0.00019982376,0.00016548419,0.007697555],"category_scores_gemma":[0.0006063317,0.00015129191,0.000091785696,0.00019903076,0.000056511166,0.000050741754,0.00016975874,0.0002006954,0.0003695466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031089505,0.00010477916,0.000379151,0.12094392,0.0004003833,0.0000138198575,0.0001505872,0.00016354343,0.87488663,0.0017223142,0.0010605325,0.00014326976],"study_design_scores_gemma":[0.0001616438,0.0000018315892,0.000044936845,0.009724839,0.00037126083,0.000034123208,0.00002288985,0.0053519453,0.98357666,0.000029939443,0.00053388346,0.00014602872],"about_ca_topic_score_codex":0.000012370928,"about_ca_topic_score_gemma":4.738243e-7,"teacher_disagreement_score":0.11121908,"about_ca_system_score_codex":0.00018975607,"about_ca_system_score_gemma":0.000016736958,"threshold_uncertainty_score":0.99320954},"labels":[],"label_agreement":null},{"id":"W6964101318","doi":"10.25318/3310084901-eng","title":"Number of enterprises in Canada, by enterprise size and age of owner","year":2025,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Productivity; Work (physics); Production (economics)","score_opus":0.003012097318538185,"score_gpt":0.2574636361210576,"score_spread":0.2544515388025194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964101318","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029068755,0.0002891608,0.000071321854,0.000019088604,0.00007438851,0.000048285205,0.9963205,0.0000017792581,0.000268591],"genre_scores_gemma":[0.03515519,0.0004393179,0.00016116373,0.000032120985,0.000010016612,0.000011097416,0.9620277,0.000008380061,0.0021549813],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984983,0.000016654401,0.0005529901,0.00025933384,0.0004778326,0.00019487743],"domain_scores_gemma":[0.99808526,0.0009277287,0.00052065984,0.0002777347,0.00011584283,0.00007279342],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000045301695,0.0002279169,0.0005270962,0.00008640032,0.00002234159,0.000010395573,0.0002287404,0.00011346002,0.0012060261],"category_scores_gemma":[0.0009929837,0.00021387759,0.000020004401,0.00030686034,0.000054981236,0.000021541851,0.000063187734,0.00020732042,4.647328e-8],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000176684,0.00004661306,0.0043748096,0.0012173955,0.000084802,0.000045488523,0.00001325636,7.089103e-7,0.0006984064,0.000008234145,0.99319375,0.00029889838],"study_design_scores_gemma":[0.0018500901,0.000053880674,0.029159963,0.0027296816,0.0025932577,0.000016799304,0.003038918,0.0000757974,0.12427372,0.00032910384,0.8337311,0.0021476946],"about_ca_topic_score_codex":0.9956548,"about_ca_topic_score_gemma":0.99874073,"teacher_disagreement_score":0.15946262,"about_ca_system_score_codex":0.0007727792,"about_ca_system_score_gemma":0.0018929105,"threshold_uncertainty_score":0.999707},"labels":[],"label_agreement":null},{"id":"W6979348753","doi":"","title":"DEEPEAST technique to enhance power in two-sample tests via the same-attraction function","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mahalanobis distance; Nonparametric statistics; Statistic; Test statistic; Statistical hypothesis testing; Statistical power; Homogeneity (statistics); Resampling; Estimator","score_opus":0.02706995453294863,"score_gpt":0.23482894628442377,"score_spread":0.20775899175147514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979348753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64381605,0.00014024551,0.34616718,0.00018792927,0.00011744682,0.00011534199,0.000009929239,0.00023225934,0.009213595],"genre_scores_gemma":[0.99650526,0.00002607035,0.00012012802,0.0001064317,0.0000647233,0.0000037786456,0.0000073615915,0.000016728849,0.003149521],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991246,0.000019424371,0.000117973555,0.00044104093,0.00006452966,0.00023242855],"domain_scores_gemma":[0.999295,0.00022970533,0.000038805654,0.00032636107,0.000040834664,0.000069277296],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012651649,0.00014510116,0.00012491408,0.00024729822,0.000098432065,0.000045879715,0.00022414776,0.00008110804,0.0012586964],"category_scores_gemma":[0.00007448281,0.00013303028,0.00008584584,0.0018845912,0.000045142064,0.00026857687,0.00006057369,0.0003192389,0.00019003495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038967378,0.00044057533,0.035018817,0.0001753755,0.00031343164,0.00032906485,0.00049706706,0.014852214,0.92223746,0.020264719,0.0017519199,0.0037297078],"study_design_scores_gemma":[0.00056494994,0.00019455598,0.00651356,0.000219342,0.0005717125,0.000049007256,0.0022299073,0.013980717,0.92242837,0.03278488,0.019322807,0.0011402193],"about_ca_topic_score_codex":0.0006805152,"about_ca_topic_score_gemma":0.00048377793,"teacher_disagreement_score":0.35268918,"about_ca_system_score_codex":0.00028026404,"about_ca_system_score_gemma":0.000035823123,"threshold_uncertainty_score":0.9996543},"labels":[],"label_agreement":null},{"id":"W6980112441","doi":"","title":"An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Mear-infrared Spectra","year":2020,"lang":"en","type":"article","venue":"e-Publications@Marquette (Marquette University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Medical Research Council; Agricultural Research Service; U.S. Department of Agriculture; Grand Challenges Canada; Government of Canada; Wellcome Trust; Marquette University","keywords":"Parity (physics); Pattern recognition (psychology); Tanzania; Artificial neural network; Autoencoder; Feature vector","score_opus":0.02795481317456702,"score_gpt":0.2910985877144175,"score_spread":0.2631437745398505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980112441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6698012,0.00041976816,0.24347477,0.040350813,0.00018632993,0.00090629194,0.003832972,0.001969664,0.0390582],"genre_scores_gemma":[0.65380585,0.00009744813,0.33065113,0.007176254,0.0010984346,0.000027174796,0.003881584,0.00014788446,0.0031142626],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970533,0.0001698595,0.0005135801,0.0010177345,0.0004503962,0.0007950784],"domain_scores_gemma":[0.99719214,0.00039018432,0.00037135216,0.0008099296,0.00033024146,0.00090615504],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020218038,0.00042240054,0.00064625824,0.0004839718,0.00037496592,0.0001933867,0.0007782744,0.00026889672,0.002849127],"category_scores_gemma":[0.00033873145,0.00049158477,0.00018407864,0.0030243746,0.00020310914,0.00064350094,0.00020022686,0.00051463634,0.000023017446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002841775,0.0022017625,0.2100192,0.00058476377,0.0025373874,0.00016055183,0.0033662652,0.071551576,0.49501464,0.030014677,0.16580576,0.01590165],"study_design_scores_gemma":[0.0039372966,0.00064694585,0.047280986,0.00008905021,0.0028785528,0.000009664793,0.0019376308,0.23683627,0.3449664,0.0029379784,0.35530838,0.0031708379],"about_ca_topic_score_codex":0.0007288007,"about_ca_topic_score_gemma":0.00018880646,"teacher_disagreement_score":0.18950263,"about_ca_system_score_codex":0.00023459447,"about_ca_system_score_gemma":0.00030447854,"threshold_uncertainty_score":0.9997536},"labels":[],"label_agreement":null},{"id":"W6981774571","doi":"","title":"Failure Case Studies in Civil Engineering: Structures Foundations and the Environment","year":2013,"lang":"en","type":"article","venue":"EngagedScholarship @ Cleveland State University (Cleveland State University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Foundation (evidence); Resource (disambiguation); Point (geometry); Class (philosophy)","score_opus":0.02540906479206732,"score_gpt":0.22365207220716923,"score_spread":0.19824300741510192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981774571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950114,0.00029724813,0.0016422903,0.00042718017,0.000046845726,0.00023844579,0.00016391082,0.000094319075,0.0020783932],"genre_scores_gemma":[0.9861662,0.0013310218,0.0006868852,0.000038870337,0.00001956536,9.746828e-7,0.000028400813,0.000024371147,0.011703734],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99807984,0.00029881048,0.0002535526,0.0005560047,0.00026888796,0.0005429105],"domain_scores_gemma":[0.99811417,0.0009989851,0.0001588177,0.00038370362,0.000105224135,0.00023911112],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046067816,0.00039233637,0.0004906885,0.0006286577,0.0010225131,0.00014111109,0.00041639965,0.00014907875,0.00065678486],"category_scores_gemma":[0.00021650341,0.00034302904,0.0001296433,0.0007553185,0.0004895919,0.0007924152,0.00031529894,0.0012580389,0.000027642742],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069042733,0.0024971657,0.4246853,0.00355672,0.023119207,0.098196484,0.2758434,0.03964155,0.018590076,0.07082983,0.012899433,0.023236543],"study_design_scores_gemma":[0.07915186,0.0005044894,0.11725657,0.00042034825,0.0041150013,0.003477214,0.4556637,0.00717333,0.009773173,0.024019893,0.29021305,0.008231365],"about_ca_topic_score_codex":0.0007469922,"about_ca_topic_score_gemma":0.0013201217,"teacher_disagreement_score":0.30742875,"about_ca_system_score_codex":0.0004220621,"about_ca_system_score_gemma":0.000065511485,"threshold_uncertainty_score":0.9999022},"labels":[],"label_agreement":null},{"id":"W6990941647","doi":"","title":"Evaluation of Machine Learning Techniques for Image Based Quality Assessment of Chickpea","year":2022,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Hyperspectral imaging; Random forest; Pattern recognition (psychology); Convolutional neural network; Mean squared error; Artificial neural network; Classifier (UML)","score_opus":0.03830472784640824,"score_gpt":0.3474720750804475,"score_spread":0.3091673472340392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990941647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95245194,0.0011851265,0.004964469,0.00018359633,0.00007259558,0.00057649816,0.0006799527,0.00008593497,0.0397999],"genre_scores_gemma":[0.9918333,0.000083366096,0.0031220817,0.0000029778487,0.000019962974,0.0000032727803,0.0020528778,0.000017875891,0.0028642786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982642,0.00018945725,0.0002883457,0.00023003225,0.00090499606,0.0001229796],"domain_scores_gemma":[0.9972466,0.0003322813,0.0012693466,0.0003787452,0.0007491406,0.000023848044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021033348,0.00015927694,0.0005035634,0.00024557646,0.00020195464,0.0000044777375,0.00051678525,0.00015164766,0.0045616366],"category_scores_gemma":[0.00027099188,0.0001673025,0.0003928276,0.00043975827,0.00008900433,0.00006396703,0.00006182089,0.00034719915,3.1131418e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002963505,0.00020166251,0.000795366,0.0013545767,0.00045295438,4.4537802e-7,0.0006020939,0.00013770226,0.99391645,0.0001850673,0.00012097576,0.0019363469],"study_design_scores_gemma":[0.00230955,0.00035119988,0.035215314,0.00013924173,0.009687665,7.875262e-7,0.037424415,0.018685378,0.8937366,0.0008909655,0.0010406309,0.0005182324],"about_ca_topic_score_codex":0.0016008489,"about_ca_topic_score_gemma":0.00018891018,"teacher_disagreement_score":0.100179836,"about_ca_system_score_codex":0.00021910622,"about_ca_system_score_gemma":0.00039101994,"threshold_uncertainty_score":0.9963483},"labels":[],"label_agreement":null},{"id":"W6996290876","doi":"","title":"Renvois du Plan de Louisbourg dans L’Isle Royale, au Canada","year":2016,"lang":"fr","type":"other","venue":"Brock University Digital Repository (Brock University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); White (mutation); Context (archaeology); Field (mathematics)","score_opus":0.005983630166042694,"score_gpt":0.16822908804109865,"score_spread":0.16224545787505595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996290876","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057380404,0.00023453197,0.0012921097,0.00002881163,0.0006156915,0.00018758667,0.0011169901,0.00028143742,0.93886244],"genre_scores_gemma":[0.08188144,0.00021612331,0.000051964664,0.00004125003,0.00094148953,2.1374848e-7,0.0001323328,0.00018400254,0.9165512],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99586844,0.00012256745,0.0004136885,0.0014009789,0.0007422873,0.0014520113],"domain_scores_gemma":[0.99635965,0.00040045773,0.00074289896,0.001024233,0.00028786212,0.0011849141],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000003544002,0.0010989734,0.0010308904,0.00090195524,0.0010414579,0.00019239998,0.0021173367,0.0011413401,0.0017968563],"category_scores_gemma":[0.00012282365,0.0013445292,0.00067313534,0.0011732773,0.000528699,0.00077218836,0.00090183615,0.00079162116,0.00029593962],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044046217,0.00043986613,0.31202728,0.00044493776,0.0018670523,0.0042393613,0.000055676323,0.00010647172,0.02434377,0.00038555876,0.65535927,0.00029026452],"study_design_scores_gemma":[0.0015823076,0.0000979511,0.0026847073,0.0003849746,0.0014553046,0.00034512975,0.014366973,0.0000027569183,0.009712981,0.000017012853,0.9677788,0.0015711084],"about_ca_topic_score_codex":0.28846556,"about_ca_topic_score_gemma":0.58605915,"teacher_disagreement_score":0.3124195,"about_ca_system_score_codex":0.013681092,"about_ca_system_score_gemma":0.005100276,"threshold_uncertainty_score":0.99911565},"labels":[],"label_agreement":null},{"id":"W6997064983","doi":"","title":"Ultrasound spectroscopy of biofluid properties for health assessment","year":2015,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Ultrasound; Distortion (music); Nonlinear distortion; Biological tissue; Attenuation; Nonlinear system; Sample (material)","score_opus":0.03324028782244038,"score_gpt":0.3251613253763732,"score_spread":0.29192103755393284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997064983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8840447,0.004078184,0.0000014257485,0.000033199172,0.000597055,0.0007182614,0.0039672097,0.0002899906,0.10626995],"genre_scores_gemma":[0.97274494,0.0012275041,0.0035899708,0.00016919564,0.00015232633,0.0003755211,0.0035231404,0.00031046546,0.017906958],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947834,0.00010413487,0.0015616588,0.0012742933,0.0012042543,0.0010722441],"domain_scores_gemma":[0.99571264,0.00029746073,0.001517258,0.0010684427,0.00090602954,0.00049817644],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095579214,0.0010066471,0.001795945,0.00056740607,0.0007730951,0.00009695405,0.00096836896,0.00088864943,0.00091645564],"category_scores_gemma":[0.0010446833,0.0009715574,0.00067517295,0.00088716124,0.000118932534,0.00038598172,0.000079494916,0.0012910122,0.00004062243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004737644,0.0006017254,0.00008876218,0.0034967298,0.00083947275,0.0000043878736,0.0000170903,0.000006025864,0.98371,0.0059011537,0.00022761711,0.004633277],"study_design_scores_gemma":[0.0011481175,0.00048210614,0.00007395741,0.0004701203,0.0006542557,0.000016601016,0.0012258643,0.0000073714887,0.969856,0.008352491,0.016751844,0.00096122763],"about_ca_topic_score_codex":0.0005363048,"about_ca_topic_score_gemma":0.00035935373,"teacher_disagreement_score":0.08870019,"about_ca_system_score_codex":0.0021476187,"about_ca_system_score_gemma":0.0005978751,"threshold_uncertainty_score":0.99999684},"labels":[],"label_agreement":null},{"id":"W7009122909","doi":"","title":"Development of prediction models for rapid analysis of glucosinolate content in canola meal using near-infrared spectroscopy","year":2023,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Canola; Glucosinolate; Meal; Brassica; Moisture; Rapeseed","score_opus":0.05225975687040592,"score_gpt":0.258319419531528,"score_spread":0.20605966266112208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009122909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994906,0.00025310967,0.0027754095,0.000009391409,0.000075896896,0.0001691897,0.000329499,0.00003636612,0.0014451451],"genre_scores_gemma":[0.9740489,0.00029483176,0.02108585,0.0000024724131,0.000023919294,0.00000245515,0.0023919374,0.000050420655,0.0020991727],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985131,0.000016770597,0.00041212203,0.00038626711,0.00040757254,0.0002641702],"domain_scores_gemma":[0.9983926,0.000074268435,0.0008669246,0.0002872514,0.0003207736,0.00005817578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015730616,0.00024607393,0.000982017,0.0011700058,0.00013105162,0.0000108308595,0.00032308482,0.00034735692,0.000048831404],"category_scores_gemma":[0.000028934997,0.00033204112,0.00040557582,0.0016421855,0.000067385234,0.000111799156,0.000049336304,0.0001878613,7.607232e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031049596,0.00071734464,0.05509607,0.0043011187,0.016274137,0.000019876488,0.010436474,0.015172513,0.89333725,0.00012856009,0.0005309142,0.00088079635],"study_design_scores_gemma":[0.002214155,0.00010298546,0.091616,0.00054493436,0.010983602,5.168365e-7,0.12417887,0.1367511,0.6328027,0.00010144519,0.00011719201,0.00058645394],"about_ca_topic_score_codex":0.0064483066,"about_ca_topic_score_gemma":0.1602781,"teacher_disagreement_score":0.2605345,"about_ca_system_score_codex":0.00053069106,"about_ca_system_score_gemma":0.00035377213,"threshold_uncertainty_score":0.99991316},"labels":[],"label_agreement":null},{"id":"W7014424868","doi":"","title":"Potential applications of hyperspectral imaging for the determination of total soluble solids, water content and firmness in mango","year":2006,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Hyperspectral imaging; Correlation coefficient; Partial least squares regression; Calibration; Linear regression; Content (measure theory); Mean squared error; Coefficient of determination","score_opus":0.012445803350576181,"score_gpt":0.2511368533235616,"score_spread":0.23869104997298543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014424868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99227154,0.00096170534,0.000022750946,0.000021301496,0.0001310942,0.00048365918,0.0005110603,0.00003512883,0.0055617285],"genre_scores_gemma":[0.9931087,0.00009385886,0.00049965084,0.000012246978,0.000047024983,0.00029510394,0.00090002647,0.00006480461,0.0049786107],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99798506,0.000025368601,0.00073662633,0.00050817104,0.00034146415,0.00040331652],"domain_scores_gemma":[0.998629,0.00017794744,0.00043623813,0.00037887954,0.00031955598,0.000058405683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026503857,0.0003499675,0.00055398827,0.0003250853,0.0003317054,0.00003599765,0.0003182906,0.0002984673,0.000116050396],"category_scores_gemma":[0.00007563379,0.0002830619,0.0002743479,0.00028447094,0.000093654395,0.00022046597,0.000060357463,0.00042626748,0.0000021436963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017848644,0.00026393254,0.00018052186,0.0009417705,0.00013381355,0.0000040769773,0.000023926643,0.000030246214,0.97205573,0.003001682,0.000002185235,0.023183651],"study_design_scores_gemma":[0.0009420336,0.000025885489,0.0007086679,0.000088460714,0.0005950212,0.000015711888,0.0014968225,0.00036904486,0.9925303,0.0025653306,0.00032254652,0.00034016464],"about_ca_topic_score_codex":0.0008419784,"about_ca_topic_score_gemma":0.00043408084,"teacher_disagreement_score":0.022843486,"about_ca_system_score_codex":0.00020211992,"about_ca_system_score_gemma":0.00002156126,"threshold_uncertainty_score":0.99996215},"labels":[],"label_agreement":null},{"id":"W7014606226","doi":"","title":"Pork meat quality evaluation from hyperspectral observations","year":2008,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Linear discriminant analysis; Spectroradiometer; Quality (philosophy); Pattern recognition (psychology); Quality assessment; sort","score_opus":0.07413595837356678,"score_gpt":0.32222664794810585,"score_spread":0.24809068957453906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014606226","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7950035,0.0017792669,1.8483398e-7,0.00002199153,0.000553929,0.0002834179,0.0026564,0.00039726272,0.19930407],"genre_scores_gemma":[0.9522076,0.0008942655,0.0013935552,0.00020141904,0.00028295832,0.0002259827,0.018051071,0.00024852355,0.026494598],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9932025,0.00028377533,0.0015356912,0.0017896774,0.0022805536,0.00090781396],"domain_scores_gemma":[0.99514765,0.00059003686,0.0011890432,0.0015376165,0.0011139179,0.00042173624],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00069168943,0.0011052678,0.0013241022,0.00048328534,0.0016374248,0.00012691844,0.0010685679,0.0016069551,0.008309686],"category_scores_gemma":[0.002665229,0.0012412195,0.0008633516,0.0015171743,0.000096650074,0.00076937274,0.00009432596,0.0021551992,0.00044265814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042872113,0.00096715253,0.0017790258,0.00039548127,0.0019676262,0.000061171544,0.0000610408,0.000071874645,0.9501008,0.024312183,0.00011201249,0.019742953],"study_design_scores_gemma":[0.0018074813,0.000051065683,0.014422867,0.0002264035,0.0028842683,0.000015942409,0.0020440207,0.00006329434,0.9391913,0.025463384,0.011707581,0.002122394],"about_ca_topic_score_codex":0.0031755597,"about_ca_topic_score_gemma":0.0024862562,"teacher_disagreement_score":0.17280948,"about_ca_system_score_codex":0.001929162,"about_ca_system_score_gemma":0.0002471561,"threshold_uncertainty_score":0.99968916},"labels":[],"label_agreement":null},{"id":"W7014657613","doi":"","title":"Prédiction d'un indicateur journalier de masse corporelle à partir des caractéristiques de la vache laitière et de son spectre du lait moyen-infrarouge","year":2018,"lang":"fr","type":"article","venue":"ORBi (University of Liège)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Context (archaeology); Order (exchange); Quality (philosophy)","score_opus":0.009494649564458021,"score_gpt":0.25251059491242595,"score_spread":0.24301594534796792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014657613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.943926,0.0018076791,0.013151312,0.0022099677,0.00007074327,0.000065991604,0.00008831152,0.000105805724,0.038574222],"genre_scores_gemma":[0.9698247,0.0050060023,0.0112879705,0.00015341562,0.0005030222,5.83831e-7,0.000028955908,0.000041265765,0.013154114],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979773,0.00036275908,0.00025001203,0.00039848685,0.00030427938,0.00070713845],"domain_scores_gemma":[0.99828815,0.0003033341,0.0005106661,0.000313623,0.00018785997,0.00039636868],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000834059,0.00031713347,0.0004544394,0.00024525062,0.0005811712,0.0000872516,0.0005225864,0.0006522802,0.007607897],"category_scores_gemma":[0.00008401328,0.00041308143,0.0002849523,0.0007272508,0.0011062503,0.00044789215,0.0001771997,0.0007917428,0.000093429226],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032295877,0.0009771291,0.8974609,0.0004166674,0.00092750526,0.0009747,0.014255985,0.000031532683,0.058201816,0.004209012,0.020497771,0.0017240528],"study_design_scores_gemma":[0.0023281267,0.000503895,0.35319644,0.00044078587,0.0029523398,0.0011307407,0.014685538,0.0019858016,0.57151014,0.012438461,0.037846234,0.0009814921],"about_ca_topic_score_codex":0.005295567,"about_ca_topic_score_gemma":0.00029288547,"teacher_disagreement_score":0.54426444,"about_ca_system_score_codex":0.0010157751,"about_ca_system_score_gemma":0.00054271345,"threshold_uncertainty_score":0.9998321},"labels":[],"label_agreement":null},{"id":"W7017445835","doi":"","title":"Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models","year":2023,"lang":"en","type":"article","venue":"Massey Research Online (Massey University)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Hyperspectral imaging; Blade (archaeology); Nutrient; Pattern recognition (psychology)","score_opus":0.20951595653084115,"score_gpt":0.38903131419085696,"score_spread":0.1795153576600158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017445835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9876781,0.0010412321,0.001537352,0.0010650279,0.00002613119,0.00011854006,0.00007405473,0.00016268429,0.008296878],"genre_scores_gemma":[0.98769385,0.0028731828,0.0016208026,0.000011024353,0.000112033005,9.4047965e-7,0.00008182485,0.00004253847,0.0075638094],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968461,0.00023863013,0.00028363534,0.0005959187,0.0008938523,0.0011418697],"domain_scores_gemma":[0.9980277,0.00068674755,0.00017522696,0.00046901483,0.00036620384,0.00027514793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006788038,0.00025261354,0.00041433226,0.0011107076,0.0008306099,0.00015429402,0.00058903836,0.00017621064,0.00015210865],"category_scores_gemma":[0.0005380549,0.0002271,0.00014260765,0.004712315,0.0005041411,0.0005403011,0.0005822675,0.0013600265,0.000004848062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039595205,0.0007591308,0.06588821,0.00070906663,0.0007482156,0.00057687843,0.0017344111,0.020607473,0.8913184,0.015588319,0.0005160728,0.0011578754],"study_design_scores_gemma":[0.0037491536,0.0003542507,0.0035528217,0.00038066108,0.0005977869,0.000048672217,0.07728749,0.6367014,0.24093446,0.004300886,0.030877594,0.0012148275],"about_ca_topic_score_codex":0.001137926,"about_ca_topic_score_gemma":0.00010989228,"teacher_disagreement_score":0.65038395,"about_ca_system_score_codex":0.00055034587,"about_ca_system_score_gemma":0.00042519916,"threshold_uncertainty_score":0.9260869},"labels":[],"label_agreement":null},{"id":"W7027855315","doi":"","title":"Digital image analysis of grain samples for potential use in grain cleaning","year":2002,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Contextual image classification; Feature (linguistics); Digital image; Classifier (UML); High resolution; Canola; Feature extraction","score_opus":0.019477297846693702,"score_gpt":0.23233274310625873,"score_spread":0.21285544525956504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027855315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912613,0.00016456257,0.0017847953,0.00003879977,0.000045233737,0.00009629635,0.0008410071,0.00003513138,0.005732883],"genre_scores_gemma":[0.98196745,0.00016977468,0.0019421575,0.0000032338078,0.000031039788,6.0264017e-7,0.00331568,0.000031450792,0.012538636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988067,0.00001377197,0.00021751788,0.0004000276,0.00030124135,0.0002607258],"domain_scores_gemma":[0.99879175,0.00014604614,0.00051615306,0.00030711453,0.00017870161,0.000060204024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007733837,0.0002433551,0.00078320206,0.00130271,0.00009851502,0.000047252277,0.00037417107,0.00030667172,0.00020315996],"category_scores_gemma":[0.0001272585,0.00033494656,0.00068853033,0.0013796734,0.00009023145,0.00032670458,0.000048845457,0.00021310676,0.0000032081996],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003635628,0.0029586279,0.6480744,0.0062455256,0.02835767,0.00039814462,0.0052937446,0.0013460965,0.27040172,0.0012302382,0.022146901,0.00991129],"study_design_scores_gemma":[0.005087115,0.00027155344,0.4952381,0.0005424155,0.03128803,0.000004308893,0.3916788,0.012545878,0.05698734,0.0004437874,0.003471186,0.0024414847],"about_ca_topic_score_codex":0.0035135418,"about_ca_topic_score_gemma":0.1672231,"teacher_disagreement_score":0.38638505,"about_ca_system_score_codex":0.00013751393,"about_ca_system_score_gemma":0.00003356736,"threshold_uncertainty_score":0.99991024},"labels":[],"label_agreement":null},{"id":"W7036136289","doi":"","title":"Associative Tracking and Recognition in Video","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discriminative model; Video tracking; Tracking (education); Pixel; Object (grammar); Artificial neural network; Pattern recognition (psychology); Associative property","score_opus":0.017744819626468983,"score_gpt":0.2583544091831647,"score_spread":0.24060958955669573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036136289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8015783,0.00010273045,0.00002867281,0.000094946256,0.000006083738,0.000010212494,0.0000064259493,0.000022329014,0.19815032],"genre_scores_gemma":[0.99824107,0.000023236864,0.0004849622,0.00003565436,0.00007334261,0.000005127065,0.000016623682,0.0000055973233,0.001114395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995768,0.0000061113283,0.00010270288,0.00012107657,0.00007550047,0.00011782453],"domain_scores_gemma":[0.99979144,0.00008446983,0.00004498365,0.000044456385,0.000019243727,0.00001540714],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005865968,0.00005569515,0.00009841595,0.00006119971,0.000028068624,0.000020051975,0.000030621202,0.00006116923,0.0012682318],"category_scores_gemma":[0.00007664136,0.00005733,0.000020898675,0.00020091796,0.000018372373,0.00006391512,0.0000095079195,0.00009991486,0.000011553572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017969482,0.00013241149,0.1356447,0.000039878858,0.0000321601,0.000014493681,0.00024827354,0.0000010904826,0.8423034,0.00028932508,0.0016416647,0.019634651],"study_design_scores_gemma":[0.00052008784,0.000008647989,0.026102439,0.000023610364,0.000046035584,0.000003194728,0.0003725035,0.00009390658,0.9167575,0.0556272,0.00029242158,0.00015240026],"about_ca_topic_score_codex":0.00009777213,"about_ca_topic_score_gemma":0.0000586953,"teacher_disagreement_score":0.19703592,"about_ca_system_score_codex":0.0000654027,"about_ca_system_score_gemma":0.0000070108367,"threshold_uncertainty_score":0.99964476},"labels":[],"label_agreement":null},{"id":"W7038187502","doi":"","title":"Géologie et dynamique glaciaire de la région de Kangirsuk, péninsule d'Ungava et l'apport de l'imagerie satellitaire de haute résolution à la cartographie Quaternaire","year":2019,"lang":"fr","type":"other","venue":"Archipelago (Université du Québec à Montréal)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Telmatology; Prospection; Metamorphic petrology","score_opus":0.005367088439867925,"score_gpt":0.2139034163043185,"score_spread":0.2085363278644506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038187502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8408306,0.011470044,0.02979669,0.005848441,0.000121863704,0.0003736392,0.00014381451,0.0005531398,0.11086175],"genre_scores_gemma":[0.9132254,0.013615973,0.0033624456,0.00055689795,0.00021766206,0.000027658212,0.00021407033,0.0004060561,0.068373844],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99459285,0.0014094636,0.0005593121,0.0012348554,0.00048713526,0.0017164169],"domain_scores_gemma":[0.9961021,0.0011571414,0.0007129089,0.0012067679,0.00012080034,0.0007003066],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014906515,0.0011517281,0.0012042328,0.00088298734,0.0005407054,0.00017889755,0.0012833214,0.0018283896,0.0013515673],"category_scores_gemma":[0.00040892028,0.0013475738,0.0010457111,0.0010075021,0.0011093969,0.0003552548,0.00052640226,0.00208507,0.00025466052],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014128067,0.00268223,0.58503455,0.0033662405,0.0069284756,0.01284792,0.14443037,0.0051243864,0.09936118,0.036448516,0.03944087,0.062922485],"study_design_scores_gemma":[0.009708722,0.0010261236,0.4249221,0.0029876293,0.010038967,0.0055025043,0.07847871,0.019234702,0.031215403,0.009244834,0.39973804,0.007902239],"about_ca_topic_score_codex":0.4578222,"about_ca_topic_score_gemma":0.3631826,"teacher_disagreement_score":0.36029717,"about_ca_system_score_codex":0.003790083,"about_ca_system_score_gemma":0.00431514,"threshold_uncertainty_score":0.9995613},"labels":[],"label_agreement":null},{"id":"W7042529216","doi":"","title":"Potentialité de la spectroscopie du proche infrarouge pour analyser le lait en salle de traite au moyen d'une fibre optique","year":2011,"lang":"fr","type":"article","venue":"Open Repository and Bibliography (University of Liège)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruker (Canada)","funders":"","keywords":"Analyser; Optical fiber; Analytical Chemistry (journal); Statistical analysis","score_opus":0.01260114829006227,"score_gpt":0.23325298799554964,"score_spread":0.22065183970548738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042529216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85310453,0.0031497078,0.0030541338,0.0010670582,0.00006265917,0.00019241599,0.000059185462,0.000049484028,0.13926081],"genre_scores_gemma":[0.9639812,0.0052676117,0.016467026,0.00006481888,0.00018423452,0.0000019041316,0.000017688304,0.00002934984,0.0139861265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978798,0.00037200807,0.00030816515,0.00061384443,0.00024620572,0.0005799631],"domain_scores_gemma":[0.9984269,0.00019781035,0.0003945036,0.00045064787,0.00014871119,0.00038139275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064974185,0.0003635115,0.000631356,0.0033711966,0.0006648608,0.00023377193,0.0010843873,0.00063220295,0.00080777303],"category_scores_gemma":[0.0000029010607,0.0004453871,0.00051485974,0.009390903,0.00075854285,0.00096383033,0.00048291273,0.00052100024,0.000005493846],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004797169,0.0011997717,0.7915098,0.0005402367,0.0021519342,0.00072128826,0.006034529,0.00001201989,0.18426874,0.0019379525,0.011047852,0.00009616453],"study_design_scores_gemma":[0.0026134688,0.00037789455,0.53522736,0.0003439782,0.0029964382,0.00053616473,0.017410954,0.00016990001,0.43382096,0.0024372435,0.0031477073,0.0009179148],"about_ca_topic_score_codex":0.060100105,"about_ca_topic_score_gemma":0.0001263032,"teacher_disagreement_score":0.25628245,"about_ca_system_score_codex":0.00009409763,"about_ca_system_score_gemma":0.00051035546,"threshold_uncertainty_score":0.9997998},"labels":[],"label_agreement":null},{"id":"W7046310894","doi":"","title":"Development of safe storage guidelines for prairie-grown flaxseed (Linum usitatissimum) and characterization of stored seeds using near infrared spectroscopy","year":2022,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Partial least squares regression; Food spoilage; Relative humidity; Germination; Water content; Moisture; Principal component analysis","score_opus":0.03518152215972406,"score_gpt":0.2816826671567196,"score_spread":0.24650114499699552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7046310894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99490434,0.00022497275,0.003532346,0.000067525245,0.00011244472,0.0002353051,0.00034202982,0.00002884512,0.00055216264],"genre_scores_gemma":[0.60232955,0.00040300257,0.3675015,0.000037608457,0.00022936155,0.00000876118,0.014313221,0.00022559655,0.014951415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985112,0.000023812947,0.00042679862,0.00038002644,0.0004371458,0.00022099655],"domain_scores_gemma":[0.9975937,0.00007132943,0.0013629238,0.00027158196,0.0006287047,0.0000717336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016105981,0.00029401458,0.0007588993,0.00040599366,0.00034445207,0.000017322041,0.00032469523,0.0002709934,0.00020018373],"category_scores_gemma":[0.00010500979,0.00039958296,0.00016369643,0.0005279685,0.000104763625,0.00018099669,0.00010464606,0.00019277538,5.26124e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007453831,0.00013686645,0.004453054,0.0021746808,0.00056846,0.0000042292527,0.0027720872,0.000057414807,0.98815274,0.000037114172,0.00060439965,0.00029359496],"study_design_scores_gemma":[0.0027599896,0.0002331472,0.0525601,0.00052194955,0.002112919,0.0000037508912,0.11975582,0.005009007,0.79882073,0.000048068156,0.017169988,0.0010045378],"about_ca_topic_score_codex":0.00039526753,"about_ca_topic_score_gemma":0.0053330967,"teacher_disagreement_score":0.39257485,"about_ca_system_score_codex":0.00027724158,"about_ca_system_score_gemma":0.00055964745,"threshold_uncertainty_score":0.9998456},"labels":[],"label_agreement":null},{"id":"W7097074817","doi":"","title":"Quantification of variations in machine-vision-computed features","year":2014,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kernel (algebra); Cereal grain; Pattern recognition (psychology); Identification (biology); Variation (astronomy)","score_opus":0.010522380184591321,"score_gpt":0.28652911389874247,"score_spread":0.27600673371415113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097074817","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31483388,0.0005667986,0.29448098,0.0014745522,0.00011572225,0.00009916691,0.000026089609,0.00024151821,0.38816127],"genre_scores_gemma":[0.99465823,0.0000074507466,0.003238454,0.00003312217,0.000025476063,0.000002424864,0.000029612564,0.0000050343615,0.0020001894],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994957,0.000010367288,0.00018015291,0.00013275124,0.00009895349,0.00008206804],"domain_scores_gemma":[0.9994849,0.00017402225,0.00007050591,0.00021238846,0.000036044476,0.000022137172],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000087938686,0.000060821447,0.0001286396,0.00013917635,0.000023595778,0.000011339208,0.00011963232,0.00006298848,0.0011692942],"category_scores_gemma":[0.00015595843,0.000051833988,0.000038081398,0.00046592866,0.000017976128,0.00003945375,0.00001674193,0.00007866187,0.000014649828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003404023,0.00069857645,0.050727338,0.00015129415,0.000100843,6.727103e-7,0.00021020502,0.0007333194,0.7975584,0.13543592,0.004577054,0.0097723175],"study_design_scores_gemma":[0.0004940188,0.000019530738,0.08346219,0.000015573225,0.000053509357,0.0000018733753,0.00007247653,0.032952715,0.8789977,0.0029162902,0.00085090153,0.00016323061],"about_ca_topic_score_codex":0.00027064054,"about_ca_topic_score_gemma":0.00006796778,"teacher_disagreement_score":0.67982435,"about_ca_system_score_codex":0.000016996077,"about_ca_system_score_gemma":0.000010242386,"threshold_uncertainty_score":0.99974376},"labels":[],"label_agreement":null},{"id":"W7113901560","doi":"10.1111/jfpe.70286","title":"Detection of Peanut Contamination in Wheat Flour Using a Digital Light Processing ( <scp>DLP</scp> ) Based Near‐Infrared Spectrometer and Ensemble Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Linear discriminant analysis; Random forest; Contamination; Ensemble learning; Support vector machine; Preprocessor; Normalization (sociology); Wheat flour; Pattern recognition (psychology)","score_opus":0.006961684648614864,"score_gpt":0.22891146083624617,"score_spread":0.22194977618763131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113901560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9505754,0.003889839,0.044773426,0.00001467058,0.000034260094,0.00003645745,0.00000187173,0.000026364323,0.00064772315],"genre_scores_gemma":[0.99854904,0.00002364086,0.0012481776,0.000005066458,0.00005308333,0.000001964523,0.000001937573,0.000020330694,0.000096780524],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988832,0.00000791004,0.000502533,0.00015498331,0.0002368617,0.00021452933],"domain_scores_gemma":[0.99922174,0.00012447145,0.00034167225,0.00006672226,0.00018852825,0.000056873396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017664266,0.00017801377,0.00038385848,0.0006768048,0.00006415056,0.00013962961,0.00011226251,0.00010757644,0.000005538141],"category_scores_gemma":[0.00060681254,0.000175219,0.00008292643,0.00087921874,0.00001696211,0.0004804966,0.000021532358,0.00045062325,9.2298286e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004643661,0.00008995441,0.005432076,0.0016855014,0.00014370281,0.0000136112185,0.00056920486,0.019579684,0.9688889,0.0000037866762,0.0000012773825,0.0035459101],"study_design_scores_gemma":[0.0008501684,0.00013204363,0.00046612305,0.0005209827,0.00013010578,0.000051335017,0.00039786086,0.19695073,0.8003155,0.00005173972,0.000060095215,0.000073328774],"about_ca_topic_score_codex":0.0000029471507,"about_ca_topic_score_gemma":0.0000023224482,"teacher_disagreement_score":0.17737105,"about_ca_system_score_codex":0.00013977048,"about_ca_system_score_gemma":0.00011484968,"threshold_uncertainty_score":0.7145223},"labels":[],"label_agreement":null},{"id":"W7117316425","doi":"10.1039/d5re00400d","title":"Improving the univariate calibration approach with Bayesian modeling for IR reaction monitoring","year":2025,"lang":"en","type":"article","venue":"Reaction Chemistry & Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Structural Genomics Consortium; University of Toronto; University of British Columbia","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Univariate; Calibration; Bayesian probability; Workflow; Bayesian inference","score_opus":0.010199817562912147,"score_gpt":0.22989485972080403,"score_spread":0.21969504215789187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117316425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20236813,0.00017874525,0.79289925,0.000075293014,0.00009226397,0.00009860561,0.0000042728575,0.0003206941,0.003962758],"genre_scores_gemma":[0.99453205,0.000024870127,0.0037325139,0.000005485235,0.00043558594,0.0001127292,0.000051974584,0.000035853336,0.0010689454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899125,0.000002866925,0.00024070994,0.000343192,0.00015297325,0.00026902903],"domain_scores_gemma":[0.999367,0.000083250845,0.000103972605,0.00031943628,0.00007507735,0.00005124405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118072814,0.0002175438,0.00018543807,0.00006824768,0.00020714053,0.00008981487,0.00015484805,0.0001634422,0.000008475702],"category_scores_gemma":[0.00009576498,0.00018391245,0.00008643518,0.00046524417,0.000012657043,0.00023138446,0.000024273137,0.0003374513,2.6137104e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003319466,0.00002478734,0.00007387714,0.00035493122,0.000111230496,3.7891084e-7,0.000032140957,0.05431925,0.9445131,0.000105609506,0.0000064582987,0.00042505653],"study_design_scores_gemma":[0.00019419185,0.000002256343,0.000007971805,0.000039667117,0.0001357848,0.000007323301,0.0003982157,0.44557297,0.5533298,0.000012464945,0.00017971767,0.0001196227],"about_ca_topic_score_codex":0.0000836592,"about_ca_topic_score_gemma":3.3859197e-7,"teacher_disagreement_score":0.7921639,"about_ca_system_score_codex":0.00032981284,"about_ca_system_score_gemma":0.0000612852,"threshold_uncertainty_score":0.7499732},"labels":[],"label_agreement":null},{"id":"W7117748994","doi":"10.18280/ts.420614","title":"Hybrid Denosing and Trend Removal Techniques for Modulated Spectroscopy","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Spectroscopy; Signal processing; Noise reduction","score_opus":0.013365264919937166,"score_gpt":0.28590335395186794,"score_spread":0.2725380890319308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117748994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82738733,0.013099662,0.1334471,0.0016107598,0.00026984914,0.0012098654,0.00048663895,0.00042275453,0.02206603],"genre_scores_gemma":[0.9822778,0.00068279717,0.011577489,0.00040460977,0.00048940314,0.00010671649,0.00014923925,0.00006390814,0.004248015],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99653864,0.000045787776,0.0010015384,0.0010901272,0.00038690245,0.00093702495],"domain_scores_gemma":[0.99860215,0.0003080113,0.0003248014,0.00042909445,0.00012701178,0.0002089132],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039538072,0.0006965942,0.00089917757,0.00061298755,0.00057601184,0.0003452087,0.00041591283,0.0002816786,0.0024804333],"category_scores_gemma":[0.00006500176,0.0007503214,0.00036637858,0.00068433513,0.000292436,0.00021155225,0.00014555351,0.00040245018,0.0000050287576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006489558,0.000595619,0.00071607396,0.00084736483,0.0012374204,0.000047631467,0.00015164082,0.000011764618,0.9755384,0.0014187362,0.0074978997,0.011288526],"study_design_scores_gemma":[0.0020278732,0.0003117727,0.00018229125,0.00027335572,0.002015499,0.00004756433,0.00026442079,0.0055936114,0.9732961,0.003479564,0.011859962,0.0006479991],"about_ca_topic_score_codex":0.00004523979,"about_ca_topic_score_gemma":0.000007596526,"teacher_disagreement_score":0.15489048,"about_ca_system_score_codex":0.0004263862,"about_ca_system_score_gemma":0.00016987383,"threshold_uncertainty_score":0.9994948},"labels":[],"label_agreement":null},{"id":"W7118213171","doi":"10.1145/3783779.3783810","title":"Supervised Functional Principal Component Analysis with Multiple Response Types","year":2025,"lang":"","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Principal component analysis; Categorical variable; Pattern recognition (psychology); Functional principal component analysis; Multivariate statistics; Set (abstract data type); Component (thermodynamics); Functional data analysis","score_opus":0.016471276518459345,"score_gpt":0.2655547094652063,"score_spread":0.24908343294674698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118213171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9327579,0.0018193526,0.020808771,0.0013739045,0.0001156985,0.00015908177,0.0000815467,0.00016505593,0.042718668],"genre_scores_gemma":[0.860622,0.00007524264,0.0011416689,0.00034856133,0.000080451784,0.000029877734,0.00013306925,0.000022792065,0.13754638],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99686956,0.00013542337,0.0006988091,0.0009985495,0.0006517096,0.00064591935],"domain_scores_gemma":[0.99698055,0.001244146,0.00017788968,0.0009951033,0.00036335722,0.00023894988],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004114619,0.00054502673,0.00096884253,0.0013015358,0.00033060546,0.00018067876,0.00043217133,0.00032832782,0.02703188],"category_scores_gemma":[0.00034468886,0.00045319076,0.0006109749,0.0073115174,0.00028206353,0.0001274261,0.00020885684,0.00048152145,0.0001492031],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.028514568,0.0025019401,0.7254536,0.00036796211,0.04731025,0.000074331336,0.0002510479,0.008128538,0.18160985,0.000804545,0.0045583807,0.0004249627],"study_design_scores_gemma":[0.006607858,0.00027538728,0.27327076,0.000081404294,0.036652565,0.000010903987,0.0029053825,0.066199,0.59758264,0.000057251007,0.014979978,0.0013768657],"about_ca_topic_score_codex":0.00036605855,"about_ca_topic_score_gemma":0.0002785795,"teacher_disagreement_score":0.45218286,"about_ca_system_score_codex":0.00042502073,"about_ca_system_score_gemma":0.00053483236,"threshold_uncertainty_score":0.999792},"labels":[],"label_agreement":null},{"id":"W7133081884","doi":"","title":"Abscess detection on bovine livers with a commercial smart imaging system","year":2025,"lang":"en","type":"article","venue":"Charles Sturt University Research Output (CRO)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"P&P Optica (Canada)","funders":"","keywords":"Hyperspectral imaging; Quality assurance; Quality (philosophy); Process (computing); Visual inspection; Meat packing industry; Image processing; Process control","score_opus":0.029086392735990053,"score_gpt":0.29310535294645157,"score_spread":0.2640189602104615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133081884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7462652,0.00030169124,0.0012173747,0.00063376327,0.000097944045,0.00020738953,0.000058785925,0.0002931134,0.2509248],"genre_scores_gemma":[0.9613529,0.00009971129,0.00003224653,0.000039714214,0.00009737165,0.0000026750408,0.000012562457,0.000017050297,0.03834576],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979867,0.00009277122,0.00013593331,0.0005501126,0.00062732084,0.00060716976],"domain_scores_gemma":[0.99862987,0.00027220554,0.000071543116,0.0004744143,0.00038627523,0.00016566677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031314706,0.00022488026,0.0003190852,0.00089693355,0.0010303799,0.00011966839,0.0006427588,0.00012814549,0.00034232796],"category_scores_gemma":[0.000076288896,0.00021832557,0.000115507086,0.0016775888,0.0003521166,0.00019276641,0.00030160128,0.00079343497,0.000061654835],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03782176,0.0047573666,0.55843836,0.010624079,0.008115837,0.006128185,0.010340349,0.0003786314,0.122465186,0.022068365,0.11674203,0.102119856],"study_design_scores_gemma":[0.011661771,0.0008399989,0.03952747,0.0020075666,0.0013388564,0.00006969604,0.13323471,0.0032191128,0.608665,0.00009424711,0.19726478,0.002076799],"about_ca_topic_score_codex":0.0016935852,"about_ca_topic_score_gemma":0.0005257795,"teacher_disagreement_score":0.5189109,"about_ca_system_score_codex":0.0014364925,"about_ca_system_score_gemma":0.00019334596,"threshold_uncertainty_score":0.8903059},"labels":[],"label_agreement":null},{"id":"W7135775331","doi":"","title":"Two- and three-way chemometric methods applied for characterisation of complex pharmaceutical preparations:St. John's Wort","year":2005,"lang":"en","type":"article","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Chemometrics; Partial least squares regression; Qualitative analysis; Calibration","score_opus":0.11700598828353476,"score_gpt":0.4020095498538342,"score_spread":0.28500356157029944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135775331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7141835,0.0037769172,0.042518977,0.0009596014,0.00002154972,0.001110485,0.00031616763,0.000056518064,0.23705627],"genre_scores_gemma":[0.89581853,0.00034132434,0.0144564845,0.000014754549,0.0000339249,6.661785e-7,0.00011075595,0.000016552849,0.089206986],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980606,0.00017954715,0.00026589315,0.00045440276,0.0006086266,0.00043096708],"domain_scores_gemma":[0.9972603,0.0011273676,0.00035155797,0.00052838423,0.0005320884,0.00020032568],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015989902,0.00019777658,0.0005927989,0.00044339016,0.0007899337,0.000021812524,0.0009045443,0.00015560133,0.22692207],"category_scores_gemma":[0.00015058176,0.000219149,0.00021129883,0.0016096127,0.0009785449,0.0003296237,0.00059179845,0.0003012349,0.000989351],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025414862,0.000456248,0.0000625477,0.00055375404,0.00085323903,0.000007393051,0.001791281,0.000113778595,0.69832605,0.00082980166,0.2662642,0.0282002],"study_design_scores_gemma":[0.003818712,0.00017034184,0.0072189923,0.000037732123,0.0006852417,0.000006395168,0.00647671,0.005022807,0.47352535,0.000036821708,0.5026523,0.0003486273],"about_ca_topic_score_codex":0.00021800931,"about_ca_topic_score_gemma":0.00017900784,"teacher_disagreement_score":0.23638807,"about_ca_system_score_codex":0.0002894039,"about_ca_system_score_gemma":0.00018091743,"threshold_uncertainty_score":0.9997885},"labels":[],"label_agreement":null},{"id":"W71604547","doi":"10.1007/978-1-4020-9119-3_8","title":"Nonlinear Principal Component Analysis","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Principal component analysis; Generalization; Section (typography); Mathematics; Nonlinear system; Empirical orthogonal functions; Geography; Algorithm; Artificial intelligence; Computer science; Statistics; Artificial neural network; Mathematical analysis; Physics","score_opus":0.026551325150616745,"score_gpt":0.26637980203781386,"score_spread":0.2398284768871971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W71604547","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008479757,0.0013027875,0.0002125698,0.000044782006,0.000026877302,0.00003154633,0.00009986114,0.00018712324,0.9972465],"genre_scores_gemma":[0.0071939547,0.0014838272,0.0013332447,0.000117159136,0.0003753815,0.0000035991732,0.0006597803,0.000059723505,0.98877335],"study_design_codex":"meta_analysis","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981647,0.0000020319299,0.00047203235,0.00057755684,0.0004938025,0.00028984423],"domain_scores_gemma":[0.998674,0.00006831051,0.00025202107,0.00074283784,0.000094247815,0.00016861896],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000035765228,0.00045606447,0.00093857583,0.0005747562,0.00010350361,0.000027076516,0.00037352205,0.00051753625,0.08169218],"category_scores_gemma":[0.000012730641,0.00041020755,0.0009473445,0.00022778129,0.00012205369,0.000028592078,0.00012888154,0.00051059324,0.00068469165],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009338142,0.004672438,0.021069225,0.0032657373,0.4178985,0.0059930957,0.0013728883,0.0013623093,0.030506792,0.14947453,0.3505594,0.012891265],"study_design_scores_gemma":[0.00032225467,0.000022510654,0.000034181954,0.000017693344,0.010266336,0.000028999853,0.000018073246,0.0006478836,0.02369582,0.0001673514,0.96379817,0.0009807138],"about_ca_topic_score_codex":0.00006849093,"about_ca_topic_score_gemma":0.000036599085,"teacher_disagreement_score":0.61323875,"about_ca_system_score_codex":0.00016853376,"about_ca_system_score_gemma":0.00006549765,"threshold_uncertainty_score":0.99983495},"labels":[],"label_agreement":null},{"id":"W7161991378","doi":"10.82308/14955","title":"Pork meat quality evaluation from hyperspectral observations","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Linear discriminant analysis; Spectroradiometer; Quality (philosophy); Pattern recognition (psychology); Quality assessment; sort","score_opus":0.11732247340507754,"score_gpt":0.3813495439324567,"score_spread":0.26402707052737917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161991378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7123062,0.0021667248,0.000024282466,0.00006599327,0.00018628986,0.00008451896,0.000105016115,0.00016817528,0.28489283],"genre_scores_gemma":[0.7680923,0.0007349456,0.002691455,0.00014507792,0.0008168589,0.0001027189,0.03587555,0.00008307447,0.19145803],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99747896,0.00003678955,0.0005907755,0.00063099747,0.0009742846,0.0002881663],"domain_scores_gemma":[0.99827486,0.00020019076,0.00037304222,0.00061217323,0.00043571985,0.000104000304],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013102722,0.00037526625,0.00053914683,0.0001606175,0.00022502795,0.00005855202,0.00033488357,0.0006009808,0.030385703],"category_scores_gemma":[0.00046783866,0.00037144058,0.00032250662,0.00057371124,0.00003138082,0.00013908364,0.000013585209,0.00044682843,0.00015067116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004005403,0.0012475152,0.052041113,0.00043241045,0.0030504193,0.000020342599,0.0046043103,0.00008070291,0.8812323,0.0061216024,0.046608724,0.004160015],"study_design_scores_gemma":[0.0010439546,0.000017230845,0.08588957,0.00006260137,0.0024761038,0.000002750019,0.012080341,0.0003374991,0.89082617,0.0041582747,0.0019238233,0.0011816932],"about_ca_topic_score_codex":0.0033211682,"about_ca_topic_score_gemma":0.0015714619,"teacher_disagreement_score":0.09343479,"about_ca_system_score_codex":0.00032142087,"about_ca_system_score_gemma":0.00036503674,"threshold_uncertainty_score":0.99987376},"labels":[],"label_agreement":null},{"id":"W775691128","doi":"10.6067/xcv8j102q5","title":"PROTEIN AND ORGANIC RESIDUE (FTIR) ANALYSIS, AND AMS RADIOCARBON DATING OF SAMPLES FROM SITES EgPr 7, EgPr 8, EgPs 65, EgPs 66, EgPs 67, EgPs 68, EgPs 70, EgPs 87, EhPq 3, EhPr 4, EhPr 24, AND EhPr 25, ALBERTA, CANADA","year":2011,"lang":"en","type":"article","venue":"The Digital Archeological Record (tDAR)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Chemistry; Analytical Chemistry (journal); Environmental chemistry","score_opus":0.028751627046248122,"score_gpt":0.22112480599477707,"score_spread":0.19237317894852896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W775691128","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.981697,0.003381388,0.00038895555,0.00044676583,0.00008815452,0.0005365406,0.0006105308,0.000173295,0.012677356],"genre_scores_gemma":[0.9914445,0.000993526,0.002013798,0.00026543424,0.00028944752,0.000081752354,0.0006281461,0.0001162699,0.0041670925],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99329114,0.00030083646,0.0017546706,0.0020476852,0.0010435339,0.0015621227],"domain_scores_gemma":[0.9928493,0.0034739417,0.0010655823,0.0015098653,0.00025504915,0.0008462783],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0006716293,0.0013056801,0.002292509,0.0004439507,0.00062596716,0.000404905,0.0013825969,0.00066209486,0.0031696171],"category_scores_gemma":[0.0035752712,0.0009627632,0.00045069263,0.0019807308,0.0017787128,0.00056749367,0.0013255198,0.0013700401,0.000019871273],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025428,0.0011653486,0.7960552,0.0006797958,0.006592167,0.0003025819,0.0026735712,0.000010560457,0.1568435,0.002978403,0.0017209738,0.028435111],"study_design_scores_gemma":[0.006850202,0.0024539465,0.28221893,0.0006824167,0.008333537,0.0003479763,0.011322487,0.002136782,0.6202988,0.050487243,0.008157645,0.006710029],"about_ca_topic_score_codex":0.21418932,"about_ca_topic_score_gemma":0.13643666,"teacher_disagreement_score":0.51383626,"about_ca_system_score_codex":0.0002613701,"about_ca_system_score_gemma":0.00036882094,"threshold_uncertainty_score":0.9999695},"labels":[],"label_agreement":null},{"id":"W80438734","doi":"10.1007/978-1-4615-0125-1_1","title":"Introduction to Real Time Infrared Spectroscopic Monitoring","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Infrared; Field (mathematics); Infrared spectroscopy; Remote sensing; Computer science; Sampling (signal processing); Spectroscopy; Data science; Optics; Physics; Telecommunications; Geography; Mathematics; Astronomy","score_opus":0.012687429438977712,"score_gpt":0.25534526884151326,"score_spread":0.24265783940253555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W80438734","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006909466,0.00023629362,0.000065128945,0.0005493377,0.00029826493,0.00007587893,0.000011283021,0.00028731202,0.99778557],"genre_scores_gemma":[0.00018343194,0.00040635487,0.0020107124,0.00006793926,0.005520746,0.000010736125,0.0000648668,0.0000976732,0.9916375],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981046,0.0000035798503,0.0004055127,0.0007308562,0.0003742735,0.00038119103],"domain_scores_gemma":[0.9987658,0.000036494075,0.00016179476,0.00074354425,0.00009480709,0.0001975387],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006380611,0.0004718634,0.0005854119,0.0003428011,0.00009736253,0.00007429869,0.0002686269,0.0004908898,0.09112477],"category_scores_gemma":[0.000052947933,0.00048318118,0.00019155032,0.00014448373,0.000040552466,0.00007367464,0.00007405747,0.000478229,0.003736575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007097588,0.000075711054,0.0003028469,0.00022002708,0.00090541964,0.000039073024,0.00007607283,0.000010291538,0.36264876,0.023101002,0.61177295,0.000776854],"study_design_scores_gemma":[0.00018682273,0.000053668624,0.000009957859,0.00004585671,0.0003619475,0.000011038686,0.000023613995,0.0000015136683,0.480188,0.0042836363,0.5141878,0.00064610096],"about_ca_topic_score_codex":0.000027001013,"about_ca_topic_score_gemma":0.0000010530633,"teacher_disagreement_score":0.11753924,"about_ca_system_score_codex":0.00051171205,"about_ca_system_score_gemma":0.00005310979,"threshold_uncertainty_score":0.999762},"labels":[],"label_agreement":null},{"id":"W951694666","doi":"10.1016/b978-012763560-6/50030-1","title":"Practices For Data Preprocessing For Optical Spectrophotometry","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Smoothing; Normalization (sociology); Preprocessor; Offset (computer science); Computer science; Data pre-processing; Fourier transform; Algorithm; Fast Fourier transform; Mathematics; Artificial intelligence; Computer vision","score_opus":0.08494474294259474,"score_gpt":0.3634095892532867,"score_spread":0.27846484631069196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W951694666","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054111984,0.0056271274,0.00082576857,0.00009363966,0.00012349583,0.0005184581,0.00049746677,0.00015111546,0.9921088],"genre_scores_gemma":[0.00036744922,0.0003625891,0.014480319,0.00016461186,0.0019136776,0.00015357653,0.00088235945,0.0002097192,0.9814657],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968942,0.0000033191748,0.00067008386,0.0014141914,0.00041789678,0.000600349],"domain_scores_gemma":[0.99560857,0.0008247764,0.0012688218,0.0018867184,0.000223424,0.00018771352],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037687854,0.00061560137,0.0009384406,0.00027625432,0.00025092115,0.00022199946,0.0013204789,0.00069538626,0.0016207662],"category_scores_gemma":[0.0009670129,0.0005939371,0.00037299798,0.00005345432,0.00017187589,0.00016680046,0.0003504296,0.000595721,0.00003791168],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030489825,0.000073125135,0.0000115757475,0.0023824763,0.0012103065,0.000025529956,0.00003593273,2.9685432e-7,0.0040552,0.0030990557,0.002110896,0.9866907],"study_design_scores_gemma":[0.00059644505,0.000060729577,2.0264288e-7,0.00022366017,0.0022900018,0.000045647652,0.000033035936,0.0001377106,0.018972114,0.008649807,0.9683194,0.00067121873],"about_ca_topic_score_codex":0.0000011143458,"about_ca_topic_score_gemma":0.000009205573,"teacher_disagreement_score":0.9860195,"about_ca_system_score_codex":0.00018224235,"about_ca_system_score_gemma":0.0002890453,"threshold_uncertainty_score":0.9996512},"labels":[],"label_agreement":null},{"id":"W971273825","doi":"10.1016/b978-0-12-374753-2.10015-2","title":"Classification of Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging","year":2010,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Reflectivity; Remote sensing; Infrared; Environmental science; Artificial intelligence; Geography; Computer science; Optics; Physics","score_opus":0.02837667116585925,"score_gpt":0.2921298584158928,"score_spread":0.26375318725003355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W971273825","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054652076,0.002196535,0.000026767668,0.000037564823,0.00014260288,0.00012689296,0.000051437557,0.00010706331,0.9918459],"genre_scores_gemma":[0.05198677,0.00009857679,0.0050041485,0.00006589204,0.00049605605,0.000011869581,0.000045769724,0.00015440451,0.9421365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99777704,0.000008761801,0.00068584364,0.00066778454,0.00045401294,0.00040652484],"domain_scores_gemma":[0.99790984,0.000066863024,0.0006963077,0.00097182865,0.00021963182,0.00013555112],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012147729,0.00052407885,0.0008090614,0.00021265462,0.00018517471,0.00008154284,0.00048298595,0.0005592784,0.002329953],"category_scores_gemma":[0.00004730486,0.0005464931,0.0004119092,0.000059978585,0.000531428,0.00007737617,0.000085622145,0.001078592,0.0000393692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004102385,0.00002299747,0.00014637993,0.00029259792,0.0002601783,0.000019998637,0.00021836949,9.139394e-7,0.8924373,0.0021923564,0.000036538982,0.10433137],"study_design_scores_gemma":[0.00084624457,0.000033711352,0.000061015526,0.0007798921,0.002034277,0.00012308329,0.00014307717,0.00071423396,0.6221732,0.02576524,0.3456107,0.0017153387],"about_ca_topic_score_codex":0.000002667194,"about_ca_topic_score_gemma":0.0000066646908,"teacher_disagreement_score":0.34557417,"about_ca_system_score_codex":0.00028769078,"about_ca_system_score_gemma":0.000260358,"threshold_uncertainty_score":0.99969864},"labels":[],"label_agreement":null},{"id":"W971531951","doi":"10.1007/978-3-662-04186-4_2","title":"Color Image Filtering","year":2000,"lang":"en","type":"book-chapter","venue":"Digital signal processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Image processing; Filter (signal processing); Noise (video); Human visual system model; Process (computing); SIGNAL (programming language); Signal processing; Digital image processing; Image (mathematics); Median filter; Task (project management); Digital signal processing; Engineering; Computer hardware","score_opus":0.015621954206632364,"score_gpt":0.24291402393854247,"score_spread":0.2272920697319101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W971531951","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005146533,0.0019722767,0.00048601622,0.000018743835,0.000011677879,0.00003817858,0.00015286176,0.00023845134,0.99656713],"genre_scores_gemma":[0.42533156,0.00004678066,0.00008231734,0.000054344127,0.00040301186,0.000005908483,0.00022958776,0.00011768985,0.5737288],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982941,8.452191e-7,0.0003996254,0.0005457895,0.000375896,0.0003837348],"domain_scores_gemma":[0.9992769,0.000049261405,0.00024685942,0.00021318666,0.00007094014,0.0001428528],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000024685993,0.00048601962,0.0005113401,0.0001617578,0.00016123678,0.0007172986,0.0003373204,0.00036203404,0.020608796],"category_scores_gemma":[0.000012706989,0.0004853546,0.00024529736,0.00008511651,0.00016624464,0.00053068093,0.00009203883,0.0004846363,0.000517909],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027019778,0.0001699134,0.000030043795,0.002087614,0.00061346276,0.0006075283,0.00017915573,0.00002675868,0.05054257,0.0016699806,0.0031014057,0.94070137],"study_design_scores_gemma":[0.0014120297,0.00017554316,0.000003745751,0.002525456,0.0013556624,0.00033475106,0.00019832724,0.0023725913,0.1870248,0.05656261,0.74319696,0.004837511],"about_ca_topic_score_codex":0.0000023879013,"about_ca_topic_score_gemma":3.0118642e-7,"teacher_disagreement_score":0.93586385,"about_ca_system_score_codex":0.00014073639,"about_ca_system_score_gemma":0.00011253088,"threshold_uncertainty_score":0.9997598},"labels":[],"label_agreement":null},{"id":"W972889591","doi":"","title":"Combining hyperspectral vegetation indices for a better estimation of crop chlorophyll content for application to precision agriculture","year":2001,"lang":"en","type":"article","venue":"DIGITAL.CSIC (Spanish National Research Council (CSIC))","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Hyperspectral imaging; Vegetation Index; Vegetation (pathology); Precision agriculture; Crop; Environmental science; Remote sensing; Estimation; Agriculture; Agricultural engineering; Normalized Difference Vegetation Index; Agronomy; Forestry; Geography; Leaf area index; Engineering; Biology","score_opus":0.13144320411458993,"score_gpt":0.3625235115018752,"score_spread":0.23108030738728524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W972889591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88301885,0.0006408609,0.07639353,0.0020133907,0.000097547054,0.002128458,0.0007190933,0.00015160987,0.034836683],"genre_scores_gemma":[0.9932406,0.000026222377,0.0027547185,0.00015994959,0.00031952586,0.000922688,0.00061755016,0.000038762915,0.0019200079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9935475,0.00001995672,0.0005848264,0.0006351917,0.0046703573,0.0005421671],"domain_scores_gemma":[0.9857508,0.0013956361,0.00028268827,0.00026582764,0.01209534,0.00020968779],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0018767295,0.00025086876,0.0003629373,0.00032290092,0.00039487946,0.00045058847,0.0004567502,0.00020495956,0.00006269728],"category_scores_gemma":[0.012080191,0.00022942519,0.00020592894,0.0012110346,0.00015456002,0.0007220198,0.000077351055,0.00025751223,0.00003544679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034138008,0.00310662,0.0029134988,0.0016329683,0.0011006331,0.0000069152047,0.0036501763,0.0116411615,0.8003378,0.032175604,0.05042116,0.08959967],"study_design_scores_gemma":[0.009216126,0.002071915,0.007934806,0.00057965337,0.0003748194,0.000042081963,0.003646663,0.05778479,0.6878241,0.07475481,0.15395573,0.0018145202],"about_ca_topic_score_codex":0.0000736692,"about_ca_topic_score_gemma":0.000058674505,"teacher_disagreement_score":0.11251371,"about_ca_system_score_codex":0.0026526647,"about_ca_system_score_gemma":0.0012153974,"threshold_uncertainty_score":0.99624145},"labels":[],"label_agreement":null},{"id":"W979168652","doi":"10.1016/b978-012763560-6/50024-6","title":"Infrared Spectral Correlation Charts","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Correlation; Infrared; Mathematics; Statistics; Physics; Optics; Geometry","score_opus":0.015642541917267435,"score_gpt":0.24395682538487976,"score_spread":0.22831428346761232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W979168652","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008475801,0.0021474366,0.00001447906,0.000025834392,0.00018289564,0.0001089666,0.000043465676,0.00020742182,0.99718475],"genre_scores_gemma":[0.0015504669,0.00029859648,0.00010113495,0.0001601283,0.0010359524,0.000016470176,0.00017456143,0.000120298355,0.9965424],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980962,0.0000049645387,0.00051177235,0.00056636333,0.00041792335,0.00040274288],"domain_scores_gemma":[0.9986465,0.00006178407,0.00037649777,0.00066697545,0.00008109788,0.00016716229],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007124654,0.00054030324,0.00065414974,0.00026452294,0.00013131954,0.00006460345,0.00032253485,0.0006912292,0.02524965],"category_scores_gemma":[0.000026905898,0.00055555947,0.00039184678,0.000039265153,0.00012394806,0.00005303412,0.00008026891,0.0008100557,0.00084625254],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069227775,0.000043551616,0.00019490017,0.00028666886,0.000905788,0.00024267899,0.00025849225,0.000004158835,0.0011299618,0.01763585,0.004039554,0.97518915],"study_design_scores_gemma":[0.00031632817,0.000023080365,0.000008369631,0.00014865983,0.0005665953,0.000049513834,0.000012605223,0.000015460606,0.0035971806,0.024727972,0.9699188,0.0006154511],"about_ca_topic_score_codex":6.125074e-7,"about_ca_topic_score_gemma":0.0000042420847,"teacher_disagreement_score":0.97457373,"about_ca_system_score_codex":0.00024825975,"about_ca_system_score_gemma":0.00009660215,"threshold_uncertainty_score":0.9999317},"labels":[],"label_agreement":null}]}