{"meta":{"query_hash":"97bd64f8f71c","filters":{"venue":"Journal of Chemometrics"},"cohort_total":65,"direct_labels_cover":0,"predictions_cover":65,"exported":65,"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/97bd64f8f71c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Chemometrics"},"results":[{"id":"W1483592191","doi":"10.1002/cem.2638","title":"Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Soft sensor; Principal component analysis; Kernel (algebra); Nonlinear system; Computer science; Probabilistic logic; Artificial intelligence; Principal component regression; Data mining; Kernel principal component analysis; Pattern recognition (psychology); Basis (linear algebra); Component (thermodynamics); Machine learning; Process (computing); Support vector machine; Kernel method; Mathematics","score_opus":0.017326773821269337,"score_gpt":0.24169795414670753,"score_spread":0.22437118032543818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483592191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045743636,0.00026192275,0.9944929,0.00009425964,0.000013615516,0.000024661826,0.00003932471,0.00011627796,0.00038268504],"genre_scores_gemma":[0.5693791,0.0017933109,0.41944107,0.00017650444,0.00018904098,0.00047904585,0.0007529113,0.0002181689,0.0075709014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829537,0.00072141923,0.00008756692,0.00047210255,0.00034894136,0.00007455951],"domain_scores_gemma":[0.99787736,0.0011347026,0.00033274945,0.00022973958,0.00037908554,0.000046408608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025051462,0.0015361805,0.0011250237,0.0008495184,0.0003962234,0.0011862348,0.0014047254,0.0012086994,0.001430951],"category_scores_gemma":[0.0058070887,0.0006161556,0.0013998216,0.000994946,0.0013469554,0.0017534706,0.0013667698,0.0018713867,0.00084401044],"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":[0.00014576325,0.000111456684,0.0015490477,0.00037144573,0.00020705935,0.00017728773,0.00020774173,0.85007644,0.008710083,0.05187307,0.0016954183,0.084875174],"study_design_scores_gemma":[0.0000015944296,0.000011571872,0.00015110492,0.0000034011703,0.0000060719335,0.000019190471,0.000004123517,0.99542123,0.00044544163,0.0035788028,0.00034964315,0.000007749269],"about_ca_topic_score_codex":0.0030330962,"about_ca_topic_score_gemma":0.0024079862,"teacher_disagreement_score":0.0030330962,"about_ca_system_score_codex":0.00059123035,"about_ca_system_score_gemma":0.0009559959,"threshold_uncertainty_score":0.013248682},"labels":[],"label_agreement":null},{"id":"W1504718675","doi":"10.1002/cem.2416","title":"Parameter estimation in differential equation models with constrained states","year":2012,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University; Actua; Simon Fraser University","funders":"","keywords":"Interpretability; Smoothing; Variety (cybernetics); Monotone polygon; Relaxation (psychology); Mathematical optimization; Computer science; Applied mathematics; Differential (mechanical device); Estimation theory; Estimation; Differential equation; Mathematics; Econometrics; Algorithm; Artificial intelligence; Statistics; Engineering; Mathematical analysis","score_opus":0.017564939274970406,"score_gpt":0.22327824820539516,"score_spread":0.20571330893042475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504718675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003882724,0.00007672332,0.99560827,0.0000845596,0.000007875318,0.000012643693,0.00003336342,0.000092689246,0.00020120066],"genre_scores_gemma":[0.474956,0.0005457279,0.5201528,0.00020014906,0.00010016209,0.0005139229,0.0005814991,0.00018869663,0.0027610061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984068,0.00085991033,0.0000933938,0.00034208113,0.00022649213,0.00007125186],"domain_scores_gemma":[0.9906632,0.0077919825,0.00076217006,0.00038715513,0.00031999766,0.00007540362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037078077,0.0013374833,0.0014681051,0.0010657726,0.00044356333,0.0013911354,0.0015999235,0.0016740575,0.0012871039],"category_scores_gemma":[0.016972465,0.0012177739,0.0016636407,0.0010011457,0.0017249319,0.0018420956,0.002128747,0.0026054445,0.00028906934],"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":[0.000049953178,0.000028549892,0.0010374519,0.00009522169,0.000095322896,0.00007687182,0.00012065204,0.9192618,0.0011832755,0.05382271,0.00035260062,0.023875589],"study_design_scores_gemma":[0.0000064654655,0.0000066484795,0.00008214855,0.0000061509027,0.0000057030115,0.0000055426244,0.000003206577,0.980391,0.00024392469,0.01900922,0.00023099786,0.000008960741],"about_ca_topic_score_codex":0.006290069,"about_ca_topic_score_gemma":0.0038917216,"teacher_disagreement_score":0.006290069,"about_ca_system_score_codex":0.0009302177,"about_ca_system_score_gemma":0.0014323961,"threshold_uncertainty_score":0.019609034},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011984234,0.00024131278,0.98527944,0.00020887384,0.000027767393,0.00022514154,0.000463269,0.0008808081,0.0006891404],"genre_scores_gemma":[0.13018279,0.00047688157,0.865818,0.00011706842,0.00005593943,0.0010462155,0.0012854803,0.00025399745,0.00076350506],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9935175,0.0032810455,0.000506185,0.0010210567,0.001496776,0.00017740334],"domain_scores_gemma":[0.97706336,0.015805045,0.001660495,0.0034240722,0.0017983003,0.00024878263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010135246,0.0019303912,0.0017170202,0.003226429,0.0008261994,0.003282167,0.0014394616,0.000888661,0.0029542882],"category_scores_gemma":[0.030405652,0.0007102408,0.0016543878,0.0035700612,0.0014411776,0.00182481,0.003765487,0.0018042034,0.000892306],"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":[0.0008102177,0.00034322025,0.014035165,0.0029618335,0.0012271466,0.0012301034,0.0030103063,0.13108812,0.06950411,0.11316516,0.011327079,0.6512975],"study_design_scores_gemma":[0.0000777493,0.00029204533,0.006595002,0.00024963162,0.00019810477,0.0006080041,0.00077531306,0.73277676,0.030117266,0.20892584,0.019196771,0.00018750604],"about_ca_topic_score_codex":0.0005249763,"about_ca_topic_score_gemma":0.0005398049,"teacher_disagreement_score":0.010135246,"about_ca_system_score_codex":0.0005792994,"about_ca_system_score_gemma":0.001683736,"threshold_uncertainty_score":0.053600967},"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":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028130796,0.45804375,0.1787871,0.28547546,0.016055271,0.000102142214,0.0004012002,0.0006802014,0.05764178],"genre_scores_gemma":[0.05527443,0.4840685,0.24478891,0.07865239,0.032398496,0.0002348347,0.0006983288,0.0008567697,0.103027314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98585504,0.0037737694,0.0010443628,0.0020007025,0.006990861,0.0003352803],"domain_scores_gemma":[0.9828111,0.008341943,0.0011179373,0.0015120108,0.0051120925,0.0011049308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014574851,0.0011990309,0.0014401808,0.005398532,0.001907871,0.008086345,0.0013305905,0.0033544626,0.0043924428],"category_scores_gemma":[0.019076938,0.0008825605,0.00086978124,0.008268078,0.0111750215,0.011815809,0.0028590192,0.012575578,0.004781252],"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":[0.00011164245,0.00011384179,0.001519599,0.00081070437,0.000105690764,0.00014958043,0.0008493407,0.0011240348,0.0011985125,0.37167746,0.27630278,0.34603682],"study_design_scores_gemma":[0.0000144890955,0.000067209614,0.000842682,0.0005563189,0.000019258283,0.00048397668,0.0002099957,0.0011695599,0.0010263565,0.15403003,0.841502,0.00007814943],"about_ca_topic_score_codex":0.004525146,"about_ca_topic_score_gemma":0.0034591537,"teacher_disagreement_score":0.014574851,"about_ca_system_score_codex":0.00411773,"about_ca_system_score_gemma":0.0047590127,"threshold_uncertainty_score":0.07708013},"labels":[],"label_agreement":null},{"id":"W1643861420","doi":"10.1002/cem.2430","title":"Liver functional magnetic resonance imaging analysis using a latent variables approach","year":2012,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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 Medical Centre; McMaster University","funders":"","keywords":"Postprandial; Blood-oxygen-level dependent; Hemodynamics; Hyperoxia; Cirrhosis; Medicine; Magnetic resonance imaging; Liver disease; Cardiology; Internal medicine; Pathology; Radiology; Lung; Insulin","score_opus":0.04886609685375378,"score_gpt":0.30263826650594783,"score_spread":0.25377216965219407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1643861420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10320913,0.0002549441,0.8941346,0.00036735577,0.000022778615,0.00012719112,0.00048850355,0.00079608255,0.0005994356],"genre_scores_gemma":[0.8116306,0.0001618019,0.18506789,0.000071257266,0.000063820895,0.00040071382,0.0011728418,0.00012291926,0.0013081112],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998475,0.0009448228,0.000052598967,0.00030263077,0.00011924734,0.00010566896],"domain_scores_gemma":[0.99864084,0.00081580045,0.00014669578,0.00022123507,0.00012717553,0.00004832603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030790223,0.0007979319,0.0007297526,0.0016477705,0.00045527122,0.0010145342,0.00069593714,0.0006006618,0.002742981],"category_scores_gemma":[0.004975039,0.00024722746,0.0015518662,0.0011591563,0.00067846006,0.0006158935,0.000850541,0.0009833116,0.0007079449],"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":[0.0015885149,0.0011166184,0.08977693,0.00039807838,0.002746894,0.0004922161,0.00071193284,0.19622357,0.036796525,0.02714918,0.0071607246,0.63583887],"study_design_scores_gemma":[0.000064698186,0.00030902182,0.035430256,0.000024324465,0.00017845386,0.00015031622,0.00013000029,0.9417573,0.0030901167,0.017113738,0.0016853486,0.00006651674],"about_ca_topic_score_codex":0.002430684,"about_ca_topic_score_gemma":0.002267073,"teacher_disagreement_score":0.0030790223,"about_ca_system_score_codex":0.00048796923,"about_ca_system_score_gemma":0.0008453131,"threshold_uncertainty_score":0.016283631},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032045026,0.000030620078,0.9991246,0.000010924946,0.000006740376,0.00002553465,0.0000128957345,0.00015739538,0.00031081744],"genre_scores_gemma":[0.0113777425,0.00013947429,0.98664606,0.000018958364,0.000018639308,0.00028535252,0.00008047172,0.00033685233,0.0010965064],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983059,0.0005036399,0.00011431624,0.00020730184,0.00078554626,0.000083158986],"domain_scores_gemma":[0.9969054,0.0017297448,0.00028187723,0.0002975636,0.00070236594,0.0000830904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029475526,0.0027982553,0.0015682984,0.0014034917,0.0007180585,0.0018318197,0.002038977,0.0013123945,0.0063340846],"category_scores_gemma":[0.01032233,0.0010166029,0.0012683137,0.0015550161,0.0012099538,0.0018637921,0.0022961185,0.002801545,0.003588905],"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":[0.00012697626,0.00014695311,0.00047074485,0.00063948747,0.0001651887,0.000107580585,0.00025240806,0.41006187,0.027539859,0.12190786,0.006417602,0.43216342],"study_design_scores_gemma":[0.00003718785,0.000047614583,0.00019143184,0.000035132474,0.0000185127,0.000080404694,0.000025972644,0.9565111,0.007278646,0.03059456,0.0051216846,0.000057827394],"about_ca_topic_score_codex":0.0015212036,"about_ca_topic_score_gemma":0.0023420125,"teacher_disagreement_score":0.0063340846,"about_ca_system_score_codex":0.0009680912,"about_ca_system_score_gemma":0.0022617057,"threshold_uncertainty_score":0.02118969},"labels":[],"label_agreement":null},{"id":"W1852006233","doi":"10.1002/cem.1407","title":"Chemometric analysis of gas chromatographic data—investigation of enological parameters of a bag‐in‐box white wine as affected by storage time and temperature","year":2011,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","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 Guelph","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Wine; Partial least squares regression; Principal component analysis; Chemistry; White Wine; Chromatography; Principal component regression; Gas chromatography; Absorbance; Chemometrics; Kovats retention index; Gas chromatography–mass spectrometry; Analytical Chemistry (journal); Mass spectrometry; Mathematics; Food science; Statistics","score_opus":0.03245339367968082,"score_gpt":0.22321898716205257,"score_spread":0.19076559348237176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1852006233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951325,0.0001908861,0.0042524305,0.000025256328,0.00001416361,0.000012437974,0.00018461411,0.000035769626,0.00015195098],"genre_scores_gemma":[0.99512583,0.00018586022,0.0037812754,0.0000201501,0.000009520304,0.000019999186,0.0004973681,0.00001939486,0.0003406096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997631,0.000047081732,0.000016905431,0.000052767573,0.00009839448,0.00002170299],"domain_scores_gemma":[0.9995813,0.000088144436,0.00014445234,0.00003688292,0.00012368006,0.000025647898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004786963,0.00043010482,0.0003701134,0.00048285062,0.00019374503,0.00039045681,0.00017650245,0.0003027404,0.0003463832],"category_scores_gemma":[0.001310876,0.00015942154,0.0003196287,0.0006195773,0.00023031361,0.00044433592,0.00019363534,0.00032555344,0.00014265177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007580218,0.000104672516,0.016578674,0.00008516375,0.00007323282,0.00010923589,0.00019908091,0.002213638,0.9645829,0.00005288684,0.000077169185,0.015165361],"study_design_scores_gemma":[0.000014691609,0.0010372037,0.3125412,0.00001287707,0.00010686839,0.00046539336,0.00031897923,0.0358651,0.64855456,0.00014845276,0.0008564295,0.000078341836],"about_ca_topic_score_codex":0.0013275892,"about_ca_topic_score_gemma":0.0010391962,"teacher_disagreement_score":0.0013275892,"about_ca_system_score_codex":0.00025339398,"about_ca_system_score_gemma":0.00013299398,"threshold_uncertainty_score":0.002639711},"labels":[],"label_agreement":null},{"id":"W1879465779","doi":"10.1002/cem.2576","title":"Determination of rice type by <sup>1</sup>H NMR spectroscopy in combination with different chemometric tools","year":2013,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":52,"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":"Ministry of Rural Affairs","keywords":"Principal component analysis; Chemometrics; Linear discriminant analysis; Independent component analysis; Partial least squares regression; Pattern recognition (psychology); Multivariate statistics; Chemistry; Mathematics; Analytical Chemistry (journal); Context (archaeology); Hierarchical clustering; Artificial intelligence; Nuclear magnetic resonance spectroscopy; Chromatography; Biological system; Cluster analysis; Statistics; Computer science; Stereochemistry","score_opus":0.025920293797992676,"score_gpt":0.26008619507157554,"score_spread":0.23416590127358286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879465779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8906875,0.0020016981,0.10232715,0.00022187787,0.00005371027,0.00012224559,0.0013377784,0.00050728227,0.0027408167],"genre_scores_gemma":[0.85267544,0.0011057892,0.14389782,0.00010151546,0.000044312845,0.000105511375,0.0009650313,0.000080057966,0.001024577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994242,0.00017613354,0.000049589627,0.00014315636,0.00016370736,0.000043234777],"domain_scores_gemma":[0.9989624,0.00043091463,0.00028157106,0.00008382986,0.00019781923,0.00004352289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012070592,0.00071848225,0.000638097,0.0012849695,0.00019674517,0.0005318193,0.00031170723,0.00055600837,0.0006867017],"category_scores_gemma":[0.001792742,0.0002507348,0.00033701738,0.0015765948,0.00048946624,0.00041070895,0.00026050516,0.0004914791,0.00056037126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005916852,0.00008223224,0.014102189,0.00020137287,0.00006346384,0.000084331215,0.00007902505,0.0018217308,0.9225821,0.0001692558,0.00021243123,0.060010143],"study_design_scores_gemma":[0.00004191756,0.0012570982,0.20779839,0.000024219427,0.0002773081,0.0011386352,0.000331682,0.052725583,0.73093754,0.001453332,0.003892361,0.00012188316],"about_ca_topic_score_codex":0.0005805403,"about_ca_topic_score_gemma":0.00083638314,"teacher_disagreement_score":0.0012849695,"about_ca_system_score_codex":0.00018395779,"about_ca_system_score_gemma":0.00026108476,"threshold_uncertainty_score":0.006383598},"labels":[],"label_agreement":null},{"id":"W1892839219","doi":"10.1002/cem.2533","title":"Statistical properties of signal entropy for use in detecting changes in time series data","year":2013,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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":"","keywords":"Computer science; Entropy (arrow of time); Differential entropy; Time series; Sample entropy; Maximum entropy spectral estimation; White noise; Transfer entropy; Change detection; Series (stratigraphy); System identification; Algorithm; Data mining; Principle of maximum entropy; Artificial intelligence; Mathematics; Statistics; Machine learning; Measure (data warehouse)","score_opus":0.04068541875529027,"score_gpt":0.23695692195659834,"score_spread":0.19627150320130807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892839219","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05924164,0.00084695156,0.935465,0.00027800904,0.00010965412,0.00009488171,0.00053071964,0.0010196709,0.0024136286],"genre_scores_gemma":[0.75626916,0.0010026435,0.2397661,0.00015377678,0.00034859046,0.0003333412,0.0009656979,0.00027073247,0.0008897835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985416,0.00047624027,0.00015397264,0.00021676939,0.00054540427,0.000065997854],"domain_scores_gemma":[0.98125297,0.014431455,0.0015565258,0.0016551095,0.00087067386,0.00023328733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003967441,0.0007289783,0.0008043783,0.003535658,0.0004304463,0.001431689,0.00058988424,0.00085574656,0.0014830959],"category_scores_gemma":[0.025066517,0.00027566598,0.0008361791,0.002969345,0.0014178251,0.0024269742,0.0011032772,0.0014936166,0.00043193527],"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":[0.00056292186,0.00041452213,0.03247514,0.0007986508,0.00039247045,0.0014299104,0.0010060816,0.304443,0.08124152,0.14962381,0.003719894,0.42389202],"study_design_scores_gemma":[0.000015096978,0.00021158092,0.018899366,0.00006562483,0.000051428284,0.00073664566,0.00008703501,0.90629417,0.012778534,0.057955164,0.0028112035,0.00009424649],"about_ca_topic_score_codex":0.00074826967,"about_ca_topic_score_gemma":0.00048359987,"teacher_disagreement_score":0.003967441,"about_ca_system_score_codex":0.0005279002,"about_ca_system_score_gemma":0.0006052404,"threshold_uncertainty_score":0.020982027},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043362896,0.00030196004,0.99443865,0.00013117002,0.000034585846,0.000026009055,0.000044341945,0.00011777184,0.0005691914],"genre_scores_gemma":[0.15202297,0.0018741225,0.8423188,0.00018361173,0.00021135696,0.00031295422,0.0004945037,0.00026011388,0.0023215436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99636275,0.0014355984,0.00028820755,0.00055193494,0.0012280834,0.0001334886],"domain_scores_gemma":[0.9952434,0.0025540905,0.00059843715,0.0004757007,0.00097446865,0.00015389746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004452345,0.0016102814,0.0012706751,0.0023888517,0.0007372606,0.0020156682,0.0011565423,0.0012366164,0.0012423937],"category_scores_gemma":[0.011774086,0.0005015002,0.0015091405,0.0033093435,0.0021961639,0.0028165837,0.0023398318,0.002486319,0.0007602293],"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":[0.00030015464,0.00015451373,0.0033507093,0.00053387956,0.0003254797,0.00043219156,0.0004058283,0.44063073,0.032516945,0.16115968,0.005545172,0.3546448],"study_design_scores_gemma":[0.000009681792,0.0000731653,0.00079201476,0.00003166446,0.00002423156,0.0001685472,0.000035764308,0.9521702,0.004800057,0.0390823,0.0027523919,0.000060004175],"about_ca_topic_score_codex":0.0009624614,"about_ca_topic_score_gemma":0.00080529705,"teacher_disagreement_score":0.004452345,"about_ca_system_score_codex":0.0009567505,"about_ca_system_score_gemma":0.0018235432,"threshold_uncertainty_score":0.023546576},"labels":[],"label_agreement":null},{"id":"W1963044959","doi":"10.1002/cem.2636","title":"Constrained kernelized partial least squares","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Face and Expression Recognition","field":"Computer Science","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":"National Research Council Canada","keywords":"Partial least squares regression; Nonlinear system; Kernel (algebra); Latent variable; Noise (video); Mathematical optimization; Computer science; Variable (mathematics); Mathematics; Kernel method; Algorithm; Artificial intelligence; Machine learning; Support vector machine","score_opus":0.015193314255049988,"score_gpt":0.24293819630197694,"score_spread":0.22774488204692694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963044959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023669,0.00023850372,0.99625236,0.000058824135,0.000024822242,0.00002559739,0.00009031617,0.00041920046,0.00052355207],"genre_scores_gemma":[0.17788343,0.0008375372,0.8104711,0.00022870926,0.000110378365,0.00025873925,0.0011472548,0.0005362386,0.00852669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980756,0.00061575696,0.00008393247,0.00047871357,0.0006416087,0.000104361854],"domain_scores_gemma":[0.9978193,0.0009285675,0.0002480311,0.00041629694,0.0005244988,0.00006342572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013438769,0.0013841452,0.0016513349,0.0007727362,0.00047672214,0.001255686,0.0019127303,0.0015011029,0.0040827286],"category_scores_gemma":[0.0064138626,0.00064633175,0.0011442346,0.0014521511,0.0009796591,0.0018213323,0.0019351018,0.0016864545,0.0023292324],"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":[0.0002256164,0.00012530276,0.0011240178,0.0006207005,0.00034200313,0.00017534831,0.00017626156,0.4205434,0.02038983,0.03412775,0.009521656,0.5126282],"study_design_scores_gemma":[0.000009398712,0.000021741675,0.0004128362,0.000014367273,0.0000134669235,0.00006581987,0.000013984781,0.9826401,0.0035330534,0.009674701,0.003575554,0.000024873427],"about_ca_topic_score_codex":0.0040457593,"about_ca_topic_score_gemma":0.0043576895,"teacher_disagreement_score":0.0040827286,"about_ca_system_score_codex":0.00053556025,"about_ca_system_score_gemma":0.0018110342,"threshold_uncertainty_score":0.013658106},"labels":[],"label_agreement":null},{"id":"W1967859542","doi":"10.1002/cem.1115","title":"Multivariate curve resolution of mixed bacterial DNA sequence spectra: identification and quantification of bacteria in undefined mixture samples","year":2008,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","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":"Toronto Metropolitan University","funders":"","keywords":"Multivariate statistics; Partial least squares regression; Calibration; Least-squares function approximation; Statistics; Multivariate analysis; Identification (biology); Mathematics; Biological system; Biology; Ecology","score_opus":0.06034472756730446,"score_gpt":0.2915426234654613,"score_spread":0.2311978958981568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967859542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53878796,0.00060487806,0.45774904,0.00012959655,0.000050432493,0.00008046978,0.00054485694,0.0011558197,0.0008969724],"genre_scores_gemma":[0.5748353,0.00040828233,0.42216378,0.00009265993,0.000020439285,0.00017911707,0.0006943527,0.0001480614,0.0014580265],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998099,0.0005841775,0.00009031323,0.0003653753,0.00079353753,0.0000675888],"domain_scores_gemma":[0.9975389,0.0011402604,0.00035671814,0.00031820996,0.0005503258,0.00009554687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00289404,0.0009428657,0.0006673622,0.0013845012,0.00019206975,0.00069142686,0.00053419924,0.0006251724,0.0011123996],"category_scores_gemma":[0.0048060766,0.00030355222,0.0005942825,0.0011166591,0.00051380094,0.0007097191,0.00060250255,0.00097528816,0.0007458061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004423522,0.00016497556,0.0048886864,0.00016285041,0.00008796087,0.000038260398,0.000095203206,0.0046348805,0.93709004,0.00046314212,0.00019600826,0.051735662],"study_design_scores_gemma":[0.000026152222,0.00036345923,0.014507145,0.000015920321,0.000063525695,0.0003184146,0.000072938776,0.13387117,0.84851444,0.00080235576,0.0013702504,0.000074193245],"about_ca_topic_score_codex":0.0004181112,"about_ca_topic_score_gemma":0.0004348139,"teacher_disagreement_score":0.00289404,"about_ca_system_score_codex":0.0002089315,"about_ca_system_score_gemma":0.0002873583,"threshold_uncertainty_score":0.01530534},"labels":[],"label_agreement":null},{"id":"W1969323816","doi":"10.1002/cem.913","title":"A Bayesian online inferential model for evaluation of analyzer performance","year":2005,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Syncrude (Canada)","funders":"","keywords":"Bayesian probability; Computer science; Weighting; Spectrum analyzer; Algorithm; Data mining; Artificial intelligence","score_opus":0.02992134045723372,"score_gpt":0.28604869998110444,"score_spread":0.2561273595238707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969323816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022387266,0.00017368818,0.97386044,0.0004143416,0.000018656061,0.0000893453,0.00013605252,0.00035112625,0.0025690966],"genre_scores_gemma":[0.82295483,0.0002615978,0.17119607,0.00018233407,0.000079333964,0.0005145591,0.00033425464,0.00010697811,0.0043699984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946292,0.0029693516,0.00018460328,0.000749591,0.001132382,0.00033489813],"domain_scores_gemma":[0.9821078,0.013571135,0.0013580482,0.0007992676,0.0018940012,0.00026973634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014524799,0.0016609628,0.0019798996,0.0018837263,0.0006135169,0.0027774903,0.0033360224,0.0029724794,0.0035880646],"category_scores_gemma":[0.04075603,0.0013934956,0.0010255792,0.0012951919,0.0025331695,0.00398254,0.0019004055,0.002385434,0.0010771994],"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":[0.00015185412,0.00007538028,0.0009920793,0.000033982975,0.00005926928,0.00006587187,0.00007883057,0.9443796,0.0006000523,0.035605058,0.0004969693,0.017460974],"study_design_scores_gemma":[0.000007549698,0.000012627542,0.000102844795,0.0000038769563,0.0000055075707,0.0000066270977,0.0000023116036,0.99119896,0.000103468796,0.008469567,0.000079848345,0.000006711402],"about_ca_topic_score_codex":0.0093501685,"about_ca_topic_score_gemma":0.0048594233,"teacher_disagreement_score":0.014524799,"about_ca_system_score_codex":0.0026200823,"about_ca_system_score_gemma":0.0017994856,"threshold_uncertainty_score":0.07681543},"labels":[],"label_agreement":null},{"id":"W1969341360","doi":"10.1002/cem.1078","title":"Sequential design of mixture experiments for the development of new products","year":2007,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","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","funders":"U.S. Department of Energy","keywords":"Set (abstract data type); Design of experiments; Product (mathematics); Computer science; Property (philosophy); Process engineering; Biochemical engineering; Mathematics; Engineering; Statistics","score_opus":0.3834768506986345,"score_gpt":0.4891570911188014,"score_spread":0.10568024042016688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969341360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045547146,0.00051020185,0.95088977,0.00006435446,0.00007640569,0.0018084061,0.000098295925,0.00036297197,0.00064244296],"genre_scores_gemma":[0.14234439,0.000310212,0.8527213,0.000056207366,0.00003172317,0.003959813,0.000131522,0.00004493953,0.00039988576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9759822,0.016914936,0.00089139777,0.0022820488,0.0035608895,0.0003684674],"domain_scores_gemma":[0.97409713,0.020328041,0.002158415,0.0015686637,0.0015117967,0.00033593588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029531576,0.0019506931,0.003392955,0.0018208018,0.000564336,0.0014473124,0.0020114703,0.0009912589,0.0022594386],"category_scores_gemma":[0.024019703,0.001434135,0.0019314243,0.0013124426,0.0014497401,0.0013641274,0.0018306411,0.0019132749,0.00033553736],"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":[0.0128541,0.0039553377,0.005097212,0.0027997154,0.0019062299,0.00020303635,0.00047561445,0.4592602,0.13242806,0.03638754,0.00081681827,0.34381607],"study_design_scores_gemma":[0.0015903197,0.012931248,0.0023124455,0.000058937218,0.00046170203,0.00008624063,0.00005039903,0.8803566,0.06972904,0.026119566,0.006139241,0.00016427517],"about_ca_topic_score_codex":0.0007540129,"about_ca_topic_score_gemma":0.0011315908,"teacher_disagreement_score":0.029531576,"about_ca_system_score_codex":0.0013646816,"about_ca_system_score_gemma":0.00251681,"threshold_uncertainty_score":0.15617979},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036694428,0.0002121846,0.99237293,0.00034108665,0.000038009603,0.00007008279,0.00018906407,0.00052967016,0.0025774369],"genre_scores_gemma":[0.24890105,0.0007194539,0.74040395,0.00033527828,0.00015744612,0.0005293906,0.0012470513,0.00044506093,0.0072613126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99522,0.0027576732,0.00009880891,0.00049020594,0.0012265526,0.00020676394],"domain_scores_gemma":[0.9953845,0.0029419705,0.0002562403,0.0006610923,0.0006638741,0.000092316644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007208836,0.0019051697,0.0013777117,0.0023258938,0.0012310217,0.0030461112,0.0024575863,0.001523259,0.004734506],"category_scores_gemma":[0.014928127,0.0006443009,0.001760117,0.0026701791,0.0016504193,0.0015785543,0.0022833908,0.003950205,0.0014936008],"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":[0.00011611896,0.00013942915,0.0037994487,0.0002946904,0.00043989866,0.00025034952,0.0005476251,0.42150912,0.0029428825,0.2645818,0.0094649615,0.29591367],"study_design_scores_gemma":[0.000007467228,0.000025781294,0.000633888,0.000019520872,0.000019388734,0.000053960208,0.000040135445,0.91841006,0.00071003573,0.07558322,0.0044648834,0.000031626816],"about_ca_topic_score_codex":0.0057913084,"about_ca_topic_score_gemma":0.004534952,"teacher_disagreement_score":0.007208836,"about_ca_system_score_codex":0.0017114067,"about_ca_system_score_gemma":0.0018775378,"threshold_uncertainty_score":0.038124442},"labels":[],"label_agreement":null},{"id":"W1982573319","doi":"10.1002/cem.709","title":"‘Stretch’ vs ‘slice’ methods for representing three‐way structure via matrix notation","year":2002,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Korea Institute of Energy Research","keywords":"Slicing; Notation; Representation (politics); Matrix (chemical analysis); Matrix representation; Set (abstract data type); Computer science; Diagonal; Flexibility (engineering); Array data structure; Algorithm; Theoretical computer science; Mathematics; Arithmetic; Programming language; Computer graphics (images); Geometry; Group (periodic table)","score_opus":0.08810379124293947,"score_gpt":0.4277878576391258,"score_spread":0.3396840663961863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982573319","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002779725,0.00011869172,0.99583495,0.00009065103,0.00003428612,0.000013419137,0.00003776779,0.00027397304,0.00081657775],"genre_scores_gemma":[0.0613515,0.000384352,0.9359149,0.00011036808,0.00006543889,0.00013348363,0.00017603615,0.00021112978,0.0016527235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984504,0.00082922773,0.00010484845,0.00018198474,0.000363052,0.00007052099],"domain_scores_gemma":[0.99694616,0.0013721192,0.00045402304,0.0007670511,0.0003745167,0.00008625528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025833983,0.0014044972,0.0006020938,0.0010241293,0.00038381518,0.0017056802,0.0012237733,0.0008216035,0.005655949],"category_scores_gemma":[0.0067327814,0.0002878524,0.00091585587,0.0016483448,0.001850462,0.003614847,0.0016479723,0.001331618,0.0019267742],"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":[0.00016163704,0.00004755861,0.0005465937,0.00029459453,0.00007498621,0.00016666017,0.00060745084,0.10610843,0.019782776,0.5447955,0.005890958,0.32152283],"study_design_scores_gemma":[0.00002230118,0.00012682295,0.0003383489,0.00005508226,0.000033168915,0.0002492958,0.00017923166,0.6981044,0.013585115,0.26512247,0.022106629,0.000077164696],"about_ca_topic_score_codex":0.0012507109,"about_ca_topic_score_gemma":0.0011572107,"teacher_disagreement_score":0.005655949,"about_ca_system_score_codex":0.00044388353,"about_ca_system_score_gemma":0.00079467735,"threshold_uncertainty_score":0.018921018},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00872521,0.000471215,0.98894775,0.00026072326,0.000043824683,0.0000935269,0.00005706345,0.00030501583,0.001095674],"genre_scores_gemma":[0.11877306,0.0010291757,0.8782953,0.00012923886,0.0001166583,0.00034170822,0.00022933327,0.00014191054,0.0009437084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905716,0.0063853837,0.00027070244,0.00071555027,0.0019367386,0.000120070916],"domain_scores_gemma":[0.9658322,0.024464661,0.0011385597,0.0041452106,0.00424111,0.00017814417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015447248,0.0014772711,0.0007993878,0.00083466986,0.000526891,0.0011902755,0.0016261194,0.0008789154,0.0048244074],"category_scores_gemma":[0.08592981,0.0005087924,0.0006829486,0.0021369755,0.0016970218,0.002986091,0.001281847,0.0020963526,0.0011251732],"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":[0.0008471388,0.00079938304,0.0020194256,0.0014317661,0.00022672827,0.00023753667,0.0003125319,0.35072762,0.035213716,0.095210195,0.0037396094,0.50923437],"study_design_scores_gemma":[0.00014094831,0.0005145537,0.0011876064,0.00007109245,0.00003380505,0.00019160891,0.000049959675,0.91374165,0.03318662,0.0423434,0.008454863,0.00008377729],"about_ca_topic_score_codex":0.00196325,"about_ca_topic_score_gemma":0.0013040067,"teacher_disagreement_score":0.015447248,"about_ca_system_score_codex":0.000707224,"about_ca_system_score_gemma":0.0009660168,"threshold_uncertainty_score":0.08169383},"labels":[],"label_agreement":null},{"id":"W2003787220","doi":"10.1002/cem.665","title":"An index formalism that generalizes the capabilities of matrix notation and algebra to n‐way arrays","year":2001,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":38,"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; Korea Institute of Energy Research","keywords":"Notation; Formalism (music); Algebra over a field; Computer science; Matrix (chemical analysis); Context (archaeology); Matrix algebra; Theoretical computer science; Mathematics; Arithmetic; Pure mathematics; Physics","score_opus":0.045832148677248406,"score_gpt":0.33749582474370843,"score_spread":0.29166367606646004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003787220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007560649,0.00043766107,0.9639755,0.0006888705,0.0004190026,0.0000549442,0.00022168862,0.000709296,0.025932387],"genre_scores_gemma":[0.17977978,0.0010894331,0.79801816,0.0012242433,0.00068563136,0.0003077285,0.0005014892,0.00063145254,0.01776211],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99835974,0.00048080515,0.00022976738,0.0002788627,0.00052759907,0.0001231693],"domain_scores_gemma":[0.99817014,0.00046935448,0.00023640372,0.00068196986,0.00029729793,0.00014484557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028346893,0.00066298735,0.00081190036,0.0015623367,0.0014238165,0.0042917198,0.0015031119,0.0010384248,0.0049316846],"category_scores_gemma":[0.0038450342,0.00042887128,0.0015607212,0.0024499553,0.0033831706,0.008474883,0.0026309208,0.0021581592,0.0022928787],"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":[0.00001529019,0.000014156281,0.000093911796,0.000035982994,0.0000065002832,0.0001013458,0.00026054587,0.0013930497,0.0017583217,0.9799644,0.0025913909,0.0137650585],"study_design_scores_gemma":[0.000009605773,0.000024787074,0.00009213239,0.000029066376,0.000010204503,0.00022450357,0.00007499531,0.017021064,0.0014027032,0.94181186,0.039268736,0.000030229266],"about_ca_topic_score_codex":0.0013651353,"about_ca_topic_score_gemma":0.0018145476,"teacher_disagreement_score":0.0049316846,"about_ca_system_score_codex":0.0008780445,"about_ca_system_score_gemma":0.0016306768,"threshold_uncertainty_score":0.016498148},"labels":[],"label_agreement":null},{"id":"W2006831848","doi":"10.1002/cem.1020","title":"An adaptive regression adjusted monitoring and fault isolation scheme","year":2006,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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","keywords":"Fault detection and isolation; Multivariate statistics; Fault (geology); Constant false alarm rate; Computer science; ALARM; Dimension (graph theory); Scheme (mathematics); Regression; Isolation (microbiology); Chart; Regression analysis; False alarm; Data mining; Statistics; Artificial intelligence; Mathematics; Machine learning; Engineering","score_opus":0.014644105384619611,"score_gpt":0.24052040699870542,"score_spread":0.2258763016140858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006831848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0139563335,0.00009543905,0.98415494,0.000052591717,0.00003453647,0.00004056964,0.000046498462,0.0010292272,0.0005899317],"genre_scores_gemma":[0.45018625,0.00008951007,0.5470797,0.000091604954,0.00008989881,0.000109246634,0.00020977465,0.000080025085,0.0020640001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881566,0.00024167015,0.00008347955,0.00036960916,0.00038311188,0.00010642967],"domain_scores_gemma":[0.9988262,0.0003020272,0.00022865622,0.00019160831,0.0003923775,0.000059232683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010462636,0.00072807295,0.0008802078,0.0010376252,0.0003162002,0.0008902615,0.0014339616,0.00089314004,0.0023660115],"category_scores_gemma":[0.0030859238,0.0002569861,0.00059427373,0.0006144447,0.00045062325,0.0011285379,0.0009862623,0.0012111092,0.0005944615],"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":[0.0008250842,0.00024835355,0.002300351,0.00016648785,0.00012237915,0.00020264248,0.00016251302,0.15078735,0.12579872,0.018742269,0.0027056593,0.6979382],"study_design_scores_gemma":[0.00003510598,0.00013778039,0.00081953243,0.0000069142675,0.000025180463,0.000094911426,0.0000061295996,0.9760074,0.018400522,0.0027790193,0.0016588855,0.000028545668],"about_ca_topic_score_codex":0.001220057,"about_ca_topic_score_gemma":0.0008544755,"teacher_disagreement_score":0.0023660115,"about_ca_system_score_codex":0.00051948405,"about_ca_system_score_gemma":0.0007459135,"threshold_uncertainty_score":0.00791508},"labels":[],"label_agreement":null},{"id":"W2033393326","doi":"10.1002/cem.1206","title":"Modeling multi‐way data with linearly dependent loadings","year":2009,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":134,"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":"Generalization; Context (archaeology); Linear model; Mathematics; Computer science; Algorithm; Biological system; Statistics; Mathematical analysis","score_opus":0.1329767601637393,"score_gpt":0.3669497680814434,"score_spread":0.23397300791770412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033393326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033395533,0.00011938056,0.9649174,0.0001707779,0.000030085015,0.00007596536,0.0001908917,0.00050038745,0.0005995291],"genre_scores_gemma":[0.58149785,0.0003195884,0.41360164,0.00016459475,0.00008746915,0.000343021,0.00074771856,0.00021373763,0.003024387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974439,0.0011591738,0.000106024134,0.000571589,0.00049840304,0.0002207997],"domain_scores_gemma":[0.99508435,0.003455783,0.00043220012,0.00048531342,0.00044701196,0.000095371324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004021342,0.0012402968,0.0010283994,0.0011671076,0.00047891086,0.001534026,0.0011631049,0.0012713658,0.0023169806],"category_scores_gemma":[0.008790661,0.00064619374,0.0017624655,0.0014965929,0.0011520452,0.0014521497,0.0013938202,0.002413634,0.0006948664],"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":[0.0001720269,0.00023800625,0.007420628,0.00020077717,0.00034759057,0.000248936,0.00043396317,0.83036435,0.009241204,0.017154345,0.0013615296,0.13281669],"study_design_scores_gemma":[0.0000041755,0.000022728567,0.00080053555,0.0000055928263,0.000007871062,0.000022188935,0.000024462102,0.99336064,0.0006005308,0.0046746265,0.00046170424,0.0000148771405],"about_ca_topic_score_codex":0.008844052,"about_ca_topic_score_gemma":0.008476739,"teacher_disagreement_score":0.008844052,"about_ca_system_score_codex":0.00075732433,"about_ca_system_score_gemma":0.0011828509,"threshold_uncertainty_score":0.021267176},"labels":[],"label_agreement":null},{"id":"W2042663327","doi":"10.1002/cem.810","title":"Shifted factor analysis—Part III: <i>N</i>‐way generalization and application","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Western University","funders":"","keywords":"Chemometrics; Generalization; Mode (computer interface); Factor analysis; Factor (programming language); Set (abstract data type); Mathematics; Data set; Algorithm; Statistics; Computer science; Applied mathematics; Machine learning; Mathematical analysis","score_opus":0.03673406376145951,"score_gpt":0.3140604343626678,"score_spread":0.2773263706012083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042663327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011639541,0.0004666703,0.9859387,0.00029330445,0.00007540876,0.00004412019,0.0000547834,0.00031492792,0.0011724954],"genre_scores_gemma":[0.20334958,0.00093690597,0.7908724,0.0001897801,0.00015169686,0.00016520963,0.00029826266,0.00029273413,0.0037433468],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799854,0.0010268237,0.000092868286,0.00040095748,0.0003967831,0.00008402979],"domain_scores_gemma":[0.99562126,0.0015750558,0.0003028111,0.0015518831,0.000866755,0.00008221433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004694944,0.0012221716,0.0007749334,0.00074430835,0.00054460374,0.0011711618,0.0010721812,0.0007617093,0.005603725],"category_scores_gemma":[0.010473192,0.0004247769,0.0016843611,0.001516698,0.0015243471,0.0019577092,0.0016421276,0.0014226963,0.0018932095],"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":[0.00023143241,0.0001080907,0.0023308948,0.00033645,0.00018768889,0.0002825393,0.00060914684,0.17191488,0.023924114,0.11851038,0.006316448,0.67524785],"study_design_scores_gemma":[0.000017667762,0.00012272038,0.0012378531,0.000057654463,0.00002614535,0.0002546514,0.00009668408,0.8770908,0.0067356736,0.104629666,0.009687731,0.000042713422],"about_ca_topic_score_codex":0.0030151054,"about_ca_topic_score_gemma":0.0018157226,"teacher_disagreement_score":0.005603725,"about_ca_system_score_codex":0.0006717158,"about_ca_system_score_gemma":0.0010443496,"threshold_uncertainty_score":0.024829507},"labels":[],"label_agreement":null},{"id":"W2047527848","doi":"10.1002/cem.926","title":"Mathematical improvements to maximum likelihood parallel factor analysis: theory and simulations","year":2005,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Dalhousie University","funders":"","keywords":"Algorithm; Computation; Representation (politics); Basis (linear algebra); Covariance; Heteroscedasticity; Function (biology); Computer science; Variety (cybernetics); Covariance matrix; Mathematics; Noise (video); Mathematical optimization; Applied mathematics; Statistics; Artificial intelligence","score_opus":0.03508056897040344,"score_gpt":0.34350867179711275,"score_spread":0.3084281028267093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047527848","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016913313,0.000102828264,0.99651575,0.00012293336,0.000017285543,0.000028443652,0.000023102066,0.00019608029,0.0013022794],"genre_scores_gemma":[0.09106396,0.00039850554,0.9052248,0.0001171432,0.00007919641,0.00051749364,0.0001369168,0.00018105813,0.002280848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982546,0.00090288295,0.00007989622,0.000146436,0.00054449716,0.00007167674],"domain_scores_gemma":[0.99241817,0.0053730737,0.00034761216,0.00078029244,0.00097472436,0.000106166626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043013827,0.00089883135,0.00083766325,0.0010256774,0.0006582142,0.0011175243,0.0018941938,0.0012470423,0.0062949765],"category_scores_gemma":[0.022537239,0.0007293507,0.00096827757,0.001478773,0.0015338201,0.0022153407,0.001900277,0.0017372221,0.0019642175],"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":[0.00004186515,0.00003751884,0.00030278493,0.00011675448,0.000022948985,0.000071777744,0.00004831778,0.8699882,0.0006884292,0.082126215,0.0014398452,0.045115333],"study_design_scores_gemma":[0.000007649977,0.000005356127,0.00002501327,0.000005002215,0.0000012777423,0.000011702227,0.0000027469748,0.98438346,0.00016867855,0.014756727,0.0006284828,0.0000037530372],"about_ca_topic_score_codex":0.0044861096,"about_ca_topic_score_gemma":0.0042203967,"teacher_disagreement_score":0.0062949765,"about_ca_system_score_codex":0.0009951153,"about_ca_system_score_gemma":0.0012282213,"threshold_uncertainty_score":0.022748172},"labels":[],"label_agreement":null},{"id":"W2048856095","doi":"10.1002/(sici)1099-128x(200003/04)14:2<79::aid-cem578>3.0.co;2-o","title":"Choice of latent explanatory variables: a multiobjective optimization approach","year":2000,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Latent variable; Mathematical optimization; Mathematics; Computer science; Multivariable calculus; Set (abstract data type); Data mining; Algorithm; Statistics; Engineering","score_opus":0.012352702537313383,"score_gpt":0.21393187083983323,"score_spread":0.20157916830251985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048856095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00244731,0.000098490855,0.9969901,0.00008931439,0.0000050772073,0.000028240825,0.000018053213,0.000053034582,0.0002704102],"genre_scores_gemma":[0.15608542,0.00036862277,0.84101963,0.00010438565,0.000047681973,0.0005508604,0.00015170536,0.000106174186,0.0015655728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978225,0.0014359717,0.00007868314,0.00020978609,0.0003458326,0.000107266336],"domain_scores_gemma":[0.9978097,0.0016359019,0.00019155997,0.00008409982,0.0002173563,0.00006144184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046969294,0.0018458948,0.0018021651,0.001984585,0.00066261465,0.001707739,0.0017006351,0.0019283298,0.0023226873],"category_scores_gemma":[0.0053293156,0.0010427098,0.0013197723,0.0015457651,0.001129422,0.001768978,0.0017574087,0.0018839657,0.0004599635],"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":[0.000056746845,0.00008622004,0.00049426086,0.00016088325,0.00013004715,0.000061769184,0.0000753742,0.9204461,0.0015062704,0.022239791,0.00058598316,0.05415655],"study_design_scores_gemma":[0.000012906998,0.000019546183,0.000069781556,0.000015892101,0.000010855626,0.0000072415573,0.000010171953,0.99081856,0.0003165446,0.008369796,0.000339659,0.000008985222],"about_ca_topic_score_codex":0.0017545277,"about_ca_topic_score_gemma":0.0016730218,"teacher_disagreement_score":0.0046969294,"about_ca_system_score_codex":0.001174495,"about_ca_system_score_gemma":0.0018804567,"threshold_uncertainty_score":0.024840057},"labels":[],"label_agreement":null},{"id":"W2049045581","doi":"10.1002/cem.816","title":"Induction of decision trees using fuzzy partitions","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","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":"Partenariat Canadien Contre Le Cancer","keywords":"Decision tree; Subspace topology; Decision tree learning; Mathematics; Incremental decision tree; Fuzzy logic; Data mining; Artificial intelligence; Classifier (UML); Computer science; Pattern recognition (psychology); Partition (number theory); Machine learning","score_opus":0.04340556262687248,"score_gpt":0.27043590294851716,"score_spread":0.22703034032164468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049045581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01002007,0.0002001612,0.9881881,0.000069211936,0.000052819785,0.00007279231,0.00010982981,0.00032912308,0.000957991],"genre_scores_gemma":[0.19030592,0.0002505047,0.8066193,0.00008330434,0.000096412514,0.0002548996,0.0007194242,0.00010062961,0.0015696064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977623,0.000716167,0.00014746869,0.00040704812,0.0008451548,0.000121752615],"domain_scores_gemma":[0.99607915,0.0024388582,0.00021313025,0.0003132032,0.00082581904,0.00012977277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019380086,0.00054344325,0.00092779024,0.0014645144,0.00056171475,0.0009792728,0.00086702214,0.0005580606,0.0020928662],"category_scores_gemma":[0.008211371,0.00043350173,0.0009647852,0.0010309996,0.0007576943,0.0013072067,0.0013037738,0.0015195183,0.0006826284],"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":[0.00030607596,0.00017796585,0.0019477084,0.0004921911,0.00020031491,0.00022190304,0.00037508796,0.36137176,0.016909868,0.09542595,0.0060540223,0.5165171],"study_design_scores_gemma":[0.000037660036,0.000060676117,0.00026995482,0.00004799473,0.000027742248,0.000039853512,0.000022216203,0.9224684,0.0055151377,0.06800994,0.003486768,0.00001361744],"about_ca_topic_score_codex":0.0009590837,"about_ca_topic_score_gemma":0.0012093656,"teacher_disagreement_score":0.0020928662,"about_ca_system_score_codex":0.00071778556,"about_ca_system_score_gemma":0.00071794086,"threshold_uncertainty_score":0.010249317},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055487975,0.00071276067,0.94025004,0.00015764161,0.00007612807,0.00010456954,0.000056973044,0.0007728478,0.0023811723],"genre_scores_gemma":[0.2847884,0.0007160046,0.71122545,0.00009630241,0.000068286674,0.00019310148,0.00043827164,0.000346855,0.002127432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955664,0.0017714474,0.00029701827,0.00044922606,0.0016838164,0.00023193481],"domain_scores_gemma":[0.97557914,0.015067742,0.0014273629,0.0017811299,0.0059350897,0.00020962585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009261224,0.0014032694,0.0008272483,0.0024177479,0.000625005,0.0019828253,0.0010389849,0.0011642333,0.0021159854],"category_scores_gemma":[0.032463588,0.00042548552,0.00086079736,0.002874863,0.0011763407,0.0025113798,0.0011513617,0.0012059888,0.00073968474],"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":[0.0007332991,0.00026160528,0.008776743,0.00035418593,0.00025225375,0.00009647233,0.0002702236,0.16102244,0.01549765,0.0613522,0.0021033399,0.74927956],"study_design_scores_gemma":[0.000060226055,0.00032479034,0.0019725505,0.000048381786,0.00006622302,0.00024659382,0.00007342975,0.96130544,0.019497562,0.011556292,0.004801764,0.000046774603],"about_ca_topic_score_codex":0.0019038898,"about_ca_topic_score_gemma":0.0010090369,"teacher_disagreement_score":0.009261224,"about_ca_system_score_codex":0.00064705533,"about_ca_system_score_gemma":0.0012413345,"threshold_uncertainty_score":0.048978627},"labels":[],"label_agreement":null},{"id":"W2060836485","doi":"10.1002/cem.1143","title":"Dynamic contrast‐enhanced MRI diagnostics in oncology via principal component analysis","year":2008,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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. Joseph’s Healthcare Hamilton; McMaster University","funders":"McMaster University","keywords":"Principal component analysis; Dynamic contrast; Contrast (vision); Magnetic resonance imaging; Dynamic contrast-enhanced MRI; Computer science; Artificial intelligence; Medical physics; Pattern recognition (psychology); Nuclear medicine; Medicine; Radiology","score_opus":0.021181806447394497,"score_gpt":0.3441456467071163,"score_spread":0.3229638402597218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060836485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013874348,0.0009496816,0.9838256,0.00034786938,0.00002580963,0.000032115488,0.000053618773,0.0003128434,0.0005780284],"genre_scores_gemma":[0.35713732,0.0017660885,0.6391457,0.0000775687,0.00013437954,0.000103199454,0.00021796077,0.000098380115,0.0013194333],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986467,0.0007968722,0.00004153223,0.0001658318,0.0003028043,0.000046207842],"domain_scores_gemma":[0.99875236,0.00077121746,0.0001611922,0.00011916801,0.00015918227,0.000036864774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020553833,0.00068704336,0.0007794489,0.0016296532,0.00030294133,0.0011943994,0.0005491106,0.0005299339,0.0010201941],"category_scores_gemma":[0.005104548,0.00039387998,0.00069704524,0.0014763415,0.00090420543,0.00082563073,0.0007880308,0.0008703546,0.00053987803],"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":[0.00025465904,0.00021011585,0.007253884,0.00037191948,0.00030825325,0.00026632182,0.00016866112,0.2464313,0.022436352,0.055763625,0.004144563,0.66239035],"study_design_scores_gemma":[0.000015428395,0.000102037106,0.003375993,0.000026570793,0.00004793588,0.000204716,0.000026016942,0.9594262,0.004325598,0.029448442,0.0029557897,0.00004526781],"about_ca_topic_score_codex":0.0017563978,"about_ca_topic_score_gemma":0.001257695,"teacher_disagreement_score":0.0020553833,"about_ca_system_score_codex":0.0004038165,"about_ca_system_score_gemma":0.0009691133,"threshold_uncertainty_score":0.01086998},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009556186,0.00068902085,0.97732717,0.0021501,0.00027875887,0.000071115945,0.0004951721,0.00026298367,0.009169583],"genre_scores_gemma":[0.61470026,0.0024658912,0.36811206,0.001952726,0.0009443126,0.0009916255,0.001458033,0.000792743,0.008582311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97086227,0.019398943,0.0010523136,0.00229375,0.005443083,0.00094979355],"domain_scores_gemma":[0.8959424,0.08224759,0.007999459,0.004734906,0.008418919,0.0006567722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029055767,0.001456328,0.0011720569,0.0043061725,0.0005366689,0.0058945687,0.002769589,0.00291198,0.009051931],"category_scores_gemma":[0.12644336,0.00071140757,0.0012237208,0.0057950243,0.004623471,0.0064755566,0.0027073813,0.008243369,0.0021336165],"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":[0.000037387803,0.000030494844,0.0017928022,0.00016252484,0.00006692383,0.00011157701,0.0004610479,0.032176193,0.00037333203,0.93729067,0.004170543,0.023326505],"study_design_scores_gemma":[0.000041395073,0.000057923888,0.0019099916,0.00028601743,0.00004708083,0.00017467962,0.00034911197,0.13021885,0.0005640034,0.8549828,0.011298834,0.00006931511],"about_ca_topic_score_codex":0.00346698,"about_ca_topic_score_gemma":0.0011090582,"teacher_disagreement_score":0.029055767,"about_ca_system_score_codex":0.0017135711,"about_ca_system_score_gemma":0.0018034831,"threshold_uncertainty_score":0.1536634},"labels":[],"label_agreement":null},{"id":"W2074970237","doi":"10.1002/cem.778","title":"Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":276,"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":"Latent variable; Statistical process control; Autocorrelation; Process (computing); Computer science; Batch processing; Trajectory; Principal component analysis; Partial least squares regression; Multivariate statistics; Process control; Statistics; Data mining; Mathematics; Artificial intelligence; Machine learning","score_opus":0.02778604446015669,"score_gpt":0.2917564515311766,"score_spread":0.2639704070710199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074970237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009229709,0.0002700249,0.9892133,0.00025365836,0.0000438784,0.000038189206,0.00015199422,0.00024705444,0.0005521491],"genre_scores_gemma":[0.7334278,0.0010353749,0.26021925,0.0001405725,0.0001642992,0.0004173817,0.00087279704,0.000201337,0.0035211244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668807,0.0015764269,0.00020309331,0.0006783061,0.00067086885,0.00018332279],"domain_scores_gemma":[0.9915965,0.0059425724,0.00097269873,0.00050515955,0.00084278214,0.00014017368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057788105,0.001157225,0.0014446368,0.0015252742,0.000565113,0.0024914239,0.0018031098,0.0012592161,0.0022481177],"category_scores_gemma":[0.012096407,0.00061149854,0.0020748521,0.002045246,0.0012998477,0.0017631021,0.0012984681,0.002988052,0.00039759648],"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":[0.000106514286,0.00007833256,0.0030474106,0.00018414027,0.000117808304,0.00009888895,0.00016223533,0.87029517,0.0017152469,0.07896486,0.0010135049,0.044215966],"study_design_scores_gemma":[0.0000020541606,0.000013552924,0.00032127654,0.000009922881,0.0000059181634,0.0000045304096,0.000012802521,0.9896974,0.00022604411,0.009147941,0.0005497217,0.00000872204],"about_ca_topic_score_codex":0.010628335,"about_ca_topic_score_gemma":0.004955236,"teacher_disagreement_score":0.010628335,"about_ca_system_score_codex":0.002085619,"about_ca_system_score_gemma":0.0015552894,"threshold_uncertainty_score":0.030561626},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018333694,0.0011669323,0.97679543,0.0007177666,0.00006426625,0.00005892388,0.00019100297,0.00020275582,0.0024692046],"genre_scores_gemma":[0.6728711,0.003457086,0.313234,0.0004458081,0.00038864795,0.0006634016,0.00096638995,0.00037571968,0.0075978287],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99596417,0.0021912274,0.00015495221,0.00083869614,0.00063798035,0.00021306379],"domain_scores_gemma":[0.9861748,0.008778279,0.0016255906,0.0016659467,0.0015192026,0.00023607111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009274903,0.0015793503,0.0013566924,0.0019763685,0.00077430153,0.002792489,0.0017134087,0.0018168594,0.0049826712],"category_scores_gemma":[0.029818624,0.00083152973,0.002816857,0.00251249,0.0034124723,0.0050981296,0.0021591173,0.0023352604,0.0015662519],"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":[0.00006931198,0.00005059249,0.004034155,0.00028046733,0.00028325454,0.00020077828,0.00056345004,0.24161254,0.0012487828,0.67275274,0.0041174227,0.07478637],"study_design_scores_gemma":[0.000009650154,0.00003153491,0.00074698566,0.00005967035,0.00002564956,0.00009634711,0.000052688534,0.53861535,0.00020546386,0.4580311,0.0020939175,0.00003150632],"about_ca_topic_score_codex":0.0043410505,"about_ca_topic_score_gemma":0.00189256,"teacher_disagreement_score":0.009274903,"about_ca_system_score_codex":0.0014936279,"about_ca_system_score_gemma":0.0012036557,"threshold_uncertainty_score":0.049050927},"labels":[],"label_agreement":null},{"id":"W2105136387","doi":"10.1002/cem.809","title":"Shifted factor analysis—Part II: Algorithms","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Blind Source Separation Techniques","field":"Computer Science","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":"Western University","funders":"","keywords":"Computation; Algorithm; Position (finance); Factor (programming language); Computer science; Factor analysis; Synthetic data; Conjecture; Mathematics; Machine learning; Discrete mathematics","score_opus":0.03123449909931067,"score_gpt":0.2855000179432603,"score_spread":0.25426551884394966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105136387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001235819,0.0003647718,0.99736917,0.00012805042,0.000039192226,0.00003378288,0.00004557769,0.00025663126,0.0005269924],"genre_scores_gemma":[0.062024012,0.0010275672,0.93204457,0.0002571183,0.00024039723,0.00038002737,0.00051593746,0.00023535824,0.0032749898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783784,0.0009693364,0.00013014802,0.00053821446,0.00041329887,0.0001111115],"domain_scores_gemma":[0.99613535,0.002005437,0.00023064931,0.0009978384,0.0005469122,0.00008389003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034950003,0.0014925608,0.0011814242,0.0016052176,0.0005983177,0.0016529071,0.0018266878,0.0017228606,0.009036465],"category_scores_gemma":[0.014718224,0.00083844824,0.0014533187,0.0023500635,0.0015070438,0.0034103002,0.0022492786,0.002013788,0.0042807483],"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":[0.00019813607,0.00007774875,0.0008653717,0.00032793925,0.00015367263,0.000075519696,0.00015174302,0.17806296,0.0037048813,0.13314188,0.010283082,0.67295706],"study_design_scores_gemma":[0.00004124975,0.000044895864,0.0002836781,0.000045062814,0.000020300093,0.00009426929,0.000028800949,0.7855072,0.0018685607,0.20418154,0.00785845,0.000025993326],"about_ca_topic_score_codex":0.002178585,"about_ca_topic_score_gemma":0.0012238497,"teacher_disagreement_score":0.009036465,"about_ca_system_score_codex":0.0008827493,"about_ca_system_score_gemma":0.0015898016,"threshold_uncertainty_score":0.030229926},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017856397,0.00024101142,0.97450656,0.00009425659,0.00021964463,0.0004227701,0.0003920697,0.0037658708,0.0025014821],"genre_scores_gemma":[0.06368469,0.00035581438,0.9300919,0.00012394358,0.00007195243,0.0009342735,0.0012319592,0.0007856707,0.002719878],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99102914,0.001983499,0.0006428262,0.0016953375,0.0044153016,0.00023388238],"domain_scores_gemma":[0.9884244,0.0013311659,0.00090908835,0.0030689125,0.00613824,0.00012820959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074986513,0.0014676593,0.0010615906,0.0032733856,0.0010135252,0.0024006502,0.0024918907,0.0010843488,0.005038441],"category_scores_gemma":[0.015634326,0.0010770367,0.00088030676,0.0030892426,0.00095055293,0.0017923709,0.0020109678,0.0021710785,0.0045952573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003035071,0.0001869074,0.00434874,0.00048187232,0.0001478517,0.00009872021,0.00046906315,0.008420381,0.49098912,0.010737138,0.008419133,0.47539747],"study_design_scores_gemma":[0.000059738944,0.00043401297,0.012513577,0.0000884807,0.00016704854,0.0004624986,0.00019841742,0.05701159,0.8573817,0.0056681405,0.06580175,0.00021311398],"about_ca_topic_score_codex":0.00088714703,"about_ca_topic_score_gemma":0.0012718932,"teacher_disagreement_score":0.0074986513,"about_ca_system_score_codex":0.0011197195,"about_ca_system_score_gemma":0.0016941347,"threshold_uncertainty_score":0.039657116},"labels":[],"label_agreement":null},{"id":"W2122640592","doi":"10.1002/cem.1245","title":"An efficient algorithm for Parafac with uncorrelated mode‐A components applied to large <i>I</i> × <i>J</i> × <i>K</i> data sets with <i>I</i> &gt;&gt; <i>JK</i>","year":2009,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Algorithm; Uncorrelated; Mode (computer interface); Product (mathematics); Constraint (computer-aided design); Computer science; Mathematics; Statistics","score_opus":0.04417632818794799,"score_gpt":0.33191053844305907,"score_spread":0.2877342102551111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122640592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013359385,0.000032779644,0.9973636,0.000049156195,0.000014904279,0.00005658621,0.000023104858,0.00090110983,0.00022290029],"genre_scores_gemma":[0.008366331,0.00003173919,0.9907803,0.000019728974,0.000011871681,0.00014898412,0.00007674343,0.00011673797,0.0004474793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987997,0.00031998177,0.00008788254,0.0002516966,0.0004543519,0.00008644046],"domain_scores_gemma":[0.99823713,0.0007953047,0.00013662406,0.00028307113,0.00048762895,0.000060228867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021264572,0.001551019,0.0011431817,0.001437867,0.0012224277,0.0010433502,0.0016442875,0.0011759688,0.006916148],"category_scores_gemma":[0.005835771,0.0007834433,0.0011649372,0.0019951882,0.0008089349,0.0013594001,0.002042204,0.002332497,0.005762828],"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":[0.00017826789,0.00013006064,0.00047285616,0.0001763028,0.000114317,0.00013885782,0.00023529724,0.06392454,0.020479113,0.015904421,0.007040258,0.8912058],"study_design_scores_gemma":[0.00011947576,0.000111259404,0.00046531274,0.000014784717,0.000030009733,0.00022449974,0.00006463059,0.9597439,0.01438441,0.01590987,0.008874812,0.000057115598],"about_ca_topic_score_codex":0.006607063,"about_ca_topic_score_gemma":0.009315288,"teacher_disagreement_score":0.006916148,"about_ca_system_score_codex":0.0006770258,"about_ca_system_score_gemma":0.002325728,"threshold_uncertainty_score":0.023136795},"labels":[],"label_agreement":null},{"id":"W2122949291","doi":"10.1002/cem.789","title":"Maximum likelihood parallel factor analysis (MLPARAFAC)","year":2003,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Dalhousie University","funders":"","keywords":"Algorithm; Covariance; Noise (video); Covariance matrix; Variety (cybernetics); Computer science; Least-squares function approximation; Uncorrelated; Mathematics; Statistics; Artificial intelligence","score_opus":0.05454327994311516,"score_gpt":0.33079010294682387,"score_spread":0.27624682300370873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122949291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010831928,0.000105394836,0.9970356,0.000052109335,0.000018140816,0.00006222816,0.000086722794,0.0011139687,0.0004426131],"genre_scores_gemma":[0.016435446,0.00013320275,0.98178977,0.000032624186,0.000022067263,0.0002647916,0.00026128124,0.00017367031,0.0008872331],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962774,0.0014733258,0.00019518795,0.0006716664,0.0012188096,0.00016368288],"domain_scores_gemma":[0.9942076,0.002751995,0.00075424014,0.0008689086,0.0012774249,0.00013982703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004939185,0.003011586,0.0016758043,0.001998328,0.001027499,0.0020248129,0.002024733,0.0015315091,0.009969987],"category_scores_gemma":[0.016799117,0.001048214,0.0021704454,0.002439205,0.001197612,0.0026307215,0.0027122588,0.0024050532,0.0067253713],"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":[0.00023528028,0.00014878562,0.0009536929,0.00064624037,0.00022743052,0.00018443898,0.00030515782,0.09561748,0.013875006,0.03024854,0.009640366,0.84791756],"study_design_scores_gemma":[0.00007419843,0.00015010351,0.0014581312,0.000078025594,0.00006228425,0.00041787975,0.00009040378,0.8958025,0.016439622,0.060754303,0.024524586,0.00014807968],"about_ca_topic_score_codex":0.00163416,"about_ca_topic_score_gemma":0.0021990184,"teacher_disagreement_score":0.009969987,"about_ca_system_score_codex":0.0005626394,"about_ca_system_score_gemma":0.0018068064,"threshold_uncertainty_score":0.03335291},"labels":[],"label_agreement":null},{"id":"W2124251904","doi":"10.1002/cem.1230","title":"Algorithms for DEDICOM: acceleration, deceleration, or neither?","year":2009,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Matrix Theory and Algorithms","field":"Computer Science","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":"McGill University","funders":"","keywords":"Monotonic function; Convergence (economics); Eigenvalues and eigenvectors; Computation; Extrapolation; Acceleration; Algorithm; Polynomial; Simple (philosophy); Matrix (chemical analysis); Mathematics; Computer science; Decomposition; Mathematical optimization","score_opus":0.051647291405399344,"score_gpt":0.3183347952125155,"score_spread":0.26668750380711614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124251904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004256963,0.00038350114,0.9921765,0.0004762351,0.000100897305,0.000037282374,0.000026997926,0.0005434503,0.0019981642],"genre_scores_gemma":[0.092258975,0.0003804773,0.9012295,0.00030795464,0.0001735054,0.00012977862,0.00018903633,0.00029430588,0.0050364714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899954,0.0002989523,0.00007922249,0.00023774352,0.0002944916,0.00009013449],"domain_scores_gemma":[0.99688417,0.0013023964,0.00021888874,0.0008016221,0.0006043686,0.00018847518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021699066,0.0009429331,0.00085373403,0.0010330904,0.0006930623,0.0015985827,0.0018445239,0.001166863,0.008303605],"category_scores_gemma":[0.007923622,0.0004254997,0.0008081213,0.0011216273,0.0013388233,0.0031781406,0.0029187307,0.0026401584,0.0030507427],"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":[0.00056881254,0.00016336377,0.00141889,0.00038295545,0.00006608883,0.00015041338,0.0002725448,0.071479484,0.006532364,0.23145299,0.020655893,0.66685617],"study_design_scores_gemma":[0.00012800664,0.00012654983,0.00049465435,0.00010154668,0.000023099268,0.0003827299,0.00015790107,0.7636851,0.0075992527,0.20266737,0.02458805,0.00004579827],"about_ca_topic_score_codex":0.0009452434,"about_ca_topic_score_gemma":0.0010227624,"teacher_disagreement_score":0.008303605,"about_ca_system_score_codex":0.0006699478,"about_ca_system_score_gemma":0.0007635371,"threshold_uncertainty_score":0.027778327},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091589145,0.00016838938,0.90563,0.00041886204,0.000041985044,0.00010776447,0.00021391848,0.00062543125,0.0012045143],"genre_scores_gemma":[0.7380015,0.00021913416,0.25900766,0.0001412488,0.00008479492,0.00021339631,0.00060784054,0.00024805532,0.0014763427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99005085,0.0069963457,0.0001983909,0.0010577458,0.0013979166,0.00029881662],"domain_scores_gemma":[0.97355145,0.021203429,0.0014941836,0.0018057368,0.0015642148,0.0003809227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0160329,0.0017417091,0.0012754501,0.0016319954,0.0009266421,0.0020799767,0.0019028444,0.0013506365,0.00361141],"category_scores_gemma":[0.050802357,0.00065714173,0.0018689397,0.0021989243,0.0019455049,0.0022779384,0.0020796482,0.0021048798,0.0010162799],"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":[0.0013350334,0.00033639692,0.027225662,0.00037749726,0.0009043934,0.0005648207,0.00039989498,0.6730365,0.010359329,0.048099536,0.0036602654,0.23370063],"study_design_scores_gemma":[0.00002168975,0.00012273534,0.0016597589,0.000011365434,0.000029138773,0.000048868118,0.000034007047,0.9796652,0.0016352029,0.016315525,0.00043651843,0.00001989574],"about_ca_topic_score_codex":0.002463634,"about_ca_topic_score_gemma":0.001300205,"teacher_disagreement_score":0.0160329,"about_ca_system_score_codex":0.00066862686,"about_ca_system_score_gemma":0.0018048297,"threshold_uncertainty_score":0.084791124},"labels":[],"label_agreement":null},{"id":"W2139346949","doi":"10.1002/cem.2712","title":"A Bayesian sparse reconstruction method for fault detection and isolation","year":2015,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Fault detection and isolation; Bayesian probability; Gibbs sampling; Covariance matrix; Computer science; Pattern recognition (psychology); Matrix (chemical analysis); Noise (video); Bayesian inference; Algorithm; Posterior probability; Artificial intelligence; Mathematics; Statistics","score_opus":0.021578136843001326,"score_gpt":0.2600267921542379,"score_spread":0.23844865531123657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139346949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007953487,0.000074827236,0.9986947,0.000042835953,0.000008142929,0.00000989115,0.00001797164,0.00010346029,0.00025291523],"genre_scores_gemma":[0.1302845,0.0005496025,0.8648759,0.00022081162,0.00016317707,0.00017018245,0.0004627435,0.00017492643,0.0030980648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984282,0.0005176021,0.000070360154,0.0002706531,0.00060416997,0.00010904309],"domain_scores_gemma":[0.9975835,0.0013853316,0.000286626,0.000189122,0.00046897912,0.000086446926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020206815,0.0009954543,0.0015917986,0.001699413,0.0005817805,0.00091396034,0.0017866198,0.001396945,0.0024361426],"category_scores_gemma":[0.007507191,0.00075229537,0.0014849282,0.0013905102,0.00096984056,0.0016806728,0.0018194919,0.0021434482,0.001018286],"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":[0.0002497479,0.00012596659,0.0016267846,0.00029126057,0.00019218816,0.00020097362,0.00024400534,0.5412025,0.015141736,0.06938832,0.004719321,0.36661714],"study_design_scores_gemma":[0.000012320134,0.000024671863,0.00021497304,0.000012274736,0.000016511181,0.00008082597,0.000010110634,0.98556167,0.0015605452,0.0110721355,0.0014165386,0.000017411678],"about_ca_topic_score_codex":0.0051232087,"about_ca_topic_score_gemma":0.0036711013,"teacher_disagreement_score":0.0051232087,"about_ca_system_score_codex":0.0007515011,"about_ca_system_score_gemma":0.0017209478,"threshold_uncertainty_score":0.010686517},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008959023,0.0072373427,0.9488646,0.01759754,0.0005761719,0.000067956586,0.00036848983,0.00043469924,0.015894188],"genre_scores_gemma":[0.6368013,0.011598567,0.31781882,0.0054125413,0.0026759931,0.0006581251,0.0008641887,0.0006738413,0.023496518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98833305,0.008215335,0.00018562336,0.0011107532,0.0019432071,0.00021207426],"domain_scores_gemma":[0.9913312,0.005835713,0.00042702723,0.0010327769,0.0011963831,0.00017687494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013187858,0.0019424196,0.0015348018,0.002145251,0.0011016284,0.004796678,0.003159713,0.0022750078,0.005816594],"category_scores_gemma":[0.02156846,0.0007373576,0.0017586842,0.0033893534,0.005346526,0.004295442,0.004189111,0.00683668,0.0018162938],"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":[0.000044456003,0.000029771498,0.0009616935,0.00021215044,0.00023316371,0.00012239291,0.00024767112,0.04243804,0.00019296486,0.88599056,0.010936036,0.05859106],"study_design_scores_gemma":[0.000015128547,0.000027382008,0.00030875776,0.000058006586,0.000021868826,0.000085809974,0.000073519615,0.1297727,0.00022062144,0.85691416,0.012475673,0.000026334375],"about_ca_topic_score_codex":0.0042128786,"about_ca_topic_score_gemma":0.0025324384,"teacher_disagreement_score":0.013187858,"about_ca_system_score_codex":0.0021205442,"about_ca_system_score_gemma":0.002478493,"threshold_uncertainty_score":0.069744885},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26569015,0.0003623785,0.7288688,0.00014050958,0.00006023736,0.000121300225,0.0003715219,0.0033559003,0.0010291805],"genre_scores_gemma":[0.55601025,0.00018493892,0.44071394,0.00006266127,0.00002310933,0.00013981332,0.0008683637,0.00012203542,0.0018747608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996214,0.00004781771,0.000037090645,0.00007816101,0.00016474068,0.000050811326],"domain_scores_gemma":[0.99884903,0.00045347112,0.00014815072,0.00008881226,0.0004186731,0.00004184274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015619849,0.00083002966,0.00080984115,0.0019844605,0.0004487404,0.0009351389,0.0006346693,0.00075863925,0.0016988921],"category_scores_gemma":[0.0033186404,0.00031236585,0.0010440731,0.00095575425,0.0002530396,0.0011770233,0.0004214875,0.00087306486,0.00078505377],"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":[0.0007063688,0.00032872532,0.006292147,0.00017715948,0.000107994056,0.00011190072,0.00018385184,0.059650157,0.14410073,0.0009925155,0.0020591717,0.7852894],"study_design_scores_gemma":[0.000019093368,0.0002888978,0.01053564,0.000015252888,0.00007428508,0.00008317456,0.00013629228,0.9197477,0.06717585,0.0009157848,0.0009713872,0.000036638066],"about_ca_topic_score_codex":0.0038772284,"about_ca_topic_score_gemma":0.005118071,"teacher_disagreement_score":0.0038772284,"about_ca_system_score_codex":0.0006252397,"about_ca_system_score_gemma":0.000937809,"threshold_uncertainty_score":0.008260667},"labels":[],"label_agreement":null},{"id":"W2281018831","doi":"10.1002/cem.2769","title":"Structure‐interaction relationship study of <i>N</i>‐(4‐phenylsubstituted) cyanoacetamides by multivariate methods","year":2015,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Analytical Chemistry and Chromatography","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":"Science and Engineering Research Board; Reseau canadien de recherche respiratoire; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja","keywords":"Substituent; Chemistry; Chemometrics; Ring (chemistry); Molecule; Principal component analysis; Multivariate statistics; Solvent; Hydrogen bond; Benzene; Computational chemistry; Ultraviolet visible spectroscopy; Organic chemistry; Chromatography; Mathematics","score_opus":0.06553662565531461,"score_gpt":0.3631946624470105,"score_spread":0.2976580367916959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281018831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9658797,0.0016435775,0.030380215,0.0000881561,0.000035689416,0.000048125676,0.00056903315,0.0002252472,0.0011303956],"genre_scores_gemma":[0.9812354,0.0007725715,0.016811652,0.000039172992,0.000017404307,0.000054208696,0.0005120355,0.00002555032,0.0005319515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999663,0.00006866847,0.000015473004,0.00005694951,0.00015897499,0.00003691701],"domain_scores_gemma":[0.99964,0.00013870023,0.00011552865,0.000016657548,0.000068472684,0.000020619726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057426566,0.0004543118,0.00045264885,0.0008350513,0.00014983826,0.00036312867,0.00021669193,0.0002666952,0.00094139396],"category_scores_gemma":[0.0009441246,0.00013448989,0.0007434297,0.0008617193,0.00023633694,0.0002896473,0.00020970726,0.0005073531,0.00015861822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076969224,0.00057381566,0.012245201,0.0004519358,0.0004220925,0.00019296867,0.00012371011,0.011110618,0.9248644,0.0005496196,0.00042767794,0.048268378],"study_design_scores_gemma":[0.000034501747,0.0014792827,0.11285712,0.000026402446,0.00045891144,0.0004877257,0.00016069402,0.24250749,0.6382405,0.00036107886,0.0032412969,0.00014495733],"about_ca_topic_score_codex":0.001371995,"about_ca_topic_score_gemma":0.0011860563,"teacher_disagreement_score":0.001371995,"about_ca_system_score_codex":0.00023385204,"about_ca_system_score_gemma":0.00025769617,"threshold_uncertainty_score":0.003149271},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028666195,0.00009033717,0.99561477,0.00006169149,0.000029579893,0.000032600004,0.000042813415,0.00038276688,0.00087878644],"genre_scores_gemma":[0.046093285,0.0003266698,0.9509564,0.00009035229,0.000032100725,0.0001874081,0.0003477982,0.00040127634,0.0015646153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762636,0.0007638659,0.00016078597,0.00036958492,0.0009781176,0.00010118667],"domain_scores_gemma":[0.9965172,0.0016644008,0.0004473701,0.0004339001,0.00089194963,0.000045264762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030898785,0.002084572,0.00085131207,0.0011962047,0.00048742714,0.0019163523,0.0011073801,0.0009871663,0.0030737927],"category_scores_gemma":[0.008122184,0.00047201724,0.0010223604,0.0017121402,0.0009358837,0.0018384798,0.0011004589,0.0022941804,0.0024253768],"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":[0.0001939491,0.00020171172,0.0015776952,0.00048294585,0.00010198231,0.00012144319,0.00018751282,0.15958165,0.058957182,0.028552854,0.006383202,0.7436579],"study_design_scores_gemma":[0.000012624652,0.00010723189,0.0010313616,0.000059788417,0.000027704462,0.00017226282,0.000074181444,0.9272868,0.051809236,0.009778243,0.009586202,0.00005445007],"about_ca_topic_score_codex":0.0020635377,"about_ca_topic_score_gemma":0.0022112539,"teacher_disagreement_score":0.0030898785,"about_ca_system_score_codex":0.0005113824,"about_ca_system_score_gemma":0.0014405086,"threshold_uncertainty_score":0.01634103},"labels":[],"label_agreement":null},{"id":"W2290255086","doi":"10.1002/cem.2792","title":"Robust online algorithm for adaptive linear regression parameter estimation and prediction","year":2016,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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; Alberta Innovates - Technology Futures","keywords":"Ordinary least squares; Computer science; Autoregressive model; Algorithm; Linear regression; Regression; Computation; Robust regression; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.028435332286884336,"score_gpt":0.2458768427174479,"score_spread":0.21744151043056356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290255086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004675379,0.00008989098,0.9986407,0.00003849342,0.000020198495,0.000012552526,0.000012758896,0.00045002188,0.00026786778],"genre_scores_gemma":[0.08823165,0.00036207313,0.9061322,0.00019735857,0.00017762737,0.00038456699,0.00027011117,0.00032589023,0.0039184494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986766,0.000350841,0.000088383196,0.00034911826,0.00043821445,0.00009683991],"domain_scores_gemma":[0.9978776,0.0011934462,0.00019566093,0.00028510558,0.00040692688,0.000041223964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016709556,0.001400523,0.0013142857,0.00087722926,0.00049828907,0.0010958894,0.0019340421,0.0014369183,0.0044409735],"category_scores_gemma":[0.0072736638,0.0006267931,0.000841537,0.0012244234,0.0007502253,0.0015010808,0.0015863978,0.0028671778,0.0032855666],"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":[0.00016411641,0.00011154367,0.0006468393,0.00021346268,0.00012974086,0.00009914911,0.00010973886,0.4063491,0.0087972665,0.03951594,0.0071096746,0.5367535],"study_design_scores_gemma":[0.000015016022,0.000027805037,0.00008909151,0.000008074406,0.000008267895,0.000036157307,0.0000058016612,0.9884262,0.0015855423,0.0071172817,0.0026673968,0.000013343115],"about_ca_topic_score_codex":0.0033390056,"about_ca_topic_score_gemma":0.0024361114,"teacher_disagreement_score":0.0044409735,"about_ca_system_score_codex":0.0005492197,"about_ca_system_score_gemma":0.0015216788,"threshold_uncertainty_score":0.014856517},"labels":[],"label_agreement":null},{"id":"W2528153187","doi":"10.1002/cem.2830","title":"Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods","year":2016,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","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":"OmniActive Health Technologies (Canada); University of Prince Edward Island","funders":"","keywords":"Chemometrics; Osteoarthritis; Joint (building); Joint disease; Pattern recognition (psychology); Computer science; Diagnostic model; Artificial intelligence; Machine learning; Data mining; Medicine; Pathology; Engineering","score_opus":0.13041814707194072,"score_gpt":0.3451259306165164,"score_spread":0.2147077835445757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528153187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20966025,0.0016260004,0.78390837,0.0005687086,0.00011499289,0.0003541912,0.00092804956,0.0011925374,0.0016469733],"genre_scores_gemma":[0.8306125,0.00072663696,0.1660124,0.00010698666,0.000031243668,0.00045788582,0.0012852028,0.000057443274,0.00070970703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917287,0.0002332935,0.00009347598,0.00017269491,0.00024733396,0.00008016253],"domain_scores_gemma":[0.99796414,0.0010952104,0.00031725422,0.000094108254,0.00044844332,0.00008083924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033182257,0.0011488978,0.00084661366,0.0025205896,0.00030537805,0.0015454016,0.0009006612,0.00077259965,0.0005441399],"category_scores_gemma":[0.0052120797,0.00033949674,0.0017118492,0.0007777602,0.00045897678,0.0009193895,0.0007399219,0.00095380575,0.0003383539],"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":[0.0013685359,0.0011148572,0.041689623,0.0008388981,0.00076476677,0.00035848582,0.00028343368,0.53769755,0.075933255,0.008860061,0.0036007464,0.32748976],"study_design_scores_gemma":[0.000018351533,0.00023317411,0.0041784653,0.000038065282,0.00009840397,0.00010156842,0.00003726458,0.9810836,0.010551982,0.002705116,0.00090910646,0.00004480091],"about_ca_topic_score_codex":0.0036168569,"about_ca_topic_score_gemma":0.0028029827,"teacher_disagreement_score":0.0036168569,"about_ca_system_score_codex":0.0014650055,"about_ca_system_score_gemma":0.0011806593,"threshold_uncertainty_score":0.01754862},"labels":[],"label_agreement":null},{"id":"W2550790092","doi":"10.1002/cem.2852","title":"Estimation of Flat‐topped Gaussian distribution with application in system identification","year":2016,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian; Gaussian random field; Gaussian process; Probability density function; Mathematics; Gaussian noise; Random variable; Applied mathematics; Gaussian filter; Algorithm; Gaussian function; Mathematical optimization; Statistics","score_opus":0.004928063804508289,"score_gpt":0.20755585390700929,"score_spread":0.202627790102501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550790092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004682367,0.0001527799,0.9947062,0.00003193517,0.000009422677,0.000008617757,0.000015107624,0.00013339917,0.000260244],"genre_scores_gemma":[0.7059368,0.0012293996,0.29045042,0.00013171011,0.000063453,0.000076731514,0.00024158104,0.00010846149,0.0017614182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989974,0.00029586212,0.000043540604,0.00022261462,0.00035588542,0.00008467842],"domain_scores_gemma":[0.9985114,0.0008136932,0.0001788661,0.0001680205,0.00029638564,0.000031603104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014063194,0.0008515733,0.0006445773,0.0010290316,0.00041104128,0.00065961777,0.00089005794,0.0008418586,0.0007963755],"category_scores_gemma":[0.005701719,0.00033273175,0.0007711146,0.0011764348,0.0008803093,0.0014488174,0.00093430455,0.0008537967,0.00025398828],"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":[0.00014699955,0.00005374864,0.0023336303,0.00025276732,0.00011501687,0.00038827764,0.00021007191,0.7604501,0.011527663,0.046441533,0.0011238647,0.17695627],"study_design_scores_gemma":[0.0000041908843,0.00003076554,0.00042512422,0.000008002712,0.000010451828,0.00008550982,0.000014485844,0.98793423,0.0026765803,0.008320874,0.000471292,0.000018503733],"about_ca_topic_score_codex":0.0040031914,"about_ca_topic_score_gemma":0.002039973,"teacher_disagreement_score":0.0040031914,"about_ca_system_score_codex":0.0005964936,"about_ca_system_score_gemma":0.000945747,"threshold_uncertainty_score":0.007959783},"labels":[],"label_agreement":null},{"id":"W2579613151","doi":"10.1002/cem.2873","title":"Sample‐wise spectral multivariate calibration desensitized to new artifacts relative to the calibration data using a residual penalty","year":2017,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Calibration and Measurement Techniques","field":"Engineering","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":"Reseau canadien de recherche respiratoire; National Science Foundation","keywords":"Calibration; Residual; Outlier; Tikhonov regularization; Sample space; Mathematics; Computer science; Sample (material); Sample size determination; Algorithm; Statistics; Inverse problem; Physics","score_opus":0.17252947876614153,"score_gpt":0.3452454414825862,"score_spread":0.17271596271644465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579613151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025373109,0.000055267526,0.9731982,0.0000701722,0.00002132239,0.000024993791,0.000031020256,0.0007195631,0.0005063997],"genre_scores_gemma":[0.36986053,0.00013933757,0.62555945,0.00011880203,0.000034388064,0.00013300046,0.00040450224,0.0004570298,0.003292983],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987921,0.0002676048,0.00006431476,0.0003080841,0.00050549046,0.00006252962],"domain_scores_gemma":[0.9981483,0.00041684747,0.00031242042,0.0006441988,0.00043262827,0.000045547396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017500486,0.0010372872,0.00063571724,0.00065331726,0.00041480438,0.0008115306,0.0011692647,0.0007566989,0.0013619134],"category_scores_gemma":[0.004115908,0.0003782342,0.00090768375,0.00080186425,0.0008204999,0.0011670536,0.0014397585,0.0017167283,0.00054664916],"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":[0.00034820876,0.00030309273,0.0052028503,0.0002359199,0.00025697256,0.0001303751,0.0003287152,0.30801913,0.22367081,0.01732669,0.0032149428,0.44096228],"study_design_scores_gemma":[0.000008649188,0.00008522769,0.0013266221,0.0000076206484,0.000021359148,0.000082007246,0.000023920495,0.9449227,0.048868496,0.002498175,0.0021194578,0.000035701167],"about_ca_topic_score_codex":0.0013160601,"about_ca_topic_score_gemma":0.001890378,"teacher_disagreement_score":0.0017500486,"about_ca_system_score_codex":0.0004754721,"about_ca_system_score_gemma":0.0009877754,"threshold_uncertainty_score":0.00925529},"labels":[],"label_agreement":null},{"id":"W2727480534","doi":"10.1002/cem.2907","title":"Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles","year":2017,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","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":"Concordia University","funders":"","keywords":"Particle swarm optimization; Photocatalysis; Titanium dioxide; Adaptive neuro fuzzy inference system; Materials science; Degradation (telecommunications); Nanoparticle; Biological system; Chemical engineering; Computer science; Fuzzy logic; Catalysis; Nanotechnology; Chemistry; Machine learning; Artificial intelligence; Fuzzy control system; Composite material; Organic chemistry; Engineering","score_opus":0.06166751818524347,"score_gpt":0.29918763269264065,"score_spread":0.23752011450739718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727480534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24904656,0.0006821151,0.74060947,0.0002243926,0.000060249713,0.00009844621,0.00012875341,0.00082971406,0.008320275],"genre_scores_gemma":[0.944987,0.00033163128,0.051543705,0.000027550217,0.000012132535,0.00017162426,0.00009937606,0.00002429576,0.0028026162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999361,0.000014744835,0.0000055397327,0.000014291975,0.000020679721,0.000008729388],"domain_scores_gemma":[0.9998807,0.00005893537,0.00002459485,0.0000060283314,0.000025363453,0.000004448236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021665206,0.00061457825,0.00032851944,0.00032109258,0.00014907667,0.00050266983,0.00033502578,0.0005990783,0.0005449568],"category_scores_gemma":[0.0005133286,0.00025503535,0.0004837467,0.0001542536,0.00019848601,0.00032760616,0.0001884189,0.00036123965,0.00010779383],"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":[0.000022397036,0.000046402205,0.0006347843,0.0000374659,0.000022419688,0.000044196422,0.000020356962,0.9802069,0.008855862,0.0010367881,0.00010271439,0.008969694],"study_design_scores_gemma":[0.000001714601,0.000013328752,0.00008694829,0.0000013550087,0.0000033143167,0.000003799629,0.0000022878262,0.9985562,0.0010812926,0.00014408334,0.00010415055,0.0000015694351],"about_ca_topic_score_codex":0.0043338346,"about_ca_topic_score_gemma":0.0033320247,"teacher_disagreement_score":0.0043338346,"about_ca_system_score_codex":0.00040621203,"about_ca_system_score_gemma":0.000401035,"threshold_uncertainty_score":0.008617222},"labels":[],"label_agreement":null},{"id":"W2767441807","doi":"10.1002/cem.2967","title":"Structure‐based statistical modeling and analysis of peptide affinity and cross‐reactivity to human senile osteoporosis <scp>OSF SH3</scp> domain","year":2017,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Computational Drug Discovery Methods","field":"Computer Science","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":"Shanghai University of Medicine and Health Sciences; Centre for Integrated Computer Systems Research","keywords":"Peptide; Chemistry; SH3 domain; Osteoclast; Polyproline helix; Computational biology; Proto-oncogene tyrosine-protein kinase Src; Biochemistry; Biology; Signal transduction; Receptor","score_opus":0.03698419853484479,"score_gpt":0.3500323459143755,"score_spread":0.3130481473795307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767441807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92181766,0.00010881726,0.07714874,0.000053485393,0.000005824293,0.00002020013,0.00013654874,0.00026266498,0.0004460552],"genre_scores_gemma":[0.9915201,0.000043965418,0.00795404,0.000011979561,0.0000021646974,0.00003277778,0.00016509304,0.000022793758,0.00024717825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973136,0.0001131765,0.000012418898,0.000048791077,0.00006497098,0.000029313913],"domain_scores_gemma":[0.999173,0.0005251272,0.0001082204,0.000063121326,0.00010825178,0.000022219863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000977465,0.00052154384,0.00040796728,0.00046513302,0.00019857025,0.00025464443,0.00054012047,0.00046593355,0.0004435769],"category_scores_gemma":[0.0014587687,0.00024677528,0.0008366506,0.00027041236,0.00025297687,0.0002264091,0.00019774366,0.0004363325,0.000111719004],"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":[0.000057333276,0.00007698983,0.00258832,0.00001614033,0.000045027977,0.00003048719,0.000012908423,0.9831687,0.009820684,0.0005493383,0.000060564955,0.0035734233],"study_design_scores_gemma":[9.3854834e-7,0.00001796277,0.00032355828,2.344986e-7,0.000002402223,0.000002949396,9.651676e-7,0.9983759,0.0012201287,0.000038431648,0.00001534242,0.0000011279993],"about_ca_topic_score_codex":0.005528971,"about_ca_topic_score_gemma":0.002906051,"teacher_disagreement_score":0.005528971,"about_ca_system_score_codex":0.00063217303,"about_ca_system_score_gemma":0.00048695234,"threshold_uncertainty_score":0.01099354},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07477246,0.00015543227,0.9223729,0.00008663757,0.000040531195,0.00014401137,0.000117981814,0.0007281384,0.0015819354],"genre_scores_gemma":[0.42874467,0.00015173228,0.5675189,0.000096464566,0.00003220563,0.00024753748,0.0005032315,0.00019730609,0.002507982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988047,0.0005894765,0.000064389824,0.00019277373,0.00028335094,0.00006528549],"domain_scores_gemma":[0.99810743,0.0012899541,0.00015473427,0.000110305,0.00030495803,0.000032665044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041408683,0.0013486069,0.00088069186,0.0016933277,0.00043787214,0.00087424315,0.00077013165,0.00053464214,0.0017622145],"category_scores_gemma":[0.0048616696,0.0003889698,0.0007535718,0.0013186202,0.00039411752,0.0006944894,0.00089170266,0.0009498178,0.000488632],"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":[0.00043981103,0.00027641264,0.006269393,0.00015781338,0.00017443213,0.00015076305,0.000054687338,0.6857911,0.02140415,0.0038037708,0.0011943161,0.2802834],"study_design_scores_gemma":[0.000008786743,0.000031775544,0.0007321805,0.0000040444215,0.000012231618,0.000009720002,0.00000625845,0.9925425,0.005590982,0.00063943584,0.00041350664,0.000008654016],"about_ca_topic_score_codex":0.0023299926,"about_ca_topic_score_gemma":0.002484195,"teacher_disagreement_score":0.0041408683,"about_ca_system_score_codex":0.00037349836,"about_ca_system_score_gemma":0.0008672936,"threshold_uncertainty_score":0.021899283},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33355036,0.0001092419,0.6590327,0.00023537163,0.000101312435,0.00016471013,0.0006760329,0.0011996828,0.004930652],"genre_scores_gemma":[0.9013793,0.00007532885,0.09624261,0.000055631903,0.000006681022,0.0001948844,0.0005273101,0.00008081775,0.0014374247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962807,0.00008340168,0.000025254938,0.000054682703,0.00016283846,0.000045760713],"domain_scores_gemma":[0.99853516,0.00088676764,0.0001162352,0.00015872491,0.00026696405,0.00003614261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074159144,0.0005146028,0.00029621398,0.00038367583,0.0003122045,0.00037240048,0.00056770974,0.00064871693,0.0019923674],"category_scores_gemma":[0.002352681,0.00017240955,0.0003574125,0.00056169176,0.00044598914,0.0003458854,0.000307871,0.0005836004,0.00034425716],"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":[0.00025387382,0.0001254927,0.0037778695,0.00012165629,0.000024731606,0.00021375324,0.0001730939,0.94138265,0.025998466,0.007538292,0.0011775447,0.019212691],"study_design_scores_gemma":[0.0000045840143,0.000038666505,0.00047744875,0.0000047928174,0.0000027732774,0.000021893735,0.000024354074,0.9856031,0.012763156,0.000612128,0.00043886673,0.0000082351025],"about_ca_topic_score_codex":0.0035365399,"about_ca_topic_score_gemma":0.0025791924,"teacher_disagreement_score":0.0035365399,"about_ca_system_score_codex":0.0004932494,"about_ca_system_score_gemma":0.0006429097,"threshold_uncertainty_score":0.0070319176},"labels":[],"label_agreement":null},{"id":"W2968512646","doi":"10.1002/cem.3174","title":"On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars","year":2019,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","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":"Brampton Civic Hospital; York University","funders":"Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; Ontario Centre of Excellence for Child and Youth Mental Health","keywords":"Mars Exploration Program; Laser-induced breakdown spectroscopy; Calibration; Partial least squares regression; Principal component analysis; Computer science; Cluster analysis; Data mining; Mean squared error; Algorithm; Martian; Multivariate statistics; Linear regression; Remote sensing; Mathematics; Statistics; Machine learning; Artificial intelligence; Spectroscopy; Geology","score_opus":0.022839795518264847,"score_gpt":0.2692334403150054,"score_spread":0.24639364479674056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968512646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062038384,0.00039469308,0.9348732,0.0007329234,0.00006371002,0.00010672594,0.00012812194,0.0006208393,0.0010412437],"genre_scores_gemma":[0.60090035,0.0005549027,0.39098188,0.00043067875,0.00011920972,0.00037130792,0.0007706868,0.0002178581,0.0056530996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991899,0.0003935365,0.000034969413,0.00018525372,0.00013694448,0.00005934044],"domain_scores_gemma":[0.9966529,0.0024195935,0.00013901261,0.00012974156,0.0005977442,0.00006105608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005081348,0.0007561021,0.0010891223,0.00094769895,0.0006829405,0.0016120357,0.0017799719,0.0015283471,0.0016716152],"category_scores_gemma":[0.007466351,0.00039987906,0.0013626018,0.000921644,0.0005374444,0.0012747452,0.0010172394,0.0016322308,0.00065157836],"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":[0.00013659782,0.00013220323,0.00266242,0.00005068871,0.00010129204,0.000046854635,0.000063319276,0.92405313,0.0016257501,0.0048210835,0.0011435188,0.06516307],"study_design_scores_gemma":[0.0000018687418,0.000007335563,0.0001280819,0.0000019078288,0.0000032980613,0.0000032846585,0.0000031025054,0.99921215,0.00012734711,0.00041505345,0.00009328729,0.0000031602563],"about_ca_topic_score_codex":0.015141937,"about_ca_topic_score_gemma":0.010145623,"teacher_disagreement_score":0.015141937,"about_ca_system_score_codex":0.00090731104,"about_ca_system_score_gemma":0.0013190057,"threshold_uncertainty_score":0.030107617},"labels":[],"label_agreement":null},{"id":"W3028186432","doi":"10.1002/cem.3247","title":"Two‐stage approach for the inference of the source of high‐dimensional and complex chemical data in forensic science","year":2020,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Forensic Fingerprint Detection Methods","field":"Social Sciences","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 du Québec à Trois-Rivières","funders":"National Institute of Justice","keywords":"Inference; Computer science; Bayes' theorem; Leverage (statistics); Statistical inference; Forensic science; Kernel (algebra); Data mining; Machine learning; Artificial intelligence; Bayesian probability; Mathematics; Statistics; Archaeology","score_opus":0.15428559141657874,"score_gpt":0.38596322449956844,"score_spread":0.2316776330829897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028186432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037024168,0.000069149835,0.9956657,0.00010689483,0.000013496191,0.000057522022,0.000037192494,0.00007341493,0.00027432645],"genre_scores_gemma":[0.15448442,0.00019917029,0.8405949,0.00018259106,0.00012212317,0.00044887394,0.00022494537,0.00009183129,0.0036510897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941164,0.0031232222,0.00027811527,0.00082821806,0.0013951432,0.0002588046],"domain_scores_gemma":[0.9736389,0.021839313,0.0010851708,0.0013663308,0.0016623923,0.00040775054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01678272,0.001038973,0.0016295206,0.004349849,0.0014217726,0.0026839185,0.0036151116,0.003191867,0.0049471688],"category_scores_gemma":[0.028229777,0.0013035783,0.0023356536,0.0018427733,0.002920417,0.0029317888,0.0039688717,0.0030528724,0.0010074155],"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":[0.0008045111,0.0007830919,0.015214722,0.00084352883,0.0009541713,0.0011502606,0.0011007357,0.31430623,0.017293192,0.37081385,0.0031854236,0.27355033],"study_design_scores_gemma":[0.000025530591,0.000084202395,0.0013414414,0.000028882807,0.00004293204,0.00014755252,0.00003748379,0.9279507,0.0020621486,0.066986084,0.0012388189,0.00005424107],"about_ca_topic_score_codex":0.003424222,"about_ca_topic_score_gemma":0.003969954,"teacher_disagreement_score":0.01678272,"about_ca_system_score_codex":0.0014629965,"about_ca_system_score_gemma":0.003398524,"threshold_uncertainty_score":0.08875656},"labels":[],"label_agreement":null},{"id":"W3028421557","doi":"10.1002/cem.3235","title":"CATTELL'S parallel proportional profiles","year":2020,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Blind Source Separation Techniques","field":"Computer Science","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":"Western University","funders":"","keywords":"Rotation (mathematics); Covariance; Decomposition; Tensor (intrinsic definition); TRACE (psycholinguistics); Simple (philosophy); Computer science; Factor (programming language); Mathematics; Applied mathematics; Algorithm; Statistics; Artificial intelligence; Pure mathematics; Chemistry","score_opus":0.03842134327046663,"score_gpt":0.26661377679371945,"score_spread":0.2281924335232528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028421557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004650804,0.0009349023,0.93042535,0.0035627917,0.0005266735,0.000163381,0.00033527586,0.00035342737,0.059047442],"genre_scores_gemma":[0.35546732,0.0021975993,0.5936915,0.005195659,0.0011016498,0.0011062433,0.00058265805,0.00075024547,0.039907105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99210745,0.003091447,0.000350576,0.0016546928,0.002485212,0.0003106653],"domain_scores_gemma":[0.9904298,0.0045609414,0.0005797952,0.0020128326,0.0019561392,0.000460353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010844131,0.0012604701,0.00068620086,0.0033403977,0.0021079339,0.0046388973,0.0021230602,0.0022098024,0.016979467],"category_scores_gemma":[0.0322291,0.00090067834,0.0016684462,0.0029179023,0.009662794,0.0085285995,0.004983047,0.0048056166,0.0043870225],"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":[0.000032770262,0.00001053424,0.00018546116,0.00004521714,0.00001447566,0.000048542377,0.0001895759,0.0017549066,0.00023611133,0.9654525,0.0047172867,0.027312547],"study_design_scores_gemma":[0.000016602828,0.000022663831,0.00018676117,0.000049125607,0.0000074680524,0.00013331127,0.00004507619,0.0145751005,0.00044765798,0.9600486,0.024444625,0.000022974698],"about_ca_topic_score_codex":0.0028353299,"about_ca_topic_score_gemma":0.0017852335,"teacher_disagreement_score":0.016979467,"about_ca_system_score_codex":0.0023968427,"about_ca_system_score_gemma":0.002530241,"threshold_uncertainty_score":0.05734992},"labels":[],"label_agreement":null},{"id":"W3082200404","doi":"10.1002/cem.3299","title":"Partial least squares discrimination applied to a few samples dataset: A case for predicting the presence of pesticide in lettuce","year":2020,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","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":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Partial least squares regression; Linear discriminant analysis; Statistics; Reliability (semiconductor); Mathematics; Computer science; Wilcoxon signed-rank test; Pattern recognition (psychology); Artificial intelligence","score_opus":0.07200196356014445,"score_gpt":0.28473690830841103,"score_spread":0.2127349447482666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082200404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96663785,0.00035849892,0.03132658,0.00042305214,0.000051569103,0.000049801918,0.00044054908,0.00016891789,0.0005431556],"genre_scores_gemma":[0.97433984,0.000116871925,0.024433922,0.00006880132,0.000021085845,0.0000334626,0.00055621634,0.000015455964,0.00041429044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991635,0.00027835942,0.000060153016,0.00023712765,0.00019468233,0.00006629623],"domain_scores_gemma":[0.9983961,0.0009047181,0.00011622667,0.00020281565,0.0003144597,0.00006563294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019615607,0.0007109769,0.00075362285,0.00079194113,0.0006764124,0.0010432131,0.00059727585,0.00119487,0.0004117221],"category_scores_gemma":[0.002721221,0.00015373646,0.00113272,0.00094613136,0.00048809993,0.0003885016,0.00042376536,0.0008371331,0.00020927563],"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":[0.002465158,0.0023839856,0.23849627,0.00087185187,0.00068669853,0.009560866,0.0011001027,0.34712556,0.13481233,0.0015515295,0.0038554808,0.25709018],"study_design_scores_gemma":[0.000047263227,0.0007812991,0.09625943,0.000049175705,0.00014877533,0.000889199,0.00071704993,0.86348224,0.032903127,0.0021416119,0.0024935775,0.000087291926],"about_ca_topic_score_codex":0.005522347,"about_ca_topic_score_gemma":0.0059975977,"teacher_disagreement_score":0.005522347,"about_ca_system_score_codex":0.00046046343,"about_ca_system_score_gemma":0.0005419042,"threshold_uncertainty_score":0.010980368},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96650857,0.00024373675,0.030487489,0.0002768835,0.00005625595,0.0014730559,0.00009636588,0.00006274171,0.0007949474],"genre_scores_gemma":[0.9710854,0.00020598498,0.026494116,0.00007813783,0.000046761976,0.0011701405,0.00012577711,0.00001983264,0.0007737985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945148,0.004373384,0.00013560917,0.00043681051,0.00038116207,0.00015824754],"domain_scores_gemma":[0.9888889,0.006266756,0.0003937046,0.0008440561,0.0030176695,0.00058896723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017701168,0.0007057119,0.0007076707,0.0006031038,0.00055203994,0.00063777145,0.00044561477,0.00038156263,0.0019209883],"category_scores_gemma":[0.01450345,0.00027667917,0.0012833293,0.00058730366,0.0006216102,0.00081624795,0.00060248876,0.0006965462,0.0004015526],"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":[0.012563054,0.046016302,0.34213287,0.0010310177,0.0010224803,0.0008788919,0.011453257,0.01227334,0.032778054,0.0036739274,0.003166388,0.5330104],"study_design_scores_gemma":[0.0034495424,0.24799852,0.51924515,0.00027355168,0.001975591,0.0010900847,0.013433173,0.17332877,0.020654641,0.0038482207,0.01439852,0.00030421355],"about_ca_topic_score_codex":0.002569664,"about_ca_topic_score_gemma":0.0016855451,"teacher_disagreement_score":0.017701168,"about_ca_system_score_codex":0.0005324338,"about_ca_system_score_gemma":0.0019437658,"threshold_uncertainty_score":0.0936138},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00944191,0.000085107466,0.9875881,0.00016903559,0.00003163084,0.000038011887,0.00007901072,0.0009732839,0.0015938414],"genre_scores_gemma":[0.10806781,0.00005972717,0.89075434,0.000042154647,0.000011619414,0.00010376369,0.00013422617,0.00014561457,0.0006807397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991491,0.00019847587,0.000055213404,0.0001222188,0.0004098401,0.000065077955],"domain_scores_gemma":[0.99686074,0.0016594453,0.00017773504,0.00044300538,0.0007533343,0.0001057566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015054259,0.0005852208,0.0006362837,0.0009193031,0.0005758376,0.0010952735,0.0009973628,0.00067293295,0.0029723777],"category_scores_gemma":[0.008203313,0.00031786226,0.0003414065,0.001080517,0.0006617423,0.0011401116,0.0014229924,0.0012394435,0.0011021585],"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":[0.00026546474,0.00019476784,0.002062307,0.00031757855,0.000061638435,0.00026476945,0.0002672718,0.5116497,0.066072,0.093191616,0.011676703,0.3139762],"study_design_scores_gemma":[0.000011573338,0.000012984487,0.00016968272,0.000005340106,0.0000016970068,0.00003184968,0.000011668314,0.98922616,0.0044955662,0.0047968943,0.0012252558,0.00001124227],"about_ca_topic_score_codex":0.0031987317,"about_ca_topic_score_gemma":0.0031381156,"teacher_disagreement_score":0.0031987317,"about_ca_system_score_codex":0.0006874904,"about_ca_system_score_gemma":0.0014936528,"threshold_uncertainty_score":0.009943545},"labels":[],"label_agreement":null},{"id":"W4224315481","doi":"10.1002/cem.3400","title":"A comparative performance of machine learning algorithms on laser‐induced breakdown spectroscopy data of minerals","year":2022,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Vector Institute","keywords":"Laser-induced breakdown spectroscopy; Meteorite; Partial least squares regression; Computer science; Spectroscopy; Artificial neural network; Machine learning; Algorithm; Predictive modelling; Biological system; Artificial intelligence; Physics; Astrobiology","score_opus":0.04894535181495153,"score_gpt":0.2890019713508906,"score_spread":0.2400566195359391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224315481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89658713,0.005771643,0.08111987,0.0017954654,0.0006069371,0.00021990058,0.0028699609,0.0063063046,0.0047227396],"genre_scores_gemma":[0.9417591,0.00060435914,0.049825877,0.00022148428,0.00009598546,0.00011818861,0.005776582,0.00011592928,0.0014825382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776065,0.00091068755,0.00022108245,0.0005365192,0.00034203762,0.0002289466],"domain_scores_gemma":[0.99223316,0.0049041067,0.0003349829,0.0007072269,0.0015635098,0.00025701712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069556395,0.0015994345,0.0011756092,0.0025739095,0.00063930324,0.0013597771,0.0013572043,0.0018497348,0.0010623402],"category_scores_gemma":[0.011346786,0.0002691682,0.0013494189,0.0016555948,0.00047972985,0.0019397645,0.00093303336,0.0015018272,0.00082183804],"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":[0.0013270432,0.00085354777,0.0273116,0.0002812201,0.00057388184,0.00010411646,0.00009086761,0.63650835,0.0023773918,0.0009986305,0.0074437675,0.32212964],"study_design_scores_gemma":[0.00001155483,0.00008180115,0.0022298011,0.000010757905,0.000016543623,0.00001637915,0.000025670055,0.99607986,0.0009392806,0.00032796655,0.0002530617,0.0000072624593],"about_ca_topic_score_codex":0.012531748,"about_ca_topic_score_gemma":0.007213632,"teacher_disagreement_score":0.012531748,"about_ca_system_score_codex":0.0012968557,"about_ca_system_score_gemma":0.0013065153,"threshold_uncertainty_score":0.036785364},"labels":[],"label_agreement":null},{"id":"W4288697545","doi":"10.1002/cem.3438","title":"Application of chemometrics on Raman spectra from Mars: Recent advances and future perspectives","year":2022,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","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":"York University","funders":"Horizon 2020 Framework Programme; Ministerio de Economía y Competitividad; European Commission; H2020 European Research Council; National Aeronautics and Space Administration","keywords":"Mars Exploration Program; Martian; Chemometrics; Raman spectroscopy; Exploration of Mars; Remote sensing; Computer science; Astrobiology; Spectrometer; Environmental science; Earth science; Systems engineering; Geology; Machine learning; Engineering; Physics; Optics","score_opus":0.00926084730607622,"score_gpt":0.23273352602786526,"score_spread":0.22347267872178905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288697545","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13111745,0.55730736,0.26945347,0.014132001,0.0011561929,0.00022206978,0.0010217202,0.0015334085,0.024056384],"genre_scores_gemma":[0.5149167,0.25727186,0.2187422,0.0016100709,0.0021132983,0.00015734148,0.0012491966,0.000261948,0.0036773365],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99793565,0.0006090157,0.0001060137,0.00043700638,0.00082720985,0.00008523219],"domain_scores_gemma":[0.9931766,0.0029776657,0.0006199218,0.00050893147,0.0024561095,0.00026077128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008004975,0.00090517994,0.00088089536,0.0036311927,0.00039211617,0.0027263202,0.001451104,0.00093125703,0.0018464669],"category_scores_gemma":[0.0058754124,0.0003808035,0.00092811114,0.005044019,0.001603932,0.0025038347,0.0015664658,0.001356234,0.00067614537],"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":[0.00012925446,0.00025616025,0.018676365,0.0025214206,0.00028140403,0.00011875861,0.00026359136,0.009664524,0.018492715,0.013980733,0.0057549956,0.92986006],"study_design_scores_gemma":[0.00006595335,0.0012088356,0.10177348,0.0029777882,0.0005382342,0.0018784902,0.0028551577,0.31260282,0.106579006,0.0768503,0.39193523,0.00073465036],"about_ca_topic_score_codex":0.0018160164,"about_ca_topic_score_gemma":0.001288855,"teacher_disagreement_score":0.008004975,"about_ca_system_score_codex":0.0009049743,"about_ca_system_score_gemma":0.0008873994,"threshold_uncertainty_score":0.042334855},"labels":[],"label_agreement":null},{"id":"W4292510267","doi":"10.1002/cem.3439","title":"A Raman spectroscopy–compositional–structural investigation of lunar surface materials and analogues","year":2022,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","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 Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Canada Foundation for Innovation; University of Winnipeg","keywords":"Raman spectroscopy; Enstatite; Plagioclase; Forsterite; Olivine; Meteorite; Context (archaeology); Mineral; Analytical Chemistry (journal); Geology; Pyroxene; Mineralogy; Melt inclusions; Materials science; Chemistry; Quartz; Astrobiology; Physics; Chondrite; Optics","score_opus":0.013938034864797865,"score_gpt":0.23007693024816678,"score_spread":0.21613889538336892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292510267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99786514,0.00017500052,0.0013935962,0.000004828961,8.8499644e-7,0.0000035689689,0.00004583602,0.000016239193,0.00049496855],"genre_scores_gemma":[0.998454,0.000047662532,0.0011914179,0.0000036228203,0.0000011898152,0.0000020598786,0.0000813498,0.000005244666,0.00021353003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998871,0.00001887546,0.000003054534,0.0000304891,0.000043562803,0.000016961825],"domain_scores_gemma":[0.9999217,0.00001829403,0.000013006824,0.0000102724,0.000029587236,0.0000071559807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014746045,0.00018645621,0.00013461319,0.0006837903,0.00021825891,0.0002386641,0.00017636838,0.00018357423,0.0007501081],"category_scores_gemma":[0.00016717799,0.00012249399,0.00018173632,0.00039420734,0.0001818613,0.00015531508,0.00014684348,0.00015043767,0.00014758071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005522237,0.000024625144,0.005839947,0.00002840406,0.000014553034,0.00005940648,0.00004337869,0.00045171747,0.98755217,0.000121540485,0.000018463867,0.0057904785],"study_design_scores_gemma":[0.000009063697,0.0003480773,0.16211548,0.000007908965,0.000038801085,0.00074973545,0.00025032673,0.014566667,0.8197812,0.0002767629,0.0018360614,0.000019844245],"about_ca_topic_score_codex":0.0009257218,"about_ca_topic_score_gemma":0.0009240567,"teacher_disagreement_score":0.0009257218,"about_ca_system_score_codex":0.00009631416,"about_ca_system_score_gemma":0.00007640927,"threshold_uncertainty_score":0.002509296},"labels":[],"label_agreement":null},{"id":"W4303199485","doi":"10.1002/cem.3443","title":"Sparse Multiple Factor Analysis, sparse STATIS, and sparse DiSTATIS with applications to sensory evaluation","year":2022,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Centre for Addiction and Mental Health","funders":"","keywords":"Interpretability; Computer science; Principal component analysis; Set (abstract data type); Pattern recognition (psychology); Sparse approximation; Artificial intelligence; Data mining; Machine learning; Mathematics; Algorithm","score_opus":0.09783862073216033,"score_gpt":0.3274506206186623,"score_spread":0.229611999886502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303199485","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013684612,0.0002885597,0.9843722,0.00031603774,0.000037661906,0.00006044665,0.00014811373,0.00030623455,0.00078622677],"genre_scores_gemma":[0.23406802,0.00041735047,0.7629691,0.00017215198,0.00013975237,0.00025630114,0.00052524626,0.00014864067,0.0013033766],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976109,0.0011113863,0.00016693107,0.00036828246,0.00064111553,0.00010134426],"domain_scores_gemma":[0.9866851,0.009551197,0.0007309055,0.001125186,0.0016793851,0.00022820137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00370448,0.001146248,0.0009599952,0.0021354519,0.000517629,0.0014083721,0.00080228,0.0008014991,0.0028415003],"category_scores_gemma":[0.01863723,0.00041632343,0.0014428893,0.0019200753,0.0014652237,0.0016048951,0.0019579178,0.0017574982,0.0005800492],"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":[0.00042988342,0.00017017136,0.005006771,0.00045610435,0.00024214727,0.00019655442,0.00041948174,0.24983951,0.012432176,0.04827367,0.0041734474,0.67836004],"study_design_scores_gemma":[0.00002562438,0.00008407986,0.0018704357,0.000039731363,0.00002442308,0.00007252148,0.00007412193,0.94489926,0.0031073142,0.047342774,0.0024159583,0.000043756216],"about_ca_topic_score_codex":0.0031910506,"about_ca_topic_score_gemma":0.0035518492,"teacher_disagreement_score":0.00370448,"about_ca_system_score_codex":0.0007010611,"about_ca_system_score_gemma":0.0011624531,"threshold_uncertainty_score":0.019591391},"labels":[],"label_agreement":null},{"id":"W4383875882","doi":"10.1002/cem.3508","title":"Planetary and space science special issue","year":2023,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","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; York University","funders":"","keywords":"Space (punctuation); Work (physics); Space Science; Field (mathematics); Engineering ethics; Political science; Environmental ethics; Sociology; Computer science; Operations research; Engineering; Mathematics; Philosophy","score_opus":0.017795870385505598,"score_gpt":0.23907162477771912,"score_spread":0.22127575439221353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383875882","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060370524,0.012081939,0.00089848693,0.018249514,0.85697895,0.00023267433,0.0016269464,0.0007000433,0.10862772],"genre_scores_gemma":[0.003923775,0.01630688,0.0007949537,0.009812994,0.77496374,0.00020658187,0.0029264076,0.0008559461,0.19020873],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99719363,0.00023849013,0.00026735646,0.00052058406,0.0014094253,0.00037054982],"domain_scores_gemma":[0.99113375,0.001253246,0.0005892018,0.0011857492,0.0036646791,0.0021733667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033554381,0.002366116,0.0025125353,0.0031937715,0.0026251501,0.010671378,0.0024495614,0.0049209297,0.2300534],"category_scores_gemma":[0.009948275,0.00083931716,0.0021294127,0.0015044077,0.001602719,0.0050329533,0.0035579333,0.006066684,0.13645191],"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":[0.000034919816,0.000045052333,0.00023365967,0.0003019035,0.00001940628,0.0001344041,0.00002345792,0.000058547263,0.00028937386,0.0018967877,0.9711901,0.025772372],"study_design_scores_gemma":[0.000008411637,0.000032987144,0.00051356846,0.00015376236,0.000009422672,0.00020877348,0.000026774736,0.00004391008,0.00010057974,0.001148747,0.9977458,0.0000071445506],"about_ca_topic_score_codex":0.0009309687,"about_ca_topic_score_gemma":0.001207295,"teacher_disagreement_score":0.2300534,"about_ca_system_score_codex":0.002051827,"about_ca_system_score_gemma":0.003355636,"threshold_uncertainty_score":0.7696053},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21718876,0.0004414733,0.77775156,0.00006943439,0.00012658969,0.00034395355,0.0007276887,0.0017268241,0.0016237217],"genre_scores_gemma":[0.5753147,0.0003104719,0.41966107,0.00007416466,0.00003384946,0.0005212398,0.0013753661,0.00049327384,0.0022158343],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99708503,0.001385066,0.00015496973,0.000631515,0.0006676034,0.00007575229],"domain_scores_gemma":[0.9966664,0.00104361,0.00047158467,0.0006247613,0.0011510357,0.000042604996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047286027,0.0010957808,0.0005969677,0.0011641596,0.00039341938,0.00095145765,0.0012256555,0.0005135554,0.0012666803],"category_scores_gemma":[0.008674957,0.00036033455,0.0011224844,0.0015577422,0.0002964112,0.0004368207,0.00071245723,0.0007657438,0.0008961137],"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":[0.0007631666,0.0003832706,0.1939773,0.0009147258,0.0011132456,0.00017258037,0.00077368174,0.07232445,0.104988195,0.0032899822,0.0027537895,0.6185457],"study_design_scores_gemma":[0.00006849298,0.0020198878,0.23552582,0.00020038841,0.0008186047,0.0005292964,0.000671188,0.5773488,0.16356747,0.0045392574,0.014449795,0.00026097716],"about_ca_topic_score_codex":0.0031528117,"about_ca_topic_score_gemma":0.005057104,"teacher_disagreement_score":0.0047286027,"about_ca_system_score_codex":0.00041358254,"about_ca_system_score_gemma":0.0010527346,"threshold_uncertainty_score":0.025007546},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04356114,0.002333252,0.9423373,0.006237664,0.0004795835,0.00044627662,0.00069479074,0.0007897808,0.0031202151],"genre_scores_gemma":[0.65590453,0.0015285697,0.33454645,0.0030702273,0.00054522225,0.0013289324,0.0006956209,0.00044329592,0.0019371698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86405253,0.108395465,0.0052242805,0.011638645,0.009397097,0.0012919708],"domain_scores_gemma":[0.53181314,0.43205643,0.013235308,0.018084204,0.004248929,0.00056202983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15127999,0.00323634,0.00272109,0.0020529795,0.0017048783,0.005319469,0.0031455217,0.0033663614,0.0030548251],"category_scores_gemma":[0.35973892,0.0014790024,0.0036108526,0.0030921502,0.0059493063,0.005941216,0.0042745904,0.006924669,0.00059427856],"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":[0.0020314986,0.00064662687,0.095365584,0.0032231426,0.0077064317,0.004759963,0.008478478,0.27564198,0.007985393,0.3413134,0.010071281,0.24277627],"study_design_scores_gemma":[0.00022623308,0.00036476107,0.014413319,0.0007209629,0.0012197971,0.0005872196,0.0009174983,0.3354417,0.0072204,0.6254234,0.013199385,0.00026533596],"about_ca_topic_score_codex":0.007921215,"about_ca_topic_score_gemma":0.005251981,"teacher_disagreement_score":0.15127999,"about_ca_system_score_codex":0.0025463232,"about_ca_system_score_gemma":0.0029728594,"threshold_uncertainty_score":0.8000546},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019474624,0.00009631966,0.97962254,0.00008033339,0.000008162465,0.000037009595,0.000024116687,0.000350699,0.00030606618],"genre_scores_gemma":[0.15808806,0.00011327474,0.8401297,0.00006620106,0.000018275516,0.00015099533,0.000081953076,0.00008343964,0.0012682371],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988722,0.00034661533,0.000041624928,0.00019200238,0.0004998801,0.000047569767],"domain_scores_gemma":[0.998176,0.0011008489,0.00019868786,0.00012874111,0.00035261668,0.00004309739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025557,0.0007831104,0.00073814887,0.00066750724,0.0003376915,0.00063544617,0.00097355305,0.000711995,0.0007944794],"category_scores_gemma":[0.0031412754,0.0004985409,0.0006172639,0.00073954655,0.00070353015,0.000643303,0.00075613236,0.0010080929,0.00037976034],"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":[0.00035637576,0.0004314959,0.0030684604,0.0002985788,0.00016640051,0.0001499449,0.00012699235,0.36119765,0.24575813,0.010316955,0.002262608,0.37586644],"study_design_scores_gemma":[0.00001202848,0.00007592851,0.00046537962,0.000004547341,0.000010101897,0.000027827458,0.000009257186,0.9782618,0.01875306,0.001893771,0.0004749086,0.0000113264905],"about_ca_topic_score_codex":0.002695875,"about_ca_topic_score_gemma":0.0032426408,"teacher_disagreement_score":0.002695875,"about_ca_system_score_codex":0.00052846776,"about_ca_system_score_gemma":0.0015621181,"threshold_uncertainty_score":0.010712326},"labels":[],"label_agreement":null},{"id":"W4401261653","doi":"10.1002/cem.3594","title":"Investigation of the Physiological and Post‐training Effects of Ecdysteroid Supplementation by Multivariate Analysis of the Human Serum Metabolome","year":2024,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","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":"World Anti-Doping Agency","keywords":"Placebo; Ecdysteroid; Metabolomics; Ecdysone; Internal medicine; Hormone; Multivariate analysis; Metabolome; Endocrinology; Pharmacology; Medicine; Chemistry; Chromatography; Pathology","score_opus":0.05524279322849623,"score_gpt":0.3292719625864878,"score_spread":0.27402916935799154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401261653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875119,0.0022382317,0.007748289,0.00009675677,0.00002614201,0.000050567534,0.001664547,0.000090433554,0.0005732187],"genre_scores_gemma":[0.99096054,0.001187379,0.005092016,0.00010160868,0.000027383232,0.000060686398,0.0016889573,0.000022174834,0.00085919775],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980897,0.000050756247,0.00001311934,0.000060515293,0.0000464279,0.000020136984],"domain_scores_gemma":[0.9998596,0.000033317603,0.000045738627,0.000018165083,0.000028973263,0.000014244373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033496573,0.00034496453,0.00036624426,0.0005710397,0.00013781697,0.00032647004,0.000103527425,0.00023968183,0.0008869374],"category_scores_gemma":[0.00038531533,0.000089106325,0.00031803473,0.0005212215,0.00015093252,0.000100477344,0.00023783224,0.00019588806,0.00017591407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032526243,0.00015248328,0.063518174,0.0002730905,0.00036283446,0.00013717548,0.00009872579,0.00053579576,0.88198483,0.000086334156,0.00029034066,0.04930764],"study_design_scores_gemma":[0.000047235666,0.0016766243,0.7690739,0.000026660322,0.00031774814,0.0008577392,0.000143439,0.006946724,0.21801125,0.0002142016,0.0026479026,0.000036585545],"about_ca_topic_score_codex":0.00085815374,"about_ca_topic_score_gemma":0.0009717576,"teacher_disagreement_score":0.0008869374,"about_ca_system_score_codex":0.0001132563,"about_ca_system_score_gemma":0.00014826197,"threshold_uncertainty_score":0.0029671192},"labels":[],"label_agreement":null},{"id":"W4409203171","doi":"10.1002/cem.70025","title":"Data Quality: Importance of the ‘before analysis’ domain (Theory of Sampling, TOS)","year":2025,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Data Quality and Management","field":"Decision Sciences","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":"Sampling (signal processing); Quality (philosophy); Domain (mathematical analysis); Statistics; Mathematics; Computer science; Philosophy; Mathematical analysis; Epistemology","score_opus":0.3448048197220179,"score_gpt":0.4975400213069517,"score_spread":0.1527352015849338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409203171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012160005,0.010782294,0.9068371,0.054910347,0.0027462419,0.0008135345,0.0007243565,0.00060785096,0.010418123],"genre_scores_gemma":[0.27224666,0.009349802,0.689455,0.018617496,0.005108481,0.0019132457,0.00084724283,0.0006156298,0.0018465644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7478144,0.15624568,0.019040266,0.016053392,0.059077892,0.0017683975],"domain_scores_gemma":[0.32289982,0.55772847,0.02418469,0.04972361,0.043400012,0.0020634013],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22074713,0.0011305565,0.00337881,0.004208704,0.0027167238,0.0138277095,0.0042661577,0.0047253314,0.0025292966],"category_scores_gemma":[0.48716143,0.0012249314,0.0019449345,0.004565561,0.024928864,0.014521454,0.008052277,0.011782817,0.0008524907],"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":[0.00038122892,0.00013319496,0.017245576,0.0048120874,0.0006517734,0.00034198407,0.005091732,0.014550251,0.0022231191,0.6647265,0.014585188,0.2752573],"study_design_scores_gemma":[0.00012767708,0.00044537205,0.008602646,0.0036050514,0.00020726427,0.0007849085,0.0020729126,0.029682428,0.004956972,0.8706357,0.07861673,0.00026236713],"about_ca_topic_score_codex":0.0055764653,"about_ca_topic_score_gemma":0.002778682,"teacher_disagreement_score":0.22074713,"about_ca_system_score_codex":0.008628158,"about_ca_system_score_gemma":0.014216157,"threshold_uncertainty_score":0.9609575},"labels":[],"label_agreement":null},{"id":"W4409203243","doi":"10.1002/cem.70021","title":"Data Quality: Importance of the ‘Before Analysis’ Domain [Theory of Sampling (TOS)]","year":2025,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Census and Population Estimation","field":"Mathematics","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":"Sampling (signal processing); Quality (philosophy); Domain (mathematical analysis); Computer science; Statistics; Mathematics; Physics; Mathematical analysis","score_opus":0.19400646165840033,"score_gpt":0.437399032937883,"score_spread":0.2433925712794827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409203243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017172419,0.006529849,0.9156993,0.03877855,0.0010327821,0.00046495566,0.0006415308,0.00046136198,0.019219257],"genre_scores_gemma":[0.57922006,0.005678587,0.3990628,0.009854122,0.0018602214,0.0010090068,0.0008474502,0.00049245666,0.0019752905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.804377,0.118436664,0.014689544,0.01382825,0.04672375,0.0019448237],"domain_scores_gemma":[0.3484796,0.48275042,0.03409214,0.056225378,0.074964225,0.0034882869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16074677,0.0008729055,0.0027627267,0.005362566,0.0027660734,0.01747216,0.004340428,0.0038842717,0.002966671],"category_scores_gemma":[0.3995466,0.0011714575,0.0019318351,0.007117862,0.02599913,0.018622464,0.009090134,0.0071735126,0.0006356969],"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":[0.00015470671,0.00008544881,0.013475518,0.001804857,0.0002646663,0.0001738779,0.003075773,0.010091773,0.00088049663,0.8343232,0.006890939,0.12877865],"study_design_scores_gemma":[0.000039021434,0.000181876,0.0061197607,0.0022096587,0.00010986025,0.00033001212,0.001439299,0.0312598,0.0020417643,0.92574996,0.030346569,0.00017248256],"about_ca_topic_score_codex":0.0077947215,"about_ca_topic_score_gemma":0.0033274768,"teacher_disagreement_score":0.16074677,"about_ca_system_score_codex":0.009119349,"about_ca_system_score_gemma":0.014202364,"threshold_uncertainty_score":0.8501203},"labels":[],"label_agreement":null}]}