{"meta":{"query_hash":"9ff97219d1a5","filters":{"topic":"Sensory Analysis and Statistical Methods"},"cohort_total":428,"direct_labels_cover":0,"predictions_cover":428,"exported":428,"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/9ff97219d1a5","api":"https://metacan.xera.ac/api/v1/cohort?topic=Sensory+Analysis+and+Statistical+Methods"},"results":[{"id":"W138230538","doi":"10.1007/bf03395369","title":"The Effect of Changes in Availability on Food Selection at Different Levels of DeprIvation","year":2000,"lang":"en","type":"article","venue":"The Psychological Record","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Mount Allison University","funders":"","keywords":"Food consumption; Body weight; Selection (genetic algorithm); Food intake; Animal science; Consumption (sociology); Statistics; Food science; Psychology; Mathematics; Biology; Computer science; Economics; Endocrinology; Agricultural economics; Artificial intelligence","score_opus":0.0757558112780407,"score_gpt":0.3327552605522418,"score_spread":0.2569994492742011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W138230538","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":[0.9970331,0.0001762334,0.0013477241,0.000051427214,0.000023085508,0.000029564571,0.000301268,0.000013594962,0.001023942],"genre_scores_gemma":[0.9967693,0.00011120474,0.0012636455,0.00008676961,0.000021424023,0.00009316845,0.00042367392,0.00004365839,0.001187225],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99911374,0.00028224822,0.000082583494,0.000191643,0.00020162929,0.00012822328],"domain_scores_gemma":[0.9909218,0.0065713273,0.0006405087,0.00071761524,0.0004908024,0.00065795984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084777863,0.0003441761,0.00065412937,0.00046389012,0.0004405231,0.00095723144,0.0003227019,0.000499366,0.0030580186],"category_scores_gemma":[0.006136437,0.0003842219,0.00034893694,0.00045888143,0.0011135469,0.00070430327,0.0007416833,0.0015076073,0.00020121202],"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":[0.04252414,0.0009522553,0.080179945,0.00046313074,0.00043762024,0.00019483113,0.0009998427,0.0011394146,0.8531454,0.0005465884,0.00032482616,0.019092025],"study_design_scores_gemma":[0.00013070297,0.004436471,0.9328085,0.000013965216,0.00019945491,0.00020890459,0.00033541923,0.0015637579,0.059033155,0.00029783242,0.00092321786,0.00004867441],"about_ca_topic_score_codex":0.0016054858,"about_ca_topic_score_gemma":0.0035530238,"teacher_disagreement_score":0.0030580186,"about_ca_system_score_codex":0.0005049285,"about_ca_system_score_gemma":0.00033051363,"threshold_uncertainty_score":0.010230064},"labels":[],"label_agreement":null},{"id":"W1478010011","doi":"10.1079/9780851990323.0075","title":"Food neophobia in humans.","year":2006,"lang":"en","type":"book-chapter","venue":"CABI eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":211,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Neophobia; Novel food; Psychology; Developmental psychology; Food science; Biology","score_opus":0.050004496000767014,"score_gpt":0.24574565560345227,"score_spread":0.19574115960268526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1478010011","genre_codex":"review","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":[0.006288716,0.48894647,0.0096242605,0.010767449,0.0046014776,0.000099087636,0.00046820685,0.00068671384,0.47851762],"genre_scores_gemma":[0.03575329,0.34474054,0.014062107,0.009996964,0.0012805536,0.00017680836,0.00058364094,0.00017157753,0.5932345],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99991655,0.000013792523,0.000003456866,0.000015874853,0.000041225234,0.000009091436],"domain_scores_gemma":[0.99994934,0.000018458888,0.000005079037,0.0000041073163,0.000011663701,0.000011322713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022056702,0.00050997274,0.00030662084,0.00043161446,0.00035505067,0.00069904036,0.00036222592,0.0010339982,0.020445121],"category_scores_gemma":[0.0003483511,0.00017554995,0.00017037333,0.0002600706,0.0005270201,0.0007683109,0.00059804035,0.0012557362,0.010476048],"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":[0.000044295677,0.00008816358,0.00070120377,0.00078943523,0.000009850344,0.0007565814,0.00075451634,0.00011823868,0.0040972573,0.023570983,0.20149893,0.76757056],"study_design_scores_gemma":[0.00000914403,0.00015450621,0.003906952,0.0006971324,0.0000069433877,0.0076283324,0.00023036776,0.00011048614,0.0007160175,0.019898584,0.9666228,0.000018698598],"about_ca_topic_score_codex":0.00082822435,"about_ca_topic_score_gemma":0.0021036402,"teacher_disagreement_score":0.020445121,"about_ca_system_score_codex":0.000265397,"about_ca_system_score_gemma":0.00038566338,"threshold_uncertainty_score":0.068395674},"labels":[],"label_agreement":null},{"id":"W1485448714","doi":"10.1002/9780470057339.vnn075","title":"Analysis of Variance, Multivariate ( <scp>MANOVA</scp> )","year":2012,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multivariate analysis of variance; Statistics; Analysis of variance; Variance (accounting); Multivariate statistics; Multivariate analysis; Confidence interval; Random effects model; Mathematics; Set (abstract data type); Placebo; Repeated measures design; Econometrics; Medicine; Computer science; Meta-analysis; Internal medicine","score_opus":0.02392652336403519,"score_gpt":0.2562643222733116,"score_spread":0.2323377989092764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485448714","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":[0.030826649,0.0010542935,0.84579414,0.0007959397,0.0033039427,0.011726405,0.036754537,0.028299322,0.0414448],"genre_scores_gemma":[0.08150681,0.0007440548,0.83998185,0.00063347275,0.00035337915,0.047396753,0.0073150536,0.0040973313,0.017971376],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98340225,0.0068784785,0.0017567045,0.0024848464,0.0050124116,0.00046533035],"domain_scores_gemma":[0.96152943,0.024416419,0.0030295302,0.0062220404,0.0044297106,0.00037283995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014042446,0.0024888914,0.0028390142,0.0024036304,0.00079817645,0.0022024456,0.0025677008,0.0008899956,0.06776069],"category_scores_gemma":[0.03764234,0.0007057132,0.0018939648,0.005015985,0.0016797811,0.0018270733,0.0015689124,0.0030741622,0.009822573],"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":[0.006885073,0.0021695644,0.005348469,0.009373465,0.0025623743,0.0007771842,0.001814227,0.009168969,0.043971557,0.05020502,0.22692236,0.6408018],"study_design_scores_gemma":[0.002162818,0.01628145,0.10023688,0.003792255,0.0019213118,0.0014646249,0.0015864267,0.1294901,0.060026497,0.07027518,0.61192024,0.00084221544],"about_ca_topic_score_codex":0.0015669888,"about_ca_topic_score_gemma":0.0017214213,"teacher_disagreement_score":0.06776069,"about_ca_system_score_codex":0.0010289875,"about_ca_system_score_gemma":0.002184172,"threshold_uncertainty_score":0.22668207},"labels":[],"label_agreement":null},{"id":"W1493882241","doi":"10.1002/0470013192.bsa427","title":"Multitrait–Multimethod Analyses","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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","funders":"","keywords":"Statistical analysis; Linear discriminant analysis; Set (abstract data type); Mathematics; Computer science; Statistics; Data mining","score_opus":0.0854941919307344,"score_gpt":0.41432936703986634,"score_spread":0.32883517510913196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493882241","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":[0.02123526,0.0014915834,0.93447226,0.00092228124,0.0010591516,0.006875586,0.012240119,0.0031271267,0.018576533],"genre_scores_gemma":[0.12748684,0.0010006297,0.8231895,0.0004854062,0.00028268207,0.021940662,0.006454913,0.0019953165,0.017164042],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9437858,0.033582658,0.004172878,0.006992828,0.010365003,0.0011007994],"domain_scores_gemma":[0.8797782,0.081248,0.00558738,0.019267585,0.01346165,0.0006571608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044670247,0.002637618,0.0029975933,0.007186667,0.0017924751,0.0042093773,0.004117779,0.001313807,0.041784916],"category_scores_gemma":[0.20177175,0.000957288,0.0033149254,0.010935141,0.001775891,0.0033702992,0.0033117395,0.0032475346,0.0064148437],"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":[0.000978929,0.0013276552,0.03252506,0.0054757446,0.00495977,0.00082813104,0.004948025,0.0142392265,0.0031962134,0.14303386,0.10480003,0.6836873],"study_design_scores_gemma":[0.00042460122,0.0020655268,0.108381495,0.002903588,0.0027831227,0.0012719567,0.004457532,0.1621101,0.01751602,0.3128273,0.38470846,0.000550358],"about_ca_topic_score_codex":0.0039440766,"about_ca_topic_score_gemma":0.0058361413,"teacher_disagreement_score":0.044670247,"about_ca_system_score_codex":0.0018142135,"about_ca_system_score_gemma":0.00492029,"threshold_uncertainty_score":0.23624164},"labels":[],"label_agreement":null},{"id":"W1505355220","doi":"10.1002/9781118445112.stat06808","title":"Fisher's exact test including power and mid‐P value","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Simon Fraser University","funders":"","keywords":"Exact test; Contingency table; p-value; Statistics; Value (mathematics); Mathematics; Binomial (polynomial); Test (biology); Binomial test; Power (physics); Mann–Whitney U test; Econometrics; Negative binomial distribution; Statistical hypothesis testing; Poisson distribution; Physics","score_opus":0.0590373046719061,"score_gpt":0.3187245466696214,"score_spread":0.2596872419977153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1505355220","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":[0.031890225,0.009308858,0.73043257,0.0046206703,0.004912373,0.0070052138,0.051714923,0.007731469,0.15238379],"genre_scores_gemma":[0.32678518,0.0059856754,0.5336153,0.002728922,0.0026620969,0.023544734,0.02377248,0.005294065,0.075611554],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98086953,0.0051913653,0.002199005,0.001981441,0.009176487,0.00058228895],"domain_scores_gemma":[0.9389013,0.04484716,0.003779763,0.004293784,0.007542103,0.0006359063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01668788,0.0009306835,0.0019693614,0.004559266,0.00094416115,0.0021669765,0.002001379,0.0014920024,0.093612194],"category_scores_gemma":[0.10919208,0.00047110996,0.001257798,0.0046240897,0.0017455535,0.0025978484,0.0015890887,0.0022683148,0.014479323],"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":[0.0023714728,0.00035691724,0.016729036,0.0048331004,0.0004624379,0.0010765301,0.00057805894,0.0034522645,0.004225882,0.04537166,0.25767547,0.6628671],"study_design_scores_gemma":[0.00051750324,0.0020148803,0.04789182,0.004229844,0.00069564366,0.0041515133,0.00077274424,0.02445903,0.01466232,0.12319241,0.777088,0.00032426897],"about_ca_topic_score_codex":0.0011788798,"about_ca_topic_score_gemma":0.00085139484,"teacher_disagreement_score":0.093612194,"about_ca_system_score_codex":0.0009537439,"about_ca_system_score_gemma":0.002679446,"threshold_uncertainty_score":0.313164},"labels":[],"label_agreement":null},{"id":"W1523675078","doi":"10.1002/jsfa.6672","title":"Consumer acceptance of quality protein maize (<scp>QPM</scp>) in East Africa","year":2014,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian International Development Agency","keywords":"Quality (philosophy); Likert scale; Aroma; Tanzania; Trait; Biotechnology; Affect (linguistics); Taste; Malnutrition; Food science; Business; Biology; Psychology; Mathematics; Medicine; Statistics; Socioeconomics; Computer science; Economics; Communication","score_opus":0.03704324791278847,"score_gpt":0.2705237977401086,"score_spread":0.23348054982732014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523675078","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":[0.99980694,0.000016546941,0.000013631433,0.000009617646,5.0806943e-7,0.0000043204604,0.000011863066,3.1149054e-7,0.00013629111],"genre_scores_gemma":[0.99972326,0.00004834778,0.00006636665,0.000011491338,7.13961e-7,0.000006884944,0.000012170585,4.1939512e-7,0.00013034805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971324,0.000107217245,0.000019577436,0.000034582583,0.00007031659,0.000055108136],"domain_scores_gemma":[0.9992286,0.00033186714,0.00025625245,0.00001994626,0.00010040658,0.000062930216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008933754,0.00020830949,0.00015017224,0.00030662463,0.00032411836,0.00056016404,0.0001086399,0.00024259754,0.0025490618],"category_scores_gemma":[0.0016761036,0.00012298813,0.00017204022,0.00033854038,0.0003007488,0.00036968794,0.0003353236,0.00032310732,0.00012390771],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018530915,0.0008310768,0.89831024,0.00036918063,0.000078049954,0.00096258725,0.044599157,0.00011800272,0.014642492,0.0003414281,0.0004679212,0.037426732],"study_design_scores_gemma":[0.000021435952,0.0014867033,0.9693568,0.0000554384,0.000035350437,0.00033124132,0.025845097,0.00039470752,0.0011904337,0.000061840816,0.0012061659,0.000014789742],"about_ca_topic_score_codex":0.0040241177,"about_ca_topic_score_gemma":0.003872581,"teacher_disagreement_score":0.0040241177,"about_ca_system_score_codex":0.000320968,"about_ca_system_score_gemma":0.0001545127,"threshold_uncertainty_score":0.008527458},"labels":[],"label_agreement":null},{"id":"W1527992236","doi":"10.1002/9780470870709.ch5","title":"Testing a Covariate","year":2006,"lang":"en","type":"other","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Covariate; Statistics; Computer science; Mathematics","score_opus":0.09957874235041529,"score_gpt":0.29528240191973104,"score_spread":0.19570365956931574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527992236","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":[0.05624365,0.00055691396,0.8743721,0.004919318,0.001981218,0.00078898855,0.005814603,0.011662642,0.04366058],"genre_scores_gemma":[0.327718,0.0006057612,0.6074291,0.0037254007,0.0007105233,0.0026306377,0.010221081,0.008323365,0.03863625],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98302066,0.007438182,0.0009816701,0.0041563595,0.003998033,0.00040512875],"domain_scores_gemma":[0.8734243,0.09906234,0.0034632222,0.01453863,0.008533906,0.0009775643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016977342,0.0013147064,0.0012379356,0.0007760052,0.0009008564,0.00194273,0.0018970763,0.0017369557,0.04187229],"category_scores_gemma":[0.15587491,0.00061307184,0.0018451429,0.0011044355,0.0017692518,0.0040635443,0.0028006888,0.0027773832,0.010423113],"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":[0.0018178415,0.00063329807,0.04305474,0.0009294694,0.00046654674,0.00048610856,0.0014224428,0.005613507,0.015524603,0.07392905,0.09297148,0.7631509],"study_design_scores_gemma":[0.0009853893,0.0057952614,0.11107255,0.0012770565,0.00085944927,0.0023392427,0.0018178151,0.11983282,0.07004609,0.32967848,0.35584393,0.0004518737],"about_ca_topic_score_codex":0.0012256317,"about_ca_topic_score_gemma":0.0011283843,"teacher_disagreement_score":0.04187229,"about_ca_system_score_codex":0.00087765226,"about_ca_system_score_gemma":0.002256973,"threshold_uncertainty_score":0.14007676},"labels":[],"label_agreement":null},{"id":"W1548778766","doi":"10.1002/0470013192.bsa717","title":"<scp>Y</scp> ule, <scp>G</scp> eorge <scp>U</scp> dny","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Encyclopedia; Citation; Library science; George (robot); Operations research; Psychology; Computer science; Artificial intelligence; Mathematics","score_opus":0.030698784937929328,"score_gpt":0.31017197352111575,"score_spread":0.2794731885831864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548778766","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010757209,0.0013775852,0.023317832,0.0035454028,0.002331039,0.00031472387,0.078342654,0.012990901,0.8767041],"genre_scores_gemma":[0.006752078,0.0017290808,0.019768346,0.00085885375,0.0004128508,0.00031761613,0.0367273,0.0065777046,0.92685616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993304,0.00008627476,0.00004041731,0.000104644045,0.00038184813,0.000056535326],"domain_scores_gemma":[0.99685305,0.0004948319,0.00016158713,0.00046235925,0.0016177928,0.00041040205],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011817546,0.0010068518,0.00081849226,0.0021923357,0.00072592736,0.002284425,0.0013793986,0.00085474295,0.5639137],"category_scores_gemma":[0.006283337,0.00028909554,0.0004220538,0.0027829714,0.0005986762,0.0011777539,0.0012773527,0.0010086299,0.4722928],"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":[0.000042517644,0.00001765942,0.000113721806,0.00012335725,0.000004563028,0.000021064105,0.000015767546,0.00016701508,0.00032474715,0.0025100044,0.904809,0.09185068],"study_design_scores_gemma":[0.000027759314,0.000015192508,0.0013724824,0.0000969287,0.0000075307057,0.00004710396,0.00003246241,0.0006705474,0.0011583628,0.0051402017,0.9914163,0.00001505846],"about_ca_topic_score_codex":0.016432153,"about_ca_topic_score_gemma":0.03834684,"teacher_disagreement_score":0.5639137,"about_ca_system_score_codex":0.0015525705,"about_ca_system_score_gemma":0.0031349878,"threshold_uncertainty_score":0.6220242},"labels":[],"label_agreement":null},{"id":"W154967080","doi":"10.1533/9781845691400.1.62","title":"Sensory analysis of food flavor","year":2006,"lang":"it","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Flavor; Sensory system; Food science; Sensory analysis; Psychology; Chemistry; Cognitive psychology","score_opus":0.04655505415728594,"score_gpt":0.27430582502708206,"score_spread":0.22775077086979612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W154967080","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":[0.16560782,0.052329164,0.54751843,0.0008681755,0.0017667081,0.000963771,0.024506686,0.008929459,0.19750977],"genre_scores_gemma":[0.3340308,0.031945895,0.28299257,0.0014126208,0.00034182114,0.0013466235,0.016221924,0.0014950559,0.33021268],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951756,0.00005536878,0.000022707402,0.0000974228,0.000273303,0.00003354871],"domain_scores_gemma":[0.999762,0.00006399243,0.000012773345,0.000023529272,0.00012358709,0.000014185751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006130408,0.0008975672,0.0005882717,0.0015299013,0.0002927799,0.0009826261,0.00059555913,0.0003974078,0.020748535],"category_scores_gemma":[0.00062430603,0.00040850157,0.00075594033,0.001268716,0.00030650976,0.0006006071,0.0006291782,0.00084004004,0.0071171075],"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":[0.00027484616,0.00010860217,0.0010844615,0.0007397262,0.00006351592,0.00006044431,0.00008223115,0.00037671384,0.5074258,0.0010962355,0.010204585,0.47848284],"study_design_scores_gemma":[0.000041813386,0.0014280895,0.096062176,0.0004277864,0.0002507099,0.0019845976,0.00054480304,0.011979714,0.72326833,0.007404359,0.15636565,0.00024195775],"about_ca_topic_score_codex":0.0021771933,"about_ca_topic_score_gemma":0.003992227,"teacher_disagreement_score":0.020748535,"about_ca_system_score_codex":0.0003283281,"about_ca_system_score_gemma":0.000394003,"threshold_uncertainty_score":0.06941074},"labels":[],"label_agreement":null},{"id":"W1554986951","doi":"10.3389/fpsyg.2015.00903","title":"Achieving across-laboratory replicability in psychophysical scaling","year":2015,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; McMaster University; InteraXon (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of Science; Canadian Institutes of Health Research","keywords":"Scaling; Psychophysics; Magnitude (astronomy); Psychology; Power function; Statistical physics; Statistics; Function (biology); Set (abstract data type); Computer science; Mathematics; Physics; Perception; Mathematical analysis","score_opus":0.06218418818740336,"score_gpt":0.3823854350011022,"score_spread":0.3202012468136989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554986951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12299194,0.0009479775,0.85726357,0.0005799188,0.0010900805,0.0021159588,0.00088311854,0.0020498468,0.012077506],"genre_scores_gemma":[0.56444234,0.0004000497,0.4210686,0.0010359602,0.00026688198,0.005439542,0.0016001834,0.0022771757,0.0034693354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.92896646,0.034685064,0.005615369,0.015007442,0.014473501,0.0012522342],"domain_scores_gemma":[0.78530616,0.07249534,0.011871209,0.10395887,0.02495513,0.0014132118],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07210086,0.001984585,0.0016693256,0.0018769599,0.002083752,0.0038898108,0.003499783,0.0020420554,0.0055986047],"category_scores_gemma":[0.187951,0.0013990613,0.001936834,0.0017679855,0.006013937,0.0032053145,0.008447508,0.003798748,0.0024430763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019506477,0.0026121277,0.042871293,0.0036840136,0.0026160816,0.0009924395,0.011155925,0.014414078,0.6553757,0.046354566,0.00675381,0.21121933],"study_design_scores_gemma":[0.0006450682,0.009941161,0.18254891,0.001159308,0.0016223424,0.0021762548,0.0026161114,0.0530286,0.53086036,0.13784263,0.07673875,0.0008205631],"about_ca_topic_score_codex":0.0017914507,"about_ca_topic_score_gemma":0.0023796265,"teacher_disagreement_score":0.9278991,"about_ca_system_score_codex":0.001236644,"about_ca_system_score_gemma":0.0016501303,"threshold_uncertainty_score":0.38131034},"labels":[],"label_agreement":null},{"id":"W1557423190","doi":"10.1016/s1570-7946(03)80070-6","title":"Multivariate weighted least squares as an alternative to the determinant criterion for multiresponse parameter estimation","year":2003,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Multivariate statistics; Monte Carlo method; Weighting; Statistics; Applied mathematics; Generalized least squares; Equivalence (formal languages); Least-squares function approximation; Estimation theory; Ordinary least squares; Explained sum of squares; Covariance; Total sum of squares; Non-linear least squares; Lack-of-fit sum of squares; Estimator","score_opus":0.026463763018282897,"score_gpt":0.26276246324457797,"score_spread":0.23629870022629507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557423190","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":[0.00084530166,0.000094063944,0.99870884,0.000022145667,0.000017232875,0.000008699613,0.000012595919,0.00012072372,0.00017042432],"genre_scores_gemma":[0.030152958,0.00025199947,0.96707624,0.0000683309,0.000050010643,0.000110065965,0.00012527384,0.00041608282,0.0017489046],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950794,0.0032271375,0.00018491877,0.00040241526,0.0010158226,0.00009035207],"domain_scores_gemma":[0.99397427,0.0043449197,0.00025565625,0.00054960477,0.0008144689,0.00006101417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004810492,0.0013162944,0.0016842016,0.0012366569,0.00041004544,0.0010638032,0.0018386329,0.0013578384,0.0027890482],"category_scores_gemma":[0.0146978665,0.00094040827,0.0014176982,0.002772907,0.00086629664,0.0019472537,0.0016194631,0.002250487,0.0013781729],"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":[0.00037486706,0.00020925989,0.0012209899,0.0005692026,0.00052020524,0.00013951006,0.00023861861,0.14755534,0.029106371,0.15419796,0.0062161423,0.65965164],"study_design_scores_gemma":[0.000029804467,0.00013208238,0.0008686529,0.000035801393,0.00007444713,0.00016033584,0.00003167487,0.9312091,0.00541356,0.055132225,0.0068372265,0.00007511874],"about_ca_topic_score_codex":0.0023944129,"about_ca_topic_score_gemma":0.0045430805,"teacher_disagreement_score":0.004810492,"about_ca_system_score_codex":0.00049031305,"about_ca_system_score_gemma":0.0013382279,"threshold_uncertainty_score":0.025440633},"labels":[],"label_agreement":null},{"id":"W155918022","doi":"10.1016/b0-08-043076-7/00673-2","title":"Signal Detection Theory: Multidimensional","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Dimension (graph theory); Mathematics; Separable space; Observer (physics); Statistics; Pure mathematics; Mathematical analysis; Physics; Quantum mechanics","score_opus":0.03148460687925489,"score_gpt":0.25430715223822026,"score_spread":0.22282254535896537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W155918022","genre_codex":"methods","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":[0.00090462697,0.08053333,0.83301014,0.0026793936,0.0022920407,0.000044947676,0.0005018526,0.001391404,0.07864223],"genre_scores_gemma":[0.09003581,0.13946733,0.57954127,0.0035191767,0.010643487,0.0009974359,0.0025357427,0.0020448884,0.17121494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987779,0.00042411088,0.000085775064,0.00023703546,0.00042784383,0.000047362577],"domain_scores_gemma":[0.9977574,0.0012980027,0.00009325289,0.00028343525,0.0005034233,0.000064436514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016283806,0.0026307644,0.002430204,0.0026231133,0.00056674576,0.004593177,0.0023677386,0.0036998368,0.02978048],"category_scores_gemma":[0.004402984,0.001202098,0.0007730415,0.005176269,0.002275782,0.0053558713,0.0017559554,0.00404155,0.019229759],"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":[0.000025989304,0.00006402157,0.00014169604,0.0011581394,0.000051807452,0.00017033877,0.000284269,0.011382143,0.0011825438,0.58174807,0.114675805,0.28911516],"study_design_scores_gemma":[0.000012811053,0.000018476332,0.00023510033,0.00035553236,0.000029567756,0.00036032585,0.00006849677,0.039642487,0.00048313357,0.8072087,0.1515362,0.000049111844],"about_ca_topic_score_codex":0.0012435705,"about_ca_topic_score_gemma":0.0010210985,"teacher_disagreement_score":0.02978048,"about_ca_system_score_codex":0.0012523142,"about_ca_system_score_gemma":0.00077209336,"threshold_uncertainty_score":0.09962565},"labels":[],"label_agreement":null},{"id":"W1560127983","doi":"","title":"A Influência do Gesto na Performance em Percussão: Análise Fatorial de Correspondências sobre Dados Experimentais.","year":2013,"lang":"pt","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.03456011550047655,"score_gpt":0.2898718975148775,"score_spread":0.25531178201440097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560127983","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":[0.92205524,0.00095238676,0.059139326,0.00024024461,0.00021123611,0.00070265227,0.001326123,0.00044876797,0.014923951],"genre_scores_gemma":[0.9619865,0.00048320534,0.030516392,0.0001248903,0.000041180443,0.0007358395,0.00074265467,0.00018850231,0.005180745],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9948881,0.0013815406,0.00039219737,0.0010647177,0.00204511,0.00022824696],"domain_scores_gemma":[0.9803381,0.012360144,0.0020080784,0.0016332343,0.003251642,0.00040870908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051802555,0.0008105754,0.00061725563,0.0015091684,0.0010490777,0.0020675599,0.00085438264,0.00094480766,0.0053976253],"category_scores_gemma":[0.0315916,0.00044530732,0.0006407092,0.0014849904,0.0015030525,0.0012090161,0.0022639246,0.0010727338,0.00089775026],"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":[0.0047429893,0.0009147377,0.1519037,0.0046395203,0.00082695094,0.0007174011,0.045709226,0.006644329,0.44273108,0.004852327,0.0032632907,0.3330545],"study_design_scores_gemma":[0.000093026494,0.00520762,0.8239209,0.00065761805,0.00071050966,0.00093776104,0.02100741,0.012227637,0.09960193,0.006694019,0.02854246,0.00039919297],"about_ca_topic_score_codex":0.002486881,"about_ca_topic_score_gemma":0.0039298334,"teacher_disagreement_score":0.0053976253,"about_ca_system_score_codex":0.0006003401,"about_ca_system_score_gemma":0.00057579647,"threshold_uncertainty_score":0.027396142},"labels":[],"label_agreement":null},{"id":"W1565800967","doi":"10.1300/j038v08n01_03","title":"Modelling Perceived Quality in Fruit Products","year":2002,"lang":"en","type":"article","venue":"Journal of Food Products Marketing","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"La Cité Collégiale; Université du Québec; Université du Québec en Outaouais","funders":"","keywords":"Quality (philosophy); Perception; Product (mathematics); Marketing; Structural equation modeling; Perceived quality; Psychology; Order (exchange); Mathematics; Business; Statistics","score_opus":0.13241071803428103,"score_gpt":0.29744022821141897,"score_spread":0.16502951017713793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565800967","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":[0.9623848,0.00006026333,0.03590708,0.00005305333,0.0000055741666,0.00007519108,0.00018566164,0.000060614,0.001267727],"genre_scores_gemma":[0.9932834,0.000028213537,0.0061691003,0.000005763006,0.0000018870516,0.000049236118,0.00011843774,0.0000046547298,0.00033931815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897987,0.0006489766,0.000030045969,0.0001319179,0.000117040305,0.00009215152],"domain_scores_gemma":[0.99466765,0.0044260593,0.00037142547,0.00016911457,0.00027868722,0.00008720055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025712568,0.00048465826,0.0002701104,0.0005855304,0.0002095312,0.0011176073,0.00064588315,0.0005756129,0.0038892692],"category_scores_gemma":[0.009345175,0.00025167054,0.0010204074,0.0005073309,0.0004562613,0.000665233,0.0005832065,0.0004707506,0.00021778309],"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":[0.0016506263,0.0013591388,0.42276138,0.00043800712,0.0005357971,0.0002888272,0.0027221069,0.44341844,0.009352062,0.023130035,0.0008924559,0.09345116],"study_design_scores_gemma":[0.00006176615,0.00070591207,0.11631126,0.000036813166,0.00010738206,0.00004444795,0.00042677415,0.87319666,0.00115826,0.007226575,0.00068753987,0.000036624526],"about_ca_topic_score_codex":0.011452207,"about_ca_topic_score_gemma":0.0073810313,"teacher_disagreement_score":0.011452207,"about_ca_system_score_codex":0.0013240548,"about_ca_system_score_gemma":0.00063604116,"threshold_uncertainty_score":0.02277106},"labels":[],"label_agreement":null},{"id":"W1579992806","doi":"","title":"An Empirical Investigation of Perceptual Incongruence Between Sales Managers and Salespeople as to Sales Force Control System Application","year":2004,"lang":"en","type":"preprint","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Humanities; Political science; Management; Business administration; Psychology; Business; Economics; Art","score_opus":0.024648974062028452,"score_gpt":0.275465956002023,"score_spread":0.2508169819399946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579992806","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":[0.9988562,0.000022641445,0.0003256085,0.000021929445,0.000002203906,0.0000144091655,0.000009432132,0.000001414587,0.00074614485],"genre_scores_gemma":[0.9990891,0.000032533626,0.00033815927,0.00003830022,0.0000042558913,0.00004973756,0.00002522953,0.0000025496065,0.00042012398],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961747,0.0020619896,0.00019560516,0.00043246784,0.0008704417,0.000264781],"domain_scores_gemma":[0.968116,0.024265135,0.0035490964,0.0011997744,0.0023300082,0.0005400844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059670988,0.00026678623,0.00030557258,0.0011444717,0.0011869237,0.0017051063,0.00041588093,0.00060432195,0.0037168055],"category_scores_gemma":[0.043073405,0.00026649312,0.00022774278,0.0006613233,0.0017371848,0.0012150065,0.001556273,0.00070284726,0.00019947752],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013839648,0.0009793313,0.6665726,0.00031626274,0.00014065282,0.00063912274,0.26393753,0.00037751655,0.018381376,0.0011725446,0.0005164261,0.04558262],"study_design_scores_gemma":[0.000044202352,0.0011762286,0.7921114,0.000057697704,0.000032459357,0.00038238356,0.20031244,0.0008458135,0.002487675,0.0005643114,0.0019308535,0.000054551343],"about_ca_topic_score_codex":0.002617275,"about_ca_topic_score_gemma":0.0027910767,"teacher_disagreement_score":0.0059670988,"about_ca_system_score_codex":0.00048755173,"about_ca_system_score_gemma":0.0003791127,"threshold_uncertainty_score":0.03155738},"labels":[],"label_agreement":null},{"id":"W1580725167","doi":"","title":"Correspondence analysis and 2-way clustering","year":2005,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Cluster analysis; Row; Novelty; Pattern recognition (psychology); Representation (politics); Row and column spaces; Computer science; Correspondence analysis; Artificial intelligence; Matrix (chemical analysis); Multiple correspondence analysis; Correlation clustering; Clustering high-dimensional data; Simple (philosophy); Fuzzy clustering; Mathematics; Data mining; Machine learning; Psychology","score_opus":0.03323390858136604,"score_gpt":0.28455606444815845,"score_spread":0.2513221558667924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580725167","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":[0.002214544,0.00017019565,0.9958234,0.000116878975,0.00006279919,0.00008561599,0.00014671459,0.0004415857,0.00093833247],"genre_scores_gemma":[0.0705987,0.0002897076,0.9241474,0.00013526325,0.00015175733,0.0007778798,0.0008238402,0.00033485866,0.0027406174],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.982866,0.0076713166,0.0008404007,0.0045346054,0.0035061692,0.00058142166],"domain_scores_gemma":[0.9869272,0.007495907,0.0010732687,0.0020845311,0.0021683776,0.00025067592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008271633,0.0019352353,0.0028236264,0.006657352,0.002789913,0.0053556524,0.0030013993,0.0024682179,0.0071998253],"category_scores_gemma":[0.025187034,0.001074794,0.0033549839,0.008077897,0.0031833034,0.004054966,0.0036367902,0.002556896,0.0030219604],"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":[0.00047033242,0.0002629489,0.004033244,0.0009563794,0.0008531574,0.00033085363,0.0017154274,0.20729063,0.0055988473,0.30907363,0.01102115,0.45839328],"study_design_scores_gemma":[0.00005255124,0.00012356734,0.0025894505,0.000072318966,0.00007856763,0.00036176317,0.0003534232,0.65791947,0.0041143536,0.3139796,0.02020287,0.00015204522],"about_ca_topic_score_codex":0.0031324301,"about_ca_topic_score_gemma":0.0032308993,"teacher_disagreement_score":0.008271633,"about_ca_system_score_codex":0.0019175985,"about_ca_system_score_gemma":0.0026465268,"threshold_uncertainty_score":0.0437451},"labels":[],"label_agreement":null},{"id":"W1602526694","doi":"10.1111/j.2044-8317.2012.02052.x","title":"A unified approach to multiple‐set canonical correlation analysis and principal components analysis","year":2012,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health; University of British Columbia; McGill University","funders":"","keywords":"Canonical correlation; Principal component analysis; Set (abstract data type); Data set; Variance (accounting); Reduction (mathematics); Mathematics; Data mining; Computer science; Function (biology); Data reduction; Canonical analysis; Variance reduction; Algorithm; Statistics","score_opus":0.08086236820783665,"score_gpt":0.34052989123659283,"score_spread":0.2596675230287562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602526694","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":[0.0002466561,0.00013749942,0.9987968,0.00012536632,0.000033369586,0.00002926052,0.00002229926,0.000056436365,0.00055231986],"genre_scores_gemma":[0.016475132,0.00057985744,0.9807916,0.00013119022,0.00020625108,0.00043529205,0.00013356954,0.00015705485,0.0010900166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99227446,0.003942751,0.00047316527,0.0011137193,0.001952857,0.00024304267],"domain_scores_gemma":[0.995061,0.0017455345,0.00046706575,0.0008346955,0.0017198054,0.00017204508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073343995,0.0022700247,0.002081948,0.0035558022,0.0013271827,0.0040472797,0.0028283389,0.0019762246,0.0036859787],"category_scores_gemma":[0.015460104,0.0011290476,0.0031952974,0.0055310763,0.0027443082,0.0035074563,0.004303744,0.004816591,0.0018690678],"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":[0.000037834263,0.000076710196,0.0006725806,0.00037181456,0.00028883526,0.00017534356,0.00041636173,0.09129787,0.004690757,0.67085844,0.007896453,0.22321703],"study_design_scores_gemma":[0.000026435746,0.00008496323,0.0009895733,0.0001144526,0.000088267436,0.0002684561,0.00012124013,0.49372005,0.0022557534,0.4657951,0.036387146,0.00014850912],"about_ca_topic_score_codex":0.0028428328,"about_ca_topic_score_gemma":0.0035468694,"teacher_disagreement_score":0.0073343995,"about_ca_system_score_codex":0.0013224266,"about_ca_system_score_gemma":0.0040314435,"threshold_uncertainty_score":0.038788438},"labels":[],"label_agreement":null},{"id":"W1615052087","doi":"10.1002/jsfa.7334","title":"Use of descriptive analysis and preference mapping for early‐stage assessment of new and established apples","year":2015,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wind Energy Institute of Canada; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Preference; Descriptive statistics; Stage (stratigraphy); Biology; Food science; Statistics; Mathematics","score_opus":0.17500312335944013,"score_gpt":0.3056640508617367,"score_spread":0.13066092750229658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1615052087","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":[0.93345207,0.00014080314,0.059698164,0.0000597072,0.0000315084,0.00066812156,0.0013214503,0.00027704466,0.0043510823],"genre_scores_gemma":[0.9529282,0.000051411705,0.045160294,0.000020172783,0.00000996382,0.00072632724,0.00063115574,0.00003237762,0.00044009736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99692553,0.0013773106,0.00036883584,0.00044998736,0.00076049985,0.000117825315],"domain_scores_gemma":[0.98281085,0.0121483775,0.0017067177,0.00097533676,0.0020537795,0.00030491978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006624785,0.0006836782,0.0005071416,0.0047006295,0.0004220347,0.00154806,0.0004924822,0.0003219552,0.0023908892],"category_scores_gemma":[0.014514911,0.00016728212,0.00097402395,0.0023193096,0.0004799297,0.0010095979,0.0007647776,0.00059918454,0.00030374184],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044936216,0.0014166594,0.5292999,0.0011463397,0.00080775,0.00041819323,0.0067539653,0.0034898478,0.050584413,0.003466403,0.0012635732,0.39685917],"study_design_scores_gemma":[0.00014429718,0.004136739,0.8932947,0.00016625096,0.00042275738,0.00074512063,0.008603807,0.05347868,0.027404489,0.0055619054,0.005767532,0.00027370598],"about_ca_topic_score_codex":0.0016922083,"about_ca_topic_score_gemma":0.001927051,"teacher_disagreement_score":0.006624785,"about_ca_system_score_codex":0.00068540446,"about_ca_system_score_gemma":0.0006156053,"threshold_uncertainty_score":0.03503567},"labels":[],"label_agreement":null},{"id":"W175862117","doi":"","title":"Comportamiento de la flora microbiana durante la elaboración industrial de kefir a partir de leche de vaca","year":2001,"lang":"es","type":"article","venue":"Alimentaria","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Kefir; Art; Biology; Lactic acid; Bacteria","score_opus":0.04260807487092651,"score_gpt":0.3250112502776967,"score_spread":0.2824031754067702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W175862117","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":[0.9985661,0.00035417225,0.00039712293,0.0000100387615,0.000004690383,0.000019400166,0.00013726656,0.000009243203,0.000502145],"genre_scores_gemma":[0.99476296,0.0004699444,0.0019120475,0.000023739374,0.000010255038,0.00004565845,0.00044855196,0.000008748233,0.0023181937],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997193,0.000046117933,0.000018903058,0.00006733287,0.00007733922,0.0000708563],"domain_scores_gemma":[0.99968946,0.000072751485,0.000055536173,0.000023683639,0.000091005124,0.00006754914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030616077,0.00039478784,0.00040138996,0.0002838011,0.00035874723,0.0006539084,0.00020965129,0.0003531954,0.00071313477],"category_scores_gemma":[0.00036591463,0.00017560169,0.00037628994,0.000378711,0.00023774747,0.00030349146,0.0002973917,0.0003314726,0.0002565951],"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":[0.00046365947,0.00008463807,0.0077943257,0.00005195221,0.000009918326,0.00009483148,0.00025502124,0.000067244684,0.9874786,0.000022284186,0.000014401217,0.0036631934],"study_design_scores_gemma":[0.000016893899,0.0047267284,0.22589892,0.000029764282,0.00006973257,0.00028459044,0.0008671199,0.0008391499,0.7650217,0.000050632876,0.0021746824,0.000020141979],"about_ca_topic_score_codex":0.0057900515,"about_ca_topic_score_gemma":0.0059455847,"teacher_disagreement_score":0.0057900515,"about_ca_system_score_codex":0.00031172714,"about_ca_system_score_gemma":0.000381223,"threshold_uncertainty_score":0.011512697},"labels":[],"label_agreement":null},{"id":"W18023542","doi":"10.1016/j.vetmic.2007.10.006","title":"Η παραγοντική ανάλυση των αντιστοιχιών (Correspondence analysis) και εφαρμογή της, με χρήση του Spss, σε δεδομένα έρευνας για την αξιοποίηση Τεχνολογιών Πληροφορίας και Επικοινωνίας (ΤΠΕ) στην πρωτοβάθμια εκπαίδευση","year":2013,"lang":"en","type":"dissertation","venue":"Veterinary Microbiology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Categorical variable; Correspondence analysis; Multiple correspondence analysis; Raw data; Set (abstract data type); Computer science; Missing data; Multivariate analysis; Multivariate statistics; Data set; Data mining; Data science; Artificial intelligence; Mathematics; Machine learning; Statistics","score_opus":0.04428887611097034,"score_gpt":0.3021217238367067,"score_spread":0.2578328477257364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W18023542","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":[0.7504336,0.0028729946,0.098719165,0.0054142317,0.000995783,0.0038311786,0.011788273,0.00095935503,0.12498544],"genre_scores_gemma":[0.91770905,0.0019238763,0.046455972,0.0004402659,0.00026723935,0.003785257,0.0021949671,0.00018909543,0.027034255],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9912213,0.0036949757,0.00074657326,0.0013370413,0.00243683,0.0005632491],"domain_scores_gemma":[0.978516,0.016558487,0.0015577533,0.0011170158,0.001955528,0.0002953366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055689,0.00029918275,0.0007101975,0.0025906516,0.0009974655,0.0017209887,0.00036517184,0.00042060652,0.044040106],"category_scores_gemma":[0.024441479,0.0003208059,0.0006117209,0.0052194004,0.0011020718,0.0012928784,0.00078438595,0.00073875056,0.0054247766],"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":[0.002315098,0.0005907053,0.17003617,0.002803042,0.000511188,0.0010425936,0.024655458,0.0014319618,0.012071437,0.028973984,0.049958263,0.7056101],"study_design_scores_gemma":[0.0003779638,0.0022950359,0.6682469,0.001713552,0.00049697387,0.002084651,0.042670235,0.012521303,0.01015289,0.051913377,0.20723657,0.00029053268],"about_ca_topic_score_codex":0.004263635,"about_ca_topic_score_gemma":0.004561911,"teacher_disagreement_score":0.044040106,"about_ca_system_score_codex":0.0013519768,"about_ca_system_score_gemma":0.0022249795,"threshold_uncertainty_score":0.14732885},"labels":[],"label_agreement":null},{"id":"W187143880","doi":"10.22237/jmasm/1209614580","title":"On Measuring the Relative Importance of Explanatory Variables in a Logistic Regression","year":2008,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; St. Francis Xavier University; University of British Columbia; Carleton University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Logistic regression; Mathematics; Statistics; Econometrics; Variables; Regression analysis; Binomial regression; Linear regression; Variable (mathematics); Logit","score_opus":0.13941036750323246,"score_gpt":0.3638800808284937,"score_spread":0.22446971332526125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W187143880","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":[0.012783079,0.0017673973,0.9832932,0.00036165965,0.00006753828,0.00023755524,0.000101395184,0.0001427706,0.0012453587],"genre_scores_gemma":[0.18492769,0.0037400734,0.80803865,0.0003090255,0.00032334225,0.001327225,0.0004806072,0.00013108944,0.0007222972],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8998017,0.08179304,0.003524601,0.0042464375,0.010037619,0.0005965806],"domain_scores_gemma":[0.5614678,0.4075972,0.01302219,0.010111554,0.007259789,0.00054143893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08474177,0.0028759912,0.002684281,0.009897176,0.0013237838,0.0041300477,0.0030749487,0.001872895,0.0020108444],"category_scores_gemma":[0.32239458,0.0010918608,0.002212842,0.0136312125,0.0043280916,0.006373362,0.0040255436,0.0050246795,0.00092430983],"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":[0.00046769908,0.0004039866,0.096861675,0.0030674462,0.0012346597,0.0009779045,0.002310479,0.066328675,0.005186714,0.19775116,0.0026564398,0.6227532],"study_design_scores_gemma":[0.00012376379,0.0014717927,0.06952634,0.0022358352,0.0005288606,0.0022120217,0.002159833,0.38719705,0.0066761044,0.5111133,0.016187347,0.000567773],"about_ca_topic_score_codex":0.0013056534,"about_ca_topic_score_gemma":0.001192852,"teacher_disagreement_score":0.08474177,"about_ca_system_score_codex":0.0014921998,"about_ca_system_score_gemma":0.002125213,"threshold_uncertainty_score":0.44816267},"labels":[],"label_agreement":null},{"id":"W1876928653","doi":"10.1111/j.1745-4557.2009.00275.x","title":"BAKING AND SENSORY CHARACTERISTICS OF MUFFINS INCORPORATED WITH APPLE SKIN POWDER","year":2009,"lang":"en","type":"article","venue":"Journal of Food Quality","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture","funders":"","keywords":"Ingredient; Food science; Food industry; Wheat flour; Dietary fiber; Health benefits; Food additive; Food products; Taste; Business; Chemistry; Medicine; Traditional medicine","score_opus":0.06575782202482335,"score_gpt":0.3110192558007941,"score_spread":0.24526143377597076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1876928653","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":[0.99919194,0.00019557236,0.00016262312,0.000006613453,0.0000042452625,0.000011942661,0.00006890101,0.000004359078,0.0003537461],"genre_scores_gemma":[0.99774665,0.00015681435,0.00069735915,0.00002533417,0.0000026013265,0.000012718525,0.00020028207,0.00000620244,0.0011519217],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998486,0.000028737035,0.000009972824,0.000029750023,0.0000599605,0.000022952407],"domain_scores_gemma":[0.99979943,0.00004918179,0.000039076935,0.000009786252,0.00006870806,0.000033844306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002574393,0.00034360893,0.0002588318,0.00023380389,0.00020087599,0.0002873572,0.0001090686,0.0002627762,0.002301092],"category_scores_gemma":[0.00030839627,0.00013218219,0.00028257802,0.00020828466,0.00016036085,0.0002928846,0.00018052649,0.00040299702,0.00015424508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006892238,0.000068081776,0.0021086559,0.00005951896,0.000015129276,0.00004797928,0.00007883417,0.000045317134,0.99552023,0.000013565029,0.000018328192,0.0013351947],"study_design_scores_gemma":[0.000024488094,0.0070054806,0.24953848,0.00003479899,0.00013123173,0.00030175288,0.00057619635,0.00089868123,0.74012995,0.000035364665,0.0012832411,0.000040227496],"about_ca_topic_score_codex":0.001167312,"about_ca_topic_score_gemma":0.0015828724,"teacher_disagreement_score":0.002301092,"about_ca_system_score_codex":0.00015783594,"about_ca_system_score_gemma":0.00007888179,"threshold_uncertainty_score":0.00769794},"labels":[],"label_agreement":null},{"id":"W1925262858","doi":"10.3968/5916","title":"The Experimental Analysis of How the Consumer Value Formed","year":2014,"lang":"en","type":"article","venue":"Studies in sociology of science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Value (mathematics); Consumption (sociology); Product (mathematics); Perception; Advertising; Consumer behaviour; Marketing; Experimental research; Psychology; Business; Sociology; Mathematics; Statistics","score_opus":0.11634867272971244,"score_gpt":0.39855517232979076,"score_spread":0.2822064996000783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1925262858","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":[0.96276546,0.00018134294,0.017877288,0.00026114305,0.00012963876,0.0008381322,0.00027609334,0.00004125571,0.017629584],"genre_scores_gemma":[0.9830049,0.00016176834,0.012167168,0.00013530721,0.000056394867,0.0010416176,0.00015236695,0.000024333442,0.0032562022],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9963482,0.0017219003,0.0002538672,0.0007018803,0.0008135644,0.00016063434],"domain_scores_gemma":[0.97418875,0.019800404,0.0017798623,0.0024577037,0.0014421409,0.00033114385],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004244593,0.00040116557,0.00027649695,0.0003950041,0.00047921884,0.0012595538,0.0004593197,0.0007026832,0.010131751],"category_scores_gemma":[0.020801406,0.00029282822,0.00038149187,0.00043982136,0.0015240975,0.0011568613,0.0008311911,0.00090954726,0.0005476849],"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":[0.009794199,0.018704887,0.070804566,0.0030261485,0.0004967609,0.0004301601,0.028062096,0.002987297,0.600853,0.08688182,0.0024985296,0.17546055],"study_design_scores_gemma":[0.0013873026,0.049604695,0.33990505,0.00043130422,0.0008285176,0.000813478,0.01284512,0.018145373,0.48085997,0.06782445,0.026904173,0.00045057706],"about_ca_topic_score_codex":0.000294843,"about_ca_topic_score_gemma":0.00023638744,"teacher_disagreement_score":0.9995208,"about_ca_system_score_codex":0.00050631317,"about_ca_system_score_gemma":0.00043852188,"threshold_uncertainty_score":0.033894062},"labels":[],"label_agreement":null},{"id":"W1948631799","doi":"10.1111/j.1745-4603.2010.00279.x","title":"USE OF A PANEL KNOWLEDGEABLE IN MATERIAL SCIENCE TO STUDY SENSORY PERCEPTION OF TEXTURE","year":2011,"lang":"en","type":"article","venue":"Journal of Texture Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"University of Guelph","funders":"","keywords":"Perception; Texture (cosmology); Stiffness; Sensory system; Sensory analysis; Toughness; Psychology; Mathematics; Computer science; Artificial intelligence; Statistics; Cognitive psychology; Materials science; Composite material","score_opus":0.2895216349290144,"score_gpt":0.36834219740561647,"score_spread":0.07882056247660207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948631799","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":[0.9973719,0.000019497806,0.00077436963,0.00002141019,0.000007774894,0.000041678984,0.000034476267,0.000006160092,0.001722787],"genre_scores_gemma":[0.9970931,0.000046074183,0.0012912806,0.00006009917,0.000007791757,0.00005766874,0.00009861251,0.000005996334,0.0013393374],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983699,0.00059095543,0.00006984462,0.0002407665,0.0005544827,0.00017403011],"domain_scores_gemma":[0.9925591,0.003631253,0.001233178,0.0006226093,0.0013378643,0.0006160056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026809333,0.0003855141,0.00037420887,0.0008512411,0.0006731091,0.0013736836,0.00042533787,0.0006716813,0.0044700564],"category_scores_gemma":[0.009571886,0.00027525524,0.00042522294,0.000283856,0.0004847733,0.0006524132,0.0011708412,0.0008774348,0.00066655525],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035471038,0.0061476016,0.649537,0.0003618934,0.00022525313,0.0010876664,0.030118417,0.00091127184,0.22341979,0.00043521656,0.0010755361,0.08313326],"study_design_scores_gemma":[0.000067127185,0.006907897,0.9464972,0.00008374766,0.000108377,0.00076893583,0.019543795,0.003313758,0.01886746,0.0003885544,0.003342267,0.00011074138],"about_ca_topic_score_codex":0.0014292274,"about_ca_topic_score_gemma":0.0018254165,"teacher_disagreement_score":0.0044700564,"about_ca_system_score_codex":0.00036092364,"about_ca_system_score_gemma":0.00024116518,"threshold_uncertainty_score":0.014953792},"labels":[],"label_agreement":null},{"id":"W1954307028","doi":"10.5539/jfr.v4n5p64","title":"Branding, Ingredients and Nutrition Information: Consumer Liking of a Healthier Snack","year":2015,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Auckland University of Technology, New Zealand","keywords":"Taste; Advertising; Nothing; Psychology; Perception; Health claims on food labels; Naturalness; Marketing; Food science; Business","score_opus":0.28092695455191763,"score_gpt":0.42333571457723934,"score_spread":0.1424087600253217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1954307028","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":[0.9984894,0.00008627238,0.00022881335,0.000051299146,0.0000073722017,0.0000148954105,0.000017945922,0.000004428574,0.0010996137],"genre_scores_gemma":[0.9984409,0.00007569523,0.0007828361,0.00007491123,0.000009643772,0.000009992775,0.00004812041,0.0000035860885,0.0005544661],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995142,0.00012809668,0.00003646347,0.000065988344,0.00022751142,0.000027798364],"domain_scores_gemma":[0.99810225,0.00089691323,0.00049224444,0.00012349329,0.000250764,0.00013433954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012338697,0.00017978974,0.00020841617,0.00015136952,0.00017067933,0.0006443255,0.00011575729,0.00039505892,0.0032456827],"category_scores_gemma":[0.0031192503,0.00010478751,0.00032030744,0.00009845496,0.00023536436,0.00064946513,0.00030657114,0.0005449187,0.00025852697],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.018005421,0.0055554225,0.4258114,0.0015806151,0.000561649,0.0004417263,0.006012825,0.0007313797,0.40024737,0.00075327937,0.0007773923,0.13952158],"study_design_scores_gemma":[0.00010106754,0.0116763,0.95817506,0.00006463641,0.00028114158,0.00035266447,0.0017055473,0.0011792599,0.024406888,0.00035620315,0.0016452739,0.000055871937],"about_ca_topic_score_codex":0.0010125446,"about_ca_topic_score_gemma":0.0010993681,"teacher_disagreement_score":0.0032456827,"about_ca_system_score_codex":0.00013994772,"about_ca_system_score_gemma":0.00012829083,"threshold_uncertainty_score":0.01085788},"labels":[],"label_agreement":null},{"id":"W1956663762","doi":"10.1002/9781118445112.stat06509","title":"Scaling Asymmetric Matrices","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Asymmetry; Variety (cybernetics); Diagonal; Antisymmetric relation; Scaling; Multidimensional scaling; Statistical physics; Mathematics; Physics; Statistics; Geometry; Particle physics; Mathematical physics","score_opus":0.05292690433724054,"score_gpt":0.31500129772321434,"score_spread":0.2620743933859738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956663762","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":[0.060009148,0.00080689543,0.8525434,0.0010392484,0.00090297917,0.00023648332,0.0023719901,0.001142059,0.080947824],"genre_scores_gemma":[0.7230525,0.0012425798,0.23045912,0.0012455697,0.0013875498,0.0005742095,0.0029154147,0.00060465164,0.038518377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967463,0.0007845782,0.00024989652,0.001006631,0.00094581937,0.0002667914],"domain_scores_gemma":[0.98904514,0.0036067695,0.0016793325,0.0027568208,0.0024262588,0.0004858229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015808421,0.001016582,0.00071587105,0.0026984776,0.0010928655,0.002905319,0.0010586835,0.00089724234,0.030132245],"category_scores_gemma":[0.019548412,0.00043634875,0.0007241982,0.002474594,0.0025633106,0.004191029,0.0017549603,0.0011963175,0.0058458913],"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":[0.00007964994,0.000036876732,0.00124258,0.0001403003,0.00003435021,0.0002403381,0.00030935425,0.006975699,0.003982144,0.90943587,0.013535997,0.063986965],"study_design_scores_gemma":[0.000017900627,0.00004251786,0.000955467,0.000051083993,0.000010578406,0.00032332187,0.00013765687,0.03468062,0.0017816774,0.939964,0.021999115,0.000036144713],"about_ca_topic_score_codex":0.0010266036,"about_ca_topic_score_gemma":0.00095517345,"teacher_disagreement_score":0.030132245,"about_ca_system_score_codex":0.00072777644,"about_ca_system_score_gemma":0.00064925593,"threshold_uncertainty_score":0.10080236},"labels":[],"label_agreement":null},{"id":"W1964368381","doi":"10.1016/s1499-4046(06)60386-2","title":"Applying the Theory of Planned Behavior to Women's Behavioral Attitudes on and Consumption of Soy Products","year":2004,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign; U.S. Department of Agriculture","keywords":"Theory of planned behavior; Consumption (sociology); Psychology; Economics; Sociology; Social science","score_opus":0.06960264361223181,"score_gpt":0.35309813573195586,"score_spread":0.283495492119724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964368381","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":[0.38576713,0.00024545297,0.607664,0.0007681422,0.0002823603,0.0012959071,0.0001597787,0.00028990023,0.0035273551],"genre_scores_gemma":[0.83355355,0.00022376464,0.1626611,0.00020175909,0.00005301921,0.0024058984,0.00012078911,0.00004058914,0.00073952304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98947376,0.008851201,0.00021060288,0.00061896123,0.00059120887,0.0002543815],"domain_scores_gemma":[0.9510553,0.04612943,0.0011526451,0.00063541596,0.0008724014,0.00015473452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014127361,0.0011126926,0.001094422,0.0011103748,0.0007676105,0.0013804887,0.0008138027,0.0008780914,0.0023176542],"category_scores_gemma":[0.039532892,0.000680638,0.002080733,0.0008745326,0.0017024502,0.0010200352,0.0007453532,0.0025244518,0.00022328603],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029884952,0.0057278024,0.33739743,0.0014448643,0.0042639864,0.00061289885,0.018995108,0.1860978,0.0056503885,0.10866948,0.0032161847,0.3249356],"study_design_scores_gemma":[0.00084959855,0.0078039505,0.13585432,0.0003428362,0.0015596882,0.0003902643,0.0035150696,0.6951753,0.0036113607,0.14793962,0.0027801688,0.00017780316],"about_ca_topic_score_codex":0.014304587,"about_ca_topic_score_gemma":0.015022663,"teacher_disagreement_score":0.014304587,"about_ca_system_score_codex":0.0020094202,"about_ca_system_score_gemma":0.0031306066,"threshold_uncertainty_score":0.07471353},"labels":[],"label_agreement":null},{"id":"W1965951536","doi":"10.1016/j.foodres.2014.11.004","title":"An investigation of matches of bottom fermented red beers with cheeses","year":2014,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Food science; Fermentation; Mathematics; Chemistry; Business","score_opus":0.12135622699954021,"score_gpt":0.37292852360370643,"score_spread":0.25157229660416625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965951536","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":[0.999148,0.00005945044,0.00011998341,0.000007817159,0.00000709486,0.000011058244,0.00013303796,0.000003291735,0.0005102257],"genre_scores_gemma":[0.99840987,0.000044892968,0.00034103057,0.000014116234,0.0000045163656,0.0000069189978,0.00025003136,0.000010214299,0.0009183551],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992507,0.00015535904,0.00004092922,0.00014398158,0.00028439568,0.00012460697],"domain_scores_gemma":[0.99862957,0.0003711294,0.00022324623,0.00007226865,0.00046079635,0.00024296438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007142023,0.0003439629,0.0004376378,0.00084659556,0.00075605913,0.0011269201,0.0002963075,0.000369818,0.0022650633],"category_scores_gemma":[0.0028119055,0.00025146452,0.0005101458,0.000763373,0.0002697465,0.00032674966,0.0008356065,0.00036639968,0.00044192013],"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":[0.036689915,0.0006954204,0.49678352,0.0004090213,0.0006069615,0.00094598636,0.005007717,0.00055574876,0.42167035,0.00032573097,0.0005709123,0.03573873],"study_design_scores_gemma":[0.000009255189,0.0011580103,0.96717256,0.00001329765,0.00010703907,0.00024384793,0.0025944957,0.0007040564,0.026849424,0.00004879945,0.0010792366,0.00001986515],"about_ca_topic_score_codex":0.01132243,"about_ca_topic_score_gemma":0.00984161,"teacher_disagreement_score":0.01132243,"about_ca_system_score_codex":0.00056673394,"about_ca_system_score_gemma":0.00039689688,"threshold_uncertainty_score":0.022513092},"labels":[],"label_agreement":null},{"id":"W1966124307","doi":"10.1017/s0007114512002188","title":"Increases in fruit intakes in older low consumers of fruit following two community-based repeated exposure interventions","year":2012,"lang":"en","type":"article","venue":"British Journal Of Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University","keywords":"Wine tasting; Medicine; Fruit juice; Psychological intervention; Toxicology; Food science; Biology","score_opus":0.06335757764970672,"score_gpt":0.32478708438545745,"score_spread":0.2614295067357507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966124307","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":[0.99954814,0.00010340453,0.000081379316,0.000019959583,0.000006302516,0.00007688235,0.000015056444,0.000006621219,0.0001422352],"genre_scores_gemma":[0.9983342,0.0001324682,0.0006762117,0.000051609662,0.00001759751,0.000183379,0.00005883518,0.0000013361898,0.0005443641],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99929094,0.00026533156,0.000055914603,0.00013192323,0.000117955846,0.00013802804],"domain_scores_gemma":[0.9991997,0.00024240394,0.00018705409,0.000057122586,0.000072194496,0.00024159318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092764525,0.00040200015,0.0007744613,0.00032162046,0.00027708174,0.00027989084,0.00036139775,0.00074661925,0.0018804794],"category_scores_gemma":[0.0022849957,0.00024361927,0.0006349585,0.00015688674,0.00020535827,0.00030135614,0.0006769536,0.00043388145,0.00015424921],"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":[0.25463146,0.11574435,0.077322386,0.0024541088,0.001984347,0.00044744246,0.0029603613,0.0013768653,0.086927675,0.00015354602,0.00056823273,0.45542917],"study_design_scores_gemma":[0.010052187,0.3933465,0.5853467,0.00012433727,0.0010871799,0.00011088473,0.0005315755,0.00066902925,0.0070214863,0.00012376292,0.0015283593,0.000058076588],"about_ca_topic_score_codex":0.0019396924,"about_ca_topic_score_gemma":0.003634001,"teacher_disagreement_score":0.0019396924,"about_ca_system_score_codex":0.0003048396,"about_ca_system_score_gemma":0.0003137259,"threshold_uncertainty_score":0.0062907934},"labels":[],"label_agreement":null},{"id":"W1966679907","doi":"10.3136/nskkk.48.378","title":"Laboratory methods for sensory analysis of food: Part 2","year":2001,"lang":"en","type":"article","venue":"Nippon Shokuhin Kagaku Kogaku Kaishi","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Sensory system; Sensory analysis; Computer science; Food science; Psychology; Neuroscience; Biology","score_opus":0.09607136204411082,"score_gpt":0.3775302644886547,"score_spread":0.28145890244454386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966679907","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":[0.05849297,0.0346395,0.86133707,0.0004545886,0.0010878422,0.007731799,0.0066509303,0.0045170896,0.025088228],"genre_scores_gemma":[0.12996118,0.035933107,0.7470671,0.001274156,0.000560734,0.021046462,0.009055828,0.0012917408,0.053809807],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966749,0.00059360126,0.00028584132,0.0004593739,0.001827244,0.00015901608],"domain_scores_gemma":[0.99837494,0.00038681945,0.00013898547,0.00030218856,0.0007164187,0.00008050062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002199932,0.0021791197,0.0009708761,0.003441064,0.0008555802,0.0012711339,0.0015995884,0.00089445966,0.01094307],"category_scores_gemma":[0.002133169,0.0009926556,0.0014374199,0.0017680547,0.0008861886,0.0010618354,0.001152919,0.0015428776,0.0067520244],"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":[0.0004151721,0.00046494126,0.0014510243,0.0020337587,0.00010275353,0.00010153657,0.00015446846,0.00038308173,0.7058304,0.0010433237,0.0048657213,0.2831538],"study_design_scores_gemma":[0.00013934956,0.0033665558,0.044066865,0.0005728316,0.0003424656,0.0016957028,0.00037210307,0.0046327026,0.8343583,0.0038415533,0.10628244,0.00032914453],"about_ca_topic_score_codex":0.0013943638,"about_ca_topic_score_gemma":0.0022484802,"teacher_disagreement_score":0.01094307,"about_ca_system_score_codex":0.00043824772,"about_ca_system_score_gemma":0.0012987792,"threshold_uncertainty_score":0.03660828},"labels":[],"label_agreement":null},{"id":"W1968089291","doi":"10.1207/s15327752jpa8502_14","title":"Dual Scaling for the Analysis of Categorical Data","year":2005,"lang":"en","type":"article","venue":"Journal of Personality Assessment","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Contingency table; Scaling; Correspondence analysis; Multidimensional scaling; Dual (grammatical number); Data set; Rank (graph theory); Set (abstract data type); Table (database); Contingency; Statistics; Categorization; Psychology; Data mining; Econometrics; Computer science; Mathematics; Artificial intelligence; Combinatorics; Epistemology","score_opus":0.21158043640637117,"score_gpt":0.4330320806078602,"score_spread":0.22145164420148902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968089291","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":[0.00071533496,0.0005096846,0.9945695,0.00032236218,0.00046016937,0.00035746218,0.0002853414,0.0005401063,0.002240024],"genre_scores_gemma":[0.01029977,0.00048325112,0.98357487,0.0002297142,0.0004162853,0.0034862256,0.00032787354,0.0004371687,0.0007448733],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8753803,0.08084272,0.009850539,0.010977004,0.021871328,0.0010781729],"domain_scores_gemma":[0.77056587,0.16878475,0.008186914,0.03380475,0.017250802,0.0014069964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.067155965,0.0032321282,0.003948693,0.007824401,0.0035637415,0.00826327,0.003984684,0.0025117085,0.017975803],"category_scores_gemma":[0.24220222,0.0014863611,0.0044085477,0.013415556,0.007944034,0.006659689,0.007199703,0.010982664,0.006396465],"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":[0.00029376466,0.00020711546,0.0018719257,0.002044205,0.00047208116,0.00040529025,0.003083867,0.0065162918,0.0029852071,0.64598477,0.029375346,0.3067602],"study_design_scores_gemma":[0.00009618491,0.00024910335,0.0020925615,0.00077939953,0.000103910075,0.0006291178,0.00057806546,0.052289695,0.001764715,0.8360072,0.105175085,0.00023499988],"about_ca_topic_score_codex":0.0013829034,"about_ca_topic_score_gemma":0.0010385935,"teacher_disagreement_score":0.067155965,"about_ca_system_score_codex":0.0029744175,"about_ca_system_score_gemma":0.00468698,"threshold_uncertainty_score":0.35515893},"labels":[],"label_agreement":null},{"id":"W1968168018","doi":"10.5539/jfr.v1n1p179","title":"Sensory Analysis of Pawpaw (Asimina triloba) Pulp Puree: Consumer Appraisal and Descriptive Lexicon","year":2012,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pulp (tooth); Sensory analysis; Flavor; Sensory system; Food science; Lexicon; Chemistry; Mathematics; Biology; Computer science; Medicine; Artificial intelligence; Dentistry","score_opus":0.26996883477262607,"score_gpt":0.43367324268994883,"score_spread":0.16370440791732277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968168018","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":[0.99343044,0.00013188402,0.0037484372,0.000023833027,0.000007413346,0.00010893541,0.0006211024,0.000036193895,0.0018917747],"genre_scores_gemma":[0.990203,0.00028088206,0.0061478317,0.00004931272,0.0000063534762,0.00013922305,0.001059482,0.000030318834,0.0020834194],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996458,0.000076030825,0.0000369664,0.00003908163,0.00017727246,0.000024786703],"domain_scores_gemma":[0.99916756,0.00022334712,0.00009480427,0.00003689231,0.00043170186,0.00004572131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091579044,0.00034127408,0.00031952054,0.0010940821,0.0002763578,0.0006272472,0.00018557985,0.00020185344,0.0017983322],"category_scores_gemma":[0.0010286592,0.0001195923,0.00029489302,0.0009569346,0.0003142977,0.0005309155,0.00050827954,0.0004371181,0.00026451965],"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":[0.0030318762,0.00042782447,0.049995914,0.00083969923,0.0001518455,0.00049055397,0.0030185718,0.00088997814,0.8674419,0.0006914826,0.00083826063,0.07218214],"study_design_scores_gemma":[0.000029264182,0.003428537,0.8375568,0.00007862865,0.0001675977,0.0014823283,0.004985117,0.0040748892,0.1427729,0.0007613526,0.0045207636,0.00014181194],"about_ca_topic_score_codex":0.0010324474,"about_ca_topic_score_gemma":0.0016588515,"teacher_disagreement_score":0.0017983322,"about_ca_system_score_codex":0.00020254789,"about_ca_system_score_gemma":0.00017044369,"threshold_uncertainty_score":0.006016016},"labels":[],"label_agreement":null},{"id":"W1969036230","doi":"10.3166/ria.22.421-442","title":"Classification de données fonctionnelles par décomposition de mélange Apports de la visualisation dans le cas des distributions de probabilité","year":2008,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.11900817474237264,"score_gpt":0.33099103772094474,"score_spread":0.2119828629785721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969036230","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":[0.25825572,0.0007954342,0.73171973,0.00059426716,0.00010980799,0.00014432266,0.0006663924,0.0052435873,0.0024706093],"genre_scores_gemma":[0.6124244,0.0007293952,0.38044593,0.00007640402,0.00006460573,0.00015779234,0.0009291676,0.00056713115,0.004605165],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989531,0.00018795571,0.00006639969,0.00023932324,0.00041951094,0.0001336215],"domain_scores_gemma":[0.99622273,0.002085867,0.00019950794,0.00032406568,0.0010119971,0.0001558728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001654098,0.0011892959,0.0008664032,0.0033227375,0.0005688391,0.0031091233,0.000502661,0.0012655377,0.0039394824],"category_scores_gemma":[0.006166715,0.000436443,0.0012862093,0.0015355224,0.00067989866,0.001337787,0.0009910956,0.0011716277,0.0009468483],"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":[0.0015983075,0.00018837773,0.0104348585,0.0004932328,0.00021369579,0.00068382855,0.0013106451,0.04571912,0.2670723,0.0055752746,0.0046912753,0.6620191],"study_design_scores_gemma":[0.0000367674,0.00019876438,0.021812331,0.00012545624,0.0001320023,0.0008477747,0.0006834993,0.8978011,0.06725213,0.0048088557,0.0062271375,0.000074244424],"about_ca_topic_score_codex":0.008462615,"about_ca_topic_score_gemma":0.006401661,"teacher_disagreement_score":0.008462615,"about_ca_system_score_codex":0.00067369814,"about_ca_system_score_gemma":0.0007310239,"threshold_uncertainty_score":0.01682669},"labels":[],"label_agreement":null},{"id":"W1971188830","doi":"10.1007/s00217-007-0807-5","title":"Influence of variety and commercial yeast preparation on red wine made from autochthonous Hungarian and Canadian grapes. Part II. Oral sensations and sensory : instrumental relationships","year":2007,"lang":"en","type":"article","venue":"European Food Research and Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Brock University","funders":"","keywords":"Wine; Sensory system; Food science; Sensory analysis; Psychology; Chemistry; Cognitive psychology","score_opus":0.10029232911860918,"score_gpt":0.3174502410250881,"score_spread":0.21715791190647893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971188830","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":[0.9988933,0.0005417061,0.000056863344,0.000008880641,0.0000062221475,0.000006303867,0.00008069734,0.0000028817587,0.0004031577],"genre_scores_gemma":[0.99870443,0.00025922974,0.0001469559,0.000027712287,0.000003768858,0.0000042028696,0.00019099607,0.00000973799,0.0006528637],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997582,0.000053791126,0.000016407337,0.000064987034,0.000054971457,0.00005164007],"domain_scores_gemma":[0.9994686,0.00019283235,0.000071744675,0.00002788913,0.00011030027,0.00012857903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003193847,0.000322974,0.0002854023,0.00026725035,0.0004478705,0.00073704706,0.0001811949,0.00017300123,0.0015378308],"category_scores_gemma":[0.00071786076,0.00018357318,0.00039103007,0.00030471495,0.0005374749,0.00019757517,0.00034665724,0.0003379891,0.00012178505],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01869267,0.00034050032,0.026116617,0.00022679572,0.00023339145,0.00027444144,0.0005421424,0.00024061579,0.94425315,0.000082300394,0.00018460002,0.008812871],"study_design_scores_gemma":[0.000081061444,0.0051477314,0.7381997,0.000033362165,0.00046346022,0.00030608597,0.0013355154,0.0004581739,0.25261244,0.00006565195,0.0012462817,0.000050501778],"about_ca_topic_score_codex":0.035914253,"about_ca_topic_score_gemma":0.07029891,"teacher_disagreement_score":0.96408576,"about_ca_system_score_codex":0.0008684832,"about_ca_system_score_gemma":0.0005351494,"threshold_uncertainty_score":0.07141042},"labels":[],"label_agreement":null},{"id":"W1971465998","doi":"10.1111/j.1745-459x.2001.tb00302.x","title":"EVALUATION OF WINE COMPETITION JUDGE PERFORMANCE USING PRINCIPAL COMPONENT SIMILARITY ANALYSIS","year":2001,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of British Columbia","funders":"","keywords":"Principal component analysis; Wine; Similarity (geometry); Outlier; Statistics; Competition (biology); Mathematics; Computer science; Artificial intelligence; Food science","score_opus":0.31337905918566156,"score_gpt":0.40231391406433475,"score_spread":0.0889348548786732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971465998","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":[0.97963643,0.000090306516,0.013747847,0.000046789766,0.00003282131,0.0001622782,0.00019347822,0.000129203,0.0059609045],"genre_scores_gemma":[0.9923822,0.00003608443,0.006223912,0.000007011663,0.00001137617,0.00006600988,0.0001983728,0.000022259655,0.0010527164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99565315,0.0013276259,0.00023123456,0.0003294841,0.0022740224,0.00018450928],"domain_scores_gemma":[0.9873469,0.0048730182,0.0010116033,0.0004930936,0.005705046,0.00057019753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057687894,0.00037909768,0.00047708512,0.0022963034,0.0005726025,0.0010974669,0.00035428518,0.00030883378,0.0029161074],"category_scores_gemma":[0.018468518,0.00012215269,0.00037307988,0.0015315339,0.00048886053,0.00055998977,0.0006193429,0.000405113,0.0006994038],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00614555,0.00084972807,0.4323024,0.00060649775,0.0005439074,0.0004502357,0.011504032,0.009963862,0.12012067,0.0030144244,0.008602597,0.4058962],"study_design_scores_gemma":[0.000082907434,0.0030214947,0.91406566,0.000040671803,0.000108714754,0.0003043403,0.004704524,0.047209825,0.024332033,0.0016600902,0.0043172124,0.00015251411],"about_ca_topic_score_codex":0.0017321669,"about_ca_topic_score_gemma":0.0027296115,"teacher_disagreement_score":0.0057687894,"about_ca_system_score_codex":0.00035281968,"about_ca_system_score_gemma":0.00033034038,"threshold_uncertainty_score":0.030508637},"labels":[],"label_agreement":null},{"id":"W1974762170","doi":"10.1080/21551197.2014.927304","title":"Characterizing Commercial Pureed Foods: Sensory, Nutritional, and Textural Analysis","year":2014,"lang":"en","type":"article","venue":"Journal of Nutrition in Gerontology and Geriatrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Medicine; Food science; Sensory analysis; Recipe; Biology","score_opus":0.04231631854526305,"score_gpt":0.2937389578641672,"score_spread":0.2514226393189042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974762170","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":[0.9889255,0.00081932225,0.0057562254,0.000024985782,0.000015611953,0.0003548616,0.0015354208,0.0000480474,0.0025200527],"genre_scores_gemma":[0.95696783,0.0015259433,0.032773457,0.000109144974,0.000018835652,0.00044400687,0.004060214,0.000087484936,0.004013135],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989562,0.00016185008,0.00012875996,0.00014150258,0.00054661045,0.00006512579],"domain_scores_gemma":[0.99775,0.00046164132,0.00031098592,0.000113029564,0.0012771245,0.000087265464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020169904,0.00067162624,0.00050522725,0.0030606452,0.0006720319,0.0011882896,0.00038806334,0.00043297576,0.0020234596],"category_scores_gemma":[0.0021722214,0.00017806828,0.000598371,0.0028714628,0.00057631807,0.00076512236,0.00081397564,0.0005494428,0.00046572063],"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":[0.0038054292,0.001271794,0.15407649,0.0019273823,0.0006485966,0.00059047487,0.0022561552,0.0007580813,0.7369174,0.00050264725,0.0006902241,0.09655545],"study_design_scores_gemma":[0.000035405614,0.004485915,0.8309953,0.00013143194,0.00051109755,0.0009292563,0.0038040725,0.002003669,0.15128453,0.00047800955,0.005231578,0.00010973123],"about_ca_topic_score_codex":0.0041520204,"about_ca_topic_score_gemma":0.009735851,"teacher_disagreement_score":0.0041520204,"about_ca_system_score_codex":0.0004429849,"about_ca_system_score_gemma":0.0003765302,"threshold_uncertainty_score":0.010667026},"labels":[],"label_agreement":null},{"id":"W1981818034","doi":"10.1080/15378020.2010.524541","title":"Perceived Match of Wine and Cheese and the Impact of Additional Food Elements: A Preliminary Study","year":2010,"lang":"en","type":"article","venue":"Journal of Foodservice Business Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"George Brown College","funders":"","keywords":"Wine; Perception; Sensory system; Psychology; Food science; Social psychology; Mathematics; Cognitive psychology; Biology","score_opus":0.07432566821198165,"score_gpt":0.3906915749216612,"score_spread":0.3163659067096795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981818034","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":[0.9995455,0.0000136357985,0.000038710572,0.000006046603,0.000001772609,0.000018826233,0.000018853974,8.1549314e-7,0.00035583324],"genre_scores_gemma":[0.99870515,0.000037151978,0.00038377562,0.000022882492,0.0000050438794,0.000039577124,0.000051819938,0.00000193304,0.00075264054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99944896,0.00017546008,0.000046209436,0.000075420554,0.00018982872,0.000064112515],"domain_scores_gemma":[0.9966,0.0017737421,0.0004325545,0.00017466051,0.00044134926,0.0005777206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019956517,0.0002332401,0.00035505422,0.00034589894,0.0005678391,0.0010602509,0.00021688687,0.00043117628,0.0038531595],"category_scores_gemma":[0.0056626145,0.00025721014,0.00045766044,0.00019492797,0.00035729865,0.00053408265,0.00070707133,0.0007278666,0.00028535354],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008567306,0.017411117,0.8549119,0.00048330275,0.00034837762,0.0009906063,0.033770084,0.00032437418,0.04738521,0.0004605029,0.00032634824,0.035020925],"study_design_scores_gemma":[0.00006977718,0.0127218915,0.97185487,0.000019518955,0.000095292635,0.00022040503,0.010555672,0.00046251182,0.0029854137,0.0001222245,0.00086394907,0.000028373786],"about_ca_topic_score_codex":0.0015779291,"about_ca_topic_score_gemma":0.0027191085,"teacher_disagreement_score":0.0038531595,"about_ca_system_score_codex":0.00023298629,"about_ca_system_score_gemma":0.00026032192,"threshold_uncertainty_score":0.01289016},"labels":[],"label_agreement":null},{"id":"W1984842026","doi":"10.1016/j.foodqual.2012.02.017","title":"Extraction of consumer texture preferences for yogurt: Comparison of the preferred attribute elicitation method to conventional profiling","year":2012,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vineland Research and Innovation Centre; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Ambiguity; Profiling (computer programming); Product (mathematics); Computer science; Sensory analysis; Strengths and weaknesses; Marketing; Sensory system; Set (abstract data type); Psychology; Business; Cognitive psychology; Social psychology; Mathematics; Statistics","score_opus":0.4167371071886555,"score_gpt":0.45805555628131717,"score_spread":0.04131844909266169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984842026","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9002637,0.00027277644,0.09636037,0.000060997427,0.000020788786,0.00033602476,0.0011091424,0.00012967282,0.0014464896],"genre_scores_gemma":[0.87564313,0.0003450317,0.12031689,0.00010374131,0.000020278998,0.0005439589,0.0012395168,0.00006474739,0.0017226195],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989054,0.0004468597,0.00006520663,0.00018088699,0.0003346783,0.00006679702],"domain_scores_gemma":[0.9976229,0.0013264236,0.0003109505,0.00019515747,0.0004590828,0.00008549182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001677506,0.0003848321,0.00048731494,0.0010321406,0.00022367512,0.00050197676,0.00025981164,0.0002808596,0.0019100542],"category_scores_gemma":[0.004574303,0.00017539035,0.00044530927,0.0011595401,0.00020493536,0.00038848436,0.0005585566,0.0005098782,0.00047775227],"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":[0.0045416476,0.0005769279,0.06005407,0.00095054583,0.00019859806,0.00011891176,0.0013059124,0.00086067047,0.7072453,0.0005154935,0.00046389946,0.223168],"study_design_scores_gemma":[0.00039720142,0.0055966806,0.6492589,0.000098611454,0.000468437,0.0014708659,0.001918316,0.027998967,0.30587426,0.001474514,0.0052136164,0.00022969826],"about_ca_topic_score_codex":0.0007606206,"about_ca_topic_score_gemma":0.0020105084,"teacher_disagreement_score":0.0019100542,"about_ca_system_score_codex":0.00017633205,"about_ca_system_score_gemma":0.00028082315,"threshold_uncertainty_score":0.008871615},"labels":[],"label_agreement":null},{"id":"W1996700555","doi":"10.1371/journal.pone.0044738","title":"Describing the Situational Contexts of Sweetened Product Consumption in a Middle Eastern Canadian Community: Application of a Mixed Method Design","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Situational ethics; Exploratory factor analysis; Thematic analysis; Context (archaeology); Construct validity; Psychology; Consumption (sociology); Predictive validity; Construct (python library); Applied psychology; Product (mathematics); Confirmatory factor analysis; Content validity; Qualitative research; Social psychology; Exploratory research; Psychometrics; Structural equation modeling; Developmental psychology; Computer science; Statistics; Sociology; Mathematics; Geography","score_opus":0.4287249345864631,"score_gpt":0.30552126606155317,"score_spread":0.12320366852490994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996700555","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":[0.80265665,0.0017508221,0.07391798,0.0011261811,0.000146827,0.10388643,0.0015860009,0.00014758467,0.014781607],"genre_scores_gemma":[0.49695122,0.0017159127,0.36377946,0.00097207085,0.000049480506,0.13086905,0.00063750334,0.00006836122,0.0049570226],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97724473,0.015870145,0.0012412865,0.001844656,0.0025368955,0.001262262],"domain_scores_gemma":[0.9850332,0.007399788,0.0012731025,0.0010569072,0.0044817654,0.00075516023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032345906,0.0012379522,0.0009493478,0.0054914933,0.009641567,0.003212347,0.0026390473,0.00085026794,0.004157979],"category_scores_gemma":[0.020777628,0.0008908436,0.0013397933,0.0047297245,0.0036934938,0.0011969045,0.0044268398,0.0011909283,0.00023577585],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010993051,0.0020256254,0.039518867,0.004820542,0.00026496156,0.0012765254,0.6402277,0.0016619451,0.009003462,0.006708683,0.0022694,0.29112303],"study_design_scores_gemma":[0.0014524447,0.005135884,0.15511559,0.003964525,0.00075370993,0.00036771252,0.72958267,0.006429604,0.009542569,0.008571043,0.078525014,0.00055923965],"about_ca_topic_score_codex":0.33725137,"about_ca_topic_score_gemma":0.6491574,"teacher_disagreement_score":0.66274863,"about_ca_system_score_codex":0.020760512,"about_ca_system_score_gemma":0.042163953,"threshold_uncertainty_score":0.6705767},"labels":[],"label_agreement":null},{"id":"W1996979899","doi":"10.1080/17470218.2014.989865","title":"Virtual experiments in megastudies: A case study of language and emotion","year":2014,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":86,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Bootstrapping (finance); Computer science; Collinearity; Natural language processing; Range (aeronautics); Artificial intelligence; Scope (computer science); Outcome (game theory); Cognitive psychology; Psychology; Econometrics; Statistics; Mathematics","score_opus":0.04742567270322111,"score_gpt":0.3845434603056153,"score_spread":0.3371177876023942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996979899","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":[0.9822479,0.00024322553,0.011205795,0.0005299701,0.00003377391,0.00024971637,0.00009554542,0.000051146322,0.0053429175],"genre_scores_gemma":[0.9811047,0.00012722434,0.016188795,0.00023943909,0.00002377191,0.00062053406,0.0001185384,0.000054009015,0.0015231705],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9848081,0.012838249,0.000294746,0.0008824937,0.0007728803,0.0004036147],"domain_scores_gemma":[0.931743,0.05633745,0.001725179,0.0068147643,0.0013090307,0.0020706553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011766986,0.00037587853,0.00072629267,0.0011132822,0.003632459,0.0022815785,0.0021638153,0.0016171618,0.003941157],"category_scores_gemma":[0.029622713,0.00032427168,0.00058924983,0.0014240803,0.0043564793,0.0021589212,0.004272809,0.0018296205,0.00047592894],"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":[0.0052349013,0.01256972,0.10520807,0.0020065552,0.0005075546,0.008351933,0.43976358,0.0062698936,0.03937033,0.08637993,0.008654974,0.28568256],"study_design_scores_gemma":[0.0014068153,0.017184546,0.29034477,0.00081540155,0.00036088887,0.007524261,0.28624508,0.027093425,0.039230216,0.1239556,0.20520346,0.00063551555],"about_ca_topic_score_codex":0.0006813886,"about_ca_topic_score_gemma":0.0019097095,"teacher_disagreement_score":0.011766986,"about_ca_system_score_codex":0.0007872581,"about_ca_system_score_gemma":0.000573089,"threshold_uncertainty_score":0.062230527},"labels":[],"label_agreement":null},{"id":"W1998049624","doi":"10.3389/fpsyg.2014.01548","title":"On the efficacy of procedures to normalize Ex-Gaussian distributions","year":2015,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Outlier; Normality; Transformation (genetics); Gaussian; Normal distribution; Parametric statistics; Skewness; Mathematics; Percentile; Statistics; Data transformation; Exponential family; Power transform; Applied mathematics; Econometrics; Computer science; Data mining; Physics; Discrete mathematics","score_opus":0.05730943982861437,"score_gpt":0.3476683201539722,"score_spread":0.29035888032535784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998049624","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":[0.029350266,0.00070938864,0.966234,0.00027145102,0.000119986726,0.00026771138,0.00013851542,0.0010093403,0.0018992934],"genre_scores_gemma":[0.21907203,0.0013977295,0.77516955,0.00030908757,0.0001121406,0.0010997407,0.00053336803,0.0010971093,0.0012092766],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98497045,0.008520115,0.00094594347,0.001819698,0.0034637575,0.00028018196],"domain_scores_gemma":[0.92403287,0.057638027,0.0039389953,0.007325315,0.0067605358,0.00030426457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030403094,0.0015050531,0.0008161422,0.0022051644,0.0012349519,0.0021547514,0.0015393127,0.001345424,0.0030355803],"category_scores_gemma":[0.12635669,0.00045885995,0.0013642794,0.0027893942,0.0030497971,0.0026538065,0.0019453596,0.0023776032,0.00138884],"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":[0.00108605,0.0004248592,0.009094578,0.0013250414,0.00052744715,0.00025048447,0.0025769037,0.03355513,0.043185044,0.07190562,0.0041499264,0.83191895],"study_design_scores_gemma":[0.00050095364,0.0024948744,0.058365963,0.0012885255,0.00090887805,0.002303062,0.0024685517,0.4335234,0.23055442,0.20928016,0.05750614,0.0008051184],"about_ca_topic_score_codex":0.0015429093,"about_ca_topic_score_gemma":0.0014655781,"teacher_disagreement_score":0.030403094,"about_ca_system_score_codex":0.0007763914,"about_ca_system_score_gemma":0.0015763051,"threshold_uncertainty_score":0.16078883},"labels":[],"label_agreement":null},{"id":"W1999213988","doi":"10.3758/bf03196847","title":"Choice and response time processes in the identification and categorization of unidimensional stimuli","year":2004,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Université Laval","funders":"","keywords":"Categorization; Accumulator (cryptography); Stimulus (psychology); Decision process; Psychology; Identification (biology); Perception; Cognitive psychology; Artificial intelligence; Computer science; Algorithm","score_opus":0.03384253685487145,"score_gpt":0.30805108249822755,"score_spread":0.2742085456433561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999213988","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":[0.9506231,0.0003579162,0.044806477,0.00027021443,0.000093879,0.00011612914,0.00018923251,0.000107818145,0.003435296],"genre_scores_gemma":[0.97774875,0.00017942536,0.018748378,0.00012395668,0.00005173614,0.00016183117,0.00023775667,0.00021359434,0.0025345671],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9974867,0.0010524652,0.00014864959,0.00049806025,0.0006269657,0.00018714917],"domain_scores_gemma":[0.95311856,0.040622126,0.0024104354,0.001532565,0.0012549713,0.0010613297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006635783,0.0003370664,0.00070324395,0.0010371874,0.000383332,0.0024777693,0.00086439116,0.001389962,0.0055086366],"category_scores_gemma":[0.0578014,0.0006454367,0.0008352988,0.00081379956,0.0011738625,0.003334393,0.0016535234,0.0015194692,0.0006206672],"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":[0.020811092,0.0014558365,0.08739306,0.00081808027,0.00053964753,0.0005686051,0.005811989,0.021623947,0.5876194,0.07494566,0.0019120047,0.19650066],"study_design_scores_gemma":[0.0006148475,0.0009961296,0.4368132,0.00009681562,0.00029405192,0.00096296414,0.000954916,0.36056888,0.04150065,0.15468124,0.0021812338,0.00033505264],"about_ca_topic_score_codex":0.0016388413,"about_ca_topic_score_gemma":0.0011668247,"teacher_disagreement_score":0.006635783,"about_ca_system_score_codex":0.0005992728,"about_ca_system_score_gemma":0.00055813085,"threshold_uncertainty_score":0.035093784},"labels":[],"label_agreement":null},{"id":"W2000944114","doi":"10.1016/s1499-4046(06)60095-x","title":"Preschool Children's Acceptance of a Novel Vegetable Following Exposure to Messages in a Storybook","year":2002,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taste; Psychology; Affect (linguistics); Head start; Reading (process); Logistic regression; Developmental psychology; Medicine; Communication","score_opus":0.033865080257555635,"score_gpt":0.3030568076891425,"score_spread":0.26919172743158687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000944114","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":[0.99933976,0.00002499805,0.000024662886,0.000021280028,0.0000050038075,0.000009619517,0.000050110066,0.000006912825,0.00051761686],"genre_scores_gemma":[0.99718326,0.00011412107,0.00018603112,0.00003900648,0.0000049568007,0.00004354177,0.00016200457,0.0000075415855,0.0022594421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961275,0.00008021518,0.000029096263,0.00007087003,0.00011189056,0.00009526052],"domain_scores_gemma":[0.99378175,0.003248363,0.0015776313,0.0002012378,0.0005521893,0.00063886156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008190813,0.00039729927,0.0003764681,0.0005103445,0.0005371359,0.0011236751,0.00046642928,0.00088300765,0.0056557716],"category_scores_gemma":[0.009997879,0.0004723438,0.00043942992,0.00036727428,0.00051932345,0.0006248138,0.00071393483,0.001533885,0.0007050296],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008470251,0.004491463,0.7627747,0.0006067764,0.00024927594,0.0033784728,0.12708837,0.0002852095,0.061015826,0.00033064716,0.0017187743,0.029590229],"study_design_scores_gemma":[0.000064780754,0.002093676,0.9555661,0.00006226495,0.00014686474,0.00047993433,0.033574246,0.00027616273,0.0063914587,0.00007789914,0.0012194241,0.000047044272],"about_ca_topic_score_codex":0.014597571,"about_ca_topic_score_gemma":0.025489924,"teacher_disagreement_score":0.014597571,"about_ca_system_score_codex":0.0005959683,"about_ca_system_score_gemma":0.00046937523,"threshold_uncertainty_score":0.029025197},"labels":[],"label_agreement":null},{"id":"W2002203721","doi":"10.1016/j.meatsci.2010.11.010","title":"Perceptual attributes of poultry and other meat products: A repertory grid application","year":2010,"lang":"en","type":"article","venue":"Meat Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Ministry of Agriculture and Forestry; Agriculture Food and Rural Development; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Life Sciences Research Board; University of Alberta","keywords":"Poultry meat; Business; Product (mathematics); Food science; Food products; Quality (philosophy); Fish products; Repertory grid; Poultry farming; Fish <Actinopterygii>; Agricultural science; Marketing; Biotechnology; Biology; Mathematics; Fishery","score_opus":0.03719389194864835,"score_gpt":0.28244077307276355,"score_spread":0.2452468811241152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002203721","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":[0.84576,0.00019331463,0.1504877,0.000087419634,0.00002822811,0.0001582132,0.0006540629,0.000611429,0.0020196005],"genre_scores_gemma":[0.9137889,0.0000791052,0.08525717,0.000014901962,0.000004374351,0.000092365684,0.00024297526,0.00003434383,0.000485818],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997292,0.0001193121,0.000024993034,0.00005476193,0.000049647544,0.000022004484],"domain_scores_gemma":[0.9977094,0.0016057057,0.000096995,0.00020580972,0.0002885819,0.00009345388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081380893,0.00032926758,0.00046787164,0.0010403547,0.0002773508,0.000594926,0.00038487485,0.00022008618,0.0025103528],"category_scores_gemma":[0.0044762692,0.0001530207,0.0005656252,0.0014957137,0.00030148574,0.00055515656,0.000632077,0.00026804264,0.00024976084],"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":[0.0035239842,0.00082363083,0.08229876,0.0012460934,0.00029493117,0.0008645471,0.004245838,0.07770405,0.033793587,0.0095706675,0.0032878502,0.782346],"study_design_scores_gemma":[0.00019615976,0.0009716369,0.111497,0.00009765274,0.00018882175,0.0008832591,0.0033457794,0.8542562,0.0070130313,0.018248694,0.0032306,0.00007104381],"about_ca_topic_score_codex":0.004404299,"about_ca_topic_score_gemma":0.0036753835,"teacher_disagreement_score":0.004404299,"about_ca_system_score_codex":0.00025951207,"about_ca_system_score_gemma":0.00026904015,"threshold_uncertainty_score":0.008757353},"labels":[],"label_agreement":null},{"id":"W2003073543","doi":"10.2333/bhmk.30.87","title":"A Note on Between-Set Distances in Dual Scaling and Correspondence Analysis","year":2003,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Comunidad Autónoma de la Región de Murcia; University of Toronto; Kwansei Gakuin University; Natural Sciences and Engineering Research Council of Canada; Fundación Séneca","keywords":"Row; Mathematics; Column (typography); Set (abstract data type); Scaling; Row and column spaces; Multidimensional scaling; Dimension (graph theory); Categorical variable; Data set; Distance matrix; Algorithm; Combinatorics; Computer science; Statistics; Geometry","score_opus":0.07852326030512424,"score_gpt":0.3485703243926371,"score_spread":0.27004706408751283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003073543","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":[0.0022902922,0.0025883398,0.98494905,0.004066766,0.0016158244,0.000033623626,0.00011632016,0.00028915898,0.0040506693],"genre_scores_gemma":[0.08479922,0.003237423,0.89356124,0.0027981687,0.0061283614,0.0003842385,0.00024436254,0.0012359817,0.007611022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97804856,0.012696268,0.0017449047,0.0031608648,0.0039687147,0.00038075543],"domain_scores_gemma":[0.8684264,0.10508843,0.0013627056,0.018387994,0.005613696,0.0011208007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025515791,0.0013951561,0.0028306947,0.0028169146,0.0035972309,0.005830207,0.0051214886,0.0042836354,0.0066368612],"category_scores_gemma":[0.112315625,0.0011101495,0.0036670365,0.005221991,0.013398994,0.016114157,0.008837296,0.014599116,0.0024165278],"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":[0.00013200774,0.00005563225,0.00091815676,0.00028269528,0.000105800355,0.00024130405,0.0005454002,0.0037755538,0.0014957091,0.8398691,0.01819151,0.13438714],"study_design_scores_gemma":[0.000017336666,0.00003236094,0.0006381889,0.00007915614,0.000041067346,0.0002012235,0.00009288951,0.021630656,0.0008112416,0.95602095,0.020369388,0.00006545424],"about_ca_topic_score_codex":0.0027032504,"about_ca_topic_score_gemma":0.0032956349,"teacher_disagreement_score":0.025515791,"about_ca_system_score_codex":0.0014692652,"about_ca_system_score_gemma":0.0015078167,"threshold_uncertainty_score":0.13494205},"labels":[],"label_agreement":null},{"id":"W2007214990","doi":"10.1167/10.7.975","title":"The Visual Perception of Correlation in Scatterplots","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","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":"University of British Columbia","funders":"","keywords":"Correlation; Mathematics; Standard deviation; Statistics; Range (aeronautics); Set (abstract data type); Pattern recognition (psychology); Artificial intelligence; Computer science; Geometry; Materials science","score_opus":0.01816543249818752,"score_gpt":0.3368255429952132,"score_spread":0.3186601104970257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007214990","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":[0.8894002,0.00042008696,0.106872745,0.000064987915,0.000056337038,0.00008986327,0.00017771036,0.00056759384,0.0023506081],"genre_scores_gemma":[0.98203385,0.00006497002,0.017465578,0.000025293475,0.000012733687,0.000031192107,0.00012114706,0.00010543292,0.00013977506],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99309325,0.002753581,0.00035019172,0.0013759917,0.002145737,0.00028121122],"domain_scores_gemma":[0.93994725,0.043548565,0.004103236,0.007315465,0.0043595284,0.0007258882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008479529,0.00046785022,0.00070916035,0.0015243306,0.00033231775,0.0016786011,0.0006179473,0.0005982889,0.001179092],"category_scores_gemma":[0.07186388,0.00045530678,0.00048476402,0.00087274093,0.0013904718,0.0015970956,0.0014914554,0.0008932596,0.00020066524],"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":[0.008762113,0.00045627472,0.06659042,0.0012519473,0.0009598445,0.0004224978,0.0062416955,0.043125276,0.63466096,0.010066614,0.0019997573,0.2254625],"study_design_scores_gemma":[0.00034359217,0.0035694982,0.5998024,0.00027852182,0.0003744966,0.0016511466,0.0012389595,0.1989015,0.17406352,0.015672427,0.0036454254,0.00045850116],"about_ca_topic_score_codex":0.0010796747,"about_ca_topic_score_gemma":0.0006440796,"teacher_disagreement_score":0.008479529,"about_ca_system_score_codex":0.0005971717,"about_ca_system_score_gemma":0.00037797054,"threshold_uncertainty_score":0.044844627},"labels":[],"label_agreement":null},{"id":"W2011215626","doi":"10.1007/s10260-014-0259-6","title":"Some new aspects of taxicab correspondence analysis","year":2014,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; National Strength and Conditioning Association","keywords":"Correspondence analysis; Equivalence (formal languages); Singular value decomposition; Mathematics; Diagonal; Singular value; Metric (unit); Row; Combinatorics; Pure mathematics; Computer science; Algorithm; Statistics; Geometry; Physics; Engineering; Eigenvalues and eigenvectors","score_opus":0.04605877483563271,"score_gpt":0.3798728019833073,"score_spread":0.3338140271476746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011215626","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":[0.003896265,0.0009114232,0.9919241,0.0005772847,0.00019448988,0.000031859036,0.00011072446,0.00022701662,0.0021268686],"genre_scores_gemma":[0.093617864,0.0020021806,0.89416564,0.00051446684,0.0014611181,0.00021611138,0.00047930083,0.00044396854,0.0070993486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99561226,0.0016671321,0.00034053682,0.0010023132,0.001256694,0.000121137266],"domain_scores_gemma":[0.9909582,0.005820785,0.0003654909,0.0013840597,0.0013150434,0.00015633879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005265541,0.00081041054,0.0010823471,0.0029002372,0.0012586664,0.004603365,0.0020879842,0.0010620422,0.0046494487],"category_scores_gemma":[0.017209494,0.00071414845,0.0016657257,0.004355892,0.0028858094,0.0033833557,0.0015136653,0.0028426072,0.0013393997],"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":[0.00011344455,0.0001094533,0.0032815551,0.00035533658,0.00020636484,0.00034390288,0.0005654421,0.01762955,0.009495954,0.4967634,0.007908914,0.46322677],"study_design_scores_gemma":[0.000023594319,0.000083642415,0.004716821,0.00007934557,0.000094823314,0.0007784481,0.0002363045,0.2583488,0.0067911507,0.68112457,0.04760187,0.00012066376],"about_ca_topic_score_codex":0.0030422937,"about_ca_topic_score_gemma":0.0038756686,"teacher_disagreement_score":0.005265541,"about_ca_system_score_codex":0.0011228198,"about_ca_system_score_gemma":0.0013979978,"threshold_uncertainty_score":0.027847111},"labels":[],"label_agreement":null},{"id":"W2015980849","doi":"10.1007/s11336-010-9157-5","title":"Generalized Structured Component Analysis with Latent Interactions","year":2010,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":54,"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":"Air Force Institute of Technology","keywords":"Latent variable; Structural equation modeling; Component (thermodynamics); Computer science; Latent variable model; Latent class model; Component analysis; Econometrics; Monte Carlo method; Extension (predicate logic); Mathematics; Mathematical optimization; Statistics; Machine learning","score_opus":0.0405640907361316,"score_gpt":0.3094801017507332,"score_spread":0.2689160110146016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015980849","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":[0.0027137906,0.00014832806,0.99578685,0.00007426131,0.000037037094,0.00014274559,0.00013257295,0.00034159925,0.0006228438],"genre_scores_gemma":[0.10506296,0.00048418372,0.8890513,0.00012506581,0.00009951755,0.0014036038,0.0010428863,0.0002606225,0.0024698938],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928538,0.0048520654,0.00022733104,0.0009377317,0.00088746124,0.00024163636],"domain_scores_gemma":[0.9949751,0.003056175,0.00038005307,0.0006734588,0.0008233844,0.000091899106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004937596,0.0023327256,0.0017577042,0.0022904247,0.0008339309,0.0019282767,0.0017632766,0.0011537891,0.004889807],"category_scores_gemma":[0.015155346,0.0006356812,0.0028635047,0.0042378227,0.0012136897,0.001683078,0.002814174,0.0021801942,0.001323037],"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":[0.00028461727,0.00021097774,0.0042770836,0.00071386836,0.0012159899,0.0003404227,0.000974875,0.27259237,0.0049812268,0.21073298,0.0109168235,0.49275884],"study_design_scores_gemma":[0.000032225587,0.000120541925,0.0019785035,0.0000634348,0.00014525758,0.000117026706,0.00009662796,0.8783985,0.00083086285,0.10993595,0.008199892,0.00008116615],"about_ca_topic_score_codex":0.004778561,"about_ca_topic_score_gemma":0.0049022664,"teacher_disagreement_score":0.004937596,"about_ca_system_score_codex":0.0008167482,"about_ca_system_score_gemma":0.003013416,"threshold_uncertainty_score":0.026112795},"labels":[],"label_agreement":null},{"id":"W2016498661","doi":"10.1006/appe.2000.0353","title":"The Food Situations Questionnaire: a measure of children's willingness to try novel foods in stimulating and non-stimulating situations","year":2000,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Neophobia; Wine tasting; Test (biology); Psychology; Novel food; Measure (data warehouse); Developmental psychology; Clinical psychology; Medicine; Food science; Computer science","score_opus":0.02311730448178181,"score_gpt":0.26654894707514754,"score_spread":0.24343164259336572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016498661","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9842194,0.00028414704,0.0018922416,0.00025410726,0.000064753025,0.001661354,0.005750925,0.00020874444,0.005664298],"genre_scores_gemma":[0.94977546,0.0009989206,0.02403179,0.00044962764,0.000061482904,0.007477411,0.007279132,0.00013680207,0.0097894175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99854136,0.00034833536,0.00027103582,0.000109837434,0.00056079176,0.00016867819],"domain_scores_gemma":[0.992149,0.0027085892,0.002965931,0.0003706069,0.0009209827,0.00088490377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024769045,0.0010325168,0.0010269706,0.0010724784,0.00045350852,0.00068962405,0.0009370923,0.00089870504,0.0043969452],"category_scores_gemma":[0.007523352,0.00065596006,0.0015081831,0.00063340063,0.0005974253,0.0006974267,0.0012521704,0.0019657314,0.0007740076],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002628061,0.005620041,0.85147643,0.0015422112,0.0011842933,0.0008766065,0.012123496,0.0029463903,0.022154164,0.00089617335,0.017493442,0.08105859],"study_design_scores_gemma":[0.00015223272,0.0011301222,0.98806703,0.000048334427,0.000091716676,0.00047502576,0.0014001586,0.00080060057,0.0020795527,0.00014812623,0.005526117,0.00008108586],"about_ca_topic_score_codex":0.004945263,"about_ca_topic_score_gemma":0.012132063,"teacher_disagreement_score":0.004945263,"about_ca_system_score_codex":0.00076721894,"about_ca_system_score_gemma":0.00086320273,"threshold_uncertainty_score":0.014709234},"labels":[],"label_agreement":null},{"id":"W2021717161","doi":"10.1111/j.1745-4557.2007.00153.x","title":"CHANGES IN SOYMILK QUALITY AS A FUNCTION OF COMPOSITION AND STORAGE","year":2007,"lang":"en","type":"article","venue":"Journal of Food Quality","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Hexanal; Food science; Chemistry; Flavor; Nonanal; Octanal; Sugar; Aroma","score_opus":0.12084399148844613,"score_gpt":0.3807686170174378,"score_spread":0.25992462552899165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021717161","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":[0.9985203,0.0005084075,0.00039312357,0.000012055087,0.000006960228,0.00001119104,0.0002582311,0.000018215609,0.0002716061],"genre_scores_gemma":[0.9980494,0.00022991313,0.0006373358,0.000016788408,0.0000028379409,0.000014411593,0.00041815167,0.000014886703,0.00061626924],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99978834,0.000038900926,0.00002006453,0.000046397196,0.000070277296,0.000036066347],"domain_scores_gemma":[0.99965477,0.00004116259,0.00011336529,0.000021231488,0.00012861611,0.000040887862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024836315,0.0004443346,0.00038740283,0.00033698286,0.00017994065,0.0004540529,0.0001521444,0.0002287818,0.0017776139],"category_scores_gemma":[0.0004076168,0.00018017278,0.00036242642,0.00045342703,0.0001874541,0.0003333954,0.00021636472,0.00040281494,0.00021607506],"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":[0.0011385207,0.000046150206,0.0032221035,0.0000847766,0.000033879624,0.00008552824,0.000054203076,0.0001238646,0.99301046,0.00001009605,0.000021636026,0.002168759],"study_design_scores_gemma":[0.000019761585,0.0029170294,0.10803497,0.000032562457,0.00012812598,0.00019178452,0.00033019602,0.0008492,0.8865335,0.00003492003,0.0008972226,0.000030755953],"about_ca_topic_score_codex":0.0010249814,"about_ca_topic_score_gemma":0.0010637996,"teacher_disagreement_score":0.0017776139,"about_ca_system_score_codex":0.00023190057,"about_ca_system_score_gemma":0.00013846834,"threshold_uncertainty_score":0.0059467554},"labels":[],"label_agreement":null},{"id":"W2022741721","doi":"10.1037/h0087426","title":"Using confidence intervals for graphically based data interpretation.","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":735,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Institute of Mental Health","keywords":"Null hypothesis; Factorial; Confidence interval; Statistics; Psychology; Interpretation (philosophy); Range (aeronautics); Statistical hypothesis testing; Confidence distribution; Computer science; Cognitive psychology; Natural language processing; Artificial intelligence; Mathematics","score_opus":0.22678970764074546,"score_gpt":0.40074004458070744,"score_spread":0.17395033693996198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022741721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071759534,0.0011156924,0.9902614,0.00049489905,0.0006455057,0.00022932705,0.0006457348,0.0023230761,0.0035668905],"genre_scores_gemma":[0.044935293,0.0010643254,0.9461764,0.0006646945,0.0007455188,0.002871416,0.0009858225,0.0014258869,0.0011306307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8513966,0.10840643,0.009494238,0.00902054,0.020867532,0.00081472803],"domain_scores_gemma":[0.4287243,0.48919332,0.021439085,0.040781133,0.018618261,0.0012438984],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08723418,0.0033490306,0.0026349614,0.015671363,0.0012833305,0.009115196,0.0065341615,0.004460924,0.026157003],"category_scores_gemma":[0.53609407,0.0014762292,0.0030250566,0.011668882,0.0052652415,0.011076297,0.0073322365,0.011706077,0.006738122],"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":[0.00071075157,0.00026729552,0.0029921492,0.004624793,0.0013725728,0.00076848303,0.0034976115,0.014199474,0.0025306463,0.47020066,0.047292825,0.45154288],"study_design_scores_gemma":[0.0003157833,0.0004799219,0.003551653,0.0030986026,0.0004772642,0.0014387082,0.0011688006,0.06042357,0.0051165167,0.7709695,0.15252152,0.00043806288],"about_ca_topic_score_codex":0.0013091131,"about_ca_topic_score_gemma":0.0009013759,"teacher_disagreement_score":0.9127658,"about_ca_system_score_codex":0.0019267531,"about_ca_system_score_gemma":0.0025987457,"threshold_uncertainty_score":0.46134394},"labels":[],"label_agreement":null},{"id":"W2025194889","doi":"10.1111/j.1365-2621.2006.tb08919.x","title":"Characterization of the Fermented Milk “Laban” with Sensory Analysis and Instrumental Measurements","year":2006,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Food science; Odor; Titratable acid; Taste; Sensory analysis; Sensory system; Principal component analysis; Sweetness; Chemistry; Quantitative Descriptive Analysis; Fermentation; Mathematics; Psychology; Flavor; Statistics","score_opus":0.03707676147797213,"score_gpt":0.24871087956799953,"score_spread":0.2116341180900274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025194889","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":[0.9977946,0.00019496668,0.0014264204,0.000017931128,0.0000046755135,0.000022729693,0.00028304762,0.000016216196,0.00023945935],"genre_scores_gemma":[0.9956729,0.00014278138,0.0032981413,0.000025630416,0.0000051688794,0.000027342117,0.0004220169,0.000009580145,0.00039634763],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997814,0.000046837355,0.000013351829,0.000028458675,0.0001067939,0.000023130451],"domain_scores_gemma":[0.99973947,0.000042691554,0.00004811233,0.000012956534,0.00013421207,0.000022547021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040325086,0.00040967893,0.00028534868,0.00064591266,0.00018092405,0.0003259864,0.00022686528,0.00017553741,0.0010098298],"category_scores_gemma":[0.00068737345,0.00009830364,0.00021550513,0.00057340343,0.00018370591,0.00025450194,0.0002551409,0.00021097215,0.00014453758],"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":[0.0005495872,0.00011213134,0.0074534225,0.00012131239,0.00003481444,0.00008623315,0.00009125476,0.00017573989,0.9840157,0.00002819448,0.000038510352,0.0072930194],"study_design_scores_gemma":[0.000034488443,0.0019701945,0.17603089,0.000028857534,0.00013963264,0.00045752447,0.00051164057,0.004211248,0.81474596,0.000084670726,0.0017545128,0.000030389245],"about_ca_topic_score_codex":0.0009236588,"about_ca_topic_score_gemma":0.0011477076,"teacher_disagreement_score":0.0010098298,"about_ca_system_score_codex":0.00019000708,"about_ca_system_score_gemma":0.00012700645,"threshold_uncertainty_score":0.0033782125},"labels":[],"label_agreement":null},{"id":"W2027880001","doi":"10.1016/s1499-4046(06)60209-1","title":"The Meaning of Food in Our Lives: A Cross-Cultural Perspective on Eating and Well-Being","year":2005,"lang":"en","type":"review","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":375,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Snacking; Moderation; Food choice; Psychology; Overweight; Meaning (existential); Convenience food; Perspective (graphical); Regulatory focus theory; Pace; Overconsumption; Social psychology; Marketing; Gerontology; Food science; Medicine; Obesity; Business; Economics; Geography","score_opus":0.07130150011096387,"score_gpt":0.4266419263452668,"score_spread":0.35534042623430295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027880001","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":[0.0007898023,0.995353,0.00024402255,0.001844432,0.00030488425,0.0000019762526,0.000007772435,0.0000021195426,0.0014519761],"genre_scores_gemma":[0.0072634164,0.99104893,0.00040717245,0.0006072907,0.0002824113,0.000008541691,0.000009697326,0.0000018730959,0.00037075067],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993849,0.00028461404,0.00006336635,0.000068838584,0.00016379368,0.00003441484],"domain_scores_gemma":[0.99792,0.0015262043,0.00012358701,0.00003837466,0.00030052575,0.000091404385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002335066,0.0008175997,0.0015288608,0.0024367976,0.0006341741,0.0022105132,0.00083223294,0.0019448457,0.001633159],"category_scores_gemma":[0.002533055,0.00024951072,0.00041795772,0.004431709,0.0032423015,0.0026472444,0.0015464755,0.0022233087,0.0002957044],"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":[0.0000858091,0.00008007667,0.0015098441,0.008699336,0.00018497788,0.0003490969,0.006231794,0.00021191017,0.00047691484,0.015366279,0.014147739,0.95265615],"study_design_scores_gemma":[0.00003073643,0.0002142041,0.024728606,0.03095276,0.00031730885,0.0036982414,0.016754702,0.00022600095,0.00041634508,0.030258656,0.8922777,0.00012475709],"about_ca_topic_score_codex":0.0049735215,"about_ca_topic_score_gemma":0.013552208,"teacher_disagreement_score":0.0049735215,"about_ca_system_score_codex":0.0011466137,"about_ca_system_score_gemma":0.001987205,"threshold_uncertainty_score":0.012349188},"labels":[],"label_agreement":null},{"id":"W2033611167","doi":"10.1016/j.appet.2007.03.152","title":"Objective sensory dimensions of food as predictor of nutrient intake in the elderly.","year":2007,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Sensory system; Flavour; Food science; Food intake; Quality (philosophy); Psychology; Medicine; Chemistry; Internal medicine; Cognitive psychology","score_opus":0.029476739969638874,"score_gpt":0.27926268912419155,"score_spread":0.24978594915455268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033611167","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":[0.9970573,0.00052166084,0.0006430024,0.000021406047,0.000012099021,0.0000656414,0.00090915774,0.000014841344,0.00075489335],"genre_scores_gemma":[0.99818665,0.00016815674,0.0006925626,0.00003127834,0.000015234722,0.00008182076,0.00046147685,0.000003775604,0.0003591712],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994338,0.0002488322,0.000051362505,0.000103236336,0.000108604574,0.000054029],"domain_scores_gemma":[0.9975158,0.0012542417,0.00067234226,0.00007865993,0.0002939785,0.00018505305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001962348,0.0006398079,0.0003717836,0.000576164,0.00019208477,0.00041226053,0.00024414086,0.00028069734,0.0016447713],"category_scores_gemma":[0.0045541204,0.000106003376,0.0004393076,0.00059211877,0.00033539586,0.00023580373,0.0003206631,0.00028682617,0.00017552904],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006141717,0.0005137242,0.9737824,0.00026348798,0.00045849965,0.00007160266,0.000106454914,0.0002849153,0.0025714766,0.00003691847,0.00026064136,0.015508123],"study_design_scores_gemma":[0.000046078196,0.0019050413,0.99648404,0.000014198302,0.0001268682,0.00010547264,0.00012540857,0.00055818545,0.0004264106,0.0000485433,0.00015123782,0.000008383496],"about_ca_topic_score_codex":0.0022146483,"about_ca_topic_score_gemma":0.003139281,"teacher_disagreement_score":0.0022146483,"about_ca_system_score_codex":0.00013832029,"about_ca_system_score_gemma":0.0003352239,"threshold_uncertainty_score":0.010378003},"labels":[],"label_agreement":null},{"id":"W2033941998","doi":"10.1016/j.ando.2013.07.543","title":"L’insuline dégludec améliore le contrôle glycémique et réduit les hypoglycémies nocturnes chez les patients diabétiques de type 2 (DT2) naïfs d’insuline : résultats d’un essai clinique randomisé et son extension à 2 ans","year":2013,"lang":"fr","type":"article","venue":"Annales d Endocrinologie","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Sample size determination; Context (archaeology); Mathematics; Population; Humanities; Statistics; Sociology; Philosophy; Geography; Demography","score_opus":0.057587375710044464,"score_gpt":0.31768697877344376,"score_spread":0.2600996030633993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033941998","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":[0.99243003,0.003977879,0.0009274269,0.0001364111,0.00024616197,0.0010387514,0.00040915466,0.00003159217,0.00080245617],"genre_scores_gemma":[0.992931,0.0016082849,0.001542712,0.00030021882,0.00023759254,0.0013491745,0.0004553364,0.000018614763,0.0015570708],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9975158,0.0013996776,0.0003665491,0.00031117993,0.00022419648,0.00018276811],"domain_scores_gemma":[0.9981108,0.00086869695,0.00035022595,0.00028821852,0.00018593195,0.00019615996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003561052,0.001090765,0.002439357,0.0005100673,0.00036558986,0.0010675936,0.0004505951,0.0013236034,0.0036857233],"category_scores_gemma":[0.0038989722,0.0007117097,0.0029350535,0.0007991456,0.0008594381,0.0009526101,0.00041995483,0.0014551308,0.00041204187],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.96280247,0.0033585064,0.004704992,0.00038478535,0.003254302,0.000049039874,0.00008469776,0.00030003488,0.0038628476,0.00005115399,0.00019454055,0.02095265],"study_design_scores_gemma":[0.7284898,0.23112871,0.029912189,0.0001035444,0.005615694,0.000109171066,0.00009749179,0.0006967996,0.0020395655,0.00022633973,0.0015212428,0.000059422673],"about_ca_topic_score_codex":0.0018260838,"about_ca_topic_score_gemma":0.0024686428,"teacher_disagreement_score":0.0036857233,"about_ca_system_score_codex":0.00086905353,"about_ca_system_score_gemma":0.00067460956,"threshold_uncertainty_score":0.018832862},"labels":[],"label_agreement":null},{"id":"W2035434565","doi":"10.1177/01466216010251006","title":"The Extra-Factor Phenomenon Revisited: Unidimensional Unfolding as Quadratic Factor Analysis","year":2001,"lang":"en","type":"article","venue":"Applied Psychological Measurement","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Phenomenon; Factor (programming language); Covariance; Quadratic equation; Metric (unit); Factor analysis; Mathematics; Set (abstract data type); Applied mathematics; Statistics; Computer science; Physics; Geometry","score_opus":0.14005892954195845,"score_gpt":0.33388675036949617,"score_spread":0.19382782082753772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035434565","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":[0.031636015,0.0028703997,0.950754,0.0020930741,0.0004940338,0.00015182167,0.00006273931,0.0002870943,0.011650747],"genre_scores_gemma":[0.6019787,0.0015835117,0.39281154,0.00076516793,0.00040344827,0.0004780405,0.00010369435,0.00017720423,0.001698728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9768024,0.015578277,0.0011549154,0.002210146,0.0037453817,0.0005089307],"domain_scores_gemma":[0.9240366,0.054104444,0.0049510933,0.012277077,0.0041174605,0.0005133107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030741105,0.0015539747,0.0013113087,0.0030933425,0.0017878006,0.0049788174,0.0021313,0.0015651851,0.003220956],"category_scores_gemma":[0.11437712,0.0007261214,0.0015506152,0.007342659,0.012307242,0.011378566,0.005565487,0.00566833,0.0004294178],"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":[0.00012695808,0.000054801923,0.0036099493,0.00042312345,0.0001334637,0.00028271275,0.008362156,0.001923048,0.0016118491,0.87184507,0.0017641783,0.1098628],"study_design_scores_gemma":[0.000024537074,0.000097884695,0.004252256,0.00019210328,0.00003627981,0.00051578536,0.0012272077,0.021022942,0.00093433855,0.9628034,0.008803538,0.00008960732],"about_ca_topic_score_codex":0.0011896152,"about_ca_topic_score_gemma":0.0010006946,"teacher_disagreement_score":0.030741105,"about_ca_system_score_codex":0.0014551351,"about_ca_system_score_gemma":0.0017982046,"threshold_uncertainty_score":0.1625765},"labels":[],"label_agreement":null},{"id":"W2039205249","doi":"10.1016/s1499-4046(06)60159-0","title":"Reported Attitudes and Beliefs toward Soy Food Consumption of Soy Consumers versus Nonconsumers in Natural Foods or Mainstream Grocery Stores","year":2005,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mainstream; Consumption (sociology); Focus group; Soy protein; Grocery store; Food science; Medicine; Environmental health; Marketing; Business; Biology","score_opus":0.0794409906792265,"score_gpt":0.3612733230733773,"score_spread":0.2818323323941508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039205249","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":[0.99960476,0.000021210675,0.000011032818,0.000015045067,0.000002254754,0.00000315304,0.00005936168,8.693241e-7,0.0002822973],"genre_scores_gemma":[0.9992157,0.00005220261,0.000053232114,0.000039327635,0.000005324914,0.000005468431,0.00012159727,9.1739963e-7,0.00050615595],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997445,0.000059908707,0.000032025742,0.000035185072,0.00007594701,0.00005240751],"domain_scores_gemma":[0.99779236,0.00061141624,0.00077816297,0.00006042596,0.000259911,0.0004977175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070736825,0.00027136592,0.00026062664,0.0009016302,0.00042639012,0.0007936221,0.00020616973,0.0004836711,0.0039854203],"category_scores_gemma":[0.002453845,0.00030202055,0.0005254711,0.00052265334,0.00042808577,0.00046491402,0.00046621924,0.00066531287,0.00042272848],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007021339,0.00047035623,0.9941128,0.000019062276,0.00010481032,0.00006368646,0.0013483921,0.00004390552,0.00095296884,0.000030320396,0.00008758667,0.0020639224],"study_design_scores_gemma":[0.000026292895,0.0005459454,0.9957846,0.000010152971,0.000060190257,0.00013430743,0.0027748132,0.00022692051,0.00025731322,0.000028537455,0.00014298204,0.000008059659],"about_ca_topic_score_codex":0.008927815,"about_ca_topic_score_gemma":0.012379416,"teacher_disagreement_score":0.008927815,"about_ca_system_score_codex":0.0003071838,"about_ca_system_score_gemma":0.00016539109,"threshold_uncertainty_score":0.017751694},"labels":[],"label_agreement":null},{"id":"W2040093856","doi":"10.1006/appe.1999.0297","title":"Flavor principles in the laboratory: a matter of taste. Reply to commentators","year":2000,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Aurora College; University of Toronto","funders":"","keywords":"Taste; Flavor; Psychology; Food science; Chemistry; Neuroscience","score_opus":0.029418061854611956,"score_gpt":0.27277699768075636,"score_spread":0.2433589358261444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040093856","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019683386,0.0013888458,0.00022959775,0.98149306,0.01632534,0.000010931268,0.00010719516,0.00003810562,0.00021001088],"genre_scores_gemma":[0.0014251183,0.00081367843,0.00038729163,0.97700113,0.01910305,0.00007419812,0.000031309934,0.00003749085,0.001126717],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9864384,0.003620186,0.0022343618,0.0026814567,0.004289449,0.00073614076],"domain_scores_gemma":[0.85179776,0.104842395,0.00493123,0.0052319397,0.028916603,0.004280147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028619468,0.0020413566,0.0029869443,0.0020610844,0.005228864,0.0054995143,0.008360075,0.04284456,0.00737714],"category_scores_gemma":[0.12017339,0.0017451862,0.0032283587,0.0033294444,0.014699573,0.012523544,0.0049793697,0.091863774,0.00814091],"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":[0.00007865908,0.000027155971,0.0004457835,0.00011531425,0.000035632147,0.00014975907,0.00036112234,0.000044674063,0.00015633594,0.001679612,0.9927562,0.004149817],"study_design_scores_gemma":[0.00030430255,0.00021472583,0.007267733,0.0010097967,0.00017970365,0.0011045294,0.0040477496,0.0008635347,0.0013071238,0.027711412,0.9555694,0.0004199231],"about_ca_topic_score_codex":0.015184314,"about_ca_topic_score_gemma":0.01602662,"teacher_disagreement_score":0.04284456,"about_ca_system_score_codex":0.004588004,"about_ca_system_score_gemma":0.006727573,"threshold_uncertainty_score":0.15135604},"labels":[],"label_agreement":null},{"id":"W2040149108","doi":"10.1007/bf02294843","title":"Generalized Constrained Multiple Correspondence Analysis","year":2002,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Correspondence analysis; Block matrix; Mathematics; Set (abstract data type); Block (permutation group theory); Multivariate statistics; Multiple correspondence analysis; Column (typography); Algorithm; Computer science; Mathematical optimization; Applied mathematics; Combinatorics; Statistics; Connection (principal bundle)","score_opus":0.0885051521407948,"score_gpt":0.29685125590065503,"score_spread":0.20834610375986024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040149108","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":[0.0008027775,0.00019641628,0.9978873,0.00005266586,0.000031343454,0.000037864866,0.00008389807,0.00017492715,0.0007327892],"genre_scores_gemma":[0.06781314,0.00073452055,0.92683136,0.00020008865,0.00013658544,0.0004413367,0.0006983041,0.00028282622,0.002861851],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99113303,0.0040588398,0.0003549578,0.0016652949,0.002393834,0.0003940646],"domain_scores_gemma":[0.9947337,0.002237573,0.00052106194,0.001091382,0.0012677591,0.00014859471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046637612,0.0022684128,0.003164536,0.004233227,0.0016396272,0.003229467,0.0036913233,0.0018288941,0.005988987],"category_scores_gemma":[0.0146284485,0.0010069156,0.0032192175,0.0072784894,0.002155014,0.0031480303,0.0043978845,0.0025932454,0.0018201831],"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":[0.00013964246,0.00009445376,0.0011059743,0.00061985623,0.00071275915,0.0003462776,0.00030699951,0.24226622,0.00645286,0.19530648,0.009290249,0.54335815],"study_design_scores_gemma":[0.000025989359,0.000086795764,0.00083273277,0.0000766287,0.00009668187,0.0002949826,0.00010238185,0.7914408,0.0031235588,0.18556121,0.018239453,0.00011876104],"about_ca_topic_score_codex":0.0055755745,"about_ca_topic_score_gemma":0.005529682,"teacher_disagreement_score":0.005988987,"about_ca_system_score_codex":0.0011655971,"about_ca_system_score_gemma":0.0038908455,"threshold_uncertainty_score":0.02466464},"labels":[],"label_agreement":null},{"id":"W2042876022","doi":"10.3758/pp.70.3.456","title":"Visual statistical decisions","year":2008,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Statistics; Psychology; Variance (accounting); Mathematics; Confidence interval; Error bar; Analysis of variance; Statistical hypothesis testing; Regression analysis; Statistical significance; Statistical analysis; Bar (unit); Regression; Social psychology; Geography","score_opus":0.07722301084583688,"score_gpt":0.33858499901949984,"score_spread":0.26136198817366296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042876022","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":[0.021013739,0.0011641575,0.73066026,0.0058394778,0.0018196226,0.00039083153,0.0040798504,0.008880786,0.22615127],"genre_scores_gemma":[0.53147244,0.0012631456,0.34486887,0.0058821114,0.0018048093,0.00080738735,0.005072864,0.00546209,0.103366256],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968262,0.0008875509,0.00017762161,0.0008575617,0.0010008244,0.0002503278],"domain_scores_gemma":[0.9900356,0.004713444,0.000554632,0.0022241587,0.0019855737,0.00048669524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039207614,0.001134785,0.0006483955,0.0020384262,0.0011149017,0.0049981424,0.0013152142,0.001175551,0.06485373],"category_scores_gemma":[0.03187149,0.00047132012,0.0010389145,0.0014187598,0.0013574648,0.0031214887,0.0019623498,0.002012535,0.01734265],"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":[0.00079490105,0.00011986353,0.0021185668,0.00035988033,0.00010017685,0.00030840727,0.0005615328,0.0037138364,0.034035925,0.34155384,0.08785713,0.5284758],"study_design_scores_gemma":[0.00014360758,0.0002081328,0.005589464,0.0002524799,0.0001074134,0.0005811517,0.0005046091,0.048639867,0.038397525,0.6608858,0.24456856,0.00012131147],"about_ca_topic_score_codex":0.0020013922,"about_ca_topic_score_gemma":0.0022869087,"teacher_disagreement_score":0.06485373,"about_ca_system_score_codex":0.0011795391,"about_ca_system_score_gemma":0.001655245,"threshold_uncertainty_score":0.21695733},"labels":[],"label_agreement":null},{"id":"W2043298959","doi":"10.1037/0096-1523.35.1.195","title":"Flexible visual statistical learning: Transfer across space and time.","year":2009,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Human Perception & Performance","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Computer science; Context (archaeology); Transfer of learning; Statistical learning; Statistical hypothesis testing; Artificial intelligence; Visual learning; Space (punctuation); Function (biology); Machine learning; Cognitive psychology; Mathematics; Psychology; Geography; Statistics","score_opus":0.041784996499110445,"score_gpt":0.39975960143513123,"score_spread":0.3579746049360208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043298959","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":[0.38900447,0.0003834239,0.6021108,0.00054692774,0.000078869285,0.00017842688,0.00016450691,0.001116394,0.006416163],"genre_scores_gemma":[0.95617986,0.00011494834,0.042337194,0.00009614906,0.000026118045,0.00009674941,0.000115019175,0.000100883626,0.0009330602],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990995,0.00033610122,0.00004152254,0.0002534595,0.00021232074,0.000057004105],"domain_scores_gemma":[0.9926561,0.0035904092,0.00089450117,0.0022675612,0.00030460118,0.00028691033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027260508,0.0003292393,0.00030437496,0.0005649905,0.00026783525,0.0010429546,0.00083376875,0.00038985445,0.002038112],"category_scores_gemma":[0.022317898,0.00037734676,0.0006527599,0.00050797925,0.002024285,0.0025628058,0.0021976295,0.0011560814,0.00035139357],"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":[0.00062619423,0.00047832873,0.024495905,0.0002724585,0.00039182723,0.00040824365,0.0013192527,0.07465303,0.11586756,0.050576914,0.0017938516,0.7291164],"study_design_scores_gemma":[0.00009232039,0.00139431,0.0666027,0.00006313422,0.000102772814,0.0010397689,0.0004250457,0.58552057,0.056110118,0.2842731,0.00423553,0.00014061318],"about_ca_topic_score_codex":0.00068373914,"about_ca_topic_score_gemma":0.000468972,"teacher_disagreement_score":0.0027260508,"about_ca_system_score_codex":0.0005804271,"about_ca_system_score_gemma":0.0005238573,"threshold_uncertainty_score":0.014416873},"labels":[],"label_agreement":null},{"id":"W2045075912","doi":"10.1016/j.foodqual.2005.07.005","title":"Use of feedback calibration to reduce the training time for wine panels","year":2005,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Research Council Canada","keywords":"Panel data; Multivariate analysis of variance; Statistics; Variance (accounting); Econometrics; Mathematics; Computer science; Economics","score_opus":0.5211957845416898,"score_gpt":0.37732064392510967,"score_spread":0.14387514061658013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045075912","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":[0.49156246,0.0004972505,0.49377355,0.00042155167,0.0006829984,0.0016460222,0.000554425,0.005825466,0.0050363713],"genre_scores_gemma":[0.6734107,0.00016970113,0.3161731,0.0005587252,0.00007992389,0.0021417881,0.00054808415,0.00092312915,0.005994818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988427,0.00039390585,0.00008628004,0.00034092995,0.00024827293,0.000087788154],"domain_scores_gemma":[0.9926472,0.0037230938,0.00042965182,0.0010876373,0.0019064947,0.00020595975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026011642,0.0012528265,0.0007387235,0.00050269836,0.0006420711,0.00054151926,0.0013183006,0.0010912436,0.009340041],"category_scores_gemma":[0.014150464,0.00061183836,0.0004877767,0.0005646061,0.00027660164,0.00089995563,0.00081340806,0.0011572433,0.0013093101],"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":[0.005190205,0.005499147,0.008999411,0.0007569805,0.00014273648,0.0000701779,0.00063402357,0.01239868,0.45922717,0.0006699916,0.004560716,0.5018509],"study_design_scores_gemma":[0.00087479415,0.008337742,0.09273049,0.00023871554,0.00038275507,0.00044217266,0.00026963925,0.19047302,0.68577504,0.0021025254,0.018068358,0.00030473623],"about_ca_topic_score_codex":0.0019836542,"about_ca_topic_score_gemma":0.0035123995,"teacher_disagreement_score":0.009340041,"about_ca_system_score_codex":0.00035139488,"about_ca_system_score_gemma":0.00074448396,"threshold_uncertainty_score":0.03124559},"labels":[],"label_agreement":null},{"id":"W2049843187","doi":"10.1111/j.1750-3841.2011.02200.x","title":"Sensory Characteristics and Consumer Acceptance of Bread and Cracker Products Made from Red or White Wheat","year":2011,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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 Guelph","funders":"Mitacs","keywords":"White (mutation); Food science; Sensory system; Chemistry; Psychology; Biochemistry; Cognitive psychology","score_opus":0.09686063612872119,"score_gpt":0.2861721261257145,"score_spread":0.18931148999699332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049843187","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":[0.999729,0.000024297973,0.000075420605,0.000005096786,7.432634e-7,0.0000030431079,0.000024044342,0.0000012725292,0.0001370321],"genre_scores_gemma":[0.99942183,0.000038367558,0.0002008201,0.000011752543,0.0000013961356,0.000004148057,0.00009308121,0.0000018758806,0.00022673147],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99980325,0.000054913366,0.000016139496,0.000030284133,0.00007721099,0.000018216344],"domain_scores_gemma":[0.9993168,0.0002573532,0.00021602042,0.000035787776,0.00011697967,0.00005713026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047750416,0.00022368769,0.00014004571,0.00027235213,0.000088217326,0.0003273901,0.00007435267,0.00019340358,0.0015520251],"category_scores_gemma":[0.0009876639,0.000105593776,0.00029069054,0.00020882694,0.00016087206,0.00026482172,0.00017317139,0.00030969977,0.00014580542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008085245,0.0013664322,0.60791427,0.00035338107,0.00056276214,0.0004754645,0.0023545823,0.0006252006,0.31083182,0.00018526752,0.00034930152,0.06689627],"study_design_scores_gemma":[0.000014359072,0.0019886876,0.9920517,0.000006470512,0.000047737623,0.00015717123,0.0004591222,0.0004375538,0.0046057883,0.000034305936,0.0001883894,0.000008675313],"about_ca_topic_score_codex":0.0007797758,"about_ca_topic_score_gemma":0.0011652741,"teacher_disagreement_score":0.0015520251,"about_ca_system_score_codex":0.00008083741,"about_ca_system_score_gemma":0.000049727238,"threshold_uncertainty_score":0.005191982},"labels":[],"label_agreement":null},{"id":"W2052006920","doi":"10.1111/j.1467-842x.2004.00357.x","title":"ASSESSING CONGRUENCEAMONG DISTANCE MATRICES: SINGLE‐MALT SCOTCH WHISKIES REVISITED","year":2004,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":126,"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":"Mathematics; Distance matrices in phylogeny; Mantel test; Generalization; Distance matrix; Matrix (chemical analysis); Statistics; Congruence (geometry); Similarity (geometry); Combinatorics; Artificial intelligence; Computer science; Geometry; Image (mathematics)","score_opus":0.06358097980858496,"score_gpt":0.3183367932302847,"score_spread":0.25475581342169973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052006920","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":[0.81540954,0.00057171774,0.17676635,0.00039154247,0.00019206094,0.00026178817,0.0004870088,0.00024399128,0.005675982],"genre_scores_gemma":[0.9772728,0.00007444567,0.021948269,0.00004233078,0.00004649898,0.00009674653,0.00021478775,0.00004381066,0.00026022678],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9613332,0.020713102,0.0025144627,0.0061485367,0.008441711,0.0008490829],"domain_scores_gemma":[0.7603833,0.19885762,0.013623204,0.0146520585,0.009774096,0.0027096542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024603976,0.00088136346,0.0018140734,0.0028144082,0.0012739907,0.003178828,0.0016792534,0.0012015112,0.00589849],"category_scores_gemma":[0.1775771,0.00058398995,0.0015454502,0.0036819393,0.0044152206,0.0032249712,0.003333827,0.0022067272,0.0006415154],"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":[0.00734255,0.000609655,0.52724606,0.0012714227,0.002657476,0.0018583047,0.0076283696,0.01418,0.041236963,0.0566337,0.003909403,0.3354261],"study_design_scores_gemma":[0.0006197272,0.00577484,0.56622326,0.00032527183,0.0007029313,0.002458554,0.010082672,0.16116808,0.018777251,0.22212684,0.011342455,0.00039802145],"about_ca_topic_score_codex":0.002736968,"about_ca_topic_score_gemma":0.0018784727,"teacher_disagreement_score":0.024603976,"about_ca_system_score_codex":0.0007566603,"about_ca_system_score_gemma":0.0012765075,"threshold_uncertainty_score":0.1301198},"labels":[],"label_agreement":null},{"id":"W2053297118","doi":"10.1007/s11336-005-1314-x","title":"Fuzzy Clusterwise Generalized Structured Component Analysis","year":2007,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":90,"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":"Pooling; Component (thermodynamics); Respondent; Econometrics; Computer science; Component analysis; Fuzzy logic; Data mining; Statistics; Mathematics; Artificial intelligence","score_opus":0.043660599707087674,"score_gpt":0.31801907475441893,"score_spread":0.27435847504733124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053297118","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":[0.006794647,0.00014545901,0.99110097,0.00008581378,0.000053587675,0.00020112778,0.00023655809,0.0003847763,0.0009971176],"genre_scores_gemma":[0.14744473,0.00025055246,0.8481366,0.00008316447,0.00005929581,0.0007597605,0.0008821844,0.00015284155,0.0022308528],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970796,0.001294661,0.0001398954,0.00062452466,0.0006811888,0.00018015559],"domain_scores_gemma":[0.99678767,0.000984759,0.00018386115,0.0004667111,0.0015002097,0.00007689732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042673307,0.001335184,0.0015896321,0.0031285894,0.0011873444,0.0017734878,0.0017644193,0.0011373736,0.004952686],"category_scores_gemma":[0.009848056,0.00034889317,0.0019720139,0.0031290727,0.0010469576,0.0010776761,0.0014335663,0.0013826144,0.0010251929],"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":[0.0005144643,0.00012600319,0.003273813,0.000464542,0.00045395704,0.00019371789,0.00069756765,0.279631,0.007215767,0.09445522,0.009919003,0.603055],"study_design_scores_gemma":[0.000015516305,0.00006722778,0.0019454117,0.000030465997,0.000043016626,0.000064030864,0.00009626978,0.960085,0.0015720468,0.031936057,0.004090451,0.000054415883],"about_ca_topic_score_codex":0.007053572,"about_ca_topic_score_gemma":0.007262025,"teacher_disagreement_score":0.007053572,"about_ca_system_score_codex":0.0011327956,"about_ca_system_score_gemma":0.0024164703,"threshold_uncertainty_score":0.022568107},"labels":[],"label_agreement":null},{"id":"W2053318164","doi":"10.2333/bhmk.42.37","title":"Fuzzy Clusterwise Functional Extended Redundancy Analysis","year":2015,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fuzzy logic; Redundancy (engineering); Ordinary least squares; Computer science; Data mining; Regression analysis; Regression; Population; Linear regression; Mathematics; Statistics; Artificial intelligence; Machine learning","score_opus":0.1384300205557461,"score_gpt":0.31919870847694587,"score_spread":0.18076868792119977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053318164","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":[0.034047067,0.0001443173,0.9644618,0.000056388304,0.000026999136,0.000053861593,0.0001864533,0.00019214938,0.00083097914],"genre_scores_gemma":[0.5735202,0.00018295899,0.42198533,0.000053579217,0.000066364286,0.0002679073,0.00079390174,0.00016149093,0.0029682508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874985,0.0004960934,0.00007305879,0.00030569782,0.0002701999,0.000105071005],"domain_scores_gemma":[0.998552,0.00053453445,0.00011311169,0.0002559542,0.00049192994,0.0000523909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002430047,0.0008140842,0.0009485793,0.0015510212,0.0005876337,0.0007263072,0.0010117876,0.0003903336,0.0028083657],"category_scores_gemma":[0.0041647744,0.00022431584,0.001353731,0.0011475447,0.0005732149,0.0007636437,0.00086270436,0.00051222177,0.0004681549],"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":[0.0013714012,0.00028042973,0.004316606,0.0008324952,0.0007022705,0.0002978394,0.0005079133,0.1925797,0.03798808,0.102904595,0.0044548255,0.6537638],"study_design_scores_gemma":[0.00001725391,0.00018235105,0.0065091206,0.000028754857,0.00011125093,0.00011747056,0.00007806257,0.9609667,0.0046211593,0.025573663,0.001737754,0.000056372402],"about_ca_topic_score_codex":0.0026222416,"about_ca_topic_score_gemma":0.0022648252,"teacher_disagreement_score":0.0028083657,"about_ca_system_score_codex":0.0005206828,"about_ca_system_score_gemma":0.0011252997,"threshold_uncertainty_score":0.012851477},"labels":[],"label_agreement":null},{"id":"W2057846446","doi":"10.1007/s11634-009-0054-7","title":"Tests of ignoring and eliminating in nonsymmetric correspondence analysis","year":2009,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contingency table; Residual; Contingency; Row; Table (database); Correspondence analysis; Test (biology); Mathematics; Econometrics; Statistics; Computer science; Algorithm; Data mining; Epistemology","score_opus":0.06777325830079646,"score_gpt":0.36711674864542976,"score_spread":0.2993434903446333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057846446","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5268863,0.0008944632,0.44835395,0.002245605,0.0007813001,0.0009609953,0.0012905598,0.0005511821,0.018035663],"genre_scores_gemma":[0.929972,0.00012927373,0.06652335,0.0005969481,0.0002741492,0.0007638178,0.00082641776,0.00017768647,0.0007363779],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7809508,0.13976172,0.014353136,0.03147719,0.029582728,0.0038743312],"domain_scores_gemma":[0.20498222,0.74042416,0.013526962,0.029779831,0.008444496,0.0028422915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10081848,0.0015965264,0.0026962995,0.004592804,0.0044816867,0.004700281,0.0044045583,0.0031322944,0.007654856],"category_scores_gemma":[0.45910677,0.0008446871,0.003992441,0.005633133,0.015191981,0.009585312,0.00672,0.0067907744,0.0006789374],"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":[0.009920369,0.0015792424,0.2723689,0.0020674595,0.007796431,0.0052701924,0.010245661,0.023294145,0.012945127,0.35008425,0.009477292,0.294951],"study_design_scores_gemma":[0.00079779257,0.0039179833,0.11510503,0.00040108166,0.0013508922,0.0027897542,0.0056200554,0.14488989,0.01111158,0.7034617,0.010036682,0.000517469],"about_ca_topic_score_codex":0.00146547,"about_ca_topic_score_gemma":0.0009296139,"teacher_disagreement_score":0.10081848,"about_ca_system_score_codex":0.001687341,"about_ca_system_score_gemma":0.003761027,"threshold_uncertainty_score":0.5331855},"labels":[],"label_agreement":null},{"id":"W2058280804","doi":"10.1080/00273170903504893","title":"Simultaneous Two-Way Clustering of Multiple Correspondence Analysis","year":2010,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McGill University","funders":"","keywords":"Categorical variable; Cluster analysis; Correspondence analysis; Centroid; Variable (mathematics); Multiple correspondence analysis; Partition (number theory); Cluster (spacecraft); Statistics; Mathematics; Data mining; Contingency table; Extant taxon; Multivariate statistics; Computer science; Artificial intelligence","score_opus":0.22477204831980976,"score_gpt":0.4679075736295627,"score_spread":0.24313552530975294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058280804","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":[0.002807089,0.00005530185,0.9963092,0.000030036457,0.000030043648,0.00008976454,0.000047183912,0.00028901445,0.00034229204],"genre_scores_gemma":[0.103045635,0.00011891494,0.8942125,0.000038127593,0.000051835428,0.00069678295,0.00032371108,0.00018544172,0.0013270908],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9839092,0.007871415,0.0008611477,0.0036847817,0.0029823203,0.0006910781],"domain_scores_gemma":[0.9860356,0.0068445564,0.0011948148,0.0021325182,0.003506131,0.00028638932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008834481,0.0021529356,0.0035636942,0.0076176836,0.0028162422,0.00441756,0.0034037298,0.0020813062,0.003572839],"category_scores_gemma":[0.02605206,0.00142342,0.0048622624,0.008556469,0.001983268,0.0031551472,0.004496845,0.0026571343,0.0012364031],"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":[0.00043154156,0.0002955247,0.007142223,0.0008701043,0.0012199297,0.00037955493,0.0025274805,0.26084468,0.01155647,0.07778413,0.0059407097,0.6310076],"study_design_scores_gemma":[0.000028632483,0.00011038916,0.0021243063,0.00003957773,0.00008627061,0.00017499356,0.0002433712,0.95830256,0.0036629066,0.03020673,0.00487219,0.00014807965],"about_ca_topic_score_codex":0.0055549983,"about_ca_topic_score_gemma":0.0060770214,"teacher_disagreement_score":0.008834481,"about_ca_system_score_codex":0.0016005277,"about_ca_system_score_gemma":0.0037012063,"threshold_uncertainty_score":0.046721756},"labels":[],"label_agreement":null},{"id":"W2059187041","doi":"10.1016/j.jclinepi.2009.08.008","title":"Correspondence analysis is a useful tool to uncover the relationships among categorical variables","year":2009,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":427,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University Health Centre; McGill University; Jewish General Hospital","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Categorical variable; Contingency table; Correspondence analysis; Multivariate statistics; Multiple correspondence analysis; Pairwise comparison; Context (archaeology); Representation (politics); Variable (mathematics); Multivariate analysis; Statistics; Computer science; Variables; Mathematics; Geography","score_opus":0.37588324888730607,"score_gpt":0.47303940398440114,"score_spread":0.09715615509709508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059187041","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":[0.06490458,0.00055045914,0.9173489,0.0005669401,0.00029161177,0.0008432842,0.0058421534,0.0034854712,0.0061665876],"genre_scores_gemma":[0.49214748,0.00044600037,0.4984877,0.00025304945,0.0003574438,0.001885497,0.0030951593,0.00079378905,0.0025337813],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9640711,0.023367738,0.003073382,0.0037178616,0.005260276,0.0005097142],"domain_scores_gemma":[0.8008202,0.17118524,0.00845993,0.012317176,0.0061376416,0.0010798541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02205094,0.001273494,0.0029135204,0.012335147,0.0017950867,0.0027368197,0.001813702,0.000966268,0.019285811],"category_scores_gemma":[0.11397645,0.00059771864,0.0023040604,0.010656708,0.0013095334,0.0034716146,0.0020615014,0.002298557,0.0030656338],"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":[0.001953908,0.00091544,0.1804858,0.0019556044,0.0030926596,0.0011901577,0.0028559307,0.0074886736,0.007930698,0.04620493,0.019900955,0.7260253],"study_design_scores_gemma":[0.00045899372,0.0050798682,0.3043301,0.0008462338,0.0016645013,0.005527766,0.0067324443,0.19665922,0.022102991,0.39595315,0.059803084,0.0008416864],"about_ca_topic_score_codex":0.0014888253,"about_ca_topic_score_gemma":0.0014097751,"teacher_disagreement_score":0.02205094,"about_ca_system_score_codex":0.0007689232,"about_ca_system_score_gemma":0.0023973158,"threshold_uncertainty_score":0.11661786},"labels":[],"label_agreement":null},{"id":"W2059469391","doi":"10.1111/j.1745-4557.2001.tb00611.x","title":"SENSORY DESCRIPTIVE ANALYSIS AND CORRESPONDENCE ANALYSIS AIDS IN THE CHOOSING OF APPLE GENOTYPES FOR PROCESSED PRODUCTS<sup>1</sup>","year":2001,"lang":"en","type":"article","venue":"Journal of Food Quality","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Sensory analysis; Sensory system; Descriptive statistics; Genotype; Principal component analysis; Quantitative Descriptive Analysis; Mathematics; Flavor; Statistics; Food science; Biology; Psychology; Cognitive psychology; Genetics","score_opus":0.11220822364515333,"score_gpt":0.34684581661812297,"score_spread":0.23463759297296966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059469391","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":[0.91167116,0.000033221222,0.0838508,0.00006113458,0.000012677829,0.00038402752,0.00044120586,0.00040267297,0.0031430207],"genre_scores_gemma":[0.93563837,0.00001732983,0.06310749,0.00001728454,0.000006329386,0.00032381943,0.00024940787,0.00006730927,0.00057261356],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99643564,0.0022305348,0.00019030557,0.00034383003,0.00068102445,0.00011871893],"domain_scores_gemma":[0.98732424,0.0089596715,0.00067101227,0.0010231929,0.001762715,0.0002590933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054226085,0.00040870067,0.00040811743,0.0015242355,0.00054240075,0.001031846,0.00046343135,0.00026404546,0.004103823],"category_scores_gemma":[0.015605425,0.00023458943,0.00046064783,0.0011921887,0.00046008153,0.00061940786,0.0007006116,0.0005609117,0.0006457182],"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":[0.010326291,0.0016008693,0.18506151,0.00045260135,0.00022022745,0.0003119602,0.0047270013,0.014015435,0.2147166,0.0067271003,0.0031336762,0.5587067],"study_design_scores_gemma":[0.00024619518,0.004126323,0.6758557,0.000094231684,0.00023791307,0.0006326323,0.005801948,0.19618051,0.10018172,0.010598057,0.0057276525,0.00031711097],"about_ca_topic_score_codex":0.0009190448,"about_ca_topic_score_gemma":0.0024495814,"teacher_disagreement_score":0.0054226085,"about_ca_system_score_codex":0.00035124668,"about_ca_system_score_gemma":0.0004978957,"threshold_uncertainty_score":0.028677821},"labels":[],"label_agreement":null},{"id":"W2064809198","doi":"10.5539/jfr.v2n6p55","title":"Optimization of the Rheological and Sensory Properties of Stirred Yogurt as Affected by Chemical Composition and Heat Treatment of Buffalo Milk","year":2013,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Food science; Rheology; Casein; Chemistry; Whey protein; Sensory analysis; Total dissolved solids; Composition (language); Apparent viscosity; Sensory system; Lactic acid; Materials science; Biology; Bacteria","score_opus":0.10295619044032453,"score_gpt":0.32547975497303017,"score_spread":0.22252356453270564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064809198","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":[0.99879044,0.0004202029,0.00048070648,0.000014677478,0.0000062957924,0.00001404664,0.00008316222,0.000010731731,0.00017971882],"genre_scores_gemma":[0.9963111,0.00049286155,0.002168929,0.000021603235,0.000005803994,0.000043606782,0.00023217515,0.000019484796,0.000704263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999826,0.000044785054,0.000016185611,0.000040860483,0.000044143315,0.0000280413],"domain_scores_gemma":[0.9998894,0.00002600042,0.00003063329,0.00000527228,0.00002808475,0.000020523292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003491607,0.00028256455,0.00043824877,0.00018925848,0.00014126566,0.0003862169,0.00016272446,0.00018387818,0.00065462396],"category_scores_gemma":[0.00038322984,0.00015313776,0.00025284328,0.00025050898,0.00014376413,0.00020891057,0.0001749564,0.0003156087,0.00014297193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028045138,0.000029918489,0.00024821318,0.000027532516,0.000005983675,0.000011368847,0.000016030563,0.00006596178,0.9986166,0.0000061025303,0.000006882053,0.0006848806],"study_design_scores_gemma":[0.00002110396,0.00093851844,0.018619206,0.000010220033,0.000056476056,0.000027474402,0.000058437083,0.001626284,0.97804374,0.000009844287,0.0005763481,0.000012395475],"about_ca_topic_score_codex":0.0009441867,"about_ca_topic_score_gemma":0.0013558678,"teacher_disagreement_score":0.0009441867,"about_ca_system_score_codex":0.00018394674,"about_ca_system_score_gemma":0.00014543135,"threshold_uncertainty_score":0.0021899939},"labels":[],"label_agreement":null},{"id":"W2064851671","doi":"10.1007/s11336-012-9268-2","title":"Functional Extended Redundancy Analysis","year":2012,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Concordia University; McGill University","funders":"","keywords":"Redundancy (engineering); Multivariate statistics; Mathematics; Mathematical optimization; Set (abstract data type); Algorithm; Computer science; Applied mathematics; Statistics","score_opus":0.08257755918806066,"score_gpt":0.31565240148637175,"score_spread":0.2330748422983111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064851671","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":[0.010342509,0.00035425657,0.9869358,0.00018864407,0.00006557939,0.00009392919,0.0003210848,0.00031106392,0.001387046],"genre_scores_gemma":[0.30895218,0.000653652,0.68473643,0.00030281945,0.00019953043,0.0006627707,0.0016787404,0.00029838865,0.0025154885],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99279976,0.00398591,0.00036806017,0.0012822199,0.0013080838,0.00025587587],"domain_scores_gemma":[0.9919727,0.002966994,0.00088085106,0.002097964,0.0019026374,0.00017883316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008832665,0.0020387569,0.0016889699,0.004123849,0.0009948517,0.0019400654,0.002657148,0.000972885,0.0041125906],"category_scores_gemma":[0.01799799,0.0005273701,0.002757996,0.0037256656,0.0015221381,0.0022303455,0.0023819583,0.0013461367,0.0011539129],"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":[0.00033833223,0.00031106666,0.013103414,0.001559403,0.0023221443,0.0007170425,0.0011291968,0.17580977,0.0247652,0.25352657,0.009028372,0.51738954],"study_design_scores_gemma":[0.000059043796,0.0004463206,0.016228221,0.00016702841,0.00041285643,0.0007279799,0.00026567493,0.6797748,0.0054107537,0.27492243,0.021295844,0.00028903183],"about_ca_topic_score_codex":0.0022798553,"about_ca_topic_score_gemma":0.0017739454,"teacher_disagreement_score":0.008832665,"about_ca_system_score_codex":0.0008194267,"about_ca_system_score_gemma":0.0016814923,"threshold_uncertainty_score":0.04671216},"labels":[],"label_agreement":null},{"id":"W2070195602","doi":"10.1006/appe.1999.0290","title":"“Pass the ketchup, please”: familiar flavors increase children's willingness to taste novel foods","year":2000,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":150,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Aurora College; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Taste; Flavor; Psychology; Food science; Pet food; Advertising; Social psychology; Cognitive psychology; Chemistry; Business; Neuroscience","score_opus":0.013091737697087357,"score_gpt":0.22742490940830745,"score_spread":0.2143331717112201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070195602","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":[0.99850476,0.00008037914,0.00027021812,0.00010909515,0.000022892613,0.000016838936,0.0000694284,0.000026730759,0.0008997977],"genre_scores_gemma":[0.9937169,0.0002467298,0.0036083634,0.00028296234,0.000027799964,0.0001696142,0.00018803088,0.000039573675,0.0017200279],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961805,0.00012729014,0.000026357013,0.000086028085,0.000096984186,0.00004532428],"domain_scores_gemma":[0.9978083,0.00095606863,0.0006970771,0.00013483808,0.000079953476,0.00032383754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011503669,0.00059648737,0.00034157676,0.00033748164,0.00027816964,0.00073881855,0.00036432705,0.0010677396,0.008420818],"category_scores_gemma":[0.004420206,0.0004308985,0.0004881316,0.00026877175,0.0008848639,0.0009252835,0.0007066301,0.0016541019,0.00032105614],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.08135778,0.029152682,0.39551726,0.003143899,0.001369632,0.0011942725,0.007851012,0.0012523092,0.31956044,0.0027855644,0.010519199,0.146296],"study_design_scores_gemma":[0.0011894184,0.013887786,0.9530507,0.0001394486,0.00076209905,0.00045270068,0.0023948024,0.0017240391,0.023310382,0.0006673494,0.0023028997,0.000118383716],"about_ca_topic_score_codex":0.0023620103,"about_ca_topic_score_gemma":0.005029645,"teacher_disagreement_score":0.008420818,"about_ca_system_score_codex":0.0003293539,"about_ca_system_score_gemma":0.00031012524,"threshold_uncertainty_score":0.028170466},"labels":[],"label_agreement":null},{"id":"W2072430704","doi":"10.1111/j.1745-4603.2005.00018.x","title":"CONTRIBUTION OF GLYCEROL, ETHANOL AND SUGAR TO THE PERCEPTION OF VISCOSITY AND DENSITY ELICITED BY MODEL WHITE WINES","year":2005,"lang":"en","type":"article","venue":"Journal of Texture Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Wine; Sugar; Glycerol; Viscosity; Ethanol; Food science; Chemistry; White Wine; Materials science; Biochemistry; Composite material","score_opus":0.02869736517400063,"score_gpt":0.30996785114977105,"score_spread":0.2812704859757704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072430704","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":[0.99932003,0.00007104048,0.00040099458,0.000006202211,0.0000035293867,0.000005779691,0.000024497069,0.000003425686,0.00016440854],"genre_scores_gemma":[0.99907625,0.000092173155,0.0005729062,0.000015791027,0.0000026575412,0.0000060702027,0.0000503496,0.000004015198,0.00017986028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999255,0.000021030612,0.0000043480486,0.000010015052,0.00002654364,0.000012598569],"domain_scores_gemma":[0.99977547,0.00009012246,0.000054977652,0.0000088682045,0.000040801096,0.00002984574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019887152,0.0002668417,0.00023322234,0.00016148605,0.000082329454,0.0005091499,0.00008003265,0.00012283295,0.00085793785],"category_scores_gemma":[0.0006821566,0.00012709101,0.00016706629,0.000113491,0.00015251218,0.00017995542,0.00020539392,0.00031393862,0.000042402888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027546596,0.00011814702,0.006466213,0.00012702188,0.000041971784,0.00005778688,0.00013934642,0.00039637127,0.9842057,0.000106087755,0.000058812824,0.005527718],"study_design_scores_gemma":[0.000105095256,0.0044702557,0.3141207,0.000049803195,0.00024258716,0.000263206,0.0009563244,0.012511211,0.66572523,0.0005597262,0.00093574217,0.000060124097],"about_ca_topic_score_codex":0.001411373,"about_ca_topic_score_gemma":0.0010988949,"teacher_disagreement_score":0.001411373,"about_ca_system_score_codex":0.00016344989,"about_ca_system_score_gemma":0.0001055368,"threshold_uncertainty_score":0.0028701425},"labels":[],"label_agreement":null},{"id":"W2072554440","doi":"10.2333/bhmk.34.95","title":"Multilevel Generalized Structured Component Analysis","year":2007,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Latent variable; Mathematics; Statistics; Partial least squares regression; Latent class model; Econometrics; Component analysis; Component (thermodynamics); Generalized least squares; Path analysis (statistics); Nested set model; Group (periodic table); Computer science; Data mining","score_opus":0.064166188615028,"score_gpt":0.33115747256389966,"score_spread":0.26699128394887167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072554440","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":[0.0108803995,0.00015372575,0.98432356,0.00008744823,0.00007952129,0.00019844987,0.001317894,0.0014971248,0.0014620334],"genre_scores_gemma":[0.1928082,0.00023823566,0.78884095,0.000115515126,0.00008718832,0.0016454681,0.0061476775,0.0016026273,0.008514217],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975249,0.0010389669,0.00011626465,0.0007424071,0.0003482507,0.00022915287],"domain_scores_gemma":[0.99601656,0.0016810214,0.0001674494,0.0011019204,0.00093240745,0.00010058712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024039506,0.0010920095,0.001448338,0.0013640416,0.00082028296,0.0013729137,0.0016795067,0.0006932066,0.019778922],"category_scores_gemma":[0.0114831915,0.00049897516,0.0037645497,0.0018223518,0.00047653203,0.0008029852,0.0019011616,0.001518858,0.0044634254],"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":[0.0008272122,0.0001987422,0.0064775893,0.0005734544,0.0014527062,0.000144938,0.00049978844,0.057047747,0.009192771,0.054593448,0.025884738,0.8431069],"study_design_scores_gemma":[0.00012239607,0.0003235613,0.023797767,0.00013702964,0.00075618573,0.00020170078,0.00024250743,0.8429249,0.005605066,0.09259691,0.033142477,0.00014955133],"about_ca_topic_score_codex":0.005590922,"about_ca_topic_score_gemma":0.010840858,"teacher_disagreement_score":0.019778922,"about_ca_system_score_codex":0.00049269845,"about_ca_system_score_gemma":0.0019135617,"threshold_uncertainty_score":0.066167116},"labels":[],"label_agreement":null},{"id":"W2072848950","doi":"10.1002/cjs.5550340201","title":"Regression and correlation for 3 × 3 rotation matrices","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rotation (mathematics); Estimator; Independence (probability theory); Mathematics; Rotation matrix; Matrix (chemical analysis); Regression analysis; Linear regression; Least-squares function approximation; Regression; Rank (graph theory); Statistics; Design matrix; Permutation (music); Algorithm; Applied mathematics; Combinatorics; Geometry","score_opus":0.028133489790747195,"score_gpt":0.2531385318061856,"score_spread":0.22500504201543842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072848950","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":[0.047054283,0.00030703688,0.9500357,0.00023032876,0.00006698604,0.000075444535,0.00022058314,0.0003260642,0.0016835101],"genre_scores_gemma":[0.8078908,0.000693319,0.181847,0.0002072842,0.00010776852,0.00038244863,0.0006525446,0.0003384861,0.007880312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9912752,0.004863158,0.00028241274,0.0017408596,0.0012253852,0.0006129],"domain_scores_gemma":[0.9627335,0.02478216,0.004786792,0.0044858255,0.002913402,0.00029829392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013749322,0.0010584434,0.0012386102,0.0015672476,0.00046786983,0.0017949091,0.0012306115,0.0011533013,0.0058126473],"category_scores_gemma":[0.055884235,0.00071071676,0.001732501,0.0026129843,0.0025224905,0.0021759227,0.001462263,0.0015401843,0.0019890035],"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":[0.00032529907,0.000157647,0.025739823,0.0002230144,0.00031703844,0.0005539369,0.0006316659,0.5145525,0.004741712,0.3367618,0.004377436,0.11161812],"study_design_scores_gemma":[0.000015981124,0.00008798341,0.007934234,0.00004496389,0.00002997916,0.00018531455,0.000056267563,0.91371536,0.0010349461,0.07394238,0.0028909822,0.00006151969],"about_ca_topic_score_codex":0.0074829077,"about_ca_topic_score_gemma":0.003527548,"teacher_disagreement_score":0.013749322,"about_ca_system_score_codex":0.001179974,"about_ca_system_score_gemma":0.0013526665,"threshold_uncertainty_score":0.07271421},"labels":[],"label_agreement":null},{"id":"W2074559029","doi":"10.1007/s10508-011-9742-4","title":"Further Validation of Blanchard’s Typology: A Reply to Nuttbrock, Bockting, Rosenblum, Mason, and Hwahng (2010)","year":2011,"lang":"en","type":"letter","venue":"Archives of Sexual Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Typology; Psychology; Public health; Quality of Life Research; Sexual behavior; Social psychology; Sociology; Medicine; Nursing; Anthropology","score_opus":0.06422732248645475,"score_gpt":0.285236588146019,"score_spread":0.22100926565956425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074559029","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009735064,0.00056123646,0.00014675096,0.9919503,0.0068363175,0.0000030311667,0.000015224732,0.000008962577,0.00038082147],"genre_scores_gemma":[0.0016729194,0.00031395908,0.00035868314,0.98330957,0.01281212,0.000027666247,0.000010757289,0.000021850079,0.0014725537],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.984201,0.0056765247,0.0021019753,0.0027587279,0.004044961,0.0012167643],"domain_scores_gemma":[0.8719887,0.095189825,0.003986156,0.004460517,0.018902497,0.0054722712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027777564,0.0010668305,0.0024449378,0.0017874314,0.008315193,0.008105192,0.0051188795,0.07749553,0.0066520846],"category_scores_gemma":[0.11505689,0.0012380712,0.0015494714,0.0022568216,0.01936209,0.012462528,0.00582148,0.10361046,0.005624646],"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":[0.000036402533,0.00001980132,0.0002366316,0.000045415934,0.000011717795,0.00017852982,0.0006958177,0.00002226522,0.00007803951,0.007638988,0.98457503,0.0064613703],"study_design_scores_gemma":[0.00009755123,0.000040164396,0.0016560049,0.00076426804,0.00003511955,0.00061778736,0.0026199883,0.00039250267,0.0003065588,0.046454165,0.9468581,0.00015775043],"about_ca_topic_score_codex":0.014399257,"about_ca_topic_score_gemma":0.024604497,"teacher_disagreement_score":0.07749553,"about_ca_system_score_codex":0.007402905,"about_ca_system_score_gemma":0.007618152,"threshold_uncertainty_score":0.14690357},"labels":[],"label_agreement":null},{"id":"W2075751391","doi":"10.1007/s10681-005-5891-7","title":"Identifying flavour targets for fruit breeding: A kiwifruit example","year":2005,"lang":"en","type":"article","venue":"Euphytica","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Flavour; Sweetness; Germplasm; Cultivar; Biology; Actinidia; Preference; Sugar; Biotechnology; Flavor; Horticulture; Food science; Mathematics","score_opus":0.13320427969150997,"score_gpt":0.326441619465224,"score_spread":0.19323733977371402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075751391","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":[0.9644452,0.0007751695,0.031468857,0.00036999912,0.000012044903,0.000047928384,0.00029246096,0.0001440721,0.0024443106],"genre_scores_gemma":[0.9422694,0.00062404247,0.053557403,0.00014027189,0.000009763308,0.00002742892,0.00031054683,0.000057772282,0.0030033013],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998933,0.000038428076,0.000003956725,0.000024355493,0.000029841485,0.000010125709],"domain_scores_gemma":[0.9995183,0.0002750816,0.000033558168,0.000046109097,0.00009402184,0.000032948195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064105797,0.0003924114,0.0003172531,0.0003716305,0.00038508364,0.00050484046,0.0002696146,0.00034244245,0.000738514],"category_scores_gemma":[0.00072094105,0.00008207438,0.00029387587,0.00046649063,0.00020475898,0.00021431196,0.00027142,0.00044235826,0.00011318331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015728128,0.0004381801,0.06590808,0.0002497899,0.00030385185,0.0008085356,0.00063760544,0.0035049247,0.7285057,0.0018411461,0.0012362509,0.19499317],"study_design_scores_gemma":[0.00014335816,0.001984888,0.7239967,0.00006121989,0.00068994134,0.0012506909,0.0014920878,0.0740067,0.1764173,0.0063732094,0.01342413,0.00015979388],"about_ca_topic_score_codex":0.007882984,"about_ca_topic_score_gemma":0.025204666,"teacher_disagreement_score":0.007882984,"about_ca_system_score_codex":0.0002625881,"about_ca_system_score_gemma":0.00019310432,"threshold_uncertainty_score":0.015674174},"labels":[],"label_agreement":null},{"id":"W2075882365","doi":"10.1016/j.foodqual.2015.02.015","title":"Product selection for liking studies: The sensory informed design","year":2015,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Ontario Universities’ Application Centre; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Product (mathematics); Product design; Computer science; Sensory system; Variety (cybernetics); Selection (genetic algorithm); Imputation (statistics); Marketing; Cognitive psychology; Psychology; Mathematics; Machine learning; Artificial intelligence; Business; Missing data","score_opus":0.776046502817835,"score_gpt":0.45167899850445614,"score_spread":0.3243675043133789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075882365","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":[0.04896764,0.00037846836,0.91534793,0.00026743428,0.00032819962,0.02851378,0.0007848601,0.0006054839,0.0048061456],"genre_scores_gemma":[0.124635845,0.00038224418,0.81069607,0.00046533067,0.00010022373,0.0610134,0.0004972052,0.00016671245,0.0020429417],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.93701196,0.04733928,0.00301873,0.003948752,0.007920251,0.00076101517],"domain_scores_gemma":[0.9592509,0.024126511,0.0032869885,0.007370763,0.005247598,0.00071723014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049931753,0.0017902615,0.0027585602,0.0021150652,0.0010308916,0.0023759175,0.0025255315,0.0018988096,0.007951893],"category_scores_gemma":[0.06874872,0.0010768858,0.002295825,0.0024076987,0.0022778853,0.0012687051,0.0022661423,0.002659568,0.0013310056],"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":[0.032319516,0.006695096,0.011978653,0.0049785124,0.0021534204,0.00020559663,0.0024020083,0.038733423,0.030375462,0.07415085,0.0055553014,0.7904522],"study_design_scores_gemma":[0.019964049,0.11539112,0.058459707,0.0017171329,0.0035923466,0.0005878671,0.0016988005,0.34134555,0.05954245,0.25050223,0.14605036,0.0011484174],"about_ca_topic_score_codex":0.00040919048,"about_ca_topic_score_gemma":0.0005891414,"teacher_disagreement_score":0.049931753,"about_ca_system_score_codex":0.0014218091,"about_ca_system_score_gemma":0.0026141838,"threshold_uncertainty_score":0.26406747},"labels":[],"label_agreement":null},{"id":"W2076626005","doi":"10.6000/1927-520x.2013.02.03.2","title":"Buffalo Sensory Analysis of Meat in the City of Medellin, Colombia, South America","year":2013,"lang":"en","type":"article","venue":"Journal of Buffalo Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariate analysis of variance; Statistics; Analysis of variance; Taste; Statistical analysis; Canonical analysis; Mathematics; Psychology; Food science; Biology","score_opus":0.05653035419985435,"score_gpt":0.2988274665914551,"score_spread":0.24229711239160073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076626005","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":[0.997331,0.00026469,0.00018450692,0.0000319477,0.000006034312,0.000019673285,0.0004329843,0.0000061945375,0.0017229061],"genre_scores_gemma":[0.9980981,0.00017435197,0.000392529,0.00001757847,0.0000035818975,0.000016853826,0.00027595263,0.0000033644965,0.001017805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998093,0.000033085613,0.000011518784,0.000045751643,0.000071916365,0.000028386878],"domain_scores_gemma":[0.9997503,0.000041507134,0.000068350724,0.000009650996,0.00009311776,0.000036981408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023189218,0.000282477,0.00025940052,0.0012220208,0.00060279865,0.00069054373,0.0001677103,0.00015324542,0.0031293523],"category_scores_gemma":[0.00046681068,0.00009320271,0.00015160727,0.0011188638,0.00032754027,0.00020501022,0.00039385183,0.0001537756,0.00018269608],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052905845,0.00015874456,0.89394975,0.0005530039,0.00010180873,0.00076233543,0.0078045917,0.0001743009,0.047877803,0.00015385491,0.0009732416,0.046961527],"study_design_scores_gemma":[0.0000013949945,0.000042374173,0.99522036,0.000010802512,0.000008969821,0.00009568128,0.0035065992,0.00006734446,0.00039151244,0.000010094971,0.0006401819,0.0000047003673],"about_ca_topic_score_codex":0.07878109,"about_ca_topic_score_gemma":0.2653685,"teacher_disagreement_score":0.07878109,"about_ca_system_score_codex":0.0007997691,"about_ca_system_score_gemma":0.0004151792,"threshold_uncertainty_score":0.15664506},"labels":[],"label_agreement":null},{"id":"W2081487426","doi":"10.1080/10942910009524622","title":"New multivariate strategy for panel evaluation using principal component similarity","year":2000,"lang":"en","type":"article","venue":"International Journal of Food Properties","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principal component analysis; Outlier; Similarity (geometry); Artificial intelligence; Multivariate statistics; Pattern recognition (psychology); Statistics; Sample (material); Mathematics; Computer science; Data mining; Chemistry","score_opus":0.37909343264011447,"score_gpt":0.3827001770502986,"score_spread":0.003606744410184104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081487426","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":[0.0028774838,0.000027590131,0.995754,0.00002628186,0.000016404316,0.00012391647,0.00004390923,0.0006005496,0.0005298189],"genre_scores_gemma":[0.063353494,0.000055648663,0.933956,0.000034535118,0.000060171464,0.0004727442,0.00045645764,0.0002849737,0.0013259475],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9917537,0.003578197,0.00056882977,0.0012751253,0.0024414533,0.00038266878],"domain_scores_gemma":[0.9922833,0.0024625908,0.00044797055,0.00087159884,0.003676665,0.00025793645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006593335,0.0015560167,0.0018324527,0.0055334144,0.0012122944,0.0021870378,0.0018100422,0.0009211286,0.006469819],"category_scores_gemma":[0.015576004,0.0006635519,0.0017492058,0.004147859,0.000801443,0.0016614003,0.002256928,0.0015137626,0.002918811],"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":[0.00023042902,0.00027256715,0.002682068,0.00020574172,0.00022398535,0.00016103974,0.00040477322,0.034264233,0.018414587,0.016871907,0.0039205356,0.9223481],"study_design_scores_gemma":[0.000037039175,0.00024679117,0.0040409383,0.000028155086,0.00010368139,0.0001394216,0.00023456589,0.954495,0.012577801,0.020204406,0.0077910847,0.000101099926],"about_ca_topic_score_codex":0.0027842592,"about_ca_topic_score_gemma":0.00208612,"teacher_disagreement_score":0.006593335,"about_ca_system_score_codex":0.00075542723,"about_ca_system_score_gemma":0.0015357117,"threshold_uncertainty_score":0.034869313},"labels":[],"label_agreement":null},{"id":"W2082720096","doi":"10.1016/j.jneb.2012.03.140","title":"Using Plate Mapping to Examine Sensitivity to Plate Size in Food Portions and Meal Composition","year":2012,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portion size; Meal; Sensitivity (control systems); Composition (language); Food science; Chemistry; Engineering; Art","score_opus":0.11778997560488215,"score_gpt":0.35813232696899655,"score_spread":0.24034235136411441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082720096","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":[0.8873405,0.00028461663,0.10830579,0.000044922184,0.00007433334,0.00018824376,0.0004584305,0.00042168805,0.002881488],"genre_scores_gemma":[0.92095846,0.00015371667,0.07495358,0.00013688358,0.00001878933,0.00034317016,0.0003240793,0.00020743292,0.00290402],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99882704,0.0003228508,0.00004575641,0.00033853095,0.00038154807,0.00008431026],"domain_scores_gemma":[0.9945075,0.003918608,0.00043617914,0.00043219124,0.0005482335,0.00015738158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013579057,0.00056459964,0.00040471274,0.00090402545,0.00032589815,0.00063204445,0.00062924146,0.0006035831,0.0026987342],"category_scores_gemma":[0.0058297347,0.0006524132,0.00046121966,0.0008830934,0.00046349145,0.0006791707,0.0006514926,0.0006620939,0.00038034024],"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":[0.0036434904,0.0004759191,0.039805043,0.00023023317,0.00021593123,0.00006774403,0.00046283472,0.0025159474,0.8959116,0.00036639196,0.00028123177,0.05602367],"study_design_scores_gemma":[0.00010539271,0.0030118243,0.5381102,0.000033205473,0.0003461433,0.0006854102,0.00061227335,0.041823912,0.41250557,0.0010590188,0.0015665289,0.00014049768],"about_ca_topic_score_codex":0.0029346868,"about_ca_topic_score_gemma":0.004725462,"teacher_disagreement_score":0.0029346868,"about_ca_system_score_codex":0.00031936672,"about_ca_system_score_gemma":0.00030954008,"threshold_uncertainty_score":0.009028137},"labels":[],"label_agreement":null},{"id":"W2085008017","doi":"10.1006/jmps.1999.1296","title":"Mathematical Studies of the Information in the Stimulus–Response Matrix","year":2001,"lang":"en","type":"article","venue":"Journal of Mathematical Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stimulus (psychology); Mathematics; Monotonic function; Mathematical analysis; Cognitive psychology; Psychology","score_opus":0.11174295089504539,"score_gpt":0.4282511150377332,"score_spread":0.31650816414268784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085008017","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":[0.056672264,0.0020652786,0.9269931,0.0029271536,0.00010865131,0.00003449464,0.00014967636,0.00010440517,0.010945049],"genre_scores_gemma":[0.7581385,0.0033278097,0.23010112,0.00058150105,0.00088885106,0.0001452031,0.00021685396,0.00030924566,0.006290885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974595,0.0012602725,0.00020825348,0.0002730718,0.0006450275,0.00015388062],"domain_scores_gemma":[0.9107902,0.078656495,0.0036559636,0.0036077988,0.002714395,0.0005752649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062755975,0.0009205285,0.0007648479,0.0026490805,0.0006252826,0.002650085,0.0019132941,0.0012831605,0.005659452],"category_scores_gemma":[0.07278927,0.00066836306,0.0010978758,0.0018800183,0.0037038887,0.00997744,0.0017923053,0.0029917634,0.0006769661],"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":[0.000032613272,0.00003623515,0.00033703446,0.00014635154,0.000026308568,0.000069385664,0.0001723724,0.020375123,0.0022656966,0.96497434,0.0006565943,0.010907964],"study_design_scores_gemma":[0.00001093003,0.000028886572,0.00069948024,0.000038287984,0.00001502786,0.00021663257,0.000037449085,0.22944243,0.0010887538,0.7671446,0.0012423284,0.000035141395],"about_ca_topic_score_codex":0.0007426405,"about_ca_topic_score_gemma":0.0005458666,"teacher_disagreement_score":0.0062755975,"about_ca_system_score_codex":0.0011213046,"about_ca_system_score_gemma":0.0010290963,"threshold_uncertainty_score":0.03318894},"labels":[],"label_agreement":null},{"id":"W2085370823","doi":"10.1016/j.jneb.2010.02.011","title":"Food Group Categories of Low-income African American Women","year":2011,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Northwestern University","keywords":"Food group; Psychology; African american; Population; Social psychology; Demography; Environmental health; Medicine; Sociology","score_opus":0.037162688996898695,"score_gpt":0.28952627751750454,"score_spread":0.25236358852060586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085370823","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":[0.9989975,0.000037990198,0.000032965243,0.000018554325,0.0000035158064,0.000008505684,0.00037162218,0.0000014211416,0.00052798045],"genre_scores_gemma":[0.9990513,0.00004122591,0.00007055784,0.000022071292,0.000003939851,0.000018958539,0.00023474825,0.0000016521989,0.0005554385],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998367,0.000044689998,0.000016229033,0.000026831844,0.000024363824,0.000051287934],"domain_scores_gemma":[0.9997099,0.000080513455,0.00008320648,0.000012512858,0.000052306186,0.00006157905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023441257,0.00025914243,0.00023009809,0.0012973524,0.00055928965,0.000397838,0.00020628436,0.00031653047,0.0046466757],"category_scores_gemma":[0.0009387652,0.00015480793,0.00025193536,0.0008775774,0.00018634406,0.0003199842,0.00038142008,0.00022013801,0.0004270188],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011158452,0.00023938387,0.9898413,0.000030620242,0.000057402834,0.0001391029,0.0011035347,0.00003264623,0.002366482,0.00007435488,0.00030492508,0.0046944665],"study_design_scores_gemma":[0.0000057451266,0.00014184727,0.9970681,0.000006259454,0.000012603764,0.000090175214,0.002224749,0.00011388947,0.00010833675,0.000033546185,0.00019068077,0.000004109829],"about_ca_topic_score_codex":0.011965454,"about_ca_topic_score_gemma":0.017819932,"teacher_disagreement_score":0.011965454,"about_ca_system_score_codex":0.00018198429,"about_ca_system_score_gemma":0.00013794213,"threshold_uncertainty_score":0.023791611},"labels":[],"label_agreement":null},{"id":"W2086111854","doi":"10.1111/j.0006-341x.2000.00893.x","title":"A Simple Test of Association for Contingency Tables with Multiple Column Responses","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Contingency table; Categorical variable; Statistics; Test (biology); Null hypothesis; Test statistic; Chi-square test; Association (psychology); Simple (philosophy); Statistic; Pearson's chi-squared test; Statistical hypothesis testing; Mathematics; Column (typography); Biometrics; Computer science; Econometrics; Artificial intelligence; Psychology","score_opus":0.04901399032510706,"score_gpt":0.29440765740321456,"score_spread":0.2453936670781075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086111854","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":[0.13077617,0.00084728823,0.8319053,0.0011417123,0.0017631791,0.004373464,0.010329833,0.0035331126,0.015329983],"genre_scores_gemma":[0.50001067,0.0003835804,0.48391432,0.0006504581,0.00069505017,0.0064198496,0.0049090404,0.00027227085,0.0027447697],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9407298,0.030383645,0.005855703,0.007891553,0.014371403,0.00076785975],"domain_scores_gemma":[0.5358943,0.41127455,0.018542496,0.017117813,0.015510317,0.0016606183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032447767,0.0010479286,0.002674444,0.007032336,0.0015696873,0.0030157964,0.0029804248,0.0018634665,0.02141011],"category_scores_gemma":[0.3125676,0.0005569461,0.0023635142,0.010311974,0.0022824784,0.0057764472,0.0024692605,0.0022925308,0.0025469118],"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":[0.0033328724,0.0014729949,0.26062474,0.0034043216,0.0057414197,0.0014695888,0.003010559,0.0063707912,0.006253542,0.07916458,0.03861671,0.590538],"study_design_scores_gemma":[0.001947878,0.0120717,0.34418297,0.001523144,0.002684855,0.011039788,0.0051805982,0.19799902,0.0133426655,0.3166894,0.092196755,0.0011411879],"about_ca_topic_score_codex":0.0006602955,"about_ca_topic_score_gemma":0.000680398,"teacher_disagreement_score":0.032447767,"about_ca_system_score_codex":0.0007681476,"about_ca_system_score_gemma":0.0019836277,"threshold_uncertainty_score":0.17160225},"labels":[],"label_agreement":null},{"id":"W2086403857","doi":"10.1300/j038v09n01_06","title":"Strategies of Differentiation in Perceived Quality: An Analysis of Factorial Invariance in Multigroup Sampling","year":2003,"lang":"en","type":"article","venue":"Journal of Food Products Marketing","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"La Cité Collégiale; Université du Québec en Outaouais; Université du Québec à Montréal","funders":"","keywords":"Quality (philosophy); Factorial analysis; Factorial; Marketing; Factorial experiment; Business; Work (physics); Sampling (signal processing); Econometrics; Statistics; Mathematics; Computer science; Engineering","score_opus":0.10308883392006035,"score_gpt":0.3409240365382813,"score_spread":0.23783520261822094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086403857","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":[0.9094962,0.000076463904,0.08715512,0.000094487696,0.000037572103,0.0005017297,0.000114348004,0.000098235265,0.0024258697],"genre_scores_gemma":[0.99047804,0.000013289744,0.008871815,0.000019638534,0.000013423618,0.00031795743,0.00012206299,0.00002802144,0.00013577584],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8826349,0.0934904,0.0031681277,0.009318863,0.009150987,0.0022367726],"domain_scores_gemma":[0.6272005,0.31385246,0.013557794,0.03653211,0.0068075596,0.0020496277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.077645026,0.000874649,0.001135583,0.002290198,0.0012588185,0.0019302013,0.0013829753,0.0010586693,0.00453944],"category_scores_gemma":[0.18370406,0.00048088038,0.0028989094,0.0021387516,0.004388435,0.0021695667,0.002068157,0.0012245219,0.00030368078],"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":[0.005823447,0.0029296542,0.7682869,0.00031668402,0.0036150157,0.00056847313,0.013664059,0.012072827,0.01355512,0.03961579,0.000982784,0.13856931],"study_design_scores_gemma":[0.0003095191,0.004451485,0.85691166,0.00005696123,0.00075116265,0.0002627516,0.003102982,0.11275408,0.0031842026,0.016958332,0.0010838468,0.0001730507],"about_ca_topic_score_codex":0.0021113348,"about_ca_topic_score_gemma":0.00094631256,"teacher_disagreement_score":0.077645026,"about_ca_system_score_codex":0.0013318672,"about_ca_system_score_gemma":0.0010936658,"threshold_uncertainty_score":0.41063106},"labels":[],"label_agreement":null},{"id":"W2086463197","doi":"10.1016/j.meatsci.2010.03.005","title":"Consumer choice of pork chops in Taiwan","year":2010,"lang":"en","type":"article","venue":"Meat Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Marbled meat; Advertising; Business; Preference; Consumer choice; Marketing; Mathematics; Biology; Statistics","score_opus":0.04198684829448333,"score_gpt":0.3177456514127743,"score_spread":0.27575880311829093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086463197","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":[0.99989605,0.000007858102,0.000007038021,0.000009183767,4.7078606e-7,8.748578e-7,0.000010619335,2.7639948e-7,0.000067560846],"genre_scores_gemma":[0.9996233,0.00001452934,0.000026202717,0.000011871808,7.9059123e-7,0.0000015303971,0.000033491164,9.2045076e-7,0.00028730519],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985063,0.0000333559,0.000011953246,0.00004190771,0.000021474863,0.000040722178],"domain_scores_gemma":[0.9995995,0.0001100367,0.00010273033,0.000020122476,0.000048829097,0.00011872506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003172501,0.0001662273,0.00018714907,0.0004915187,0.00043838707,0.000629332,0.00016674607,0.0004351539,0.0025885208],"category_scores_gemma":[0.0006969732,0.00024003402,0.0004292586,0.0005122557,0.00031097405,0.00040661418,0.00036480874,0.00042375142,0.00019456698],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023696828,0.0004975276,0.9672382,0.000056673834,0.00015051663,0.0010339012,0.011546053,0.00015256778,0.009705701,0.00015388409,0.00020488774,0.0068904012],"study_design_scores_gemma":[0.00001732167,0.00031883552,0.99102205,0.0000074593568,0.000040401308,0.00016662832,0.0072719036,0.00062158913,0.00028031846,0.000039537288,0.00020234859,0.000011672922],"about_ca_topic_score_codex":0.027044147,"about_ca_topic_score_gemma":0.048804414,"teacher_disagreement_score":0.027044147,"about_ca_system_score_codex":0.0005863894,"about_ca_system_score_gemma":0.00028694898,"threshold_uncertainty_score":0.053773463},"labels":[],"label_agreement":null},{"id":"W2086591225","doi":"10.1016/j.appet.2013.09.025","title":"Does liking or wanting determine repeat consumption delay?","year":2013,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Consumption (sociology); Psychology; Social psychology; Food consumption; Developmental psychology; Economics","score_opus":0.046537169150053126,"score_gpt":0.2785530460596942,"score_spread":0.2320158769096411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086591225","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":[0.9962359,0.00013685772,0.002398698,0.00004357211,0.000019854228,0.000017944156,0.00011710661,0.000024610496,0.0010054297],"genre_scores_gemma":[0.99802446,0.000048887265,0.0011567567,0.00004395725,0.000011298665,0.000020097055,0.00010235827,0.000026996286,0.00056520465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99953675,0.00013614597,0.000036997142,0.00013434055,0.00009805244,0.000057830202],"domain_scores_gemma":[0.9943869,0.0034389708,0.001027817,0.00053916144,0.00026595843,0.00034112582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017187267,0.00024191895,0.00044746854,0.0002489829,0.00014422108,0.0006881305,0.00022731823,0.00055400206,0.0031363785],"category_scores_gemma":[0.008501996,0.0002678664,0.00035755883,0.0002987381,0.00046165602,0.00074159703,0.00023921541,0.0007135889,0.000321572],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0067947065,0.0008389418,0.7994822,0.00024738957,0.00055889034,0.00016920904,0.0011586692,0.00061716617,0.14261165,0.00065416814,0.0004504158,0.046416655],"study_design_scores_gemma":[0.000020465091,0.00076123205,0.992029,0.000008313827,0.00007178455,0.00014209763,0.00025354,0.0020660567,0.004007035,0.0003442318,0.00027775,0.000018371298],"about_ca_topic_score_codex":0.0016056922,"about_ca_topic_score_gemma":0.0026419607,"teacher_disagreement_score":0.0031363785,"about_ca_system_score_codex":0.00015922314,"about_ca_system_score_gemma":0.00019503637,"threshold_uncertainty_score":0.010492206},"labels":[],"label_agreement":null},{"id":"W2089800891","doi":"10.3758/s13428-011-0155-z","title":"A test for psychometric function shift","year":2011,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Psychometric function; Statistics; Test statistic; Nonparametric statistics; Statistic; Homogeneity (statistics); Contingency table; Mathematics; Psychometrics; Null distribution; Weibull distribution; Statistical hypothesis testing; Psychology","score_opus":0.6640702287473397,"score_gpt":0.5703768996849142,"score_spread":0.09369332906242545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089800891","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79672617,0.00049545267,0.14701284,0.0007290382,0.00066516246,0.0008894862,0.0032842048,0.0018036376,0.048394024],"genre_scores_gemma":[0.97439945,0.00006530936,0.018022414,0.0003590638,0.00008616421,0.0012327797,0.0017417448,0.00030860386,0.003784458],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9878225,0.0029878907,0.0013725493,0.002295331,0.0048501287,0.000671722],"domain_scores_gemma":[0.87446666,0.09631515,0.006150819,0.013706909,0.007547861,0.0018125926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014501425,0.0013224946,0.0011846968,0.0037779126,0.000996089,0.0018881721,0.0015041635,0.002284249,0.019283758],"category_scores_gemma":[0.117961586,0.00029401854,0.002210503,0.0019437491,0.002062288,0.0044913557,0.0035742857,0.0020699578,0.004395745],"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":[0.008264266,0.0032594593,0.47062218,0.0007698695,0.0021973967,0.0010396177,0.0028206955,0.006939608,0.032649085,0.030021148,0.01349085,0.42792574],"study_design_scores_gemma":[0.0011458128,0.010977785,0.81697,0.00035849545,0.0008132552,0.0039006667,0.0033573683,0.054669578,0.024405483,0.063330196,0.019686101,0.0003852997],"about_ca_topic_score_codex":0.0007529104,"about_ca_topic_score_gemma":0.0003639322,"teacher_disagreement_score":0.019283758,"about_ca_system_score_codex":0.00075950264,"about_ca_system_score_gemma":0.0011109123,"threshold_uncertainty_score":0.07669175},"labels":[],"label_agreement":null},{"id":"W2092315339","doi":"10.2333/bhmk.37.111","title":"Fuzzy Cluster Multiple Correspondence Analysis","year":2010,"lang":"es","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cluster (spacecraft); Multiple correspondence analysis; Computer science; Variable (mathematics); Monte Carlo method; Extant taxon; Fuzzy logic; Population; Interpretation (philosophy); Data mining; Statistics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.0405276773651684,"score_gpt":0.32562136618422627,"score_spread":0.2850936888190579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092315339","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":[0.012352754,0.00016199648,0.98318565,0.000088790795,0.00006631864,0.00015604463,0.00028544205,0.00076345576,0.0029395497],"genre_scores_gemma":[0.25773752,0.00014584267,0.7321538,0.000061153136,0.000067139175,0.0005165611,0.0011297994,0.0004065153,0.007781622],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99562705,0.0012127361,0.00024415096,0.0014777116,0.0010884918,0.0003499006],"domain_scores_gemma":[0.996506,0.0013864118,0.00018812485,0.00047608052,0.0013324419,0.0001108367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003422348,0.0011050387,0.0018851362,0.006205781,0.0029091367,0.0027323633,0.0019202242,0.0014411844,0.010373874],"category_scores_gemma":[0.008205858,0.0006393986,0.0023812468,0.004430754,0.00092027633,0.0013812542,0.0015895927,0.001193895,0.00248103],"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":[0.000793114,0.0001749493,0.0020659291,0.00037083152,0.00035822982,0.00016196189,0.00042823862,0.11259904,0.007645961,0.04377178,0.0097448,0.82188517],"study_design_scores_gemma":[0.000026326186,0.00007099609,0.0018587419,0.00003013279,0.00007913549,0.000095888834,0.0001740942,0.9604169,0.006728448,0.024215693,0.0062533906,0.000050198643],"about_ca_topic_score_codex":0.008360917,"about_ca_topic_score_gemma":0.0073217456,"teacher_disagreement_score":0.010373874,"about_ca_system_score_codex":0.0013944947,"about_ca_system_score_gemma":0.0030663745,"threshold_uncertainty_score":0.03470403},"labels":[],"label_agreement":null},{"id":"W2094013107","doi":"10.1002/wics.1246","title":"Multiple factor analysis: principal component analysis for multitable and multiblock data sets","year":2013,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":552,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Principal component analysis; Categorical variable; Table (database); Exploratory data analysis; Data set; Data mining; Contingency table; Statistics; Computer science; Factor analysis; Mathematics; Exploratory factor analysis; Set (abstract data type)","score_opus":0.2573493746915503,"score_gpt":0.43043095032030143,"score_spread":0.17308157562875115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094013107","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017693148,0.00082366157,0.99051195,0.0005758252,0.00018890524,0.0005609721,0.0015932301,0.0028847319,0.0010914048],"genre_scores_gemma":[0.021867802,0.0006795673,0.9717681,0.0001395827,0.00015064204,0.0020901149,0.0020707762,0.000699413,0.0005339904],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9728927,0.015155199,0.0024986947,0.0045143734,0.0045213425,0.00041758874],"domain_scores_gemma":[0.93646353,0.04243232,0.004266796,0.008537377,0.0076077143,0.0006922101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026023472,0.0036278423,0.0026030056,0.007801946,0.0015912366,0.005247469,0.002923062,0.0016013583,0.014405139],"category_scores_gemma":[0.10138347,0.0014040326,0.0053017014,0.012101405,0.0018323096,0.005635738,0.004764104,0.004865884,0.0073149004],"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":[0.00047945062,0.00027749757,0.009195857,0.004048541,0.0032643287,0.00044323565,0.0019174778,0.029737327,0.0038190754,0.1058201,0.079849005,0.76114804],"study_design_scores_gemma":[0.0002483655,0.00035387123,0.01747911,0.0014263199,0.0006833896,0.0006241003,0.001158456,0.34946173,0.0045702523,0.5002123,0.12329968,0.00048247827],"about_ca_topic_score_codex":0.0027477813,"about_ca_topic_score_gemma":0.0027236198,"teacher_disagreement_score":0.026023472,"about_ca_system_score_codex":0.0015943546,"about_ca_system_score_gemma":0.0044170194,"threshold_uncertainty_score":0.13762695},"labels":[],"label_agreement":null},{"id":"W2095229143","doi":"10.15446/rce.v37n2spe.47934","title":"Recent Advances in Visualizing Multivariate Linear Models","year":2014,"lang":"en","type":"article","venue":"Revista Colombiana de Estadística","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"York University","funders":"","keywords":"Multivariate statistics; Ellipsoid; Pairwise comparison; Context (archaeology); Mathematics; Canonical correlation; Rank (graph theory); Visualization; Multivariate analysis; Extension (predicate logic); Statistics; Computer science; Data mining; Combinatorics; Geography","score_opus":0.04468509851055038,"score_gpt":0.3265081838678213,"score_spread":0.28182308535727096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095229143","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013950396,0.036599886,0.94878536,0.0028955059,0.00056377816,0.00004343702,0.00047921255,0.0042625214,0.0049752225],"genre_scores_gemma":[0.02946623,0.05432348,0.90650904,0.00075007125,0.00240332,0.00027334987,0.0012261227,0.0022141375,0.0028341752],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.988288,0.0070074056,0.0006535258,0.0013436157,0.0025111912,0.00019636088],"domain_scores_gemma":[0.95261484,0.03716075,0.0018304284,0.0036288863,0.0041022627,0.00066278805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01278205,0.002889221,0.0023685396,0.0065849912,0.0008878938,0.006946255,0.0030320091,0.0018239773,0.011755879],"category_scores_gemma":[0.050097432,0.0017233283,0.0034977815,0.009363813,0.0025217012,0.0067438106,0.0038286918,0.0049846764,0.0036265857],"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":[0.00012081284,0.00006697642,0.002310845,0.0038342937,0.0005244648,0.00017933137,0.0016431792,0.034133665,0.0032602474,0.1767847,0.034827646,0.7423138],"study_design_scores_gemma":[0.0000693455,0.0001311605,0.003444636,0.0015934124,0.00029499663,0.0008791762,0.0006571216,0.18913397,0.0054427907,0.28722444,0.5107184,0.00041056608],"about_ca_topic_score_codex":0.0058392994,"about_ca_topic_score_gemma":0.0050427434,"teacher_disagreement_score":0.01278205,"about_ca_system_score_codex":0.0017166253,"about_ca_system_score_gemma":0.0020080034,"threshold_uncertainty_score":0.06759876},"labels":[],"label_agreement":null},{"id":"W2095850045","doi":"10.1002/1521-4036(200206)44:4<410::aid-bimj410>3.0.co;2-o","title":"A Comparative Analysis of Graphical Interaction and Logistic Regression Modelling: Self-care and Coping with a Chronic Illness in Later Life","year":2002,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Logistic regression; Coping (psychology); Graphical model; Statistical graphics; Computer science; Econometrics; Regression analysis; Psychology; Context (archaeology); Management science; Mathematics; Artificial intelligence; Machine learning; Clinical psychology; Engineering","score_opus":0.12421132182608502,"score_gpt":0.3386160938905203,"score_spread":0.21440477206443526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095850045","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":[0.45227373,0.004911803,0.53220326,0.0024196096,0.00015298295,0.00017959409,0.00045461467,0.00064479664,0.006759603],"genre_scores_gemma":[0.88991445,0.0016310584,0.106369965,0.00013048337,0.00013374927,0.00027616532,0.00038736267,0.00014471126,0.001012057],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9630192,0.034136668,0.00041592642,0.0006852041,0.0013834378,0.00035950934],"domain_scores_gemma":[0.74624586,0.24441673,0.003808293,0.0030389705,0.0019577742,0.0005324592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027139667,0.0012319837,0.0010971439,0.0038013058,0.0003569771,0.00185633,0.0012441783,0.0011920421,0.0032683904],"category_scores_gemma":[0.093029395,0.00040597143,0.0031750135,0.0046035224,0.001443417,0.0022291467,0.0016492184,0.001215664,0.0003569184],"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":[0.0063185543,0.00078040356,0.21093085,0.0014864139,0.005839259,0.001461763,0.007344541,0.20450155,0.0033343371,0.15611637,0.0041143443,0.39777157],"study_design_scores_gemma":[0.00020434182,0.0017126185,0.11457005,0.00030001288,0.0010687739,0.0007198884,0.0018898863,0.79302883,0.0009103359,0.081754036,0.0035395413,0.00030173105],"about_ca_topic_score_codex":0.003337294,"about_ca_topic_score_gemma":0.003111621,"teacher_disagreement_score":0.027139667,"about_ca_system_score_codex":0.0008214712,"about_ca_system_score_gemma":0.0008970188,"threshold_uncertainty_score":0.14352995},"labels":[],"label_agreement":null},{"id":"W2095871404","doi":"10.5539/ijms.v7n5p65","title":"Sensory and Hedonic Evaluation in Response to Food-Cue Exposure: The Case of Juicing Demonstration of Fresh Oranges","year":2015,"lang":"en","type":"article","venue":"International Journal of Marketing Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Odor; Sweetness; Flavor; Food science; Orange (colour); Orange juice; Sensory system; Wine tasting; Psychology; Mathematics; Cognitive psychology; Chemistry","score_opus":0.15257349814787818,"score_gpt":0.3864943745458212,"score_spread":0.233920876397943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095871404","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":[0.9994868,0.000035895766,0.00023123316,0.000009788605,0.0000036297647,0.000020795906,0.000012841063,0.0000031787436,0.00019586174],"genre_scores_gemma":[0.998131,0.00008685459,0.0012507449,0.000044520806,0.0000081250955,0.000043627937,0.000037706963,0.0000053817776,0.00039208832],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996537,0.00011379161,0.000015278398,0.00006959836,0.000099963734,0.000047620873],"domain_scores_gemma":[0.9986614,0.00073956314,0.00025403034,0.00010002759,0.00011667451,0.00012822916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007322179,0.00032547864,0.00040185975,0.00019713784,0.00022715422,0.0004887669,0.00015542397,0.00055181235,0.0016068468],"category_scores_gemma":[0.0038464663,0.00023763189,0.00034948692,0.00016539189,0.00042703364,0.000331606,0.0005571837,0.0006073878,0.00009170459],"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":[0.01306399,0.0017613979,0.047436986,0.0005492693,0.00013400201,0.0012996574,0.007255889,0.00033197648,0.9095958,0.00017470589,0.00016782997,0.018228417],"study_design_scores_gemma":[0.00019049564,0.022290424,0.8641954,0.000048437032,0.00021758504,0.0009970365,0.004880867,0.0017378404,0.1040425,0.00036618146,0.0009283933,0.00010484202],"about_ca_topic_score_codex":0.0008568668,"about_ca_topic_score_gemma":0.0012039761,"teacher_disagreement_score":0.0016068468,"about_ca_system_score_codex":0.00016967037,"about_ca_system_score_gemma":0.00016661067,"threshold_uncertainty_score":0.005375445},"labels":[],"label_agreement":null},{"id":"W2096221952","doi":"10.1111/j.1745-459x.2001.tb00321.x","title":"A COMPARISON OF METHODS FOR EVALUATING THE PERFORMANCE OF A TRAINED SENSORY PANEL<sup>1</sup>","year":2001,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Principal component analysis; Sensory analysis; Reliability (semiconductor); Statistics; Sensory system; Conjunction (astronomy); Aroma; Quality (philosophy); Cluster (spacecraft); Computer science; Pattern recognition (psychology); Mathematics; Psychology; Artificial intelligence; Cognitive psychology; Food science; Chemistry","score_opus":0.4628711006252526,"score_gpt":0.5098913916782787,"score_spread":0.047020291053026075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096221952","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":[0.4246331,0.002634073,0.5622884,0.00023040542,0.0002577971,0.0015880215,0.00091718155,0.0015521302,0.0058989017],"genre_scores_gemma":[0.4407755,0.0010098764,0.55326355,0.000076224125,0.000059564158,0.001501048,0.0007343049,0.0002779879,0.0023018748],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9789367,0.011719331,0.0010849707,0.0015876908,0.0063747563,0.00029666748],"domain_scores_gemma":[0.93879664,0.041510195,0.0023903844,0.002649788,0.014118564,0.0005344448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021682294,0.0009978091,0.0006832155,0.003760255,0.00056731136,0.0011248715,0.0009632867,0.00078997586,0.0019379616],"category_scores_gemma":[0.036708295,0.0004520913,0.0007568255,0.001992414,0.0005588689,0.0006669705,0.0007262352,0.00056422764,0.00095856667],"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":[0.0051674736,0.0009347975,0.060478292,0.0020818068,0.00091998803,0.00014837463,0.0022369325,0.009484816,0.13524139,0.0013664657,0.0032220003,0.77871764],"study_design_scores_gemma":[0.0006712583,0.014819552,0.4418361,0.00068919815,0.0013800332,0.001748934,0.0035117462,0.2718325,0.24210078,0.0060770074,0.01434462,0.0009883207],"about_ca_topic_score_codex":0.0015852102,"about_ca_topic_score_gemma":0.003685956,"teacher_disagreement_score":0.021682294,"about_ca_system_score_codex":0.0005330286,"about_ca_system_score_gemma":0.0005889115,"threshold_uncertainty_score":0.11466831},"labels":[],"label_agreement":null},{"id":"W2098716591","doi":"10.1111/j.1365-2621.2007.01644.x","title":"A comparison of two instrumental techniques used to discriminate the cooking quality of spaghetti","year":2008,"lang":"en","type":"article","venue":"International Journal of Food Science & Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Grains Research and Development Corporation","keywords":"Analyser; Texture (cosmology); Mathematics; Ranking (information retrieval); Flavour; Quality (philosophy); Statistics; Food science; Artificial intelligence; Computer science; Chemistry; Chromatography; Physics","score_opus":0.17321226018168584,"score_gpt":0.44153667333758556,"score_spread":0.2683244131558997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098716591","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":[0.9630087,0.002101424,0.03229052,0.000043979257,0.00009274844,0.00014022812,0.00028669354,0.000133347,0.001902345],"genre_scores_gemma":[0.95707846,0.0005315722,0.040517014,0.00007270548,0.000025170231,0.00011123954,0.00037117157,0.000043135937,0.0012495703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995634,0.0015732527,0.00023276248,0.000708527,0.0016410132,0.00021041035],"domain_scores_gemma":[0.99359447,0.003053505,0.0006897365,0.00040906205,0.002082651,0.00017066298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050654677,0.0007078588,0.00050973415,0.0023422497,0.00036602316,0.0007605612,0.0005061841,0.00077707606,0.0008094724],"category_scores_gemma":[0.007186041,0.0004128845,0.00058459636,0.0011715451,0.00053968,0.00041704573,0.00047433336,0.00066184724,0.00038959438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005287111,0.00067155564,0.10591257,0.0006267887,0.0008088105,0.00012735066,0.0009243067,0.00090588105,0.78146565,0.00028513663,0.0002966629,0.10268821],"study_design_scores_gemma":[0.000102101774,0.009864756,0.68731844,0.00006673277,0.0008370896,0.0013013111,0.00078344316,0.007799572,0.28911388,0.00028603544,0.0024010139,0.0001256374],"about_ca_topic_score_codex":0.00090477295,"about_ca_topic_score_gemma":0.0015378863,"teacher_disagreement_score":0.0050654677,"about_ca_system_score_codex":0.00028869451,"about_ca_system_score_gemma":0.00021417551,"threshold_uncertainty_score":0.02678907},"labels":[],"label_agreement":null},{"id":"W2099358577","doi":"10.2307/3316080","title":"Statistical analyses for round robin interaction data","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Okanagan University College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bayesian probability; Data science; Data type; Data mining; Artificial intelligence","score_opus":0.4109348954607822,"score_gpt":0.4064214595017145,"score_spread":0.004513435959067724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099358577","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":[0.04752359,0.00048758107,0.9440643,0.00026678233,0.00016784626,0.001936622,0.0023674679,0.0014180085,0.0017678235],"genre_scores_gemma":[0.30872878,0.00027408169,0.6762366,0.00028693725,0.00014299389,0.008900931,0.0029348377,0.00086327357,0.0016315182],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8753012,0.09216014,0.005885147,0.008282056,0.01685233,0.0015192287],"domain_scores_gemma":[0.6396341,0.3072703,0.014466602,0.022317702,0.015098696,0.0012126474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06575252,0.0013046674,0.00282328,0.0039701858,0.0018574958,0.002215815,0.0025817524,0.0010738682,0.010024818],"category_scores_gemma":[0.28661335,0.0006063747,0.0036627634,0.0050784657,0.0023601805,0.0022068226,0.0025744678,0.003902578,0.0009080401],"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":[0.010989373,0.0015972335,0.07029017,0.0053867856,0.01111608,0.0016356863,0.0046576457,0.04208548,0.021989405,0.08545065,0.025426077,0.71937543],"study_design_scores_gemma":[0.0009820715,0.012026438,0.20341745,0.0015179423,0.0041491585,0.0024116603,0.002555569,0.4326767,0.032713518,0.2303358,0.07642967,0.0007839919],"about_ca_topic_score_codex":0.0019832142,"about_ca_topic_score_gemma":0.0026497145,"teacher_disagreement_score":0.06575252,"about_ca_system_score_codex":0.0013768774,"about_ca_system_score_gemma":0.00244536,"threshold_uncertainty_score":0.34773672},"labels":[],"label_agreement":null},{"id":"W2100662412","doi":"10.1016/s0167-9473(02)00289-x","title":"Visualizing categorical data in ViSta","year":2003,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Categorical variable; Computer science; Interactivity; Correspondence analysis; Visualization; Bar chart; Data visualization; Statistical analysis; Data mining; R package; Mathematics; Statistics; Machine learning; Programming language; World Wide Web","score_opus":0.16947718887655253,"score_gpt":0.3900978698834989,"score_spread":0.2206206810069464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100662412","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":[0.034363285,0.0003999895,0.81373984,0.00087100733,0.0003504767,0.00037573546,0.019088509,0.11537421,0.01543691],"genre_scores_gemma":[0.14925173,0.0005831377,0.8100759,0.0005343488,0.000115548995,0.0015645347,0.013312712,0.013822444,0.010739634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995196,0.0001515431,0.000050435447,0.000096868884,0.00012779707,0.00005372713],"domain_scores_gemma":[0.997366,0.0015605645,0.00019058539,0.00030170102,0.00043215914,0.0001490084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012049531,0.0009811817,0.00068180985,0.0030357086,0.0007951247,0.0033308845,0.0010804906,0.00067180296,0.037169322],"category_scores_gemma":[0.0050956877,0.0005931485,0.0013827019,0.002032776,0.0004477811,0.0017744919,0.002296761,0.0012394104,0.004192235],"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":[0.0015147292,0.0003356885,0.010140865,0.0022694974,0.00066126307,0.0009053698,0.0051808795,0.018263161,0.04871083,0.07455102,0.2057911,0.6316756],"study_design_scores_gemma":[0.00043092907,0.0003217948,0.0158357,0.0009364674,0.00040105876,0.0011909067,0.002172601,0.26133284,0.04794562,0.21253502,0.45648968,0.00040743436],"about_ca_topic_score_codex":0.004996094,"about_ca_topic_score_gemma":0.00807092,"teacher_disagreement_score":0.037169322,"about_ca_system_score_codex":0.00051654864,"about_ca_system_score_gemma":0.0013033225,"threshold_uncertainty_score":0.12434369},"labels":[],"label_agreement":null},{"id":"W2102746137","doi":"10.1007/s004420100716","title":"Ecologically meaningful transformations for ordination of species data","year":2001,"lang":"en","type":"article","venue":"Oecologia","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5421,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ordination; Biplot; Principal component analysis; Correspondence analysis; Detrended correspondence analysis; Euclidean distance; Biology; Multivariate statistics; Canonical correspondence analysis; Univariate; Ecology; Multivariate analysis; Distance measures; Distance matrices in phylogeny; Statistics; Variance (accounting); Canonical analysis; Mathematics; Computer science; Artificial intelligence; Bioinformatics; Species richness","score_opus":0.170818306498357,"score_gpt":0.3309361309345823,"score_spread":0.1601178244362253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102746137","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":[0.015413714,0.00010224824,0.9753211,0.0001715029,0.00013736245,0.00028455292,0.002151937,0.0051773475,0.0012401576],"genre_scores_gemma":[0.121331744,0.00008779824,0.86855686,0.00011760023,0.0000759858,0.0023100043,0.00391992,0.0025441109,0.0010560127],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9852643,0.008726792,0.0014693524,0.0019751925,0.0020761008,0.000488264],"domain_scores_gemma":[0.9704681,0.018353079,0.0010013054,0.007822814,0.0018798053,0.00047492338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010584032,0.0015545128,0.0011842613,0.0039110305,0.0013371162,0.002573431,0.0014349628,0.0007856472,0.012860489],"category_scores_gemma":[0.06476939,0.00054311083,0.0020545814,0.0052627916,0.002239246,0.0018377185,0.0025512557,0.004269803,0.0033130571],"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":[0.0020723727,0.001264512,0.008451157,0.0014356398,0.00066179875,0.0005623771,0.0025972899,0.011830822,0.05729076,0.114668176,0.023694267,0.77547085],"study_design_scores_gemma":[0.00062916504,0.0016264407,0.048195206,0.00041740225,0.0003663083,0.0015046967,0.0024867614,0.18995298,0.10268837,0.54119235,0.11058001,0.00036037754],"about_ca_topic_score_codex":0.00096548256,"about_ca_topic_score_gemma":0.0014829725,"teacher_disagreement_score":0.012860489,"about_ca_system_score_codex":0.0008243396,"about_ca_system_score_gemma":0.0023261097,"threshold_uncertainty_score":0.055974364},"labels":[],"label_agreement":null},{"id":"W2104482761","doi":"10.5539/jfr.v2n6p12","title":"Influence of Fat and Moisture Content in the Processing of Light Requeijão","year":2013,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Linear regression; Moisture; Food science; Mathematics; Regression analysis; Response surface methodology; Statistics; Chemistry; Animal science; Biology","score_opus":0.19867647862149265,"score_gpt":0.3857451839698511,"score_spread":0.18706870534835843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104482761","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":[0.99862444,0.0002614117,0.00052894215,0.000016485354,0.000007665087,0.000014441393,0.000039612933,0.0000057658217,0.00050124864],"genre_scores_gemma":[0.9959533,0.00026986122,0.0024501681,0.00006808055,0.000005255759,0.000022519524,0.00010291883,0.000016479124,0.0011114608],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998117,0.00003971343,0.000012558356,0.000045710494,0.000065431625,0.000024989782],"domain_scores_gemma":[0.9997862,0.00006942945,0.00007144855,0.000014430175,0.00003704154,0.000021520058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041603402,0.00020908435,0.00022015102,0.00019101359,0.00017346854,0.00044852763,0.0001332893,0.00020241141,0.001499353],"category_scores_gemma":[0.00051191135,0.00012716085,0.00031225957,0.00017279187,0.00015988189,0.0003065466,0.00020239037,0.00035104915,0.00019835925],"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":[0.004668129,0.00024386738,0.004792707,0.00024073309,0.00003257981,0.00010810327,0.00015974097,0.00016931277,0.97747415,0.0001066142,0.000052399402,0.011951571],"study_design_scores_gemma":[0.00009425146,0.008666604,0.16312096,0.00007209301,0.00032604867,0.00031391068,0.00056631776,0.002529436,0.8192772,0.00017429738,0.004806611,0.000052243155],"about_ca_topic_score_codex":0.0008886403,"about_ca_topic_score_gemma":0.0015363473,"teacher_disagreement_score":0.001499353,"about_ca_system_score_codex":0.00013495814,"about_ca_system_score_gemma":0.00012495636,"threshold_uncertainty_score":0.00501585},"labels":[],"label_agreement":null},{"id":"W2106836460","doi":"10.1177/0022022102033003009","title":"Assessing the Cross-Cultural Applicability of Affective and Cognitive Components of Attitude","year":2002,"lang":"en","type":"article","venue":"Journal of Cross-Cultural Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Psychology; Cognition; Ambivalence; Cross-cultural; Acculturation; Context (archaeology); Social psychology; Cultural group selection; Confirmatory factor analysis; Affect (linguistics); Cognitive psychology; Structural equation modeling; Sociology; Ethnic group; Communication; Computer science; Geography","score_opus":0.14081524119910352,"score_gpt":0.4773623475231175,"score_spread":0.33654710632401397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106836460","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":[0.9942625,0.00009151672,0.0033654473,0.00003504049,0.00002678132,0.00004180728,0.00004272494,0.000008010418,0.0021261082],"genre_scores_gemma":[0.9984773,0.00003738787,0.0011763376,0.000020051904,0.000007786168,0.000040465962,0.00010863885,0.0000054213797,0.00012659279],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99356866,0.004057634,0.00041261816,0.0006394294,0.0011022832,0.0002194028],"domain_scores_gemma":[0.9694662,0.018085293,0.0024728943,0.0047161253,0.004588883,0.0006705715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013120074,0.0004883338,0.00038472994,0.001162339,0.0007050292,0.0012258401,0.0002911368,0.00039198817,0.0014146584],"category_scores_gemma":[0.028743017,0.00022969146,0.00086164544,0.00074512523,0.0012125648,0.000772448,0.0014307983,0.00071249116,0.00018165282],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037378748,0.00027736195,0.94326454,0.00010193635,0.0011217202,0.00013943441,0.009790398,0.00084618287,0.005010349,0.0013426911,0.00014611013,0.03758548],"study_design_scores_gemma":[0.000029428624,0.0005738775,0.98551995,0.000048028698,0.00024115217,0.00033018284,0.0054500103,0.0033937115,0.0020243654,0.0014296571,0.0009156884,0.000043968197],"about_ca_topic_score_codex":0.0032520741,"about_ca_topic_score_gemma":0.0036080074,"teacher_disagreement_score":0.013120074,"about_ca_system_score_codex":0.00038986412,"about_ca_system_score_gemma":0.0005708946,"threshold_uncertainty_score":0.06938636},"labels":[],"label_agreement":null},{"id":"W2107642135","doi":"10.1111/1750-3841.12173","title":"Systematic Comparison of Hedonic Ranking and Rating Methods Demonstrates Few Practical Differences","year":2013,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Agriculture and Agri-Food Canada","funders":"","keywords":"Ranking (information retrieval); Rank (graph theory); Bivariate analysis; Statistics; Psychology; Econometrics; Mathematics; Marketing; Computer science; Business; Information retrieval","score_opus":0.11781469021025445,"score_gpt":0.40860373909584774,"score_spread":0.2907890488855933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107642135","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":[0.58447576,0.044948492,0.28335986,0.0019373955,0.002193438,0.010643032,0.0028519959,0.0008140339,0.06877598],"genre_scores_gemma":[0.8414469,0.0072310423,0.1420766,0.00073673425,0.0003481067,0.003815619,0.0010282603,0.000345721,0.0029710983],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.8205173,0.1277653,0.014641067,0.006474732,0.02995024,0.0006513284],"domain_scores_gemma":[0.5588377,0.34215197,0.0144013595,0.03498947,0.048615903,0.0010036218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14489807,0.0007983384,0.00146948,0.0043717744,0.00078152464,0.0025836467,0.0013693033,0.0007703376,0.0025225254],"category_scores_gemma":[0.2567116,0.0004928685,0.001511081,0.0036998226,0.002134888,0.001770159,0.0020698346,0.00085249444,0.0010476406],"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":[0.0062279515,0.0012255467,0.15017508,0.009071655,0.0065205656,0.00012150379,0.009251835,0.0014131869,0.012421917,0.014239421,0.004123634,0.7852077],"study_design_scores_gemma":[0.0009903858,0.012642503,0.86179996,0.0057862727,0.0032711325,0.0010604379,0.007865574,0.007302514,0.026608186,0.02093989,0.051073197,0.0006600188],"about_ca_topic_score_codex":0.0008285914,"about_ca_topic_score_gemma":0.0022205305,"teacher_disagreement_score":0.14489807,"about_ca_system_score_codex":0.0010084285,"about_ca_system_score_gemma":0.0011582876,"threshold_uncertainty_score":0.7663034},"labels":[],"label_agreement":null},{"id":"W2108160164","doi":"10.1542/peds.2007-0858","title":"Early Determinants of Fruit and Vegetable Acceptance","year":2007,"lang":"en","type":"article","venue":"PEDIATRICS","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":330,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Medicine; Breastfeeding; Calorie; Pediatrics; Food science; Biology","score_opus":0.03322647125267536,"score_gpt":0.29319767321821877,"score_spread":0.2599712019655434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108160164","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":[0.9996797,0.0000851818,0.000022058779,0.00001364511,7.729485e-7,0.0000021494538,0.0000231264,9.610587e-7,0.00017254177],"genre_scores_gemma":[0.9996288,0.000052248255,0.00009956991,0.0000063619655,0.0000020276782,0.000004927491,0.000032942127,0.0000010530808,0.00017212605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997192,0.000110107925,0.00001646664,0.00004003136,0.000055449604,0.000058827194],"domain_scores_gemma":[0.9979988,0.0007029509,0.000740849,0.000054987664,0.00016715834,0.00033528332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004889621,0.00018497372,0.00016292723,0.0001717896,0.00014865452,0.00034581238,0.00015504238,0.0003046894,0.0026113396],"category_scores_gemma":[0.0030082304,0.00012539967,0.00016036972,0.00013417377,0.00017781007,0.00019655918,0.00025351046,0.00031372232,0.0001866106],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006378567,0.00028305192,0.98732823,0.000041740805,0.000046866397,0.00019486478,0.00061200705,0.000027477365,0.0057638385,0.000023501203,0.000050685343,0.004989759],"study_design_scores_gemma":[0.0000022151098,0.00023139732,0.99909437,0.0000053052254,0.000008269564,0.00012062288,0.00015797379,0.000033228094,0.00027121286,0.000007975608,0.00006579294,0.0000018218739],"about_ca_topic_score_codex":0.0017238188,"about_ca_topic_score_gemma":0.0015334827,"teacher_disagreement_score":0.0026113396,"about_ca_system_score_codex":0.00013130496,"about_ca_system_score_gemma":0.00016182917,"threshold_uncertainty_score":0.008735776},"labels":[],"label_agreement":null},{"id":"W2108754680","doi":"10.1002/jsfa.2190","title":"The development of a goat's milk yogurt","year":2005,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Nova Scotia Department of Agriculture","funders":"","keywords":"Food science; Titratable acid; Organoleptic; Carrageenan; Chemistry; Pectin; Mathematics","score_opus":0.02346490630194746,"score_gpt":0.2540805392987545,"score_spread":0.23061563299680704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108754680","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":[0.99188894,0.0011613548,0.0057813562,0.000059593203,0.000020478661,0.00014525099,0.00007399813,0.000041416188,0.0008277158],"genre_scores_gemma":[0.9833794,0.00076861354,0.013532567,0.000054059376,0.000009770211,0.00007214894,0.00024577483,0.000026450134,0.001911115],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988604,0.000018933926,0.000005760748,0.000030809624,0.000042885167,0.000015522694],"domain_scores_gemma":[0.99993753,0.000010242024,0.000013097323,0.000004366068,0.000018865183,0.000015926702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002645189,0.00022017011,0.00023007987,0.00013295659,0.000120706376,0.00027245344,0.00025895546,0.00019782115,0.00079472363],"category_scores_gemma":[0.00020109062,0.00012097313,0.0002802671,0.00007052364,0.00017824427,0.00022375789,0.00036159452,0.00030142078,0.00018553826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004061763,0.000024583194,0.00017272134,0.000039562008,0.000003803587,0.000030440407,0.000016347944,0.000024285555,0.99810565,0.000028970224,0.00001269578,0.0015002647],"study_design_scores_gemma":[0.000021249129,0.0013010986,0.0063737636,0.000012333264,0.000036047397,0.0002097752,0.000039960578,0.00068769325,0.988177,0.000017682834,0.0031175902,0.0000058355304],"about_ca_topic_score_codex":0.0006925793,"about_ca_topic_score_gemma":0.0012367117,"teacher_disagreement_score":0.00079472363,"about_ca_system_score_codex":0.00019079317,"about_ca_system_score_gemma":0.00017757015,"threshold_uncertainty_score":0.0026586652},"labels":[],"label_agreement":null},{"id":"W2110043583","doi":"10.3168/jds.2012-6287","title":"Descriptive analysis and early-stage consumer acceptance of yogurts fermented with carrot juice","year":2013,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Food science; Flavor; Aroma; Starter; Taste; Chemistry; Carrot juice; Recipe; Sensory analysis; Total dissolved solids; Mathematics","score_opus":0.03287810075179517,"score_gpt":0.2758746281098904,"score_spread":0.2429965273580952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110043583","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":[0.99938226,0.000019366793,0.00006828198,0.0000035361427,7.5257594e-7,0.00001490745,0.00021366435,0.0000018373017,0.0002952806],"genre_scores_gemma":[0.9984503,0.000038106984,0.00024342956,0.000006719492,0.0000017297154,0.0000279561,0.0004925337,0.0000023671555,0.00073687435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969316,0.000047960395,0.00003378834,0.000040394407,0.00013544406,0.00004919945],"domain_scores_gemma":[0.99857855,0.00043574927,0.0003806489,0.00006245452,0.00044303408,0.00009948826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006170215,0.00017799206,0.00021873871,0.00045706404,0.00022146705,0.00041075674,0.00014630317,0.00012698866,0.0017775114],"category_scores_gemma":[0.0014455878,0.00008855902,0.00036588951,0.0005344091,0.00016480658,0.00020574614,0.00018330854,0.00023854534,0.0001952105],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013920441,0.00045707249,0.96099025,0.00011331645,0.00009924819,0.00019458406,0.0025480087,0.00013631303,0.021234393,0.000058582125,0.00018220254,0.012594075],"study_design_scores_gemma":[0.0000027007309,0.00045412788,0.99776804,0.0000030976228,0.000011574369,0.000043050124,0.0006553476,0.0001539771,0.000759149,0.0000069378198,0.00013772519,0.0000042409515],"about_ca_topic_score_codex":0.01058544,"about_ca_topic_score_gemma":0.017141815,"teacher_disagreement_score":0.01058544,"about_ca_system_score_codex":0.00037759705,"about_ca_system_score_gemma":0.00025251947,"threshold_uncertainty_score":0.021047652},"labels":[],"label_agreement":null},{"id":"W2110709463","doi":"10.5539/jas.v5n4p161","title":"Sensory Analysis of New Varieties of Citrus as a Complementary Strategy to the Brazilian Citriculture","year":2013,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Orange (colour); Citrus × sinensis; Flavor; Sensory analysis; Hybrid; Horticulture; Pera; Biology; Food science; Mathematics","score_opus":0.0416396333607096,"score_gpt":0.3036110989381154,"score_spread":0.2619714655774058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110709463","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":[0.9994043,0.000055430548,0.00018155455,0.00000619385,9.376363e-7,0.0000056019985,0.000034597426,0.0000030140532,0.0003083567],"genre_scores_gemma":[0.99859077,0.000056240882,0.0008797118,0.000013344553,9.06179e-7,0.0000089779,0.00012608772,0.0000041328226,0.00031977822],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99977523,0.00004839467,0.000012359634,0.00007829309,0.000058327427,0.000027443857],"domain_scores_gemma":[0.9997477,0.00005623006,0.000050715495,0.000029289688,0.0000685537,0.000047506506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042099156,0.00026008647,0.0002564452,0.00041112927,0.00026308853,0.00030057822,0.00015326354,0.0001505041,0.00096788927],"category_scores_gemma":[0.00042850818,0.00011610121,0.00029706242,0.00029907667,0.00031196707,0.00020823204,0.00031132528,0.00021165819,0.00007727827],"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":[0.00071513455,0.00011634038,0.08478754,0.00017683019,0.00007045386,0.00029482818,0.0018739391,0.00015552423,0.89285713,0.00017472057,0.00005882836,0.018718733],"study_design_scores_gemma":[0.000013089033,0.0011379219,0.9564402,0.000020507572,0.00008061663,0.0005389668,0.0013604253,0.0004729728,0.038237277,0.00009226855,0.0015825813,0.000023164575],"about_ca_topic_score_codex":0.004557128,"about_ca_topic_score_gemma":0.016188908,"teacher_disagreement_score":0.004557128,"about_ca_system_score_codex":0.00032326818,"about_ca_system_score_gemma":0.00010795049,"threshold_uncertainty_score":0.009061217},"labels":[],"label_agreement":null},{"id":"W2112179747","doi":"10.1111/j.1467-8659.2009.01694.x","title":"The Perception of Correlation in Scatterplots","year":2010,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Correlation; Mathematics; Bivariate analysis; Statistics; Range (aeronautics); Computer science; Artificial intelligence; Pattern recognition (psychology); Geometry","score_opus":0.0154832959407309,"score_gpt":0.25504654174623537,"score_spread":0.23956324580550448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112179747","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":[0.714622,0.00056752097,0.27524778,0.00016983821,0.00008883634,0.000080723745,0.0002611316,0.001166467,0.0077955965],"genre_scores_gemma":[0.97865903,0.00007345379,0.02077807,0.000035109526,0.000024762263,0.000023530274,0.00010249588,0.000103670245,0.00020002182],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9937644,0.0022405589,0.00030040205,0.0008094427,0.0026416997,0.00024346143],"domain_scores_gemma":[0.96657103,0.02134857,0.0037732578,0.0031065468,0.004474686,0.0007259216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056835003,0.00052005757,0.00055783807,0.0023905002,0.0004181239,0.0026835978,0.0005128979,0.0005169087,0.0024021366],"category_scores_gemma":[0.04389054,0.00039082175,0.00050701806,0.0012899234,0.0017201803,0.0026456534,0.0019090935,0.0007884308,0.0002886414],"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":[0.005377734,0.00034223977,0.1333877,0.0014301487,0.0008854829,0.000972019,0.01005612,0.061668947,0.32216123,0.055362437,0.00556643,0.40278947],"study_design_scores_gemma":[0.00022300323,0.0017835938,0.574501,0.00037708587,0.00027003256,0.002302772,0.0034032792,0.28149262,0.06643183,0.058882803,0.009757173,0.0005748585],"about_ca_topic_score_codex":0.0009551147,"about_ca_topic_score_gemma":0.0005243397,"teacher_disagreement_score":0.0056835003,"about_ca_system_score_codex":0.0008161717,"about_ca_system_score_gemma":0.0003936178,"threshold_uncertainty_score":0.03005755},"labels":[],"label_agreement":null},{"id":"W2115435663","doi":"10.1177/1359105313506760","title":"Visualising future behaviour: Effects for snacking on biscuit bars, but no effects for snacking on fruit","year":2013,"lang":"en","type":"article","venue":"Journal of Health Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":61,"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":"Economic and Social Research Council; Queen's University; Leverhulme Trust; University of Essex","keywords":"Snacking; Consumption (sociology); Environmental health; Food science; Psychology; Medicine; Biology; Aesthetics; Art","score_opus":0.06253016155523138,"score_gpt":0.41629116490566076,"score_spread":0.35376100335042937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115435663","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":[0.9963329,0.0004422294,0.0010134468,0.000109568384,0.000068585956,0.00020259466,0.00011370406,0.000036674184,0.0016804475],"genre_scores_gemma":[0.9927537,0.00031636443,0.004326093,0.00008963163,0.000027946351,0.0005987001,0.00010288799,0.000017730372,0.0017670171],"study_design_codex":"randomized_trial","study_design_gemma":"observational","domain_scores_codex":[0.9989894,0.0005426256,0.00007327046,0.0001939404,0.00012398923,0.00007681425],"domain_scores_gemma":[0.9903019,0.007873564,0.0008377801,0.00025055412,0.00020825518,0.0005279066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013950678,0.0005847282,0.0006998929,0.000165526,0.00024362208,0.0006594028,0.00037267004,0.00078863854,0.009962948],"category_scores_gemma":[0.011625277,0.00025248146,0.00075890793,0.00014454531,0.0005326543,0.0005322161,0.0005901449,0.00079157675,0.00034264932],"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":[0.40600845,0.025739232,0.063224904,0.01469146,0.002574555,0.0004172751,0.012242198,0.0035576348,0.13093251,0.0013554977,0.0015065223,0.33774978],"study_design_scores_gemma":[0.016136527,0.19054723,0.7505124,0.0015751236,0.0049656075,0.00028799256,0.0037778514,0.004110551,0.016932132,0.0030654736,0.007830197,0.0002588482],"about_ca_topic_score_codex":0.0014635315,"about_ca_topic_score_gemma":0.0020853202,"teacher_disagreement_score":0.009962948,"about_ca_system_score_codex":0.00024956377,"about_ca_system_score_gemma":0.00032653788,"threshold_uncertainty_score":0.033329368},"labels":[],"label_agreement":null},{"id":"W2115710670","doi":"10.5539/jfr.v1n3p192","title":"Technological and Economic Optimization of Functional Ready to Eat Meal","year":2012,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taste; Promotion (chess); Product (mathematics); Food science; Marketing; Business; Meal; Mathematics; Chemistry","score_opus":0.25667920864866844,"score_gpt":0.3978137861916822,"score_spread":0.14113457754301373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115710670","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":[0.9771308,0.0015682778,0.013935289,0.00013809428,0.000067960755,0.00008905789,0.0003038835,0.00006719429,0.006699501],"genre_scores_gemma":[0.9818747,0.00092747755,0.0141481515,0.000027919497,0.00001059543,0.00006762685,0.0004045903,0.00004291812,0.0024959447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998117,0.000026136096,0.0000092004975,0.000042877073,0.00006605414,0.000044125594],"domain_scores_gemma":[0.9998994,0.000018092913,0.00002308866,0.000010959349,0.000029547436,0.000018852197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035593688,0.000390465,0.0002861892,0.00082133344,0.0002797057,0.0010164251,0.0003250442,0.0004256139,0.002095505],"category_scores_gemma":[0.0004575941,0.00024301288,0.0006039767,0.0004689081,0.00016646062,0.00060050434,0.00048706977,0.0006054473,0.00040456132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010694831,0.00068145286,0.0023821115,0.00054714415,0.00005281906,0.00023051811,0.000039649734,0.023700688,0.9348848,0.0035504699,0.00043751666,0.032423396],"study_design_scores_gemma":[0.00013101753,0.0040506814,0.023459705,0.00007502553,0.00022277866,0.00026631044,0.00021968546,0.10743293,0.8510609,0.0019530281,0.011047217,0.000080675076],"about_ca_topic_score_codex":0.00072431687,"about_ca_topic_score_gemma":0.0015801506,"teacher_disagreement_score":0.002095505,"about_ca_system_score_codex":0.00041727876,"about_ca_system_score_gemma":0.0004256833,"threshold_uncertainty_score":0.0070101023},"labels":[],"label_agreement":null},{"id":"W2118065007","doi":"10.5430/jct.v3n1p10","title":"Sensory Evaluation as a Tool in Determining Acceptability of Innovative Products Developed by Undergraduate Students in Food Science and Technology at The University of Trinidad and Tobago","year":2014,"lang":"en","type":"article","venue":"Journal of Curriculum and Teaching","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":156,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Product (mathematics); Purchasing; Quality (philosophy); Test (biology); Sensory analysis; Food products; Descriptive statistics; Product testing; Marketing; Statistical analysis; Psychology; Computer science; Medical education; Food science; Mathematics; Business; Medicine; Statistics","score_opus":0.027852716750924527,"score_gpt":0.3125530885500301,"score_spread":0.28470037179910557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118065007","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":[0.97528195,0.00039594894,0.011298333,0.00019182226,0.000042190353,0.0005964129,0.0000797098,0.000105813546,0.012007743],"genre_scores_gemma":[0.96462005,0.00070299866,0.028249742,0.00016258079,0.000015782076,0.0004516482,0.00010524812,0.000028675166,0.00566333],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966337,0.0016927635,0.00016611326,0.00019080576,0.001100479,0.00021617868],"domain_scores_gemma":[0.994467,0.0030333635,0.00067197636,0.0002200043,0.0012762693,0.0003313372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066961395,0.00045123103,0.0002864567,0.0014748683,0.0005259329,0.001266281,0.00044575345,0.00039300177,0.0025881417],"category_scores_gemma":[0.007931857,0.00030224636,0.00030441617,0.0008154471,0.0007432583,0.00044309828,0.0012061232,0.0006521154,0.00044159734],"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":[0.0011935497,0.002805028,0.39453828,0.0010116647,0.000077857345,0.0004044714,0.018233785,0.0012278276,0.10470523,0.0009200658,0.0024520853,0.47243017],"study_design_scores_gemma":[0.00004418716,0.0056185443,0.9353924,0.00033105892,0.000057138222,0.0005506462,0.017235113,0.0022499203,0.027644157,0.0005061521,0.010278257,0.00009230304],"about_ca_topic_score_codex":0.0030431165,"about_ca_topic_score_gemma":0.008310813,"teacher_disagreement_score":0.0066961395,"about_ca_system_score_codex":0.00065288093,"about_ca_system_score_gemma":0.0012802905,"threshold_uncertainty_score":0.035412967},"labels":[],"label_agreement":null},{"id":"W2118940709","doi":"10.5539/jfr.v1n3p204","title":"Sensory and Instrumental Consistency of Processed Cheeses","year":2012,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Chewiness; Food science; Sensory system; Texture (cosmology); Casein; Chemistry; Mathematics; Artificial intelligence; Psychology; Computer science; Cognitive psychology","score_opus":0.2513825305351437,"score_gpt":0.408867699307171,"score_spread":0.15748516877202728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118940709","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":[0.9989453,0.00010166813,0.00021518955,0.000007651099,0.0000042389074,0.000010472923,0.00013725896,0.00000934943,0.0005688817],"genre_scores_gemma":[0.9987889,0.00005141461,0.00029769167,0.000021383023,0.000006127951,0.000010550834,0.00037362924,0.0000087478,0.00044168887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956673,0.000059223825,0.000040265993,0.00007524616,0.00021483279,0.000043635104],"domain_scores_gemma":[0.99885416,0.0002407106,0.00032461286,0.00008018733,0.0003865656,0.00011389334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059998117,0.0002809482,0.0002541639,0.0006890085,0.0001387709,0.00045864636,0.00018114413,0.00021354215,0.0016259839],"category_scores_gemma":[0.0019181261,0.00015703954,0.0003630302,0.000273771,0.00025629633,0.00019892615,0.00040190184,0.0003170747,0.00022577061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051376363,0.00024959163,0.24895306,0.00032289853,0.00044029206,0.00063779653,0.0006981799,0.00039050874,0.7280891,0.00008974285,0.00023135175,0.0147598265],"study_design_scores_gemma":[0.000018495733,0.0012460249,0.9766932,0.0000103007205,0.00007480913,0.00034550607,0.00027962463,0.00033561996,0.020661132,0.00003445004,0.0002847392,0.000016071932],"about_ca_topic_score_codex":0.0007760356,"about_ca_topic_score_gemma":0.0008557086,"teacher_disagreement_score":0.0016259839,"about_ca_system_score_codex":0.00016189723,"about_ca_system_score_gemma":0.000119667035,"threshold_uncertainty_score":0.0054394603},"labels":[],"label_agreement":null},{"id":"W2122573478","doi":"10.1007/s11336-007-9041-0","title":"Taxicab Correspondence Analysis of Contingency Tables with One Heavyweight Column","year":2007,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Université de Moncton","funders":"","keywords":"Contingency table; Column (typography); Mathematics; Correspondence analysis; Set (abstract data type); Statistics; Computer science; Connection (principal bundle); Geometry","score_opus":0.042477094881948875,"score_gpt":0.2927358553031575,"score_spread":0.2502587604212086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122573478","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":[0.029277114,0.00026175953,0.9678514,0.0001190349,0.000052703323,0.00015004304,0.0003164667,0.00067344867,0.0012980028],"genre_scores_gemma":[0.33993325,0.00037620048,0.6536986,0.00019234278,0.00027256954,0.00089859724,0.0015422195,0.0004438281,0.002642277],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98621714,0.0067775524,0.0006062669,0.0018698652,0.003960378,0.0005688779],"domain_scores_gemma":[0.8859765,0.095473886,0.004630308,0.006927294,0.006066649,0.0009253364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0100252675,0.0007985541,0.00153092,0.0040241843,0.0015490021,0.0029080259,0.0014506793,0.0006223806,0.007843982],"category_scores_gemma":[0.073306926,0.0005871721,0.0018746234,0.004491859,0.0022602484,0.0024418868,0.0019649803,0.003033102,0.0013631405],"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":[0.0013018784,0.00043354434,0.027623076,0.00091005367,0.0006926446,0.0011609299,0.00086465385,0.07297846,0.008056552,0.22556058,0.007423325,0.6529942],"study_design_scores_gemma":[0.00010883777,0.0008440982,0.0107880905,0.0001228326,0.00021628157,0.0009936645,0.00043454286,0.6782272,0.010974331,0.28704095,0.010123848,0.00012538362],"about_ca_topic_score_codex":0.0023284822,"about_ca_topic_score_gemma":0.0019922242,"teacher_disagreement_score":0.0100252675,"about_ca_system_score_codex":0.0012103677,"about_ca_system_score_gemma":0.0028493486,"threshold_uncertainty_score":0.053019285},"labels":[],"label_agreement":null},{"id":"W2123768626","doi":"10.1016/j.jclinepi.2007.04.021","title":"Radar plots: a useful way for presenting multivariate health care data","year":2008,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":220,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"","keywords":"Radar chart; Bar chart; Multivariate statistics; Radar; Computer science; Pie chart; Relevance (law); Data presentation; Multivariate analysis; Data science; Presentation (obstetrics); Field (mathematics); Plot (graphics); Graphical model; Data mining; Information retrieval; Statistics; Artificial intelligence; Data collection; Machine learning; Mathematics; Medicine","score_opus":0.7121486938718011,"score_gpt":0.5746039580667881,"score_spread":0.13754473580501303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123768626","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":[0.005443747,0.0007699878,0.9175328,0.001383494,0.0010248617,0.00048774728,0.017249722,0.05214058,0.003966935],"genre_scores_gemma":[0.060723986,0.0008970514,0.91257215,0.0006823337,0.00060604385,0.0013327822,0.008090393,0.010607688,0.004487602],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930379,0.004430921,0.0010449626,0.00042532512,0.000895635,0.00016526473],"domain_scores_gemma":[0.9095304,0.075215854,0.004419493,0.004700028,0.005019283,0.0011149399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011734549,0.002564136,0.0019204132,0.008255814,0.0007188781,0.0043466063,0.0017875675,0.0016927425,0.07123708],"category_scores_gemma":[0.087472595,0.0011815334,0.0017287509,0.007908732,0.0006807795,0.0050946097,0.0025988915,0.0030131568,0.009271609],"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":[0.0026784022,0.0003761797,0.009349278,0.002864783,0.0008528688,0.001055548,0.0023369684,0.008229723,0.00608482,0.046893492,0.32659057,0.59268737],"study_design_scores_gemma":[0.0024578916,0.0018428485,0.017954573,0.0020444554,0.0010485575,0.0050728363,0.0024088193,0.14554769,0.017517155,0.21083497,0.59215575,0.0011145067],"about_ca_topic_score_codex":0.0026357297,"about_ca_topic_score_gemma":0.0025826958,"teacher_disagreement_score":0.07123708,"about_ca_system_score_codex":0.00042100463,"about_ca_system_score_gemma":0.0011214339,"threshold_uncertainty_score":0.23831183},"labels":[],"label_agreement":null},{"id":"W2125069160","doi":"10.1111/j.1745-459x.2000.tb00266.x","title":"DEVELOPMENT OF A ‘BIPOLAR’ R‐INDEX<sup>1</sup>","year":2000,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Index (typography); Bipolar disorder; Sample (material); Psychology; Statistics; Mathematics; Computer science; Social psychology; Mood; Chemistry; Chromatography","score_opus":0.09615279297293462,"score_gpt":0.3254712913836686,"score_spread":0.22931849841073398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125069160","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":[0.049121227,0.00034085516,0.9388245,0.00021798862,0.00024891828,0.0009768077,0.0010420369,0.0016117933,0.007615886],"genre_scores_gemma":[0.17075948,0.00016869624,0.82532585,0.00012987094,0.00007508844,0.0008289618,0.0009667807,0.0002909132,0.0014543621],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.988906,0.004694815,0.0011995954,0.0012155407,0.0037867064,0.00019728055],"domain_scores_gemma":[0.961455,0.019015025,0.0025842816,0.0026342454,0.013917535,0.00039390178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017105965,0.0012938282,0.0010506174,0.0030830735,0.00063959695,0.0020755196,0.0015378981,0.0007365619,0.0062263417],"category_scores_gemma":[0.041596826,0.0007204665,0.0013302946,0.0025792758,0.0009036379,0.0017673824,0.0009658704,0.00093596434,0.0031471234],"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":[0.0022563406,0.0002943251,0.05167537,0.001312455,0.0008219308,0.00019674007,0.00069121923,0.012577095,0.070558004,0.01436004,0.015173273,0.83008325],"study_design_scores_gemma":[0.0005140903,0.004747096,0.17836219,0.0004290718,0.001560997,0.0016796301,0.00095013424,0.6016571,0.12288129,0.034502283,0.051816773,0.000899382],"about_ca_topic_score_codex":0.001873496,"about_ca_topic_score_gemma":0.0028956917,"teacher_disagreement_score":0.017105965,"about_ca_system_score_codex":0.000674107,"about_ca_system_score_gemma":0.0011438276,"threshold_uncertainty_score":0.09046608},"labels":[],"label_agreement":null},{"id":"W2128728535","doi":"10.1002/wics.101","title":"Principal component analysis","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10417,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Principal component analysis; Singular value decomposition; Correspondence analysis; Dimensionality reduction; Jackknife resampling; Multiple correspondence analysis; Mathematics; Multivariate statistics; Dimension (graph theory); Sparse PCA; Table (database); Similarity (geometry); Data set; Computer science; Statistics; Pattern recognition (psychology); Data mining; Artificial intelligence; Algorithm; Combinatorics","score_opus":0.10090262445510462,"score_gpt":0.39980465909311036,"score_spread":0.29890203463800574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128728535","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011314027,0.0698798,0.83083606,0.00362756,0.0026578992,0.0036300067,0.008993336,0.0060584894,0.06300286],"genre_scores_gemma":[0.10655219,0.07844515,0.7478409,0.0013686174,0.0015881137,0.0044720136,0.015598884,0.0013449118,0.04278931],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937184,0.0017096734,0.00055535306,0.0010740716,0.002759356,0.00018322545],"domain_scores_gemma":[0.9929363,0.001995392,0.00058984366,0.00055126013,0.0037604976,0.00016669564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005573798,0.0023007165,0.002548561,0.00614561,0.001008432,0.0049196593,0.002179685,0.0012545986,0.02312955],"category_scores_gemma":[0.015802082,0.00050003955,0.0016860121,0.011200157,0.0010895188,0.0022641767,0.0017678023,0.0016580108,0.016754033],"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":[0.0001108005,0.00007144103,0.0017666639,0.0030872317,0.00041148192,0.00012942939,0.00030326107,0.003491997,0.0022853818,0.012563812,0.050975088,0.9248033],"study_design_scores_gemma":[0.000110824025,0.00033460147,0.019844033,0.0027635312,0.0007068094,0.00104866,0.0010656673,0.02829432,0.0072027007,0.062131085,0.8762189,0.0002788907],"about_ca_topic_score_codex":0.002369246,"about_ca_topic_score_gemma":0.0021354326,"teacher_disagreement_score":0.02312955,"about_ca_system_score_codex":0.0013607338,"about_ca_system_score_gemma":0.0033375144,"threshold_uncertainty_score":0.07737607},"labels":[],"label_agreement":null},{"id":"W2132664917","doi":"10.1016/j.jneb.2006.08.002","title":"Developing the Consumer Interface for the MyPyramid Food Guidance System","year":2006,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Focus (optics); Interface (matter); Pyramid (geometry); Focus group; Psychology; Computer science; Graphics; Advertising; Multimedia; Marketing; Business; Mathematics","score_opus":0.05697171340749458,"score_gpt":0.3380088908179895,"score_spread":0.28103717741049494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132664917","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":[0.070256576,0.00018541422,0.8164108,0.00031378402,0.00012940704,0.0011276426,0.0011515327,0.10189214,0.008532699],"genre_scores_gemma":[0.3299924,0.00028155083,0.620533,0.00081340957,0.00007183364,0.0013129999,0.002737575,0.0073105134,0.03694676],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996474,0.000058406407,0.000029409312,0.00007954786,0.00014330924,0.00004181357],"domain_scores_gemma":[0.99882585,0.0006041213,0.00004906516,0.00009527825,0.00035173018,0.00007389947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011450486,0.00086244795,0.00058220455,0.00039446543,0.00020057685,0.0010372581,0.0015648878,0.0009112843,0.026120929],"category_scores_gemma":[0.0031596618,0.00049359014,0.00048169584,0.00017223884,0.00016658923,0.0009758882,0.0010091455,0.00068272965,0.006310279],"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":[0.0039690826,0.0008035971,0.009332625,0.0009510707,0.00027402514,0.002284339,0.0020087205,0.0068404838,0.25869274,0.004228184,0.046826996,0.66378826],"study_design_scores_gemma":[0.0011009529,0.0013915484,0.014767063,0.00034918226,0.0003607798,0.0034403058,0.0007637009,0.49511835,0.31046802,0.0042123115,0.16773544,0.00029225758],"about_ca_topic_score_codex":0.0012977418,"about_ca_topic_score_gemma":0.0012925196,"teacher_disagreement_score":0.026120929,"about_ca_system_score_codex":0.00025356485,"about_ca_system_score_gemma":0.0003970723,"threshold_uncertainty_score":0.08738321},"labels":[],"label_agreement":null},{"id":"W2136578764","doi":"10.1177/0956797613501169","title":"Below-Baseline Suppression of Competitors During Interference Resolution by Younger but Not Older Adults","year":2013,"lang":"en","type":"article","venue":"Psychological Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Psychology; Baseline (sea); Competitor analysis; Resolution (logic); Interference (communication); Developmental psychology; Audiology; Telecommunications; Medicine; Artificial intelligence","score_opus":0.028438370842085087,"score_gpt":0.3109304767376468,"score_spread":0.28249210589556173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136578764","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":[0.99901223,0.00008038142,0.0005633512,0.000010113853,0.000004527718,0.000010558067,0.000028792489,0.0000062594813,0.00028383173],"genre_scores_gemma":[0.9973821,0.00009508058,0.0017333651,0.00004561794,0.0000088778925,0.00004129429,0.00007957079,0.0000068922723,0.00060708687],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998167,0.00003169159,0.000025840132,0.00005166078,0.0000509177,0.000023236378],"domain_scores_gemma":[0.99917275,0.00024967155,0.00024768157,0.000096016105,0.00011359768,0.00012032981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684946,0.00019077404,0.00033410973,0.00019537473,0.00015518881,0.00029761434,0.00016165782,0.00026381394,0.0014233658],"category_scores_gemma":[0.0013863523,0.00010948478,0.00010700403,0.00010450302,0.00023628061,0.00029059607,0.0002903244,0.00036314392,0.00019795513],"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":[0.0021503263,0.000512937,0.014002532,0.00006636891,0.000019204545,0.000087958906,0.0008776037,0.00003742548,0.96707225,0.000120086115,0.00010591592,0.0149474405],"study_design_scores_gemma":[0.00021668462,0.012962897,0.632263,0.00003419322,0.00010521673,0.0008674249,0.001024665,0.0019397782,0.34802026,0.00090122246,0.0016062795,0.000058412108],"about_ca_topic_score_codex":0.0008627319,"about_ca_topic_score_gemma":0.0017404691,"teacher_disagreement_score":0.0014233658,"about_ca_system_score_codex":0.00008689582,"about_ca_system_score_gemma":0.00018877424,"threshold_uncertainty_score":0.0047616363},"labels":[],"label_agreement":null},{"id":"W2138965994","doi":"10.1111/j.1468-5884.2008.00373.x","title":"Two‐dimensional psychophysics in chickens and humans: Comparative aspects of perceptual relativity","year":2008,"lang":"en","type":"article","venue":"Japanese Psychological Research","topic":"Sensory Analysis and Statistical Methods","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":"St. Francis Xavier University","funders":"","keywords":"Psychophysics; Perception; Stimulus (psychology); Psychology; Cognitive psychology; Context effect; Communication; Mathematics; Neuroscience","score_opus":0.36392962484478597,"score_gpt":0.48041811127408446,"score_spread":0.11648848642929849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138965994","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":[0.9966703,0.00027923795,0.0019176431,0.000047009908,0.0000059696613,0.0000041671137,0.000024909696,0.000009468062,0.0010412184],"genre_scores_gemma":[0.99914944,0.00004328745,0.00069482374,0.000011595403,0.0000023235436,0.0000025928887,0.000012202684,0.0000033422957,0.00008025175],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997452,0.0000708282,0.000013445439,0.000114163464,0.000036452067,0.000019862262],"domain_scores_gemma":[0.99909294,0.00037228077,0.00020661486,0.00018201867,0.00007000914,0.00007616144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006299345,0.00016427565,0.00017751496,0.000503491,0.00017164026,0.00040922617,0.00013993465,0.0002872249,0.0014178075],"category_scores_gemma":[0.002095476,0.00017947423,0.000118221826,0.00013216984,0.0010773815,0.0003495907,0.00039548977,0.00028445097,0.00009961501],"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":[0.0032159574,0.00015680352,0.21536723,0.00043738706,0.00026274452,0.0007533032,0.011151509,0.001348348,0.7078159,0.0077691176,0.00034193837,0.051379684],"study_design_scores_gemma":[0.000025791136,0.0005774858,0.9793046,0.00003660709,0.00007223886,0.0012690744,0.0017899089,0.0014006173,0.011709054,0.002846527,0.0009311473,0.0000369822],"about_ca_topic_score_codex":0.0008112488,"about_ca_topic_score_gemma":0.000697553,"teacher_disagreement_score":0.0014178075,"about_ca_system_score_codex":0.00015833881,"about_ca_system_score_gemma":0.00009324423,"threshold_uncertainty_score":0.0047430396},"labels":[],"label_agreement":null},{"id":"W2143640743","doi":"10.1093/jpepsy/jsq022","title":"Time-Window Sequential Analysis: An Introduction for Pediatric Psychologists","year":2010,"lang":"en","type":"article","venue":"Journal of Pediatric Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Pediatric psychology; Window (computing); Psychology; Developmental psychology; Applied psychology; Clinical psychology; Computer science; World Wide Web","score_opus":0.045661888959497396,"score_gpt":0.3691933362117384,"score_spread":0.323531447252241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143640743","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":[0.00028646484,0.0018675276,0.993005,0.0010927456,0.0007826301,0.000291702,0.00014173817,0.00092952786,0.0016027149],"genre_scores_gemma":[0.003945246,0.003794135,0.9874693,0.00058224826,0.001218735,0.0014254694,0.00012286157,0.0002667428,0.001175236],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9923422,0.0055365837,0.000665848,0.00035488568,0.0010024755,0.00009794433],"domain_scores_gemma":[0.9784532,0.018052015,0.00079183944,0.00082988816,0.001544691,0.00032833996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013113513,0.0022367514,0.0011802528,0.0029392526,0.00057224376,0.0012457086,0.001780665,0.001395525,0.0102471085],"category_scores_gemma":[0.028971028,0.0009874912,0.0010441566,0.0033536449,0.002439254,0.00322825,0.0015646048,0.005421325,0.0045202817],"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":[0.00029455588,0.00023033885,0.001094991,0.0016459386,0.00015042836,0.00082538265,0.0013923771,0.0069109364,0.0046150615,0.21555455,0.12616852,0.6411169],"study_design_scores_gemma":[0.00016369301,0.0006213113,0.001644942,0.0012723787,0.000066890265,0.0020275395,0.00047922565,0.06865766,0.0028908283,0.28141078,0.64060223,0.00016261089],"about_ca_topic_score_codex":0.0008676048,"about_ca_topic_score_gemma":0.000900932,"teacher_disagreement_score":0.013113513,"about_ca_system_score_codex":0.0009638884,"about_ca_system_score_gemma":0.0020039321,"threshold_uncertainty_score":0.06935173},"labels":[],"label_agreement":null},{"id":"W2144211742","doi":"10.20982/tqmp.01.1.p001","title":"The rise of quantitative methods in psychology","year":2005,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Galileo (satellite navigation); Planck; Epistemology; Einstein; Theoretical physics; Mathematics education; Psychology; Social science; Physics; Sociology; Philosophy; Geography; Astrophysics; Quantum mechanics","score_opus":0.24742234798818458,"score_gpt":0.5837937420101684,"score_spread":0.33637139402198385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144211742","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009828565,0.048470624,0.89582294,0.024534956,0.0033468292,0.0001497492,0.00042913953,0.00091849413,0.025344335],"genre_scores_gemma":[0.084738635,0.05864303,0.81840694,0.013040124,0.01245496,0.0012801846,0.0006382469,0.0015528336,0.009245039],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9447505,0.036254108,0.0025461947,0.004697595,0.011068101,0.00068343175],"domain_scores_gemma":[0.7611553,0.20495556,0.0050541027,0.015046096,0.012158772,0.0016302159],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05673584,0.002489597,0.0032823153,0.0075610406,0.0023218167,0.0108197,0.003961365,0.0055469912,0.011056914],"category_scores_gemma":[0.15801948,0.0016629423,0.002967612,0.006774145,0.02084399,0.014128582,0.0067514777,0.0160423,0.00471731],"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":[0.00003259988,0.00004174749,0.00046520913,0.0010346503,0.00013650162,0.000054243337,0.0008851685,0.002541481,0.00041110354,0.907496,0.014858431,0.07204292],"study_design_scores_gemma":[0.000019525416,0.0000316859,0.0004328701,0.00057954644,0.000028989729,0.000083828185,0.00019283948,0.0067431047,0.0004243013,0.88043356,0.11095453,0.000075201315],"about_ca_topic_score_codex":0.0029585976,"about_ca_topic_score_gemma":0.0011167885,"teacher_disagreement_score":0.9432642,"about_ca_system_score_codex":0.0058626863,"about_ca_system_score_gemma":0.0054550692,"threshold_uncertainty_score":0.3000514},"labels":[],"label_agreement":null},{"id":"W2144747396","doi":"10.22329/amr.v13i1.2836","title":"Generalized Procrustes Analysis: A Tool for Exploring Aggregates and Persons","year":2009,"lang":"en","type":"article","venue":"Applied Multivariate Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Aggregate (composite); Multivariate statistics; Set (abstract data type); Multivariate analysis; Data set; Computer science; Statistical analysis; Econometrics; Mathematics; Statistics; Data mining; Data science","score_opus":0.26322745512381296,"score_gpt":0.406715915218674,"score_spread":0.14348846009486105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144747396","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":[0.004961728,0.00030531082,0.98701686,0.00021277046,0.00012157323,0.00032495274,0.0010479863,0.0036997334,0.0023090676],"genre_scores_gemma":[0.07521947,0.0005980941,0.9157489,0.00013447688,0.00016602574,0.0025025618,0.0019029417,0.0018396524,0.0018878415],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9796775,0.0117334295,0.0015261754,0.0027848787,0.0038010657,0.00047694848],"domain_scores_gemma":[0.9437273,0.039378725,0.0041842465,0.0069576623,0.005227616,0.0005244372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023677232,0.0041783196,0.0025397034,0.011546138,0.0018341541,0.0047510923,0.002575581,0.0014339453,0.017692862],"category_scores_gemma":[0.077893846,0.0012383731,0.0038737475,0.011525363,0.002565751,0.004506647,0.004662741,0.0038210154,0.0047779926],"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":[0.0005266174,0.00034429732,0.025835084,0.002145505,0.0026571094,0.0008114881,0.007539671,0.024274018,0.008033152,0.12527014,0.051687665,0.7508752],"study_design_scores_gemma":[0.0001979744,0.0012996534,0.04948979,0.0010385594,0.0009230472,0.0021045837,0.0051364168,0.28778276,0.01556182,0.3746265,0.26092002,0.00091891823],"about_ca_topic_score_codex":0.0032893831,"about_ca_topic_score_gemma":0.0028566439,"teacher_disagreement_score":0.023677232,"about_ca_system_score_codex":0.0009328573,"about_ca_system_score_gemma":0.002888809,"threshold_uncertainty_score":0.12521863},"labels":[],"label_agreement":null},{"id":"W2148990075","doi":"10.1111/j.1745-4557.2005.00033.x","title":"EVALUATION OF IDEAL WINE AND CHEESE PAIRS USING A DEVIATION‐FROM‐IDEAL SCALE WITH FOOD AND WINE EXPERTS","year":2005,"lang":"en","type":"article","venue":"Journal of Food Quality","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Dairy Farmers of Canada","keywords":"Wine; White Wine; Ideal (ethics); Mathematics; Food science; Preference; Scale (ratio); Statistics; Chemistry; Geography","score_opus":0.168119561866512,"score_gpt":0.3764813137139001,"score_spread":0.2083617518473881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148990075","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":[0.99290097,0.00006925816,0.0021743048,0.000047947105,0.00002815876,0.00026250267,0.00016981873,0.00002127652,0.004325656],"genre_scores_gemma":[0.9897132,0.00010582395,0.008108826,0.000042300748,0.000018537528,0.00032029292,0.00050648325,0.000011657059,0.0011728992],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962664,0.0010422948,0.000435919,0.0002257892,0.0017855581,0.00024408143],"domain_scores_gemma":[0.99181056,0.0024025412,0.0012592509,0.00034254778,0.0033546223,0.0008303709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058701313,0.00043093343,0.00044728452,0.0016931115,0.00078139553,0.0008999934,0.00047297435,0.00039594114,0.0027451941],"category_scores_gemma":[0.012355287,0.00023663143,0.0007405186,0.00066851405,0.0007640087,0.00046867417,0.001106185,0.00048810785,0.00045573036],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00370672,0.00078803406,0.90171975,0.00038894982,0.00044591684,0.0004110543,0.009489451,0.001618212,0.022138825,0.00068773713,0.0038859928,0.054719333],"study_design_scores_gemma":[0.00014238924,0.0020878636,0.96789294,0.00008845442,0.00008903543,0.00049859687,0.013073109,0.0051600076,0.0051661055,0.0007085899,0.00496784,0.00012504123],"about_ca_topic_score_codex":0.0030720849,"about_ca_topic_score_gemma":0.010263956,"teacher_disagreement_score":0.0058701313,"about_ca_system_score_codex":0.0007169747,"about_ca_system_score_gemma":0.00067380513,"threshold_uncertainty_score":0.031044602},"labels":[],"label_agreement":null},{"id":"W2149387073","doi":"10.3389/fpsyg.2013.00109","title":"On the Relevance of Assumptions Associated with Classical Factor Analytic Approaches†","year":2013,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Psychology; Relevance (law); Factor (programming language); Volume (thermodynamics); Cognitive psychology; Social psychology; Computer science","score_opus":0.09408891068979373,"score_gpt":0.304638209123134,"score_spread":0.21054929843334025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149387073","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":[0.010140812,0.0027868995,0.96572375,0.00883139,0.0007631063,0.00036523162,0.0003042883,0.00023898514,0.010845482],"genre_scores_gemma":[0.26697364,0.0032320567,0.7189199,0.004123221,0.001433256,0.0024257307,0.00049005286,0.00023577192,0.0021664163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.88891006,0.080069885,0.0055861585,0.0061441655,0.018381765,0.0009079774],"domain_scores_gemma":[0.44391623,0.4796504,0.014911682,0.03663026,0.024014112,0.0008772532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1765372,0.002461844,0.0017581157,0.0040278803,0.0032410163,0.006402526,0.0050782515,0.0036276078,0.0058260877],"category_scores_gemma":[0.47883883,0.0011356323,0.0017610062,0.005770499,0.015343419,0.014782768,0.0060843667,0.010155086,0.0025174087],"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":[0.0003455842,0.00014127656,0.007630844,0.0011538989,0.00036897536,0.00050630706,0.004162832,0.011261799,0.0005652868,0.8579766,0.008244779,0.10764188],"study_design_scores_gemma":[0.000075592645,0.00013542052,0.0026749515,0.00078073214,0.00007761943,0.00036768557,0.000848052,0.03670573,0.0006410545,0.9464965,0.011099421,0.00009728937],"about_ca_topic_score_codex":0.0044816486,"about_ca_topic_score_gemma":0.003126385,"teacher_disagreement_score":0.1765372,"about_ca_system_score_codex":0.0035479849,"about_ca_system_score_gemma":0.0037250535,"threshold_uncertainty_score":0.9336291},"labels":[],"label_agreement":null},{"id":"W2152203989","doi":"10.1111/acer.12511","title":"Alcoholic Beverage Strength Discrimination by Taste May Have an Upper Threshold","year":2014,"lang":"en","type":"article","venue":"Alcoholism Clinical and Experimental Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Orange juice; Taste; Food science; Significant difference; Alcohol consumption; Alcohol; Population; Psychology; Medicine; Mathematics; Chemistry; Statistics; Environmental health","score_opus":0.21052330288238813,"score_gpt":0.4810661677951033,"score_spread":0.2705428649127152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152203989","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":[0.9897196,0.00082586304,0.004325816,0.00013577052,0.000029766345,0.000039939114,0.00008759585,0.00005860718,0.0047770827],"genre_scores_gemma":[0.99794203,0.00012126404,0.0011845134,0.00008651957,0.000013125863,0.000016635186,0.00007590949,0.000011142462,0.0005489298],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987936,0.00020421957,0.00013076248,0.00020205758,0.0004639745,0.00020532761],"domain_scores_gemma":[0.9927483,0.003923071,0.000963247,0.0006388545,0.001248502,0.0004780717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018455267,0.0003725807,0.0005675266,0.00090962916,0.0003266375,0.0011685587,0.0004735211,0.0008293165,0.008861047],"category_scores_gemma":[0.0068300334,0.00028837018,0.0005070683,0.00043171464,0.000842123,0.0011849739,0.0014013208,0.0009211202,0.0013443071],"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":[0.00439526,0.0013651056,0.28177696,0.0010139323,0.00018760083,0.0008007192,0.0012297304,0.00074199337,0.57429767,0.001980652,0.00081242825,0.13139799],"study_design_scores_gemma":[0.00006278578,0.0052451985,0.85716426,0.00018874712,0.00015765193,0.0035671138,0.00075557444,0.0038758677,0.12253638,0.003801115,0.0025743314,0.000070993665],"about_ca_topic_score_codex":0.00078312564,"about_ca_topic_score_gemma":0.00046848436,"teacher_disagreement_score":0.008861047,"about_ca_system_score_codex":0.00029660622,"about_ca_system_score_gemma":0.00023723456,"threshold_uncertainty_score":0.029643118},"labels":[],"label_agreement":null},{"id":"W2152416281","doi":"10.1007/s11336-004-1173-x","title":"An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents","year":2006,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":68,"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; HEC Montréal","funders":"","keywords":"Categorical variable; Correspondence analysis; Multiple correspondence analysis; Multivariate statistics; Extension (predicate logic); Multivariate analysis; Categorization; Cluster (spacecraft); Extant taxon; Computer science; Homogeneous; Data mining; Mathematics; Statistics; Contingency table; Econometrics; Artificial intelligence","score_opus":0.09342433670460742,"score_gpt":0.3560124988996356,"score_spread":0.2625881621950282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152416281","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":[0.0037336668,0.00004846937,0.99413365,0.00008757498,0.000048983973,0.00024218076,0.00010170081,0.00017429757,0.0014295807],"genre_scores_gemma":[0.10877977,0.00014704236,0.8880312,0.00008853318,0.000085565785,0.0011878477,0.00020727399,0.00013858399,0.0013342823],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96825343,0.022696419,0.00087798503,0.0038401932,0.0037509631,0.000580991],"domain_scores_gemma":[0.9510472,0.035738755,0.0026928668,0.0048889406,0.0050961133,0.0005360827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021528406,0.0016713183,0.002197,0.00749638,0.0025806047,0.0031112824,0.0025955995,0.0017278822,0.009153979],"category_scores_gemma":[0.072724566,0.0008648014,0.0033513182,0.010323806,0.0025623194,0.0047952966,0.0043962835,0.0025599112,0.0016502315],"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":[0.00036118977,0.00051959814,0.015619313,0.0010239356,0.001119433,0.00053107255,0.0059129745,0.04202062,0.004371618,0.30606508,0.0063310936,0.6161241],"study_design_scores_gemma":[0.00013828778,0.00059001514,0.012103171,0.00023785901,0.000356323,0.0010387917,0.0020423182,0.54277444,0.004515756,0.40752518,0.028278014,0.00039981212],"about_ca_topic_score_codex":0.0029139179,"about_ca_topic_score_gemma":0.0027608252,"teacher_disagreement_score":0.021528406,"about_ca_system_score_codex":0.0014085887,"about_ca_system_score_gemma":0.0037445596,"threshold_uncertainty_score":0.11385447},"labels":[],"label_agreement":null},{"id":"W2153591306","doi":"10.1002/sam.11270","title":"Principal axes analysis of symbolic histogram variables","year":2015,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","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":"McMaster University","funders":"","keywords":"Histogram; Mathematics; Principal component analysis; Estimator; Symbolic data analysis; Quantile; Pattern recognition (psychology); Histogram matching; Statistics; Artificial intelligence; Computer science; Image (mathematics)","score_opus":0.2031330746131957,"score_gpt":0.39143483193317025,"score_spread":0.18830175731997453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153591306","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":[0.0072438405,0.00019684959,0.9892021,0.00008845333,0.000112960595,0.000099544304,0.00048320362,0.0008233862,0.0017496622],"genre_scores_gemma":[0.13582605,0.00044547606,0.8573837,0.00007092804,0.0002192498,0.00055227155,0.0014762465,0.0004568007,0.0035692456],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99795234,0.0004353745,0.00015494943,0.00042238005,0.000875216,0.00015969906],"domain_scores_gemma":[0.9973137,0.0007273058,0.00032738407,0.0004125481,0.0011081937,0.00011083929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012771301,0.001224319,0.0008704839,0.003786103,0.00079329434,0.0020973154,0.00094796624,0.00046692503,0.009942176],"category_scores_gemma":[0.00748667,0.00033503887,0.0012453016,0.0046083275,0.0011566184,0.0019863616,0.0012957009,0.001615542,0.0027767627],"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":[0.00026194303,0.00014837316,0.007281527,0.00047050367,0.0001883117,0.00022024392,0.00048322033,0.021493027,0.021271473,0.051533516,0.008064814,0.88858306],"study_design_scores_gemma":[0.00011255096,0.000483519,0.030017627,0.00019547787,0.00021621442,0.0006941239,0.0008618368,0.70974797,0.035029035,0.14296176,0.07933755,0.00034230435],"about_ca_topic_score_codex":0.0022334484,"about_ca_topic_score_gemma":0.0018238244,"teacher_disagreement_score":0.009942176,"about_ca_system_score_codex":0.00046844347,"about_ca_system_score_gemma":0.0014238951,"threshold_uncertainty_score":0.03325987},"labels":[],"label_agreement":null},{"id":"W2154777645","doi":"10.1111/j.1745-459x.2004.tb00140.x","title":"SELECTION OF AN ASTRINGENCY REFERENCE STANDARD FOR THE SENSORY EVALUATION OF BLACK TEA","year":2004,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Astringent; Tannic acid; Black tea; Taste; Chemistry; Food science; Psychology; Bitter taste; Alum; Organic chemistry","score_opus":0.25941229923988246,"score_gpt":0.4149709927603481,"score_spread":0.15555869352046564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154777645","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":[0.9663813,0.0009226494,0.03074356,0.0000626669,0.00006292717,0.00035981133,0.00018763987,0.00012506662,0.001154487],"genre_scores_gemma":[0.9196332,0.00060685887,0.07731085,0.00009767882,0.000019493888,0.0005384851,0.0006850488,0.000039430146,0.0010690076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778336,0.0008959407,0.00018386991,0.00021209808,0.0008475775,0.000077144185],"domain_scores_gemma":[0.9978231,0.00034080743,0.00020282455,0.00015219074,0.0013800832,0.00010101377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032607785,0.00054744276,0.00046826783,0.0010135315,0.00056736136,0.00074682606,0.0005625949,0.00075368857,0.000904735],"category_scores_gemma":[0.004065988,0.00020902157,0.00046727393,0.00063633494,0.00043197974,0.00036222383,0.0004037438,0.00050212874,0.00028698402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012230049,0.00045919538,0.0064068045,0.00015659913,0.000034379336,0.00004580549,0.00019905558,0.00038655315,0.97599185,0.00015769254,0.00014322484,0.0147958575],"study_design_scores_gemma":[0.000116682546,0.013013287,0.0587907,0.00008842446,0.00018906532,0.00034540985,0.0004733151,0.0059625097,0.91731536,0.00024427514,0.0033956352,0.00006526088],"about_ca_topic_score_codex":0.0011976492,"about_ca_topic_score_gemma":0.002164262,"teacher_disagreement_score":0.0032607785,"about_ca_system_score_codex":0.00031935063,"about_ca_system_score_gemma":0.0004029901,"threshold_uncertainty_score":0.017244816},"labels":[],"label_agreement":null},{"id":"W2155745695","doi":"10.5539/jfr.v3n1p39","title":"The Brazilian Way to Consume acai: Do guarana Extract and Sugar Concentrations Influence on Acceptance?","year":2013,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Flavor; Mathematics; Food science; Sugar; Response surface methodology; Biotechnology; Chemistry; Biology; Statistics","score_opus":0.09515603406226394,"score_gpt":0.3906871293557212,"score_spread":0.2955310952934572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155745695","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":[0.99726,0.0005372105,0.00016702247,0.00016556458,0.000012203048,0.000019431118,0.00006128279,0.000004467571,0.0017727482],"genre_scores_gemma":[0.99754566,0.0007232811,0.00043976677,0.00010793797,0.0000067198607,0.000027007687,0.00007696783,0.00000636055,0.0010661678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997881,0.00006306353,0.000009517556,0.00005671543,0.000045426423,0.00003722931],"domain_scores_gemma":[0.9996525,0.0001203248,0.0001143442,0.000023047303,0.000056815952,0.000032819295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003896213,0.00029718535,0.00037692953,0.00023822056,0.00025311028,0.0007607982,0.00015818549,0.00028731802,0.0027808838],"category_scores_gemma":[0.0012662382,0.00020267443,0.00038342865,0.0003627192,0.0004386312,0.00030765214,0.0002563403,0.0003920461,0.00024882628],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010350137,0.006465287,0.53417224,0.001964448,0.0005044052,0.0009684159,0.012080818,0.00041115345,0.21094912,0.0025751027,0.0011703862,0.21838856],"study_design_scores_gemma":[0.000092584145,0.0059977304,0.964618,0.00014686912,0.00035872232,0.00033827414,0.006608039,0.0010179771,0.011298766,0.0007337746,0.008735657,0.000053616357],"about_ca_topic_score_codex":0.009792315,"about_ca_topic_score_gemma":0.01733135,"teacher_disagreement_score":0.009792315,"about_ca_system_score_codex":0.00029832398,"about_ca_system_score_gemma":0.00035457307,"threshold_uncertainty_score":0.019470632},"labels":[],"label_agreement":null},{"id":"W2156579478","doi":"10.1016/s0306-4530(02)00108-7","title":"Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change","year":2003,"lang":"en","type":"article","venue":"Psychoneuroendocrinology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3635,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Statistics; Computation; Mathematics; Repeated measures design; Area under the curve; Growth curve (statistics); Area under curve; Duration (music); Curve fitting; Data point; Algorithm; Medicine","score_opus":0.15978579404270654,"score_gpt":0.3391802752514318,"score_spread":0.17939448120872525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156579478","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":[0.0035694644,0.0007528104,0.9919777,0.00013843997,0.00025034772,0.00015308236,0.00039134387,0.0021604632,0.0006064354],"genre_scores_gemma":[0.025326293,0.00074865954,0.96906537,0.00015509309,0.00016678109,0.0008623258,0.00058015075,0.00080109335,0.0022942584],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960224,0.0012691292,0.0005297058,0.0006792855,0.001245203,0.00025434076],"domain_scores_gemma":[0.9679252,0.023098178,0.002148883,0.002907969,0.0035479132,0.000371966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005642548,0.002581378,0.0017070237,0.0055283397,0.0004823483,0.0022232884,0.003149341,0.0027720858,0.007499478],"category_scores_gemma":[0.04661722,0.0008632849,0.0021705455,0.0029348275,0.0011164146,0.0031749893,0.0012917744,0.0035104342,0.004877669],"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":[0.0009734285,0.0004352989,0.0063821254,0.0016730278,0.00054244365,0.0002765166,0.0006263894,0.020068388,0.06523308,0.05461856,0.014668575,0.8345021],"study_design_scores_gemma":[0.0005066025,0.0019893546,0.03717531,0.00050471653,0.00093542575,0.006766257,0.00042247164,0.5992329,0.18944734,0.089041,0.07267693,0.0013016257],"about_ca_topic_score_codex":0.0018274803,"about_ca_topic_score_gemma":0.0020761755,"teacher_disagreement_score":0.007499478,"about_ca_system_score_codex":0.0009848281,"about_ca_system_score_gemma":0.00090357295,"threshold_uncertainty_score":0.029841006},"labels":[],"label_agreement":null},{"id":"W2157428947","doi":"10.1027/1614-2241/a000051","title":"Non-Graphical Solutions for Cattell’s Scree Test","year":2012,"lang":"en","type":"article","venue":"Methodology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":355,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Principal component analysis; Eigenvalues and eigenvectors; Plot (graphics); Test (biology); Computer science; Matrix (chemical analysis); Factor (programming language); Mathematics; Statistics; Artificial intelligence; Geology","score_opus":0.35031273622427517,"score_gpt":0.39547294823846263,"score_spread":0.04516021201418746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157428947","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":[0.0025788906,0.00019962437,0.9935213,0.0005084844,0.00012993523,0.00025073823,0.00008884183,0.00056959473,0.0021526702],"genre_scores_gemma":[0.09477952,0.00033355525,0.8986651,0.00041946146,0.00022128763,0.0020221965,0.00036321188,0.00059716223,0.0025985322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92328376,0.059271727,0.0030583483,0.004754713,0.008690926,0.00094059855],"domain_scores_gemma":[0.7367117,0.22949953,0.007134252,0.012953782,0.012840387,0.0008603299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046978097,0.003429899,0.0028140938,0.006930928,0.0016611392,0.0050532664,0.00533579,0.003998377,0.029310467],"category_scores_gemma":[0.340421,0.0011114405,0.0026104692,0.0063400897,0.006586189,0.00595498,0.0044863587,0.0037479035,0.008710055],"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":[0.00036455135,0.00032079022,0.004662303,0.0011475477,0.00039091054,0.0007708353,0.0019108703,0.05047138,0.0021593051,0.52500933,0.026501216,0.38629094],"study_design_scores_gemma":[0.00014437237,0.00029057846,0.0028524736,0.0003475264,0.000090972666,0.0008853965,0.00065466913,0.4752788,0.0026208502,0.49697548,0.019579766,0.00027909462],"about_ca_topic_score_codex":0.0014432047,"about_ca_topic_score_gemma":0.0011089895,"teacher_disagreement_score":0.046978097,"about_ca_system_score_codex":0.0013660067,"about_ca_system_score_gemma":0.0022115,"threshold_uncertainty_score":0.24844694},"labels":[],"label_agreement":null},{"id":"W2158145327","doi":"10.1177/0959354310384910","title":"The mythologization of regression towards the mean","year":2011,"lang":"en","type":"article","venue":"Theory & Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Galton's problem; Unobservable; Phenomenon; Regression toward the mean; Property (philosophy); Regression; Epistemology; Regression analysis; Computer science; Statistics; Econometrics; Mathematics; Philosophy","score_opus":0.14332339830538424,"score_gpt":0.3532749189369567,"score_spread":0.2099515206315725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158145327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013752773,0.011572473,0.85416216,0.04609531,0.001712816,0.00013588219,0.00029780532,0.0003832066,0.07188754],"genre_scores_gemma":[0.7332058,0.0076050274,0.2260729,0.016203351,0.006659584,0.0013701501,0.00025024902,0.0006170482,0.008015919],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9571367,0.026481256,0.0015396292,0.00713333,0.006982375,0.00072681584],"domain_scores_gemma":[0.9369049,0.0487749,0.004150521,0.0059491983,0.0037702587,0.0004502013],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.047224198,0.0010828797,0.0014710873,0.0046396563,0.0022482013,0.007423536,0.0031566203,0.0035440458,0.0035496526],"category_scores_gemma":[0.06800348,0.00062507077,0.0019022876,0.003470938,0.046244387,0.012302877,0.004952599,0.010101343,0.0009900037],"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":[0.000008085059,0.0000058129567,0.00017281626,0.00004993691,0.000012831589,0.000008201634,0.0008296141,0.00035535524,0.000046695975,0.9944582,0.00074055366,0.0033118725],"study_design_scores_gemma":[0.000013076301,0.000033922614,0.00033905523,0.000081806815,0.000008997337,0.000058613135,0.000246161,0.0020339666,0.00016167978,0.9787821,0.018223083,0.000017459552],"about_ca_topic_score_codex":0.0012733618,"about_ca_topic_score_gemma":0.0005367474,"teacher_disagreement_score":0.9527758,"about_ca_system_score_codex":0.004310822,"about_ca_system_score_gemma":0.002674532,"threshold_uncertainty_score":0.24974841},"labels":[],"label_agreement":null},{"id":"W2158157835","doi":"10.3102/10769986027002105","title":"Constrained Principal Component Analysis: Various Applications","year":2002,"lang":"en","type":"article","venue":"Journal of Educational and Behavioral Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Principal component analysis; Contingency table; Set (abstract data type); Computer science; Multivariate statistics; Reliability (semiconductor); Component (thermodynamics); Regression analysis; Variety (cybernetics); Statistics; Data mining; Econometrics; Mathematics; Machine learning","score_opus":0.08203732621707842,"score_gpt":0.34469778670554435,"score_spread":0.2626604604884659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158157835","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":[0.004347945,0.018476756,0.9604444,0.0021842304,0.00034937332,0.00032922786,0.00054053724,0.0010710444,0.012256424],"genre_scores_gemma":[0.077667885,0.024662415,0.888955,0.0005629061,0.0008259509,0.0008835557,0.0007666175,0.00061626325,0.005059389],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99268097,0.0036390575,0.00046680612,0.0010713818,0.001987218,0.00015449106],"domain_scores_gemma":[0.9843008,0.0105586955,0.00068713917,0.0015919353,0.0025843703,0.000277141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009481243,0.0022749722,0.0019426959,0.005548128,0.0013607416,0.003312462,0.0018870769,0.0021988824,0.0066000936],"category_scores_gemma":[0.036321595,0.0010516469,0.0021533496,0.014185689,0.0026160134,0.0027529267,0.0027596096,0.003035407,0.0026126155],"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":[0.000105312196,0.00011459812,0.003751042,0.0012143841,0.0005227643,0.00027819327,0.00090134714,0.0207899,0.0016857667,0.1997559,0.021454925,0.7494259],"study_design_scores_gemma":[0.00006702314,0.00009704051,0.008079606,0.00092098716,0.00018734933,0.00078239525,0.0005961031,0.13596225,0.0028908057,0.69490236,0.1552554,0.00025872234],"about_ca_topic_score_codex":0.0045851823,"about_ca_topic_score_gemma":0.0038165206,"teacher_disagreement_score":0.009481243,"about_ca_system_score_codex":0.0011704554,"about_ca_system_score_gemma":0.0023529385,"threshold_uncertainty_score":0.05014217},"labels":[],"label_agreement":null},{"id":"W2163948174","doi":"10.1002/cjs.5550330405","title":"A nonparametric test for interaction in two-way layouts","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa; York University","funders":"","keywords":"Test statistic; Nonparametric statistics; Estimator; Ranking (information retrieval); Statistics; Statistical hypothesis testing; Rank (graph theory); Statistic; Null hypothesis; Mathematics; Set (abstract data type); Test (biology); Null (SQL); Limiting; Covariance; Computer science; Data mining; Artificial intelligence; Engineering; Combinatorics","score_opus":0.05586300490728202,"score_gpt":0.3146718824202835,"score_spread":0.25880887751300147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163948174","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":[0.101594664,0.00016207554,0.8932278,0.00023437286,0.00014645937,0.00025345347,0.00073164614,0.0011017132,0.002547757],"genre_scores_gemma":[0.57152253,0.0000696305,0.42322886,0.0002550721,0.0001006471,0.0012350419,0.0015488038,0.00033608565,0.0017032797],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98196745,0.008358339,0.000806182,0.0027621558,0.0053992053,0.00070665235],"domain_scores_gemma":[0.92687386,0.058263835,0.0039655645,0.005872966,0.00413132,0.000892497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010759917,0.00078628643,0.002215639,0.00282518,0.0012299399,0.001894715,0.0022488784,0.0017173749,0.0050367275],"category_scores_gemma":[0.06496621,0.0005225022,0.0018684524,0.0025582693,0.0029816309,0.0025032035,0.002435647,0.0025634847,0.000767939],"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":[0.0035429022,0.0018204926,0.10028542,0.0009692349,0.0022659053,0.0016928093,0.001540756,0.054709606,0.092371225,0.08190651,0.008306232,0.6505889],"study_design_scores_gemma":[0.000967286,0.0071052765,0.1647619,0.00017883285,0.0007569036,0.002986856,0.0015301922,0.542903,0.053372275,0.20381926,0.020838626,0.0007796162],"about_ca_topic_score_codex":0.0006754713,"about_ca_topic_score_gemma":0.00084789004,"teacher_disagreement_score":0.010759917,"about_ca_system_score_codex":0.0009001537,"about_ca_system_score_gemma":0.0018422761,"threshold_uncertainty_score":0.056904554},"labels":[],"label_agreement":null},{"id":"W2163965546","doi":"10.1111/j.1467-8659.2012.03125.x","title":"A Taxonomy of Visual Cluster Separation Factors","year":2012,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":159,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Taxonomy (biology); Computer science; CLARITY; Artificial intelligence; Cluster (spacecraft); Coding (social sciences); Data mining; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.062713636670657,"score_gpt":0.30915848768621046,"score_spread":0.24644485101555347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163965546","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":[0.08635488,0.0014460209,0.8646597,0.0039197616,0.00022526251,0.0018890179,0.0020651834,0.0022519624,0.037188195],"genre_scores_gemma":[0.536439,0.0005584701,0.45576864,0.00032198124,0.00007308082,0.0024635335,0.0013672768,0.000514038,0.0024939172],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97086483,0.0122366855,0.0031183062,0.0024776345,0.010373855,0.0009287386],"domain_scores_gemma":[0.8854842,0.057926327,0.010082809,0.009900717,0.034644578,0.0019614818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024469526,0.0015953993,0.0007621515,0.013104451,0.0033553154,0.0095096,0.0025360074,0.00161287,0.0063578896],"category_scores_gemma":[0.11378052,0.00072097237,0.0012083442,0.010408722,0.009709783,0.012342806,0.0052943313,0.0021405811,0.00090818375],"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":[0.00039214105,0.00017041653,0.03182709,0.003968628,0.0001517019,0.00040239905,0.11435117,0.0056280303,0.0067598224,0.48691478,0.020118566,0.32931522],"study_design_scores_gemma":[0.00012151914,0.00042538342,0.03605663,0.003842288,0.00015980017,0.001382083,0.10871035,0.051196136,0.008142867,0.61208713,0.17733684,0.0005389128],"about_ca_topic_score_codex":0.005412886,"about_ca_topic_score_gemma":0.0034385778,"teacher_disagreement_score":0.024469526,"about_ca_system_score_codex":0.0053121974,"about_ca_system_score_gemma":0.005632684,"threshold_uncertainty_score":0.12940878},"labels":[],"label_agreement":null},{"id":"W2164016671","doi":"10.1080/00221309.2012.703711","title":"More Voodoo Correlations: When Average-Based Measures Inflate Correlations","year":2012,"lang":"en","type":"article","venue":"The Journal of General Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Correlation; Statistics; Measure (data warehouse); Monte Carlo method; Econometrics; Inflation (cosmology); Mathematics; Computer science; Physics; Data mining","score_opus":0.103253087144363,"score_gpt":0.3532156247165014,"score_spread":0.24996253757213843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164016671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35337347,0.0007877422,0.6347432,0.0008992627,0.000527991,0.0006001989,0.00037826717,0.00089495623,0.00779493],"genre_scores_gemma":[0.8937698,0.000105307074,0.10344796,0.0006412242,0.00006618069,0.0005709119,0.00030796832,0.0003497258,0.00074089074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94719124,0.03534925,0.0036598407,0.006191208,0.0066372827,0.00097103976],"domain_scores_gemma":[0.6099074,0.30918074,0.020226307,0.047903605,0.011670317,0.0011115604],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07220683,0.00092948455,0.0013465694,0.0018330694,0.0014394671,0.0038654064,0.0017747519,0.0019121943,0.0032118093],"category_scores_gemma":[0.33274844,0.0007605829,0.0018302891,0.0017729222,0.0034213464,0.0039928015,0.0030550603,0.0037555639,0.00038101882],"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":[0.00457923,0.0010216635,0.25836948,0.0022999172,0.005128558,0.0032656074,0.014342954,0.13797742,0.045505628,0.23467298,0.009904678,0.28293192],"study_design_scores_gemma":[0.00041489585,0.004750698,0.17498752,0.0011629717,0.0015851636,0.0027541525,0.0019957724,0.40406322,0.041379727,0.341393,0.024765281,0.0007476057],"about_ca_topic_score_codex":0.001294598,"about_ca_topic_score_gemma":0.0016271237,"teacher_disagreement_score":0.92779315,"about_ca_system_score_codex":0.0013312788,"about_ca_system_score_gemma":0.0010212074,"threshold_uncertainty_score":0.3818708},"labels":[],"label_agreement":null},{"id":"W2166516323","doi":"10.7202/900356ar","title":"Le problème de la détermination du nombre de facteurs en analyse factorielle","year":2009,"lang":"fr","type":"article","venue":"Revue des sciences de l éducation","topic":"Sensory Analysis and Statistical Methods","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":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Political science; Physics; Humanities; Philosophy","score_opus":0.2211183647678516,"score_gpt":0.4165505938590296,"score_spread":0.195432229091178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166516323","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":[0.29021093,0.005826465,0.69181293,0.0018952001,0.0003998134,0.001006899,0.0008684898,0.0004572972,0.0075219786],"genre_scores_gemma":[0.612051,0.0016703309,0.38146526,0.00045225862,0.000119194905,0.00087442785,0.0005355978,0.00019412205,0.002637721],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9544144,0.02815984,0.0034072646,0.0047189677,0.00841576,0.00088382186],"domain_scores_gemma":[0.7494947,0.2162982,0.0060938816,0.012242206,0.015156194,0.00071487745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06291342,0.0019040139,0.0022486132,0.0033889394,0.0021601398,0.004823471,0.0013937,0.0017902333,0.003218662],"category_scores_gemma":[0.18145779,0.0010780863,0.0022647541,0.0027005726,0.003692283,0.003327739,0.0017097081,0.003062916,0.0013865656],"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":[0.002923785,0.00092440104,0.10542588,0.0044110357,0.0028898763,0.0003096462,0.007020417,0.022517778,0.11659537,0.01981235,0.0032810953,0.7138884],"study_design_scores_gemma":[0.000733073,0.0077556297,0.33007333,0.0034510095,0.003128658,0.001886917,0.010775476,0.16417187,0.26211882,0.15366066,0.0613788,0.00086570124],"about_ca_topic_score_codex":0.005573196,"about_ca_topic_score_gemma":0.008730796,"teacher_disagreement_score":0.06291342,"about_ca_system_score_codex":0.0010523871,"about_ca_system_score_gemma":0.0025929492,"threshold_uncertainty_score":0.33272195},"labels":[],"label_agreement":null},{"id":"W2168285514","doi":"10.1023/a:1003769304778","title":"Sensory evaluation as a selection tool in apple breeding","year":2000,"lang":"en","type":"article","venue":"Euphytica","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":138,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Sweetness; Flavour; Aroma; Food science; Taste; Organoleptic; Sensory analysis; Titratable acid; Texture (cosmology); Mathematics; Psychology; Biology; Artificial intelligence","score_opus":0.05504603835654036,"score_gpt":0.31615571557484434,"score_spread":0.26110967721830397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168285514","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":[0.9099194,0.0006634345,0.084814064,0.000100861034,0.00004522893,0.00019461452,0.00040458172,0.00047108147,0.0033866267],"genre_scores_gemma":[0.95771515,0.0002101279,0.04035431,0.00005994387,0.00001931624,0.00015475962,0.00017418803,0.00007486841,0.0012373334],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99678266,0.0021780245,0.00013138971,0.00024003552,0.0005645679,0.00010324656],"domain_scores_gemma":[0.99645764,0.0022527575,0.00032367287,0.0002292415,0.000544756,0.00019191948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042466973,0.0005348073,0.0005681669,0.0013426752,0.00026630642,0.0008449776,0.00036675803,0.0003274491,0.0011563534],"category_scores_gemma":[0.0044783857,0.00023330172,0.00032620152,0.0011751747,0.00042595522,0.0004541672,0.00047615194,0.00064198,0.00017300718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005429022,0.0014924756,0.11500198,0.00040541537,0.0003503539,0.0002908289,0.0007757885,0.004565789,0.483681,0.0010772103,0.0011995748,0.38573056],"study_design_scores_gemma":[0.0001393467,0.00918462,0.77285534,0.00008104715,0.0004576968,0.0007990447,0.0009551469,0.057427175,0.15356626,0.0013542038,0.002986846,0.00019328375],"about_ca_topic_score_codex":0.00081885303,"about_ca_topic_score_gemma":0.0023930045,"teacher_disagreement_score":0.0042466973,"about_ca_system_score_codex":0.00028787932,"about_ca_system_score_gemma":0.00031701213,"threshold_uncertainty_score":0.02245897},"labels":[],"label_agreement":null},{"id":"W2169211258","doi":"10.1111/jtxs.12016","title":"Impact of Structure Modification on Texture of a Soymilk and Cow's Milk Gel Assessed Using the <scp>N</scp>apping Procedure","year":2013,"lang":"en","type":"article","venue":"Journal of Texture Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Vineland Research and Innovation Centre; University of Guelph","funders":"","keywords":"Food science; Casein; Soy protein; Homogenization (climate); Skimmed milk; Chemistry; Texture (cosmology); Protein isolate; Whey protein; Milk protein; Sensory system; Biology; Computer science","score_opus":0.08414558795298294,"score_gpt":0.3672292593491425,"score_spread":0.2830836713961596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169211258","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":[0.9988223,0.00020647557,0.0005942837,0.000010714013,0.0000058033606,0.000012147725,0.000030982872,0.000009414442,0.00030786608],"genre_scores_gemma":[0.99815506,0.00016199148,0.0011667542,0.000019983669,0.0000022727095,0.000012329276,0.000040222974,0.000009797606,0.0004315892],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985564,0.000031231957,0.000011152125,0.00002784878,0.000045857854,0.000028205659],"domain_scores_gemma":[0.99974805,0.00008053666,0.00008593833,0.00001683288,0.000038639067,0.000030081033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023636264,0.00023852853,0.00013948316,0.00010485427,0.00011258998,0.00023223467,0.000110560875,0.00019145942,0.0007803597],"category_scores_gemma":[0.00035330674,0.00015541419,0.00014577067,0.000089317735,0.00015824176,0.00012688214,0.00013091865,0.00024046443,0.000105571824],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010261266,0.0000103418,0.00012160247,0.000015269547,0.0000036794133,0.00001539468,0.000012305577,0.000024539144,0.9992118,0.0000042759066,0.0000037260245,0.000474506],"study_design_scores_gemma":[0.0000050595936,0.00046342876,0.0065453383,0.000004114684,0.000015736086,0.000046228968,0.00003555406,0.00036358708,0.9922569,0.00000411784,0.0002553918,0.000004614807],"about_ca_topic_score_codex":0.0006435476,"about_ca_topic_score_gemma":0.0009810672,"teacher_disagreement_score":0.0007803597,"about_ca_system_score_codex":0.0001143711,"about_ca_system_score_gemma":0.000110943394,"threshold_uncertainty_score":0.0026106238},"labels":[],"label_agreement":null},{"id":"W2183065343","doi":"10.20982/tqmp.07.1.p005","title":"Correspondence Analysis applied to psychological research","year":2011,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Psychology","score_opus":0.6817363619728046,"score_gpt":0.6368229115765854,"score_spread":0.04491345039621919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183065343","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":[0.0041033234,0.0014670804,0.9722507,0.0006573957,0.00068680954,0.0012273222,0.0008021526,0.0015960516,0.017209256],"genre_scores_gemma":[0.058240093,0.001327684,0.9277091,0.00035218158,0.00042021734,0.006650377,0.00080775074,0.0008624546,0.0036301552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94210887,0.035713337,0.0047931042,0.0069406815,0.009393052,0.0010509941],"domain_scores_gemma":[0.902142,0.06892614,0.0037536065,0.011191257,0.012948077,0.0010389211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02950168,0.0018483143,0.0024633785,0.009519298,0.0033697644,0.00768304,0.0022385695,0.0020160214,0.025616003],"category_scores_gemma":[0.14392191,0.0010943996,0.0034646038,0.017646026,0.0055775736,0.0052188877,0.007735428,0.004695942,0.005808248],"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":[0.0003421633,0.00024129334,0.004295208,0.0034907542,0.0005767327,0.0004134679,0.0071738837,0.008716539,0.002646985,0.53782153,0.02234683,0.4119346],"study_design_scores_gemma":[0.00008197204,0.00021102019,0.005386273,0.0009217969,0.00017480047,0.0005614437,0.0017638184,0.034415267,0.002433538,0.813025,0.14086546,0.00015961227],"about_ca_topic_score_codex":0.0019603008,"about_ca_topic_score_gemma":0.0012020516,"teacher_disagreement_score":0.02950168,"about_ca_system_score_codex":0.0035668195,"about_ca_system_score_gemma":0.0067832083,"threshold_uncertainty_score":0.15602165},"labels":[],"label_agreement":null},{"id":"W2187714035","doi":"10.21273/hortsci.37.7.1127","title":"Use of Digital Images for Evaluation of Factors Responsible for Visual Preference of Apples by Consumers","year":2002,"lang":"en","type":"article","venue":"HortScience","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; College of the North Atlantic; Agriculture and Agri-Food Canada","funders":"","keywords":"Malus; Preference; Nova scotia; Mathematics; Horticulture; Geography; Biology; Statistics; Archaeology","score_opus":0.3323633777378368,"score_gpt":0.3656949365243856,"score_spread":0.03333155878654881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187714035","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":[0.99806756,0.00010812758,0.00075941597,0.000020410895,0.0000035982255,0.00008790349,0.00009143613,0.0000073090705,0.0008542729],"genre_scores_gemma":[0.9942151,0.00016685524,0.0045400294,0.000035620043,0.0000057421316,0.00013664461,0.00019217232,0.0000072113053,0.00070066855],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99929714,0.00023337969,0.000043719963,0.00011863142,0.00025332064,0.0000536847],"domain_scores_gemma":[0.9978581,0.0009789123,0.00044235424,0.00012831384,0.00049439515,0.000097906996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011689378,0.00033238652,0.0002348692,0.00066181825,0.00022345153,0.0006558198,0.00022039865,0.00021275363,0.0016067305],"category_scores_gemma":[0.0041991062,0.0001600283,0.00029710546,0.00053115946,0.00033025694,0.0003689317,0.00023243493,0.0002872153,0.00012167465],"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":[0.008736233,0.0018986659,0.36552995,0.0009870757,0.00027358183,0.00041830866,0.0034493208,0.0009982949,0.47717193,0.0003138771,0.000506399,0.13971636],"study_design_scores_gemma":[0.00008634919,0.0022042112,0.9579449,0.000033289052,0.0001795886,0.00024445093,0.0013736171,0.0013750738,0.035586994,0.00011249745,0.0008123798,0.00004648267],"about_ca_topic_score_codex":0.010060145,"about_ca_topic_score_gemma":0.015440955,"teacher_disagreement_score":0.010060145,"about_ca_system_score_codex":0.0006316888,"about_ca_system_score_gemma":0.0003449313,"threshold_uncertainty_score":0.02000314},"labels":[],"label_agreement":null},{"id":"W2188162370","doi":"10.20982/tqmp.04.1.p021","title":"Eliminating Aggregation Bias in Experimental Research: Random Coefficient Analysis as an Alternative to Performing a ‘by-subjects’ and/or ‘by-items’ ANOVA","year":2008,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Analysis of variance; Statistics; Mixed-design analysis of variance; Psychology; Econometrics; Repeated measures design; Statistical analysis; Mathematics","score_opus":0.45898644711197384,"score_gpt":0.5686488432530882,"score_spread":0.10966239614111434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188162370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008107374,0.00025961763,0.98633784,0.00028691758,0.0004292995,0.0014317629,0.0001987166,0.0007074926,0.0022409519],"genre_scores_gemma":[0.051920123,0.00024738608,0.93982023,0.00033651324,0.00021327172,0.006102851,0.00017592174,0.00053062703,0.0006530554],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.66036266,0.27871522,0.012656613,0.015156669,0.031770308,0.0013385508],"domain_scores_gemma":[0.45029286,0.43960565,0.018777609,0.06985159,0.020760395,0.00071199826],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17062764,0.0026693007,0.0042203525,0.0047424017,0.0019348228,0.0041953134,0.0036270854,0.0028444433,0.006270895],"category_scores_gemma":[0.43170014,0.0013017879,0.0042968187,0.010748799,0.004635164,0.006419012,0.00414124,0.0064377487,0.0014138173],"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":[0.0026372818,0.001257581,0.01573766,0.0072045974,0.005430391,0.00076741114,0.015741263,0.015356078,0.024117861,0.2356412,0.018039256,0.65806955],"study_design_scores_gemma":[0.0017938699,0.010471987,0.08519102,0.0023722616,0.0038273165,0.0011835404,0.004007627,0.1507929,0.037088543,0.6186967,0.08314495,0.0014291806],"about_ca_topic_score_codex":0.0016616882,"about_ca_topic_score_gemma":0.002456988,"teacher_disagreement_score":0.82937235,"about_ca_system_score_codex":0.0016606704,"about_ca_system_score_gemma":0.003050421,"threshold_uncertainty_score":0.902376},"labels":[],"label_agreement":null},{"id":"W2188439980","doi":"10.6339/jds.201404_12(2).0008","title":"SENSITIVITY ANALYSIS OF BAYES FACTOR FOR CATEGORICAL DATA WITH EMPHASIS ON SPARSE MULTINOMIAL DATA","year":2021,"lang":"en","type":"article","venue":"Journal of Data Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Government of Newfoundland and Labrador","funders":"","keywords":"Categorical variable; Multinomial distribution; Contingency table; Bayes factor; Prior probability; Dirichlet distribution; Computer science; Bayes' theorem; Bayesian probability; Sensitivity (control systems); Context (archaeology); Econometrics; Statistics; Consistency (knowledge bases); Factor analysis; Data mining; Mathematics; Artificial intelligence; Machine learning","score_opus":0.2991029132364069,"score_gpt":0.4089341009435077,"score_spread":0.10983118770710076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188439980","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":[0.08319892,0.0042374507,0.9067301,0.0012968713,0.00031481712,0.0006245307,0.00038800074,0.00037481007,0.002834431],"genre_scores_gemma":[0.8185487,0.001057798,0.17733552,0.0006171935,0.00028651065,0.0008235544,0.00044555968,0.0002058557,0.0006793582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8619335,0.11706657,0.003446499,0.008240194,0.008225226,0.0010880475],"domain_scores_gemma":[0.20539136,0.7663671,0.0073815235,0.014744241,0.005489826,0.0006258885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14779235,0.0013866092,0.0029459638,0.0037972503,0.0017628303,0.003571376,0.0023622024,0.0032029673,0.0031339223],"category_scores_gemma":[0.5391139,0.0007992598,0.0031996334,0.0024976197,0.003648782,0.004704412,0.0036425155,0.0043305377,0.0003171415],"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":[0.0046742572,0.0004020953,0.07907245,0.0037791934,0.0048684264,0.0021606735,0.003304628,0.50483936,0.006767096,0.14553134,0.003977795,0.24062261],"study_design_scores_gemma":[0.00021240689,0.001334022,0.015477139,0.00094455533,0.0013087845,0.0022761533,0.0009814742,0.70171344,0.007331107,0.26216158,0.005924296,0.00033501332],"about_ca_topic_score_codex":0.0028683913,"about_ca_topic_score_gemma":0.0011798245,"teacher_disagreement_score":0.14779235,"about_ca_system_score_codex":0.0023952012,"about_ca_system_score_gemma":0.0021551668,"threshold_uncertainty_score":0.78161},"labels":[],"label_agreement":null},{"id":"W2203025214","doi":"10.1016/b978-0-12-394437-5.00134-0","title":"The Application of Sensory Science to the Evaluation of Grain-Based Foods","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sensory system; Perception; Sensory analysis; Presentation (obstetrics); Product testing; Test (biology); Quantitative sensory testing; Psychology; Quantitative Descriptive Analysis; Cognitive psychology; Computer science; Applied psychology; Mathematics; Statistics; Engineering; Medicine; Operations management; Neuroscience; Biology","score_opus":0.06994681776022155,"score_gpt":0.3301860452934372,"score_spread":0.26023922753321566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203025214","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":[0.02418029,0.2670326,0.5999355,0.0031877116,0.0019188642,0.00034070612,0.0028334563,0.0019155202,0.09865532],"genre_scores_gemma":[0.1288202,0.23480555,0.5182655,0.0028198839,0.0012855316,0.0005233377,0.0020326623,0.0010083971,0.11043899],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884087,0.00026189262,0.00006684471,0.00013725157,0.0006636919,0.00002953756],"domain_scores_gemma":[0.9985025,0.0010970597,0.00004978502,0.000075672826,0.00024376127,0.000031232936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021689918,0.0010978883,0.0009392872,0.00209883,0.00028320187,0.0023708392,0.00093717343,0.00088453933,0.008446341],"category_scores_gemma":[0.00277149,0.0004468669,0.00073907233,0.0022282656,0.0014845917,0.00140801,0.0010820208,0.001754739,0.0024129602],"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":[0.00013608488,0.0001243951,0.0010318574,0.0021579694,0.000089798334,0.00013158591,0.00025224205,0.0031759932,0.05026274,0.011755341,0.017383467,0.91349846],"study_design_scores_gemma":[0.000046820158,0.0013408056,0.039885685,0.0023009696,0.00022077974,0.0026057854,0.0013555861,0.04007596,0.09023118,0.28034836,0.54115254,0.00043559397],"about_ca_topic_score_codex":0.0020914944,"about_ca_topic_score_gemma":0.0035305107,"teacher_disagreement_score":0.008446341,"about_ca_system_score_codex":0.0006421761,"about_ca_system_score_gemma":0.00093791715,"threshold_uncertainty_score":0.02825582},"labels":[],"label_agreement":null},{"id":"W2212514950","doi":"10.2139/ssrn.1616743","title":"Profiling of Canadian Travelers to Asian Travel Destinations","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Destinations; Profiling (computer programming); Advertising; Geography; Business; Tourism; Computer science; Archaeology","score_opus":0.02351710492380034,"score_gpt":0.26338855571959324,"score_spread":0.23987145079579292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2212514950","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":[0.9864158,0.00047342258,0.00047979728,0.00021882313,0.000021367572,0.000074995536,0.0036810355,0.000022808457,0.008611904],"genre_scores_gemma":[0.9871852,0.000809875,0.0012539816,0.00013104606,0.000009541057,0.000042056432,0.0019745196,0.000017273096,0.008576535],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994622,0.000042023275,0.000018567773,0.00006287197,0.00020151483,0.00021287173],"domain_scores_gemma":[0.9987135,0.000046920708,0.00011620065,0.00001848724,0.00092636264,0.00017853535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044041037,0.0004131034,0.00025941388,0.0024752025,0.0028343347,0.0014974009,0.0006957163,0.0003396303,0.0036596723],"category_scores_gemma":[0.0011941128,0.00016902965,0.0004046884,0.00637235,0.00037495073,0.00030731325,0.000682088,0.00043117686,0.00055900053],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006899854,0.000092232425,0.90495706,0.00026452958,0.00012211158,0.00039957179,0.013750793,0.0007195358,0.0071077296,0.00054935203,0.009494704,0.061852314],"study_design_scores_gemma":[0.0000032722146,0.000046128574,0.9765841,0.000050497696,0.000036821722,0.00010740149,0.014325341,0.00051732134,0.00062966737,0.000028670278,0.007635706,0.000035061024],"about_ca_topic_score_codex":0.9894498,"about_ca_topic_score_gemma":0.9961356,"teacher_disagreement_score":0.010550201,"about_ca_system_score_codex":0.009850186,"about_ca_system_score_gemma":0.012798474,"threshold_uncertainty_score":0.07146847},"labels":[],"label_agreement":null},{"id":"W2244491512","doi":"10.1002/env.2414","title":"Analysis of aggregated functional data from mixed populations with application to energy consumption","year":2016,"lang":"en","type":"preprint","venue":"Environmetrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Class (philosophy); Consumption (sociology); Energy consumption; Energy (signal processing); Function (biology); Maximization; Mathematics; Mathematical optimization; Set (abstract data type); Measure (data warehouse); Computer science; Econometrics; Statistics; Data mining; Artificial intelligence","score_opus":0.1533387504153592,"score_gpt":0.3069626422998113,"score_spread":0.1536238918844521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244491512","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":[0.13735603,0.0002735516,0.8599414,0.00032207443,0.00003605336,0.00011301278,0.0008597187,0.0005876665,0.00051052636],"genre_scores_gemma":[0.6420836,0.00029779927,0.35323453,0.00014065969,0.00009076026,0.0005354072,0.002537496,0.00015298797,0.00092676166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949706,0.0037279704,0.00017685376,0.0005474034,0.00045144183,0.00012580016],"domain_scores_gemma":[0.95605356,0.035263266,0.002645233,0.004364027,0.0013636596,0.00031027512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012915712,0.00082355406,0.0011319951,0.003101113,0.00079793076,0.0015413756,0.0016281753,0.0013979758,0.0017972626],"category_scores_gemma":[0.05433494,0.000700436,0.002481971,0.0027479408,0.001613045,0.0014004955,0.001748439,0.0014388217,0.00023960962],"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":[0.00029538255,0.00020236244,0.06697822,0.00042138365,0.0012180331,0.00044074413,0.0008524035,0.78156346,0.0027367068,0.057931148,0.0015618816,0.085798256],"study_design_scores_gemma":[0.000016101289,0.000038336933,0.0074558393,0.00002186263,0.000025524709,0.000059192247,0.000077839024,0.9572266,0.00034434837,0.03399215,0.00071844854,0.00002379169],"about_ca_topic_score_codex":0.010421623,"about_ca_topic_score_gemma":0.0067881765,"teacher_disagreement_score":0.012915712,"about_ca_system_score_codex":0.0011053598,"about_ca_system_score_gemma":0.0007907784,"threshold_uncertainty_score":0.06830567},"labels":[],"label_agreement":null},{"id":"W2283891470","doi":"10.1016/j.ijgfs.2016.01.001","title":"A comparison of sensory properties of artisanal style and industrially processed gluten free breads","year":2016,"lang":"en","type":"article","venue":"International Journal of Gastronomy and Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Acadia University","funders":"Acadia University","keywords":"Food science; Gluten free; Flavor; Gluten; Mathematics; Chemistry","score_opus":0.0802248136833482,"score_gpt":0.31152496797464657,"score_spread":0.23130015429129835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283891470","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":[0.99922454,0.00010158478,0.000097829994,0.0000058057867,0.0000024638452,0.000004855146,0.000046624322,0.0000021396622,0.0005141048],"genre_scores_gemma":[0.99885,0.0001147185,0.00038525453,0.000022348691,0.000003097048,0.000004106289,0.00019938177,0.0000030389247,0.00041806346],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997749,0.00004145237,0.000020499287,0.0000485597,0.0000891457,0.000025417767],"domain_scores_gemma":[0.9994968,0.0001307929,0.00010555892,0.00005101756,0.00013796394,0.0000778532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046094015,0.00032237874,0.0002072909,0.000669375,0.00026741397,0.0004403241,0.0001432152,0.00021701222,0.0016885399],"category_scores_gemma":[0.0007071799,0.00012767393,0.00033129126,0.00050792866,0.00026118933,0.0002089076,0.0003233369,0.00024876057,0.00018192253],"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":[0.004555736,0.0004860045,0.28045198,0.00044839366,0.00037321937,0.0007555477,0.0033064478,0.00031381054,0.66628206,0.00008878714,0.00027639963,0.04266153],"study_design_scores_gemma":[0.000017573311,0.0010533887,0.98723245,0.000010520859,0.000043379743,0.000305149,0.0008686791,0.00014806422,0.00979642,0.00003689862,0.00047546264,0.0000120268805],"about_ca_topic_score_codex":0.00095229107,"about_ca_topic_score_gemma":0.0016276087,"teacher_disagreement_score":0.0016885399,"about_ca_system_score_codex":0.00009040721,"about_ca_system_score_gemma":0.000063065105,"threshold_uncertainty_score":0.005648732},"labels":[],"label_agreement":null},{"id":"W2300081474","doi":"10.1111/jtxs.12185","title":"Exploring the Links Between Texture Perception and Bolus Properties Throughout oral Processing. Part 1: Breakdown Paths","year":2016,"lang":"en","type":"article","venue":"Journal of Texture Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":31,"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":"Employment and Social Development Canada; Ministry of Business, Innovation and Employment","keywords":"Swallowing; Perception; Bolus (digestion); Mastication; Psychology; Medicine; Audiology; Mathematics; Dentistry; Surgery; Neuroscience","score_opus":0.25845723834584555,"score_gpt":0.3427322558674621,"score_spread":0.08427501752161654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300081474","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":[0.99913126,0.00003994918,0.00047606637,0.0000036586205,9.790663e-7,0.000010042317,0.000026122418,0.00000305744,0.0003089954],"genre_scores_gemma":[0.9992085,0.000033323126,0.00043536082,0.000004805402,0.0000014771185,0.0000060584903,0.000038097027,0.0000023004363,0.0002701029],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999083,0.000016149737,0.000005661981,0.000026944417,0.00002867901,0.000014176304],"domain_scores_gemma":[0.9991097,0.00040389257,0.0002049975,0.000052617346,0.0001405044,0.00008831104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037272705,0.0001957652,0.00017277968,0.00034134646,0.00017242813,0.00054921134,0.000095232295,0.00024354846,0.001793237],"category_scores_gemma":[0.0017313311,0.00019230375,0.00016841637,0.00016624345,0.00018697357,0.00034103924,0.00025830843,0.0002602383,0.00013994158],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036679711,0.0004629247,0.5247314,0.0002878223,0.00015702363,0.00041889364,0.008297999,0.0006704248,0.4032371,0.000209325,0.00019401187,0.057665043],"study_design_scores_gemma":[0.0000067947817,0.00065882824,0.9947233,0.0000039178776,0.000019984825,0.00011490738,0.0006923586,0.00064643385,0.0029503114,0.000060451246,0.00011439447,0.000008279526],"about_ca_topic_score_codex":0.0012408504,"about_ca_topic_score_gemma":0.001228412,"teacher_disagreement_score":0.001793237,"about_ca_system_score_codex":0.00008507186,"about_ca_system_score_gemma":0.00008399732,"threshold_uncertainty_score":0.005998969},"labels":[],"label_agreement":null},{"id":"W2307014153","doi":"10.5539/jfr.v5n2p50","title":"Brazilian Peach (Prunus Persica) and Passion Fruit (Passiflora Edulis) Nectars: Good Source of Vitamin C and Anthocyanins?","year":2016,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Passion fruit; Passiflora; Titratable acid; Food science; Chemistry; Citric acid; Vitamin C; Nectar; Prunus; Vitamin; Anthocyanin; Passifloraceae; Horticulture; Botany; Biology; Biochemistry; Pollen","score_opus":0.07860199855881948,"score_gpt":0.3437846275206562,"score_spread":0.2651826289618367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2307014153","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":[0.98779315,0.007493239,0.0006027777,0.00020640137,0.000016266542,0.000012700718,0.00027458876,0.00002177119,0.0035791174],"genre_scores_gemma":[0.9954905,0.0017614827,0.0010973598,0.0001076897,0.0000070157375,0.000004763593,0.00023404478,0.0000073266856,0.0012898073],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983716,0.000026301215,0.000010714369,0.000055029723,0.000050295916,0.000020512434],"domain_scores_gemma":[0.99982256,0.000022407476,0.00006786901,0.000016927475,0.0000551486,0.000015145616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020135078,0.00030756652,0.00022322175,0.000582085,0.0003283788,0.00060788466,0.00016042478,0.00033947895,0.0018257309],"category_scores_gemma":[0.0003466311,0.00014037566,0.00020348672,0.0006162969,0.00028577584,0.00045033768,0.00020785337,0.00018366877,0.0002492045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006515247,0.00012760572,0.16647245,0.0013934457,0.00021571993,0.0012826908,0.0014014867,0.000102759026,0.73451746,0.00082739815,0.0005184522,0.09248899],"study_design_scores_gemma":[0.000017399841,0.00069614,0.91249645,0.00014639724,0.00021740273,0.00243969,0.0016122746,0.0002718029,0.06486202,0.00041519728,0.016793976,0.000031176252],"about_ca_topic_score_codex":0.008028971,"about_ca_topic_score_gemma":0.01866975,"teacher_disagreement_score":0.008028971,"about_ca_system_score_codex":0.00034151474,"about_ca_system_score_gemma":0.00020385842,"threshold_uncertainty_score":0.015964508},"labels":[],"label_agreement":null},{"id":"W2331424697","doi":"10.1111/1750-3841.13281","title":"Assessment of Important Sensory Attributes of Millet Based Snacks and Biscuits","year":2016,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; Acadia University; University of Guelph","funders":"Ministry of Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Aftertaste; Food science; Taste; Flavor; Mathematics; Health benefits; Chemistry; Medicine; Traditional medicine","score_opus":0.05718646818696297,"score_gpt":0.3206657811207708,"score_spread":0.26347931293380783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331424697","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":[0.99843305,0.00020855617,0.0006895513,0.0000063110897,0.0000042973006,0.00003916174,0.00019627572,0.0000050673034,0.00041766683],"genre_scores_gemma":[0.9951846,0.00027631543,0.0029422129,0.000033239427,0.0000030236295,0.00004949718,0.0006533183,0.0000074619043,0.00085045065],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996582,0.00008488746,0.000038874838,0.00005564338,0.000123591,0.000038843747],"domain_scores_gemma":[0.99948287,0.0001368375,0.000109623244,0.000020151583,0.00018274697,0.00006775271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007208625,0.00054034265,0.00029658302,0.001264582,0.00029505978,0.0006976959,0.00018989007,0.00037569818,0.0012671285],"category_scores_gemma":[0.0007229552,0.000173887,0.00043396914,0.0009112487,0.0002684721,0.00040664652,0.00039807337,0.00060431525,0.0001566071],"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":[0.0034643377,0.00068938907,0.043932248,0.00037940723,0.0001277988,0.00016493112,0.0003108461,0.00045719487,0.93804455,0.0001276893,0.000056915673,0.012244718],"study_design_scores_gemma":[0.00003916396,0.0071497792,0.74532443,0.000052248615,0.00017170118,0.00037401298,0.00079376635,0.0018971016,0.24310154,0.0001447247,0.0009000781,0.000051454383],"about_ca_topic_score_codex":0.0013412201,"about_ca_topic_score_gemma":0.002351692,"teacher_disagreement_score":0.0013412201,"about_ca_system_score_codex":0.00025179295,"about_ca_system_score_gemma":0.00015255946,"threshold_uncertainty_score":0.004238963},"labels":[],"label_agreement":null},{"id":"W2384781313","doi":"10.1111/joss.12210","title":"Does Data Collection Device Affect Sensory Descriptive Analysis Results?","year":2016,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Laptop; Data collection; Descriptive statistics; Univariate; Computer science; Principal component analysis; Statistics; Sensory analysis; Analysis of variance; Affect (linguistics); Multivariate statistics; Data mining; Mathematics; Psychology; Communication","score_opus":0.22562662184135096,"score_gpt":0.37843902141656804,"score_spread":0.15281239957521708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2384781313","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":[0.9621078,0.0022052687,0.028021969,0.00060065807,0.0005438815,0.00089091173,0.0009980737,0.00020132253,0.004430213],"genre_scores_gemma":[0.97160745,0.00092038844,0.024446508,0.0005292443,0.00019495562,0.0007492517,0.00057296274,0.00015405353,0.00082518946],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.94651294,0.02971034,0.007373003,0.0028353292,0.0128504215,0.00071804394],"domain_scores_gemma":[0.68357044,0.26223034,0.021093475,0.012664397,0.019623142,0.0008183423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0434951,0.00051184936,0.0007484291,0.0009835969,0.00051579886,0.0019930454,0.0009203693,0.0007016573,0.0021145039],"category_scores_gemma":[0.169793,0.00052464806,0.0010632023,0.0018878747,0.0011551605,0.0015832456,0.0007767092,0.0005944023,0.0007755591],"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":[0.013547185,0.001472894,0.6932318,0.0031291728,0.001317002,0.00037342537,0.00499599,0.000751318,0.061478063,0.0005680272,0.0022966622,0.21683852],"study_design_scores_gemma":[0.00015815196,0.008754065,0.92232877,0.0007213266,0.0010811251,0.0007322052,0.002509995,0.0024047568,0.05555708,0.00051986927,0.005059434,0.00017314422],"about_ca_topic_score_codex":0.0011601433,"about_ca_topic_score_gemma":0.001432019,"teacher_disagreement_score":0.0434951,"about_ca_system_score_codex":0.0005450405,"about_ca_system_score_gemma":0.00073559245,"threshold_uncertainty_score":0.23002684},"labels":[],"label_agreement":null},{"id":"W2410793253","doi":"10.1111/1750-3841.13328","title":"Temporal Check‐All‐That‐Apply Characterization of Syrah Wine","year":2016,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Universities’ Application Centre","funders":"Core Research for Evolutional Science and Technology","keywords":"Wine; Food science; Ethanol; Mathematics; Ethanol content; Alcohol; Chemistry; Raw material; Fermentation; Biochemistry; Organic chemistry","score_opus":0.0714357322327381,"score_gpt":0.29227558611870447,"score_spread":0.2208398538859664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2410793253","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":[0.9744532,0.000350525,0.022255216,0.00002614748,0.00004508797,0.0001338426,0.00067394733,0.00008602388,0.0019760444],"genre_scores_gemma":[0.96683383,0.0005251685,0.028189914,0.00007680525,0.000026027186,0.00022933158,0.0012853697,0.000101501544,0.0027321754],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997112,0.00003068262,0.000017218073,0.00006279367,0.00014436836,0.000033763405],"domain_scores_gemma":[0.9995241,0.00008568808,0.000055639204,0.000037219907,0.00025930672,0.00003811818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005303498,0.00038975396,0.00035154168,0.00070639246,0.0004028069,0.00044810475,0.00020662766,0.0002121775,0.0013080513],"category_scores_gemma":[0.0007272918,0.0001429347,0.00049415464,0.00045748818,0.00020751219,0.00031279083,0.0003982279,0.0005247299,0.00028335364],"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":[0.0006993859,0.00010557275,0.016392913,0.00018856056,0.00006180644,0.00008013739,0.0005206262,0.00042713844,0.950246,0.00012334938,0.0002732307,0.0308813],"study_design_scores_gemma":[0.000019511244,0.0022622377,0.43189606,0.000043343436,0.00020099395,0.00045149805,0.001301864,0.01145062,0.5465759,0.00032650793,0.0053579686,0.00011345962],"about_ca_topic_score_codex":0.0019988557,"about_ca_topic_score_gemma":0.0060199196,"teacher_disagreement_score":0.0019988557,"about_ca_system_score_codex":0.00014944059,"about_ca_system_score_gemma":0.00024614905,"threshold_uncertainty_score":0.004375875},"labels":[],"label_agreement":null},{"id":"W2417673837","doi":"10.1037/met0000053","title":"Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data.","year":2015,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; National Institute on Drug Abuse; Agence Nationale de la Recherche; U.S. Department of Defense","keywords":"Partial least squares regression; Categorical variable; Computer science; Correspondence analysis; Multivariate statistics; Artificial intelligence; Multivariate analysis; Statistics; Machine learning; Data mining; Psychology; Mathematics","score_opus":0.2672039613477072,"score_gpt":0.4999134662670226,"score_spread":0.2327095049193154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417673837","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":[0.00038124475,0.0003473692,0.998064,0.00017321044,0.00003814884,0.00006209104,0.0002470797,0.00027137637,0.0004154243],"genre_scores_gemma":[0.018424386,0.000857368,0.9771597,0.00027045183,0.00018693478,0.00087816967,0.0009631015,0.00030542686,0.00095449685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9788032,0.013760751,0.0010268892,0.0029113693,0.003205814,0.0002919144],"domain_scores_gemma":[0.9668026,0.025222944,0.0021078514,0.0030752164,0.002397536,0.00039378012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022820622,0.003125698,0.0022170015,0.0063277464,0.0011783425,0.0041514845,0.004038975,0.0019974587,0.0067111133],"category_scores_gemma":[0.053162027,0.0014495343,0.004643038,0.008577133,0.0035426228,0.0034958974,0.0054769367,0.004936268,0.00266761],"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":[0.00021774787,0.00025897636,0.00882715,0.0017384132,0.0023239711,0.0005142605,0.0011555974,0.10879262,0.004239293,0.3943595,0.020022336,0.45755014],"study_design_scores_gemma":[0.000045142573,0.00014646686,0.0031619105,0.0001884573,0.00019027787,0.00034551084,0.00023688063,0.3050198,0.0016222432,0.65220547,0.03670119,0.0001365875],"about_ca_topic_score_codex":0.0059907474,"about_ca_topic_score_gemma":0.006655224,"teacher_disagreement_score":0.022820622,"about_ca_system_score_codex":0.001583983,"about_ca_system_score_gemma":0.005782982,"threshold_uncertainty_score":0.12068844},"labels":[],"label_agreement":null},{"id":"W2464987778","doi":"10.1016/j.jneb.2016.04.238","title":"Tastes Like Who I Want to Be: Making Foods Tastier by Linking to Desirable Social Image","year":2016,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taste; Likert scale; Psychology; Quality (philosophy); Identity (music); Social psychology; Athletes; Wine tasting; Scale (ratio); Advertising; Food science; Developmental psychology; Medicine; Business; Physical therapy; Aesthetics","score_opus":0.052479167680002145,"score_gpt":0.34770725461008534,"score_spread":0.2952280869300832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464987778","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":[0.9961383,0.00004956092,0.0008773074,0.00018653719,0.000019155439,0.000017136634,0.00003952943,0.00001139412,0.002661158],"genre_scores_gemma":[0.99615943,0.000065191736,0.0027991757,0.00014419692,0.000010546889,0.000024846831,0.000056036417,0.000012324168,0.0007281516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997906,0.000081895836,0.000005598747,0.000047221936,0.000050117713,0.00002447824],"domain_scores_gemma":[0.9987476,0.0005033481,0.00041548506,0.00006289177,0.00012021668,0.00015035142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007258118,0.00021750834,0.00016406058,0.0002673041,0.00032580088,0.0009362119,0.00017719196,0.00048685833,0.0037405859],"category_scores_gemma":[0.0039780093,0.00021879796,0.00022826192,0.00021326829,0.00056341506,0.0004715936,0.0004914857,0.0008070259,0.00020978306],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063558775,0.0056033405,0.723702,0.0007242736,0.00076564,0.0003729256,0.01090934,0.001251253,0.08507207,0.0033831112,0.0036277575,0.15823238],"study_design_scores_gemma":[0.00007533122,0.0010424833,0.98325634,0.00006341783,0.00032148632,0.00011628283,0.0031657566,0.003132998,0.0043730396,0.0033943728,0.0010133585,0.00004509873],"about_ca_topic_score_codex":0.0019793306,"about_ca_topic_score_gemma":0.0049017407,"teacher_disagreement_score":0.0037405859,"about_ca_system_score_codex":0.00023825963,"about_ca_system_score_gemma":0.00019991279,"threshold_uncertainty_score":0.012513459},"labels":[],"label_agreement":null},{"id":"W2482430091","doi":"10.1075/cilt.308.22dup","title":"Visualization, validation and seriation","year":2009,"lang":"en","type":"book-chapter","venue":"Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Seriation (archaeology); Computer science; Visualization; Context (archaeology); Natural language processing; Table (database); Row; Spelling; Simple (philosophy); Correspondence analysis; Artificial intelligence; Linguistics; Data mining; Programming language; History; Machine learning; Archaeology","score_opus":0.06879928727149978,"score_gpt":0.3585326682448972,"score_spread":0.2897333809733974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482430091","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":[0.013029656,0.0032363364,0.8944919,0.0038652732,0.0014458796,0.00063257915,0.005177563,0.021361401,0.056759384],"genre_scores_gemma":[0.10800809,0.0023604063,0.8561175,0.0005403598,0.00047073275,0.0015450101,0.0040073176,0.007848357,0.01910237],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99022055,0.0047007487,0.0007829908,0.0012721205,0.0028001887,0.00022334674],"domain_scores_gemma":[0.9716347,0.015168837,0.0011449258,0.0053374604,0.0063713724,0.0003426959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018933615,0.0017588314,0.0013422065,0.007832261,0.001640604,0.00969351,0.0015971046,0.0014741669,0.030718982],"category_scores_gemma":[0.06446221,0.0007896616,0.0012108982,0.0067197327,0.0024297556,0.0058549545,0.0038139396,0.002333779,0.009503189],"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":[0.0003104515,0.000076987264,0.0022068752,0.0017970966,0.00009758226,0.0003551779,0.005542293,0.005602602,0.009107699,0.17225906,0.11084776,0.6917964],"study_design_scores_gemma":[0.00011081308,0.00012296083,0.005580246,0.001686384,0.000088779576,0.00071677024,0.0026318633,0.06511951,0.025547419,0.23050496,0.6676487,0.00024167844],"about_ca_topic_score_codex":0.0017906688,"about_ca_topic_score_gemma":0.0011159483,"teacher_disagreement_score":0.030718982,"about_ca_system_score_codex":0.0010598707,"about_ca_system_score_gemma":0.002674696,"threshold_uncertainty_score":0.10276526},"labels":[],"label_agreement":null},{"id":"W2486334885","doi":"10.1520/mnl11507m","title":"Appendix Z: Ideal Scaling","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Continental (Canada)","funders":"","keywords":"Ideal (ethics); Scaling; Appendix; Computer science; Mathematics; Philosophy; Geology; Geometry; Epistemology; Paleontology","score_opus":0.05678861865563233,"score_gpt":0.2817780328349353,"score_spread":0.22498941417930296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486334885","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012843242,0.0014753088,0.23567894,0.001892117,0.0025927527,0.0012701836,0.015953714,0.007697022,0.7321556],"genre_scores_gemma":[0.02573955,0.003665798,0.3403108,0.0010172392,0.0008733538,0.0022090669,0.023705548,0.0066345455,0.5958441],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985085,0.00031454943,0.00012068788,0.000166616,0.0008300379,0.000059603302],"domain_scores_gemma":[0.9959467,0.0013285596,0.000116060335,0.0005250955,0.0020069978,0.00007656575],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015462015,0.0013916681,0.00075770257,0.0028578765,0.0012698645,0.002210221,0.0018577548,0.0010334245,0.42365435],"category_scores_gemma":[0.013033542,0.00067720166,0.00060172006,0.0026754148,0.00090570876,0.0027794417,0.0015094321,0.0018734774,0.22735396],"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":[0.00005353148,0.00003752299,0.00017322101,0.00042339848,0.0000037715158,0.000102731494,0.00019294347,0.0006974615,0.0006572117,0.11067711,0.71712756,0.16985357],"study_design_scores_gemma":[0.00001333496,0.000021172757,0.0003279021,0.00018729897,0.0000037479492,0.00035089764,0.00010890152,0.0014692077,0.0007490472,0.041782446,0.9549616,0.000024456558],"about_ca_topic_score_codex":0.0022657188,"about_ca_topic_score_gemma":0.002263747,"teacher_disagreement_score":0.42365435,"about_ca_system_score_codex":0.0009192462,"about_ca_system_score_gemma":0.0010127848,"threshold_uncertainty_score":0.8220871},"labels":[],"label_agreement":null},{"id":"W2489962730","doi":"10.4135/9780857020994.n11","title":"Correspondence Analysis, Multiple Correspondence Analysis, and Recent Developments","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"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":"Correspondence analysis; Correspondence problem; Computer science; Artificial intelligence; Machine learning","score_opus":0.045703181215149664,"score_gpt":0.27532840100498973,"score_spread":0.22962521978984007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489962730","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":[0.008671957,0.14698225,0.7249442,0.019391483,0.0030715782,0.00015204937,0.0005556772,0.0007120287,0.09551878],"genre_scores_gemma":[0.20309885,0.083059736,0.67458576,0.0020902252,0.007775222,0.0006885138,0.0009931249,0.00089169544,0.026816865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97269815,0.01693023,0.00083638844,0.0042021372,0.004919292,0.00041390155],"domain_scores_gemma":[0.90080047,0.08369683,0.0026184202,0.005563405,0.006735644,0.0005851942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03358836,0.0016198047,0.0022701803,0.013056867,0.0032194022,0.008285472,0.0051734117,0.0029394107,0.010581526],"category_scores_gemma":[0.07219315,0.0011970943,0.001993404,0.02709712,0.013356085,0.015101693,0.0045322166,0.004433601,0.0028826718],"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":[0.000039201976,0.000032536547,0.0011348337,0.00073948107,0.000118919445,0.00012120515,0.0013027918,0.0020355647,0.00008595765,0.729555,0.012009884,0.25282457],"study_design_scores_gemma":[0.000014178606,0.000036190464,0.0020471327,0.00045407662,0.00005612263,0.0003739677,0.0011151168,0.019056985,0.00048393357,0.8481729,0.12810506,0.00008423864],"about_ca_topic_score_codex":0.005776398,"about_ca_topic_score_gemma":0.0046173544,"teacher_disagreement_score":0.03358836,"about_ca_system_score_codex":0.0053212736,"about_ca_system_score_gemma":0.0034898475,"threshold_uncertainty_score":0.17763436},"labels":[],"label_agreement":null},{"id":"W2498209427","doi":"10.1533/9781845699284.2.189","title":"Sensory analysis of wine","year":2010,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vineland Research and Innovation Centre","funders":"","keywords":"Wine tasting; Wine; Sensory system; Winery; Sensory analysis; Quality (philosophy); Computer science; Marketing; Business; Food science; Psychology; Cognitive psychology; Biology","score_opus":0.03647177936732691,"score_gpt":0.27185110287064146,"score_spread":0.23537932350331456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498209427","genre_codex":"methods","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":[0.2911781,0.08021015,0.3811671,0.001543706,0.0024080449,0.0007691258,0.025862,0.0063180584,0.21054369],"genre_scores_gemma":[0.47387895,0.050871115,0.17446683,0.0014323583,0.00037859744,0.0005538586,0.01984661,0.0014557281,0.27711597],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995797,0.000046555924,0.000021379023,0.000091967275,0.00022715864,0.000033246393],"domain_scores_gemma":[0.99988496,0.000024638624,0.0000059349354,0.000010122804,0.00006514179,0.0000091525735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004830767,0.00084989733,0.000495728,0.0013271442,0.000340756,0.0011095094,0.00044985648,0.00041062283,0.009870293],"category_scores_gemma":[0.00042709755,0.00034556322,0.0008201122,0.0011864428,0.00024984588,0.000516496,0.0005735759,0.0006810387,0.0035997494],"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":[0.000270995,0.0000970842,0.0010412987,0.0009283013,0.00006538545,0.00014065123,0.00013939253,0.0007789408,0.59834105,0.0011830091,0.011987382,0.3850265],"study_design_scores_gemma":[0.000020512201,0.0009075625,0.06798423,0.0003865208,0.00017551819,0.0018258517,0.00063299574,0.009194904,0.6603285,0.0063323816,0.25200665,0.00020434971],"about_ca_topic_score_codex":0.002623258,"about_ca_topic_score_gemma":0.005096316,"teacher_disagreement_score":0.009870293,"about_ca_system_score_codex":0.00032435235,"about_ca_system_score_gemma":0.0003702235,"threshold_uncertainty_score":0.033019423},"labels":[],"label_agreement":null},{"id":"W2502546802","doi":"10.1016/b978-0-12-803751-5.00006-4","title":"Consumer Driven and Consumer Perceptible Food Innovation","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Marketing; Food industry; Business; Food products; Food science","score_opus":0.048310780551743586,"score_gpt":0.2694349826559186,"score_spread":0.221124202104175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502546802","genre_codex":"other","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":[0.055795424,0.026085021,0.2088957,0.006197251,0.0012673602,0.000081681006,0.0005996345,0.0006825495,0.70039535],"genre_scores_gemma":[0.40531048,0.0245735,0.06848617,0.0017781666,0.00094649155,0.00017812717,0.000536741,0.0003668527,0.4978235],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997576,0.000042396627,0.000008810715,0.00005694643,0.00011970848,0.000014646491],"domain_scores_gemma":[0.9996226,0.00024199042,0.000032713695,0.00003361023,0.000047686473,0.000021350457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005749152,0.00040180286,0.00022145124,0.0005338495,0.00019567694,0.0022625441,0.00038886344,0.00078442966,0.024185233],"category_scores_gemma":[0.0011597489,0.00016385452,0.00028709485,0.00070675946,0.0012366775,0.0019034966,0.00068818853,0.000830969,0.0020467532],"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":[0.00009930147,0.000075853844,0.0014952893,0.00077789085,0.00003936357,0.00026692456,0.0006946202,0.002923361,0.011998583,0.42990226,0.019390844,0.53233576],"study_design_scores_gemma":[0.000016956317,0.00011635021,0.011114138,0.00043711613,0.000034176315,0.0007849024,0.000892451,0.01829145,0.006520662,0.67489374,0.2868332,0.00006485803],"about_ca_topic_score_codex":0.00078672,"about_ca_topic_score_gemma":0.0010362299,"teacher_disagreement_score":0.024185233,"about_ca_system_score_codex":0.00075743336,"about_ca_system_score_gemma":0.00046659602,"threshold_uncertainty_score":0.0809077},"labels":[],"label_agreement":null},{"id":"W2506375825","doi":"10.1520/mnl11487m","title":"Appendix F: Mean versus Scale Mid-Point","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Ontario Universities’ Application Centre","funders":"","keywords":"Appendix; Scale (ratio); Point (geometry); Mathematics; Statistics; Geography; Geology; Cartography; Geometry; Paleontology","score_opus":0.06325230849074545,"score_gpt":0.2848206854386345,"score_spread":0.22156837694788903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506375825","genre_codex":"methods","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":[0.0036241373,0.0029216618,0.45609897,0.0043623946,0.010693586,0.004872321,0.053824242,0.0200221,0.44358045],"genre_scores_gemma":[0.026861647,0.0020949242,0.5650888,0.0020074765,0.0014064071,0.009126433,0.02658819,0.010987004,0.35583913],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958515,0.001165791,0.00042360203,0.0005226748,0.0019315901,0.00010479547],"domain_scores_gemma":[0.97121775,0.017566489,0.00054996775,0.0017685256,0.008638671,0.0002585343],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0043549635,0.0012893466,0.00089977257,0.0031161474,0.0013214482,0.0024656984,0.002080619,0.0014985579,0.31381905],"category_scores_gemma":[0.04658627,0.00075689895,0.0007348214,0.0027597724,0.001175214,0.0024103203,0.001200647,0.0023731163,0.14482448],"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":[0.00014189493,0.00005841667,0.0003362859,0.0006521883,0.0000096644535,0.00007331201,0.00025887456,0.0002840919,0.0008421292,0.023790898,0.7374215,0.2361307],"study_design_scores_gemma":[0.000046764675,0.00010268913,0.0026266691,0.00046666863,0.000010790796,0.0005913134,0.00023293134,0.0016240593,0.0017412949,0.022820003,0.96966994,0.000066936984],"about_ca_topic_score_codex":0.0029123665,"about_ca_topic_score_gemma":0.0029533273,"teacher_disagreement_score":0.31381905,"about_ca_system_score_codex":0.001283957,"about_ca_system_score_gemma":0.0016149345,"threshold_uncertainty_score":0.97875386},"labels":[],"label_agreement":null},{"id":"W2514043166","doi":"10.20982/tqmp.08.1.p001","title":"Decomposing interactions using GLM in combination with the COMPARE, LMATRIX and MMATRIX subcommands in SPSS","year":2012,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate analysis of variance; Analysis of variance; Statistics; Analysis of covariance; Mixed-design analysis of variance; Variance (accounting); Computer science; Range (aeronautics); Syntax; Simple (philosophy); Descriptive statistics; One-way analysis of variance; Repeated measures design; Mathematics; Natural language processing","score_opus":0.22296043839946855,"score_gpt":0.5271795009228554,"score_spread":0.3042190625233868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514043166","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":[0.001302071,0.00024376398,0.9680159,0.00028965223,0.0005022806,0.0007014106,0.002944195,0.023927283,0.0020734714],"genre_scores_gemma":[0.0052966657,0.00020064149,0.97854656,0.00024485262,0.00014633246,0.0036406417,0.0016026517,0.008652921,0.0016687254],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9778632,0.012644137,0.0027578988,0.002480379,0.0037323406,0.0005219815],"domain_scores_gemma":[0.90698695,0.07929285,0.0027028373,0.00616248,0.004414441,0.00044041866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02186668,0.005571396,0.0033319676,0.005110145,0.001362134,0.0036460408,0.0037561506,0.0015291855,0.1032034],"category_scores_gemma":[0.08459761,0.0023661982,0.0045684855,0.004455106,0.001694588,0.0046989517,0.0032867081,0.008436511,0.030954683],"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":[0.0011371898,0.0005521127,0.003594304,0.00603303,0.001290639,0.0011680241,0.003751751,0.0084507605,0.018786099,0.081631236,0.20998192,0.663623],"study_design_scores_gemma":[0.00060538045,0.0013852222,0.009967387,0.0019467442,0.0007642049,0.0013291034,0.0016871607,0.085061826,0.05097737,0.32448015,0.5209475,0.000847902],"about_ca_topic_score_codex":0.0016316957,"about_ca_topic_score_gemma":0.0019784942,"teacher_disagreement_score":0.1032034,"about_ca_system_score_codex":0.0011256593,"about_ca_system_score_gemma":0.0025295145,"threshold_uncertainty_score":0.34524977},"labels":[],"label_agreement":null},{"id":"W2515936342","doi":"10.1108/bfj-09-2015-0315","title":"Cheese perception in the North American market","year":2016,"lang":"en","type":"article","venue":"British Food Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Regione del Veneto","keywords":"Market segmentation; Consumption (sociology); Representativeness heuristic; Respondent; Sample (material); Marketing; Business; Statistics; Sociology; Mathematics; Political science","score_opus":0.027129871031674035,"score_gpt":0.2518571424787852,"score_spread":0.2247272714471112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515936342","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":[0.9963181,0.000111612535,0.000025441395,0.00023031681,0.000007005664,0.000004734279,0.00007514508,0.0000015712518,0.0032260437],"genre_scores_gemma":[0.9983677,0.00020172079,0.00004907007,0.00015285637,0.0000070643346,0.0000058013507,0.00013391166,0.0000019578822,0.0010800709],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99962175,0.00008775847,0.000009174473,0.00006656654,0.00014428001,0.00007046664],"domain_scores_gemma":[0.9990382,0.00017730027,0.00020900644,0.00003604561,0.00033212398,0.00020732732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067433703,0.00015413217,0.00023960185,0.00073747046,0.0013601532,0.0015049415,0.00025916393,0.00038785418,0.005374737],"category_scores_gemma":[0.0014131571,0.00015565904,0.00026697427,0.0009930956,0.00072316884,0.00057781686,0.000603956,0.0005289037,0.00033982442],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039113368,0.00023876278,0.9233737,0.000074161755,0.000061206825,0.00060634414,0.048256278,0.000109032764,0.0016470845,0.00069387315,0.0038132756,0.020735022],"study_design_scores_gemma":[0.000003544471,0.000036750826,0.96161145,0.00002238052,0.0000104686915,0.000091185335,0.035033055,0.00018889175,0.00007837185,0.000052312666,0.0028583235,0.000013103086],"about_ca_topic_score_codex":0.4869827,"about_ca_topic_score_gemma":0.5719912,"teacher_disagreement_score":0.4869827,"about_ca_system_score_codex":0.0019142566,"about_ca_system_score_gemma":0.0012370793,"threshold_uncertainty_score":0.9682963},"labels":[],"label_agreement":null},{"id":"W2516573604","doi":"10.5539/ijsp.v5n5p55","title":"Construct-focused Configural Invariance for Measures Showing a Multi-dimensional Structure and Application to Exchange Test Data","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Construct (python library); Measurement invariance; Metric (unit); Variance (accounting); Mathematics; Test (biology); Population; Component (thermodynamics); Statistics; Econometrics; Confirmatory factor analysis; Computer science; Structural equation modeling","score_opus":0.08891266597953677,"score_gpt":0.3265611259221039,"score_spread":0.23764845994256711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516573604","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":[0.13867544,0.0003136117,0.8535114,0.00022798621,0.000084137486,0.00046714288,0.00045364044,0.00040793174,0.0058585913],"genre_scores_gemma":[0.83402795,0.000090782894,0.16376834,0.000081960075,0.00006436194,0.0010733842,0.0004710503,0.00012557142,0.0002967029],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98619485,0.008672139,0.00096679945,0.0020622695,0.0017714866,0.00033245652],"domain_scores_gemma":[0.9505535,0.030380057,0.0038811269,0.012667597,0.002017799,0.0004999156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015471453,0.0010040102,0.0007404347,0.0035834545,0.0007305601,0.0018879818,0.0012092465,0.00068284606,0.0038996357],"category_scores_gemma":[0.07013176,0.000360627,0.0023629689,0.0039202273,0.003755886,0.0029689823,0.002604462,0.0018672406,0.00033246892],"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":[0.0006865372,0.00036221466,0.13091832,0.0008897068,0.0013061664,0.00076969876,0.00431939,0.021277094,0.027957307,0.4070783,0.0022027742,0.40223247],"study_design_scores_gemma":[0.0001471219,0.0019493672,0.29626384,0.00034710587,0.0003751059,0.0014083693,0.0018060028,0.15401775,0.010432185,0.5250729,0.007816197,0.00036411316],"about_ca_topic_score_codex":0.0009354768,"about_ca_topic_score_gemma":0.0005968343,"teacher_disagreement_score":0.015471453,"about_ca_system_score_codex":0.0008444831,"about_ca_system_score_gemma":0.0010884472,"threshold_uncertainty_score":0.08182186},"labels":[],"label_agreement":null},{"id":"W2516658355","doi":"10.20982/tqmp.06.1.p001","title":"The Application of Canonical Correlation to Two-Dimensional Contingency Tables","year":2010,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Canonical correlation; Contingency table; Canonical analysis; Correlation; Mathematics; Statistics; Canonical correspondence analysis; Computer science; Geometry; Geology; Paleontology","score_opus":0.09341193335899815,"score_gpt":0.5013317837544494,"score_spread":0.40791985039545126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516658355","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":[0.0037798034,0.00046127598,0.99061614,0.000321086,0.0003696033,0.00018787941,0.0005428068,0.00095667294,0.0027646553],"genre_scores_gemma":[0.07998505,0.0007645565,0.9151933,0.00020693743,0.00045864645,0.0009785269,0.00093423796,0.00047076982,0.0010079878],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9644059,0.024127826,0.0019341273,0.0033854083,0.005826734,0.00032005133],"domain_scores_gemma":[0.86596894,0.102419086,0.0038433599,0.014628193,0.0123494025,0.0007911686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020466575,0.0013753906,0.0015588113,0.0074257962,0.0021431115,0.005025535,0.0014967503,0.0005981121,0.009284227],"category_scores_gemma":[0.16130528,0.0008976151,0.0021918819,0.012571286,0.0029677192,0.003694978,0.0034291055,0.0032288283,0.0023037866],"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":[0.00013630548,0.000099426084,0.014963049,0.0012107345,0.00077081413,0.0004353061,0.0026725368,0.013846653,0.0021600025,0.2694777,0.026074018,0.66815346],"study_design_scores_gemma":[0.000055274253,0.00022156327,0.013545119,0.0004506146,0.00017754958,0.0018611534,0.0009192047,0.11463167,0.0032727015,0.8055899,0.05897107,0.00030412845],"about_ca_topic_score_codex":0.0025161551,"about_ca_topic_score_gemma":0.0030015192,"teacher_disagreement_score":0.020466575,"about_ca_system_score_codex":0.0011191395,"about_ca_system_score_gemma":0.0034093685,"threshold_uncertainty_score":0.108238935},"labels":[],"label_agreement":null},{"id":"W2520295263","doi":"10.1136/tobaccocontrol-2016-053192","title":"Flavour preferences in youth versus adults: a review","year":2016,"lang":"en","type":"review","venue":"Tobacco Control","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":135,"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":"Hamilton Health Sciences Foundation; National Institutes of Health; National Geographic Society","keywords":"Psychology; Advertising; Medicine; Business; Environmental health","score_opus":0.11749851686318215,"score_gpt":0.35186803488494145,"score_spread":0.23436951802175932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520295263","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":[0.00073925086,0.9989121,0.000015116982,0.00007654484,0.000034444125,0.000011614072,0.00006947322,9.885676e-7,0.00014042758],"genre_scores_gemma":[0.006789789,0.9926964,0.00009490457,0.00020178553,0.000047091788,0.00004090524,0.000068229805,0.0000011195638,0.000059652983],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980446,0.00045173193,0.00081292674,0.0002288705,0.00036546137,0.00009626094],"domain_scores_gemma":[0.9898548,0.007516784,0.0018436033,0.000063009546,0.00059844984,0.00012322723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028628989,0.0007352508,0.003376887,0.006494155,0.00036109882,0.0021665331,0.00095663744,0.0013455573,0.0037653707],"category_scores_gemma":[0.013287554,0.00055829843,0.0031830615,0.0068704193,0.00048297542,0.0018718917,0.00091751496,0.00077501865,0.000326107],"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":[0.00037583226,0.000042449523,0.0033903993,0.79362994,0.0025552385,0.00040790119,0.00077537034,0.00005896001,0.00032961773,0.00034688265,0.0028743353,0.195213],"study_design_scores_gemma":[0.00011406374,0.00038508943,0.022872765,0.8823028,0.018054498,0.0024493046,0.0018114115,0.000050743456,0.00031198503,0.0003771723,0.07122153,0.00004873551],"about_ca_topic_score_codex":0.005341401,"about_ca_topic_score_gemma":0.011845885,"teacher_disagreement_score":0.006494155,"about_ca_system_score_codex":0.0014039476,"about_ca_system_score_gemma":0.0030813827,"threshold_uncertainty_score":0.015140653},"labels":[],"label_agreement":null},{"id":"W2543067875","doi":"10.1177/1745691616674458","title":"Registered Replication Report","year":2016,"lang":"en","type":"review","venue":"Perspectives on Psychological Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":423,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Psychology; Facial expression; Replication (statistics); Likert scale; Significant difference; Meta-analysis; Social psychology; Developmental psychology; Communication; Statistics; Mathematics; Medicine","score_opus":0.2409334870398609,"score_gpt":0.5003658557954134,"score_spread":0.2594323687555525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543067875","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006641145,0.010910198,0.034539096,0.011263223,0.08820968,0.33736664,0.4125638,0.004077273,0.09442893],"genre_scores_gemma":[0.04641916,0.006682097,0.057114426,0.0065804585,0.004425364,0.7033728,0.09886435,0.003283307,0.0732581],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9036683,0.02590962,0.035104368,0.011546633,0.020908164,0.0028629627],"domain_scores_gemma":[0.77634716,0.03788135,0.015205544,0.08838963,0.078233995,0.0039423765],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.06958571,0.0025606577,0.00760193,0.008759918,0.0044811163,0.008381456,0.0053721275,0.0049245735,0.42177415],"category_scores_gemma":[0.30270734,0.003011109,0.009109194,0.012888766,0.0030681167,0.0053441096,0.0046812333,0.0047009215,0.13706265],"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":[0.008679766,0.00092238875,0.0025840912,0.12623158,0.0039955107,0.00094944023,0.0021637206,0.0005209966,0.0026370008,0.013633916,0.6318608,0.2058208],"study_design_scores_gemma":[0.012498158,0.0010883615,0.012516801,0.044679027,0.0029092764,0.00073969975,0.0012833098,0.0005538571,0.0016769165,0.012281361,0.90946776,0.00030535817],"about_ca_topic_score_codex":0.005901127,"about_ca_topic_score_gemma":0.008617283,"teacher_disagreement_score":0.9304143,"about_ca_system_score_codex":0.004918735,"about_ca_system_score_gemma":0.025936924,"threshold_uncertainty_score":0.824769},"labels":[],"label_agreement":null},{"id":"W2546157540","doi":"10.3758/s13423-016-1174-7","title":"The nature of correlation perception in scatterplots","year":2016,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Boeing","keywords":"Logarithm; Correlation; Perception; Psychology; Statistics; Entropy (arrow of time); Generality; Psychophysics; Mathematics; Statistical physics; Social psychology; Mathematical analysis; Physics; Geometry; Quantum mechanics","score_opus":0.01895681949930714,"score_gpt":0.29346661386783984,"score_spread":0.2745097943685327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546157540","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":[0.48968515,0.00988969,0.4766927,0.0009975067,0.00024646005,0.00012364345,0.00048094033,0.0014132024,0.02047066],"genre_scores_gemma":[0.9614519,0.001532818,0.035810687,0.00015054955,0.00011119347,0.000055291617,0.00019985509,0.00017474213,0.0005130626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964282,0.0011192297,0.00013875574,0.00072461786,0.0014544632,0.00013476811],"domain_scores_gemma":[0.98593706,0.008347293,0.0020082819,0.0014383002,0.0019161056,0.00035290318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042372392,0.00045877468,0.00074362947,0.0032662132,0.00041066474,0.0029748501,0.00064828957,0.0005864148,0.0021131118],"category_scores_gemma":[0.029231112,0.0007089231,0.0009225141,0.002028443,0.0028455956,0.0047346945,0.0015650182,0.0010341849,0.00036008903],"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":[0.0016463068,0.00014006924,0.07551583,0.002910117,0.0011117363,0.00064028555,0.014301908,0.021323374,0.1041764,0.1534286,0.008172221,0.6166332],"study_design_scores_gemma":[0.00012378042,0.0009975157,0.5317972,0.0009535942,0.0003029047,0.003603814,0.0054743495,0.10840069,0.025060782,0.30131733,0.021462558,0.0005055402],"about_ca_topic_score_codex":0.0012608634,"about_ca_topic_score_gemma":0.0007871737,"teacher_disagreement_score":0.0042372392,"about_ca_system_score_codex":0.00091841933,"about_ca_system_score_gemma":0.00042101325,"threshold_uncertainty_score":0.022408903},"labels":[],"label_agreement":null},{"id":"W2556161582","doi":"10.1111/joss.12241","title":"Handling missing data in consumer hedonic tests arising from direct scaling","year":2016,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McMaster University; University of Waterloo; MacEwan University","funders":"","keywords":"Imputation (statistics); Missing data; Data mining; Computer science; Multivariate statistics; Statistics; Machine learning; Mathematics","score_opus":0.23868408736417662,"score_gpt":0.38794063204240203,"score_spread":0.1492565446782254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556161582","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":[0.28687394,0.00056466757,0.70883405,0.00041176146,0.00017139726,0.00034071077,0.00085466594,0.00041508194,0.0015337315],"genre_scores_gemma":[0.84698415,0.00021127953,0.14977407,0.00023406305,0.0000667154,0.0005889728,0.0012189534,0.0001568592,0.0007649298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95525205,0.033548396,0.0019239001,0.0035752493,0.00504015,0.0006602757],"domain_scores_gemma":[0.7503068,0.20079981,0.01487635,0.02776174,0.005666651,0.0005885578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06657172,0.0009246564,0.0020105084,0.0014975971,0.0011050333,0.0023380804,0.002445032,0.0015036782,0.0033942484],"category_scores_gemma":[0.19757716,0.000798208,0.0023279993,0.0030202314,0.002314766,0.0020463823,0.00255888,0.002782798,0.00048668918],"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":[0.0050029936,0.0017596125,0.30601966,0.0028143248,0.0042715683,0.0023677428,0.0063978215,0.17463163,0.012252061,0.040782474,0.006383481,0.4373167],"study_design_scores_gemma":[0.00029455518,0.002759162,0.20766155,0.0008750467,0.0012180324,0.001599403,0.0020845034,0.6149292,0.02347668,0.13696359,0.0077336915,0.00040456597],"about_ca_topic_score_codex":0.0016675275,"about_ca_topic_score_gemma":0.0016805921,"teacher_disagreement_score":0.06657172,"about_ca_system_score_codex":0.0007088126,"about_ca_system_score_gemma":0.0010710454,"threshold_uncertainty_score":0.35206914},"labels":[],"label_agreement":null},{"id":"W2574420290","doi":"10.5539/jas.v9n2p115","title":"Physicochemical and Sensory Quality of Brown Sugar: Variables of Processing Study","year":2017,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Sugar; Aroma; Flavor; Context (archaeology); Food science; Chemistry; Taste; Commercialization; Quality (philosophy); Sweetness; Business; Marketing; Biology","score_opus":0.08382278300605998,"score_gpt":0.35928256600945124,"score_spread":0.27545978300339125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574420290","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":[0.9975321,0.0003236158,0.0006154751,0.0000130679355,0.000009519301,0.000036535053,0.00043062976,0.000008029437,0.0010309952],"genre_scores_gemma":[0.9969717,0.00025432932,0.0010649961,0.000024486279,0.000007711169,0.000029846886,0.0006182991,0.000012295156,0.0010163267],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995633,0.000060232378,0.000031414485,0.000077005476,0.00021736733,0.00005063974],"domain_scores_gemma":[0.99927694,0.0001456315,0.00015089246,0.000029067245,0.00031934096,0.000078066965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005364906,0.0003249175,0.00035177835,0.00058686826,0.00033196184,0.0008065704,0.0001502069,0.00025501833,0.0018347471],"category_scores_gemma":[0.0008912418,0.00009857614,0.00041553218,0.0013509401,0.00028686775,0.0003428248,0.00021836841,0.0005949331,0.00023770031],"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":[0.002531022,0.00085606554,0.122311495,0.00073561963,0.0002565018,0.00054423214,0.0013660404,0.001013889,0.8489583,0.000233679,0.00041553238,0.0207776],"study_design_scores_gemma":[0.00001159916,0.002608791,0.80341256,0.00004083025,0.00020748525,0.00042881837,0.0020788275,0.0011671152,0.18725885,0.00016282334,0.0025478285,0.000074496784],"about_ca_topic_score_codex":0.0025453325,"about_ca_topic_score_gemma":0.0027633247,"teacher_disagreement_score":0.0025453325,"about_ca_system_score_codex":0.00023785605,"about_ca_system_score_gemma":0.0002821239,"threshold_uncertainty_score":0.006137848},"labels":[],"label_agreement":null},{"id":"W2590486738","doi":"10.4141/cjps2013-236","title":"Consumer research explores acceptability of a new Canadian apple – Salish™","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Health Canada","keywords":"Ethnic group; Cultivar; Preference; Geography; Horticulture; Biology; Sociology; Anthropology; Mathematics","score_opus":0.16371445010517163,"score_gpt":0.33865500927134595,"score_spread":0.17494055916617432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590486738","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":[0.99267197,0.0003288875,0.0000636691,0.00017262384,0.0000053832428,0.00002131635,0.00025717713,0.0000029145099,0.0064761788],"genre_scores_gemma":[0.9960317,0.0005160956,0.00037812846,0.00015179614,0.000004742275,0.000009395007,0.00034690596,0.000003460478,0.0025578449],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99891865,0.00009185098,0.000028791848,0.00012142996,0.0007306025,0.00010868749],"domain_scores_gemma":[0.9978393,0.00031792896,0.0002937792,0.000062944084,0.0012925459,0.0001935393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014980515,0.00014062406,0.00017992075,0.00079623586,0.0017599126,0.0011962855,0.0003935043,0.00024946383,0.0030363235],"category_scores_gemma":[0.0020700227,0.00013388187,0.00036825862,0.001161129,0.00067030865,0.00047758405,0.000513883,0.00044971632,0.00021055117],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011689629,0.00025987643,0.81709766,0.000697996,0.00024107115,0.00043432257,0.04661157,0.00017643062,0.027189635,0.0008081492,0.005788732,0.09952558],"study_design_scores_gemma":[0.0000050309854,0.00015753544,0.981818,0.0000392924,0.000032533157,0.00006452786,0.011485189,0.00006548486,0.0007876645,0.00003814933,0.00548439,0.000022191865],"about_ca_topic_score_codex":0.80592054,"about_ca_topic_score_gemma":0.9447254,"teacher_disagreement_score":0.19407946,"about_ca_system_score_codex":0.005679667,"about_ca_system_score_gemma":0.0029660156,"threshold_uncertainty_score":0.39044482},"labels":[],"label_agreement":null},{"id":"W2606854604","doi":"10.23889/ijpds.v1i1.118","title":"Multiple Correspondence Analysis is a Useful Tool to Visualize Complex Categorical Correlated Data","year":2017,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saskatchewan Health Quality Council","funders":"","keywords":"Categorical variable; Confounding; Covariate; Medicine; Correspondence analysis; Population; Schizophrenia (object-oriented programming); Multiple correspondence analysis; Psychiatry; Statistics; Environmental health; Mathematics; Internal medicine","score_opus":0.33774371209896364,"score_gpt":0.48852830735674724,"score_spread":0.1507845952577836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606854604","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":[0.013223397,0.0006315066,0.8435534,0.001889032,0.0010437628,0.0014998913,0.041757822,0.08459805,0.011803162],"genre_scores_gemma":[0.06880633,0.00039747998,0.90744364,0.00039272,0.00026416386,0.0036238006,0.010166656,0.0055830446,0.0033220502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98478556,0.008417386,0.0019087149,0.001722339,0.0027932497,0.00037278488],"domain_scores_gemma":[0.83952576,0.1335225,0.008015537,0.008751864,0.009141428,0.0010428554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023751575,0.0024998279,0.0023482442,0.012424124,0.0017824967,0.004393788,0.0023178293,0.0010324823,0.08146448],"category_scores_gemma":[0.096994005,0.0011672003,0.0030685952,0.010664242,0.0011848458,0.005408803,0.0036937809,0.003461765,0.008844014],"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":[0.0012661245,0.00046489178,0.024820425,0.004491134,0.0021766112,0.0013100895,0.0056171506,0.010017402,0.003898179,0.065858886,0.36339757,0.51668155],"study_design_scores_gemma":[0.0005877601,0.0010843505,0.045541085,0.0023376993,0.0007167492,0.0014027809,0.0060567604,0.19508955,0.010114847,0.15484035,0.58131796,0.0009100702],"about_ca_topic_score_codex":0.005756084,"about_ca_topic_score_gemma":0.006492937,"teacher_disagreement_score":0.08146448,"about_ca_system_score_codex":0.0017668963,"about_ca_system_score_gemma":0.0029777111,"threshold_uncertainty_score":0.2725258},"labels":[],"label_agreement":null},{"id":"W2610998635","doi":"10.1016/j.chemolab.2017.04.008","title":"Estimation of missing values in a food property database by matrix completion using PCA-based approaches","year":2017,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Agriculture and Agri-Food Canada; Université de Sherbrooke","funders":"Institute of Psychology, Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Matrix completion; Overfitting; Property (philosophy); Matrix (chemical analysis); Computer science; Mathematics; Early stopping; Data mining; Statistics; Algorithm; Artificial intelligence; Artificial neural network","score_opus":0.197180973832631,"score_gpt":0.3314739283898,"score_spread":0.134292954557169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610998635","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":[0.07977952,0.0004665576,0.91730756,0.00014054685,0.0000484784,0.000087297,0.0009869746,0.0009156036,0.00026745634],"genre_scores_gemma":[0.42533517,0.00039344843,0.568285,0.000065550565,0.00007039178,0.00023286861,0.0048433645,0.00009471003,0.00067955174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99665713,0.0006154509,0.0003359072,0.0008607158,0.0013263412,0.00020447536],"domain_scores_gemma":[0.9913085,0.00416794,0.00080168154,0.0017440222,0.0018112267,0.00016663707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042333137,0.0010093629,0.0020202661,0.0027334406,0.0007111396,0.0014768703,0.0021657734,0.0011831056,0.0009262405],"category_scores_gemma":[0.012679189,0.0007215882,0.0016605462,0.0034644096,0.00080486975,0.0019344211,0.0014401877,0.0018397438,0.0008095707],"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":[0.0014755506,0.0015028304,0.02402986,0.0010643003,0.00065548037,0.00062630925,0.0006718286,0.12801032,0.0587447,0.0045780228,0.0046657436,0.7739751],"study_design_scores_gemma":[0.00005160523,0.00041376043,0.0159022,0.00006923208,0.00021653244,0.000489628,0.00027712173,0.94847435,0.019117162,0.0114406785,0.0034495362,0.00009812004],"about_ca_topic_score_codex":0.0035981575,"about_ca_topic_score_gemma":0.0028372488,"teacher_disagreement_score":0.0042333137,"about_ca_system_score_codex":0.0003732341,"about_ca_system_score_gemma":0.0013977748,"threshold_uncertainty_score":0.02238816},"labels":[],"label_agreement":null},{"id":"W2612713092","doi":"10.5539/jfr.v6n3p102","title":"Processing Capability of Maize Varieties Through Free Sorting and CATA Methodologies and Physicochemical Characteristics","year":2017,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Endosperm; Cultivar; Mathematics; Ecotype; Agronomy; Biology; Botany","score_opus":0.3764392959553457,"score_gpt":0.46410132479032556,"score_spread":0.08766202883497987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612713092","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":[0.99715614,0.00013531232,0.0018461748,0.000010749807,0.0000038248263,0.000027632459,0.0003187304,0.000013667636,0.00048785098],"genre_scores_gemma":[0.9937203,0.0001598383,0.0044501014,0.000015362068,0.0000039826245,0.000040262188,0.00092184194,0.000015244307,0.00067291904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995648,0.00007057352,0.000046949866,0.00014207746,0.0001212232,0.000054432006],"domain_scores_gemma":[0.99951744,0.00012945611,0.00012456572,0.000047631143,0.00014136646,0.000039611583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005132483,0.00037741207,0.00026162347,0.0014097516,0.00030538358,0.000562701,0.00016880607,0.0001857689,0.0010101281],"category_scores_gemma":[0.0007112209,0.00014069407,0.00043136478,0.0010241913,0.00028668786,0.00034136596,0.00038922633,0.0003289946,0.0002139614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005323928,0.00008653654,0.02721601,0.000078231744,0.00004845573,0.00007076687,0.0005210044,0.00018542433,0.9601423,0.0001367858,0.000044786673,0.010937366],"study_design_scores_gemma":[0.000013253101,0.0017775996,0.6475617,0.000020390036,0.00017404044,0.00046739494,0.0012957553,0.0013841398,0.34499118,0.0003043589,0.0019496819,0.000060622555],"about_ca_topic_score_codex":0.0015040761,"about_ca_topic_score_gemma":0.0020793215,"teacher_disagreement_score":0.0015040761,"about_ca_system_score_codex":0.00020605612,"about_ca_system_score_gemma":0.00014488242,"threshold_uncertainty_score":0.0033791661},"labels":[],"label_agreement":null},{"id":"W2623613399","doi":"10.1007/978-3-319-56294-0_10","title":"Outcries of Dual Scaling: The Key Is Duality","year":2017,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scaling; Duality (order theory); Row and column spaces; Dual (grammatical number); Multidimensional scaling; Premise; Space (punctuation); Computer science; Nonlinear system; Row; Field (mathematics); Theoretical computer science; Mathematics; Algorithm; Discrete mathematics; Statistics; Pure mathematics; Geometry","score_opus":0.08275875836490192,"score_gpt":0.3088625479421708,"score_spread":0.22610378957726884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623613399","genre_codex":"other","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":[0.023778342,0.018663278,0.31160098,0.024019785,0.006541314,0.000067040244,0.00031777617,0.00054665096,0.6144648],"genre_scores_gemma":[0.75359017,0.0111468695,0.0931782,0.010461753,0.010621968,0.00028496687,0.00032460116,0.0019136927,0.118477754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984585,0.0005498641,0.00007208118,0.00039727121,0.00040020552,0.00012203351],"domain_scores_gemma":[0.9970657,0.0014764743,0.00018466958,0.00070881064,0.00033580835,0.00022853941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020076632,0.0009342796,0.0015189636,0.0011032947,0.0022870556,0.0062751095,0.0011218137,0.0024165085,0.012887959],"category_scores_gemma":[0.0073041674,0.0005942451,0.00093983946,0.00080138684,0.013918894,0.011656378,0.004144714,0.007991143,0.0024124184],"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":[0.0000073994083,0.000005079204,0.00002004757,0.000016202288,0.000002019,0.000012041736,0.00006961202,0.000076262615,0.00010657165,0.99516076,0.0016088134,0.0029150553],"study_design_scores_gemma":[0.0000025771956,0.0000036535173,0.000018012506,0.000011388918,9.98595e-7,0.000037991453,0.000021063574,0.0003396961,0.00006227643,0.99265337,0.0068451082,0.0000038220273],"about_ca_topic_score_codex":0.00040564724,"about_ca_topic_score_gemma":0.00031579618,"teacher_disagreement_score":0.012887959,"about_ca_system_score_codex":0.0012995624,"about_ca_system_score_gemma":0.00091406005,"threshold_uncertainty_score":0.043114483},"labels":[],"label_agreement":null},{"id":"W2669406525","doi":"","title":"Constructing Collaborative Online Communities for Visualizing Spimes.","year":2010,"lang":"en","type":"article","venue":"Web Intelligence/IAT Workshops","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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 Regina","funders":"","keywords":"Variety (cybernetics); Software deployment; Computer science; World Wide Web; Knowledge management; Appeal; Visualization; Data science; Internet privacy; Software engineering","score_opus":0.06354106137116673,"score_gpt":0.3483002725605597,"score_spread":0.28475921118939296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2669406525","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":[0.09580064,0.00070763764,0.8605239,0.00096763554,0.00022117568,0.0013019972,0.0028521782,0.015042304,0.022582531],"genre_scores_gemma":[0.23127067,0.0002749437,0.7574248,0.00007713829,0.00005409916,0.0010296173,0.0024279484,0.001240161,0.0062005986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833137,0.0008443974,0.000089618276,0.00023516819,0.00040057665,0.00009884483],"domain_scores_gemma":[0.99156207,0.0045595844,0.00059465633,0.0014566462,0.0011440793,0.0006829336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003729597,0.0011942965,0.000508708,0.0069625983,0.0021612048,0.0040089623,0.0013288017,0.0013325631,0.011428856],"category_scores_gemma":[0.013207426,0.0007308777,0.001199032,0.0033508104,0.0010118902,0.0059782555,0.005860594,0.0010880687,0.0019844482],"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":[0.00094526436,0.0011563482,0.016767934,0.003240255,0.00030772304,0.0027992397,0.07537431,0.017733572,0.036536083,0.10254311,0.057862945,0.6847333],"study_design_scores_gemma":[0.00043233985,0.0005665159,0.018533919,0.0012179508,0.00027178487,0.0028325175,0.038592227,0.28191572,0.029828012,0.18380615,0.44146594,0.0005369453],"about_ca_topic_score_codex":0.0021437174,"about_ca_topic_score_gemma":0.0060892766,"teacher_disagreement_score":0.011428856,"about_ca_system_score_codex":0.00053775136,"about_ca_system_score_gemma":0.0012063128,"threshold_uncertainty_score":0.03823334},"labels":[],"label_agreement":null},{"id":"W2708707409","doi":"","title":"PREDICTING LISTENER’S MOOD BASED ON MUSIC GENRE: AN ADAPTED REPRODUCED MODEL OF RUSSELL AND THAYER","year":2017,"lang":"en","type":"article","venue":"International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mood; Popularity; Psychology; Musical; Categorization; Population; Social psychology; Sociology; Computer science; Artificial intelligence; Demography; Art; Visual arts","score_opus":0.0189008440330987,"score_gpt":0.2283681035304651,"score_spread":0.20946725949736641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2708707409","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":[0.892588,0.00078970264,0.069099665,0.0018305347,0.00019942565,0.0017534309,0.0012978477,0.00018660414,0.032254845],"genre_scores_gemma":[0.9836527,0.00036626382,0.013091893,0.000110162386,0.000028147359,0.00038955148,0.00027750447,0.000008759953,0.0020750565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921966,0.00030583027,0.00004952957,0.0001498259,0.00019410688,0.00008101362],"domain_scores_gemma":[0.99839765,0.0007481118,0.00017434575,0.00010107965,0.00040796213,0.00017083426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013275481,0.0006136292,0.00046329197,0.0016859969,0.0006085534,0.0016553553,0.0009579504,0.00065265974,0.0033048182],"category_scores_gemma":[0.003688263,0.00028443494,0.0013089217,0.0009104555,0.00065876055,0.0007254325,0.0007273691,0.0007702976,0.00083241257],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007471437,0.0025787142,0.7957269,0.00046441713,0.0007551719,0.0010221828,0.008636256,0.016165912,0.0058242963,0.016651336,0.004651911,0.1467757],"study_design_scores_gemma":[0.00016174727,0.0019413977,0.75053525,0.0003277543,0.0005916001,0.001058882,0.007092725,0.21024442,0.0014317494,0.021225374,0.00523937,0.00014977177],"about_ca_topic_score_codex":0.0075168256,"about_ca_topic_score_gemma":0.0068811113,"teacher_disagreement_score":0.0075168256,"about_ca_system_score_codex":0.00097226736,"about_ca_system_score_gemma":0.0008465625,"threshold_uncertainty_score":0.014946163},"labels":[],"label_agreement":null},{"id":"W2726680096","doi":"10.1111/1750-3841.13799","title":"Using Preferred Attribute Elicitation to Determine How Males and Females Evaluate Beer","year":2017,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"","keywords":"Craft; Brewing; Food science; Nova scotia; Psychology; Geography; Biology","score_opus":0.39776827896290684,"score_gpt":0.4142002243924353,"score_spread":0.01643194542952847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726680096","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":[0.995259,0.000044414006,0.0020025203,0.000034101173,0.000006568388,0.00014054285,0.00015530421,0.00000831028,0.002349417],"genre_scores_gemma":[0.9909719,0.00011741556,0.004872301,0.00010800481,0.000010299144,0.000385857,0.00029293753,0.000012666595,0.0032285189],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998616,0.0005947574,0.000060426486,0.0001921482,0.00038904426,0.00014757445],"domain_scores_gemma":[0.99671364,0.0018472237,0.00042389575,0.00018125457,0.0006570948,0.00017692603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022859396,0.0002963541,0.00022012982,0.00056252757,0.00057023036,0.0007947061,0.00018225433,0.00027852578,0.0033635406],"category_scores_gemma":[0.0067970585,0.00014524219,0.00014393326,0.00034032017,0.0003264912,0.00027625533,0.0008243494,0.0003684781,0.00061877124],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038720379,0.0011768957,0.4925917,0.00034404724,0.00010250837,0.00082015886,0.03812114,0.00069314643,0.31316945,0.0010061867,0.001962829,0.14613995],"study_design_scores_gemma":[0.00004886605,0.0014778564,0.95534146,0.00004402781,0.000031607757,0.00053906644,0.018377284,0.0022453328,0.016194897,0.00041738458,0.0052213073,0.000060932285],"about_ca_topic_score_codex":0.003995275,"about_ca_topic_score_gemma":0.008300699,"teacher_disagreement_score":0.003995275,"about_ca_system_score_codex":0.00044805047,"about_ca_system_score_gemma":0.0003366192,"threshold_uncertainty_score":0.012089372},"labels":[],"label_agreement":null},{"id":"W2743595333","doi":"10.1016/b978-0-12-805134-4.00012-2","title":"Carrot Juice Yogurts","year":2017,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Food science; Titratable acid; Starter; Carrot juice; Lactic acid; Sugar; Shelf life; Chemistry; Fruit juice; Sensory analysis; Bacteria; Biology","score_opus":0.05214735412653004,"score_gpt":0.28316553821843254,"score_spread":0.2310181840919025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743595333","genre_codex":"other","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":[0.11353844,0.3606559,0.027371647,0.0023515585,0.0025273862,0.00056446064,0.00279352,0.0017047271,0.48849243],"genre_scores_gemma":[0.15339693,0.13037577,0.024431225,0.0015358252,0.00046654695,0.00022488023,0.001995902,0.0003584478,0.6872145],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990106,0.0000108858785,0.000004979182,0.000023450657,0.000048615184,0.000010995778],"domain_scores_gemma":[0.9999685,0.0000067616957,0.0000050843005,0.0000030445453,0.00001097676,0.0000055769865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016763195,0.0007408243,0.00036021488,0.0007654896,0.00026536206,0.0006855074,0.0002501555,0.00041410723,0.030105175],"category_scores_gemma":[0.0001705918,0.00013456403,0.00033477164,0.0005307913,0.00021141637,0.0005551933,0.00044422026,0.00052117684,0.006724263],"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":[0.0005408922,0.00013070338,0.0002700536,0.0022886451,0.000026960955,0.00054883055,0.00016351251,0.0001260204,0.36485663,0.0045730164,0.0119141415,0.61456066],"study_design_scores_gemma":[0.000032441567,0.0008075676,0.007020643,0.0008988249,0.00009531379,0.0012613095,0.00018081377,0.00032468463,0.15152769,0.0019521286,0.83585733,0.00004118298],"about_ca_topic_score_codex":0.002855718,"about_ca_topic_score_gemma":0.005135821,"teacher_disagreement_score":0.030105175,"about_ca_system_score_codex":0.00035845235,"about_ca_system_score_gemma":0.0003042325,"threshold_uncertainty_score":0.10071182},"labels":[],"label_agreement":null},{"id":"W2765221530","doi":"10.1007/s41237-017-0042-8","title":"Simultaneous canonical correlation analysis with invariant canonical loadings","year":2017,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Canonical correlation; Canonical analysis; Canonical correspondence analysis; Mathematics; Canonical form; Covariance; Invariant (physics); Applied mathematics; Statistics; Pure mathematics; Mathematical physics","score_opus":0.03982958838864918,"score_gpt":0.30371834959619853,"score_spread":0.26388876120754934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765221530","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":[0.03743217,0.00018102994,0.95829767,0.000102756494,0.00012306462,0.00015131418,0.0005369191,0.0013865012,0.0017885196],"genre_scores_gemma":[0.3855289,0.00039824253,0.60232675,0.00011561534,0.00022029555,0.0012151534,0.003033085,0.0016707435,0.0054912753],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99491286,0.001966994,0.0002529492,0.001552232,0.0009042655,0.0004106464],"domain_scores_gemma":[0.9937815,0.0022741535,0.00031260267,0.0017460352,0.0016806333,0.00020507142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005174985,0.001962498,0.0011182176,0.0027045656,0.0009771116,0.0022160006,0.0011301856,0.0006397418,0.008528264],"category_scores_gemma":[0.018381895,0.0006458453,0.0029982992,0.0040531787,0.0011833942,0.0019635996,0.002275392,0.0014403736,0.0028428289],"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":[0.0008895618,0.00055026053,0.014885278,0.00044728612,0.0010964925,0.0002448718,0.0008438756,0.028620116,0.04173843,0.05155061,0.013169302,0.84596395],"study_design_scores_gemma":[0.00012253002,0.00063536136,0.081915185,0.00014720846,0.000661747,0.00055750227,0.0007007034,0.7810538,0.02994603,0.08085053,0.02303801,0.00037139622],"about_ca_topic_score_codex":0.0034900184,"about_ca_topic_score_gemma":0.0059903357,"teacher_disagreement_score":0.008528264,"about_ca_system_score_codex":0.0004558881,"about_ca_system_score_gemma":0.003057308,"threshold_uncertainty_score":0.028529882},"labels":[],"label_agreement":null},{"id":"W2765550888","doi":"10.1038/s41593-017-0008-x","title":"Elucidating the underlying components of food valuation in the human orbitofrontal cortex","year":2017,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":217,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Aging","funders":"National Institute of Mental Health","keywords":"Orbitofrontal cortex; Valuation (finance); Neuroscience; Psychology; Multivariate statistics; Prefrontal cortex; Biology; Cognitive psychology; Computer science; Cognition; Machine learning; Economics","score_opus":0.192469340653037,"score_gpt":0.3873999921289087,"score_spread":0.19493065147587169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765550888","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":[0.97417283,0.00040251936,0.023825549,0.00017793861,0.000011282945,0.000023443507,0.00019023128,0.00002548516,0.0011706746],"genre_scores_gemma":[0.99373394,0.00014067694,0.0058149737,0.0000138005935,0.00000902858,0.000008904114,0.00006473704,0.0000125238785,0.00020139743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992394,0.000021639764,0.0000041900253,0.000014292973,0.00002060095,0.000015310918],"domain_scores_gemma":[0.9995943,0.000259937,0.00005447718,0.000038110244,0.000037911883,0.000015397958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042854634,0.00017107934,0.00013124713,0.00036259388,0.00014746706,0.00072794186,0.00015972495,0.00017467803,0.00093323993],"category_scores_gemma":[0.0022244195,0.00017800243,0.00024232015,0.00048558865,0.0004049439,0.0004325665,0.00029174588,0.00024123947,0.000055292116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013165453,0.000109901586,0.07787124,0.000268118,0.00036130007,0.00037460538,0.0010248518,0.0094682295,0.6991143,0.013148735,0.0007034376,0.19623877],"study_design_scores_gemma":[0.00006188359,0.00011843665,0.8567599,0.000031813168,0.00013073647,0.00055802043,0.00042176558,0.0890224,0.026880061,0.025091622,0.0008842669,0.000039064646],"about_ca_topic_score_codex":0.0027802454,"about_ca_topic_score_gemma":0.0050617657,"teacher_disagreement_score":0.0027802454,"about_ca_system_score_codex":0.00019354137,"about_ca_system_score_gemma":0.00034933034,"threshold_uncertainty_score":0.005528152},"labels":[],"label_agreement":null},{"id":"W2770163336","doi":"10.1111/joss.12300","title":"Comparing preferred attribute elicitation to trained panelists' evaluations using a novel food product","year":2017,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Acadia University","funders":"","keywords":"Sensory analysis; Descriptive statistics; Product (mathematics); Mathematics; Statistics; Computer science; Food science; Chemistry","score_opus":0.607617147234339,"score_gpt":0.46237467837569585,"score_spread":0.14524246885864317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770163336","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":[0.9888693,0.00006302117,0.008306776,0.000041261606,0.000027201035,0.00029107113,0.00007966135,0.000046615012,0.0022751715],"genre_scores_gemma":[0.97568476,0.0001260892,0.02056322,0.000118660144,0.000032281496,0.00050733157,0.00023561866,0.000032523145,0.00269949],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9945623,0.0027218654,0.0002962697,0.0007817363,0.0014129938,0.00022474532],"domain_scores_gemma":[0.986177,0.0074225035,0.0013229991,0.0011265586,0.0035618485,0.0003889985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069955494,0.00046676063,0.0004997313,0.00057648285,0.00044403254,0.00061363005,0.00048220463,0.0005652288,0.002676359],"category_scores_gemma":[0.0134717235,0.00023766677,0.0005006374,0.00032128548,0.0003635036,0.00048462636,0.00090979727,0.00068653317,0.0005455347],"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":[0.007378732,0.004451339,0.07130332,0.0012867773,0.00036561926,0.00035546633,0.0101051815,0.0031981447,0.7162006,0.00034834142,0.0015715937,0.18343496],"study_design_scores_gemma":[0.00031264577,0.020406839,0.7696406,0.00019255468,0.0002745383,0.0006512742,0.007799562,0.015838316,0.17825,0.0006612604,0.005674083,0.00029832099],"about_ca_topic_score_codex":0.000740215,"about_ca_topic_score_gemma":0.002266096,"teacher_disagreement_score":0.0069955494,"about_ca_system_score_codex":0.00039043633,"about_ca_system_score_gemma":0.00030244066,"threshold_uncertainty_score":0.036996424},"labels":[],"label_agreement":null},{"id":"W2771297297","doi":"10.3390/nu9121337","title":"Development and Validation of the Food Liking Questionnaire in a French-Canadian Population","year":2017,"lang":"en","type":"article","venue":"Nutrients","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Cronbach's alpha; Exploratory factor analysis; Psychology; Reliability (semiconductor); Food frequency questionnaire; Population; Clinical psychology; Test (biology); Content validity; Internal consistency; Psychometrics; Medicine; Environmental health","score_opus":0.05094793025675002,"score_gpt":0.2791136091399128,"score_spread":0.22816567888316278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771297297","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":[0.9688167,0.002158035,0.0074190237,0.0014187576,0.00022716926,0.0029376394,0.005189691,0.00012754396,0.011705471],"genre_scores_gemma":[0.9368118,0.0034021381,0.0403449,0.0010446938,0.00010439334,0.0038444328,0.007141835,0.00007058745,0.0072352164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99733245,0.0006246056,0.00019397802,0.0002156657,0.0012832584,0.00034999056],"domain_scores_gemma":[0.9937767,0.0005489003,0.00024539643,0.0001929139,0.0048057106,0.0004304583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009459808,0.00056837406,0.0004960109,0.002499563,0.0031345228,0.0010780529,0.0011112584,0.0005066567,0.0026576184],"category_scores_gemma":[0.0082173105,0.00025520806,0.0010094657,0.0020556403,0.00063650415,0.00059463974,0.0008176312,0.0006967534,0.00058193103],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005125596,0.0008420205,0.7606722,0.0006915065,0.0001992882,0.00092175195,0.014774215,0.0007690235,0.0048424955,0.0016127465,0.014812088,0.19935012],"study_design_scores_gemma":[0.00006520791,0.00036615512,0.97167677,0.00020647516,0.00006864665,0.00039191477,0.0044974517,0.00088079897,0.00060851185,0.00012424673,0.021053232,0.00006063022],"about_ca_topic_score_codex":0.89839935,"about_ca_topic_score_gemma":0.91843915,"teacher_disagreement_score":0.89839935,"about_ca_system_score_codex":0.008349356,"about_ca_system_score_gemma":0.018644,"threshold_uncertainty_score":0.20439792},"labels":[],"label_agreement":null},{"id":"W2771720193","doi":"10.3389/fpsyg.2017.02137","title":"Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error","year":2017,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McGill University","funders":"","keywords":"Structural equation modeling; Uniqueness; Component (thermodynamics); Component analysis; Latent variable; Observational error; Econometrics; Computer science; Independent component analysis; Extension (predicate logic); Applied mathematics; Factor analysis; Confirmatory factor analysis; Statistics; Algorithm; Data mining; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.10923421718923362,"score_gpt":0.367250866723239,"score_spread":0.2580166495340054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771720193","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":[0.0013371096,0.0001344403,0.9978606,0.00007841467,0.000045224537,0.00008956892,0.000037260124,0.000119072625,0.00029835873],"genre_scores_gemma":[0.0495856,0.0003222059,0.94759464,0.00013453937,0.00009446692,0.0010945236,0.00028715422,0.00017374363,0.00071315566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9795659,0.01469966,0.00094699836,0.0022259492,0.0022005534,0.00036080758],"domain_scores_gemma":[0.96695286,0.020612927,0.002348304,0.0057558175,0.00396193,0.00036815717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023611272,0.0031983275,0.0021403977,0.0037525587,0.0015952368,0.0026542672,0.0033142902,0.0020564809,0.0043825945],"category_scores_gemma":[0.07934035,0.0010283113,0.004045189,0.006656696,0.0029342235,0.004810376,0.0052219606,0.0042547043,0.0014200954],"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":[0.00016188752,0.00016244025,0.00784513,0.00096258766,0.0012144297,0.00030396742,0.001212019,0.14424124,0.0037307565,0.48973277,0.007753354,0.34267938],"study_design_scores_gemma":[0.00005623877,0.00019429863,0.0030243907,0.00024311652,0.0003034188,0.00019447994,0.00016826951,0.63435715,0.0019308862,0.34888363,0.010494138,0.00015003773],"about_ca_topic_score_codex":0.0031712414,"about_ca_topic_score_gemma":0.0044136574,"teacher_disagreement_score":0.023611272,"about_ca_system_score_codex":0.0012939059,"about_ca_system_score_gemma":0.004833484,"threshold_uncertainty_score":0.12486988},"labels":[],"label_agreement":null},{"id":"W2773142873","doi":"10.1016/j.cie.2017.12.005","title":"Product recommendation algorithms in the age of omnichannel retailing – An intuitive clustering approach","year":2017,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Omnichannel; Cluster analysis; Computer science; Profitability index; Product (mathematics); Service (business); Matrix (chemical analysis); Process (computing); Fuzzy clustering; Data mining; Marketing; Business; World Wide Web; Artificial intelligence; Mathematics","score_opus":0.16122219745903377,"score_gpt":0.30235187026417365,"score_spread":0.14112967280513988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773142873","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":[0.019551132,0.0011467003,0.97483575,0.0013074639,0.00006253017,0.00005750071,0.00012031873,0.00030364748,0.0026149678],"genre_scores_gemma":[0.39460352,0.0017684706,0.5942639,0.0005665097,0.00040563665,0.00012672634,0.00047410006,0.00016853178,0.0076227086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986578,0.00043760322,0.00009310626,0.00040288683,0.00031060912,0.00009799818],"domain_scores_gemma":[0.9952448,0.002932994,0.00021761538,0.00063689426,0.0008207864,0.00014699323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025681318,0.0006369973,0.0013882007,0.0022856437,0.0010897943,0.0031167294,0.0024524669,0.0022537594,0.0021812452],"category_scores_gemma":[0.010971363,0.0007820946,0.0010200454,0.0033227452,0.0012133602,0.004802915,0.0017863648,0.0018371064,0.000847882],"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":[0.00023498206,0.0003448807,0.00826489,0.00028769867,0.0003416591,0.00034669542,0.00059306173,0.3609461,0.0037949383,0.2827302,0.010213792,0.33190107],"study_design_scores_gemma":[0.00001579676,0.00003070177,0.00114681,0.000020615438,0.00002248545,0.00010271529,0.00007994358,0.85998255,0.00046621897,0.13558692,0.0025143002,0.000030945164],"about_ca_topic_score_codex":0.008905574,"about_ca_topic_score_gemma":0.010027917,"teacher_disagreement_score":0.008905574,"about_ca_system_score_codex":0.001276584,"about_ca_system_score_gemma":0.00076809083,"threshold_uncertainty_score":0.017707467},"labels":[],"label_agreement":null},{"id":"W2773181409","doi":"10.1017/9781316871867.014","title":"Statistical Approaches for Inferring and Predicting Food-Web Architecture","year":2017,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"SystemsX.ch; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Computer science; Architecture; Web content; Content (measure theory); World Wide Web; Information retrieval; The Internet; Geography; Mathematics","score_opus":0.10313294522693868,"score_gpt":0.23950407802949752,"score_spread":0.13637113280255886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773181409","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":[0.004272569,0.0037387467,0.9872924,0.0004200536,0.00007883791,0.00003410348,0.0007610861,0.0011881876,0.002213888],"genre_scores_gemma":[0.10689934,0.0077472487,0.8761697,0.0002902968,0.00044067958,0.0003519207,0.0034405645,0.000729764,0.0039305245],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99841726,0.00066434813,0.000114331175,0.0003781176,0.00036667223,0.000059156566],"domain_scores_gemma":[0.98863494,0.009599244,0.00037985283,0.000790878,0.0004720749,0.00012291841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004592706,0.0017169219,0.0012999449,0.0049703014,0.0005430171,0.0029503356,0.003446602,0.0015795563,0.004072837],"category_scores_gemma":[0.014755417,0.0015266241,0.0023276107,0.005149366,0.0016394717,0.0034802742,0.001789189,0.0032576816,0.0024483837],"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":[0.00008225622,0.00011444523,0.009551637,0.0010198476,0.00096834067,0.00022441185,0.0003414223,0.40094286,0.0026757226,0.093120076,0.018050361,0.47290862],"study_design_scores_gemma":[0.000009465909,0.000021962602,0.0022980103,0.00009669732,0.00005754479,0.00009674084,0.00006214828,0.7371978,0.000821681,0.2537826,0.005511331,0.000043914668],"about_ca_topic_score_codex":0.006386293,"about_ca_topic_score_gemma":0.009011652,"teacher_disagreement_score":0.006386293,"about_ca_system_score_codex":0.0014400266,"about_ca_system_score_gemma":0.0010502812,"threshold_uncertainty_score":0.024288833},"labels":[],"label_agreement":null},{"id":"W2774182998","doi":"10.22237/jmasm/1509494700","title":"Using Pratt's Importance Measures in Confirmatory Factor Analyses","year":2017,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Confirmatory factor analysis; Statistics; Measure (data warehouse); Correlation; Interpretation (philosophy); Factor analysis; Correlation coefficient; Correlation ratio; Econometrics; Data mining; Structural equation modeling; Computer science; Geometry","score_opus":0.41125937643966837,"score_gpt":0.5041627269370226,"score_spread":0.09290335049735421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774182998","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":[0.017850105,0.0013093553,0.96100336,0.0009448694,0.0006159868,0.0037383158,0.0005143166,0.00067437306,0.013349258],"genre_scores_gemma":[0.14558169,0.0007766154,0.8416093,0.0003322628,0.00018588692,0.0097210435,0.0003397878,0.00025018354,0.0012031851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8663502,0.09665073,0.0058157113,0.009005111,0.021176744,0.0010015311],"domain_scores_gemma":[0.6890388,0.25188956,0.011500144,0.020245036,0.025940392,0.0013860568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1194087,0.0030456018,0.0022880621,0.0086288275,0.00350379,0.0069974405,0.0032174399,0.0020690863,0.005721771],"category_scores_gemma":[0.32535437,0.0020808794,0.0040403088,0.012176254,0.006650245,0.0074710706,0.0050259307,0.007482249,0.001529455],"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":[0.0005168893,0.00077254407,0.03267029,0.0047501693,0.0016261543,0.0005228202,0.026194066,0.01121825,0.0022156157,0.32623762,0.025509696,0.5677659],"study_design_scores_gemma":[0.0006602064,0.0021036183,0.058605157,0.004042487,0.0010143513,0.0008802606,0.007819667,0.09052579,0.004776198,0.74139136,0.08725996,0.00092092727],"about_ca_topic_score_codex":0.0035913887,"about_ca_topic_score_gemma":0.005322215,"teacher_disagreement_score":0.1194087,"about_ca_system_score_codex":0.0033172814,"about_ca_system_score_gemma":0.0076029403,"threshold_uncertainty_score":0.6315011},"labels":[],"label_agreement":null},{"id":"W2796016970","doi":"10.5539/jfr.v7n3p82","title":"Sensory Descriptive Profiling and Consumer Acceptance of Made-in-transit (MIT) Set Yoghurt","year":2018,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Starter; Food science; Skimmed milk; Shelf life; Fortification; Fermentation; Food industry; Mathematics; Chemistry; Business","score_opus":0.24510316312187866,"score_gpt":0.4130700248030346,"score_spread":0.16796686168115596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796016970","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":[0.99951327,0.000051857864,0.00015546274,0.000011200342,0.0000050010503,0.00001076189,0.000062624866,0.0000031764307,0.0001866923],"genre_scores_gemma":[0.9983719,0.000074568925,0.00056791457,0.000045301203,0.0000057170782,0.000022934139,0.00022276184,0.00000484588,0.0006841573],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997601,0.000048348273,0.000016349264,0.000045332195,0.000096747695,0.000033179786],"domain_scores_gemma":[0.99961144,0.00010171303,0.00011511623,0.00001857267,0.00010901096,0.000044213208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036823173,0.0002912648,0.00028372477,0.00021436652,0.00014832773,0.00035250446,0.0001318901,0.00029276483,0.0015901604],"category_scores_gemma":[0.00071944966,0.000119474214,0.00032842674,0.00020144507,0.00017529873,0.0002788688,0.00028466608,0.00052531814,0.00013920163],"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":[0.009848073,0.0010647884,0.04569263,0.00034870024,0.00012955582,0.00025274014,0.0012398018,0.00014074729,0.9250757,0.00007324925,0.0002546724,0.015879244],"study_design_scores_gemma":[0.000060655922,0.02438372,0.8651948,0.00003765199,0.00019577955,0.0003275668,0.0013739801,0.001136378,0.10633712,0.00004850905,0.0008607386,0.00004299213],"about_ca_topic_score_codex":0.0012867827,"about_ca_topic_score_gemma":0.0018435417,"teacher_disagreement_score":0.0015901604,"about_ca_system_score_codex":0.00014153101,"about_ca_system_score_gemma":0.000073472416,"threshold_uncertainty_score":0.0053195953},"labels":[],"label_agreement":null},{"id":"W2800617133","doi":"10.1371/journal.pone.0196236","title":"The effect of cigarillo packaging elements on young adult perceptions of product flavor, taste, smell, and appeal","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":33,"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":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill; Health Canada; University of North Carolina at Chapel Hill","keywords":"Flavor; Packaging and labeling; Taste; Perception; Product (mathematics); Food science; Advertising; Psychology; Business; Materials science; Mathematics; Chemistry; Marketing","score_opus":0.027928622072474017,"score_gpt":0.26123671326386405,"score_spread":0.23330809119139004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800617133","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":[0.9996008,0.00005720964,0.00002124776,0.000009014673,0.0000015847284,0.000005123231,0.000018789535,0.0000018724877,0.00028446494],"genre_scores_gemma":[0.9993807,0.00007950435,0.00018582848,0.000027422879,0.0000031432971,0.00000649756,0.000043496788,0.0000011241985,0.00027226878],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997341,0.00007638809,0.000023194365,0.000038797716,0.000089235036,0.000038166352],"domain_scores_gemma":[0.9976992,0.0008813232,0.0007082418,0.000075492055,0.00024101378,0.00039470414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007902801,0.00021358879,0.0001734495,0.0002455092,0.00019376849,0.0006983698,0.00011429478,0.0002663194,0.0030018848],"category_scores_gemma":[0.0029147658,0.0001894488,0.00032251215,0.00012669257,0.00019891572,0.00033594228,0.0003486837,0.00029858493,0.00028641956],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001473165,0.0010759388,0.9727333,0.000086582535,0.0001059021,0.00009417161,0.0011853045,0.0000419586,0.008310807,0.0000330858,0.00015087884,0.014708816],"study_design_scores_gemma":[0.000013911175,0.001301548,0.9971625,0.000009303128,0.000049427792,0.00006412079,0.00044830708,0.00009211169,0.0006795256,0.000013530151,0.00016136038,0.0000045012434],"about_ca_topic_score_codex":0.0023468663,"about_ca_topic_score_gemma":0.0038556566,"teacher_disagreement_score":0.0030018848,"about_ca_system_score_codex":0.00017836838,"about_ca_system_score_gemma":0.00018627051,"threshold_uncertainty_score":0.010042369},"labels":[],"label_agreement":null},{"id":"W2803344759","doi":"10.1016/j.jpsychores.2018.03.100","title":"The Self-Assessment Kiosk: Development and implementation of an internet resource for self-assessment of mental health and health behavior that provides personalized feedback","year":2018,"lang":"en","type":"article","venue":"Journal of Psychosomatic Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sinai Health System; University of Toronto","funders":"","keywords":"Osmotic dehydration; Ascorbic acid; Carotenoid; Chemistry; Food science; Dehydration; Sucrose; Biochemistry","score_opus":0.15054795432292933,"score_gpt":0.5036636511307883,"score_spread":0.3531156968078589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803344759","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5239303,0.0003499091,0.29709077,0.0017099328,0.000500687,0.05029188,0.018479113,0.08717167,0.020475764],"genre_scores_gemma":[0.3285422,0.00046029018,0.5766213,0.0010073552,0.00017877386,0.040460147,0.01865239,0.0061760303,0.02790159],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984174,0.0006176101,0.00021423773,0.00025614796,0.00034638678,0.00014829656],"domain_scores_gemma":[0.9935846,0.0027860303,0.00031571407,0.0006912623,0.0013887498,0.0012335812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050743427,0.0009125798,0.00059665885,0.0015635488,0.0005303442,0.00089322135,0.002153375,0.0005137322,0.012848196],"category_scores_gemma":[0.008827131,0.00060985715,0.00050556206,0.0006161072,0.00035595387,0.0012975242,0.0021325266,0.0010674299,0.0055228574],"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":[0.0034441755,0.010148262,0.04793487,0.00074686995,0.0002179427,0.0003007377,0.00159134,0.0012560104,0.014604031,0.0006658767,0.03619903,0.8828908],"study_design_scores_gemma":[0.007786497,0.01764876,0.53174406,0.0012877608,0.0015904977,0.0019690408,0.0039500934,0.08751488,0.10614896,0.006053666,0.23303899,0.0012667325],"about_ca_topic_score_codex":0.0021534488,"about_ca_topic_score_gemma":0.004171425,"teacher_disagreement_score":0.012848196,"about_ca_system_score_codex":0.000502473,"about_ca_system_score_gemma":0.0026860738,"threshold_uncertainty_score":0.042981505},"labels":[],"label_agreement":null},{"id":"W2804774137","doi":"10.1136/tobaccocontrol-2017-054152","title":"Sensory analysis of characterising flavours: evaluating tobacco product odours using an expert panel","year":2018,"lang":"en","type":"article","venue":"Tobacco Control","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":25,"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":"Third Health Programme; Consumers, Health, Agriculture and Food Executive Agency; European Commission","keywords":"Packaging and labeling; Flavour; Product (mathematics); Tobacco industry; Directive; Tobacco product; Business; Food science; Computer science; Marketing; Mathematics; Medicine; Environmental health","score_opus":0.2120572719951122,"score_gpt":0.38603329025305944,"score_spread":0.17397601825794723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804774137","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":[0.9490104,0.00020174276,0.041161478,0.00013619092,0.000063769454,0.0016029224,0.00043437167,0.00021624732,0.007173001],"genre_scores_gemma":[0.9064338,0.00033062501,0.0860983,0.00027666704,0.000048014837,0.0014445012,0.0010050602,0.00006825006,0.004294766],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99701643,0.0011403225,0.00023998925,0.00057851727,0.00082670257,0.00019801411],"domain_scores_gemma":[0.9926442,0.0019633137,0.00057256996,0.00039865557,0.003980073,0.00044104154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068222876,0.0007215944,0.00059128524,0.0011338497,0.00081235805,0.0007413469,0.0007221479,0.00073714537,0.003094585],"category_scores_gemma":[0.008142835,0.00032677018,0.00069661095,0.0004957776,0.00040888478,0.00053564395,0.0012521632,0.0005705715,0.0015344884],"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":[0.0058609834,0.0029393854,0.1604614,0.0015958019,0.00037154814,0.00056343374,0.015154422,0.0064349133,0.51573366,0.00029834753,0.004262017,0.28632408],"study_design_scores_gemma":[0.00031176218,0.013117,0.6930009,0.00049360906,0.00064910983,0.001371891,0.01264067,0.057354726,0.20469092,0.001042639,0.014849653,0.00047709918],"about_ca_topic_score_codex":0.0016650518,"about_ca_topic_score_gemma":0.0034985112,"teacher_disagreement_score":0.0068222876,"about_ca_system_score_codex":0.00031265098,"about_ca_system_score_gemma":0.000606529,"threshold_uncertainty_score":0.036080122},"labels":[],"label_agreement":null},{"id":"W2806528777","doi":"10.1101/333005","title":"Generalization of the minimum covariance determinant algorithm for categorical and mixed data types","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Western University; University of Toronto; Baycrest Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Eisai; Government of Ontario; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; Bristol-Myers Squibb; Eli Lilly and Company; Strong; Biogen; Ontario Brain Institute; National Institute on Aging; Alzheimer's Association","keywords":"Categorical variable; Generalization; Covariance; Data type; Ordinal data; Mathematics; Computer science; Mahalanobis distance; Algorithm; Artificial intelligence; Statistics","score_opus":0.05217688775488725,"score_gpt":0.2720762328793413,"score_spread":0.21989934512445405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806528777","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":[0.0026705312,0.00009145653,0.99610585,0.00017776208,0.00002652764,0.000060468738,0.00014806459,0.00036631804,0.00035306948],"genre_scores_gemma":[0.0647824,0.000118134536,0.9316951,0.00029026988,0.00011354736,0.0003172444,0.00094322243,0.000404427,0.0013356489],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9860065,0.0067292843,0.0011484552,0.0027255968,0.0028868825,0.0005031498],"domain_scores_gemma":[0.9546245,0.030494638,0.001782593,0.0058026374,0.0065682363,0.00072746235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016086353,0.0009295085,0.001960202,0.003763631,0.001043138,0.0036747134,0.003540898,0.0021510196,0.004812097],"category_scores_gemma":[0.068680756,0.000883822,0.003121655,0.0047240225,0.0017964903,0.0038572724,0.0046665333,0.004264315,0.0017750013],"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":[0.0004355126,0.00030078093,0.018157776,0.0005231272,0.00060042646,0.0005599526,0.0007974706,0.19420327,0.0043594018,0.24180059,0.019967893,0.5182938],"study_design_scores_gemma":[0.00004639585,0.000054497712,0.0013618219,0.000050418534,0.000027812392,0.00025447848,0.00007974667,0.81221974,0.0010253954,0.17853852,0.0063047856,0.00003629188],"about_ca_topic_score_codex":0.004852045,"about_ca_topic_score_gemma":0.00653645,"teacher_disagreement_score":0.016086353,"about_ca_system_score_codex":0.0013788161,"about_ca_system_score_gemma":0.0035964155,"threshold_uncertainty_score":0.08507377},"labels":[],"label_agreement":null},{"id":"W2810425927","doi":"10.3389/fpsyg.2018.01250","title":"Applying the Model-Comparison Approach to Test Specific Research Hypotheses in Psychophysical Research Using the Palamedes Toolbox","year":2018,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":479,"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":"Toolbox; Computer science; Statistical hypothesis testing; Estimator; Flexibility (engineering); Statistical model; Machine learning; Artificial intelligence; Test (biology); Software; Statistics; Mathematics","score_opus":0.4477466533658099,"score_gpt":0.49128078725173596,"score_spread":0.043534133885926085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810425927","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":[0.0014782222,0.000115421695,0.99373525,0.00019160591,0.000055515997,0.00017891802,0.0002501053,0.0022516476,0.0017434116],"genre_scores_gemma":[0.0259572,0.00022607445,0.96979684,0.00022346148,0.000037182606,0.001568762,0.0003135421,0.0008814294,0.0009954949],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98890483,0.0080327755,0.00063387956,0.0009823572,0.0012726617,0.00017342878],"domain_scores_gemma":[0.92912316,0.061928906,0.0019472244,0.003831008,0.0027456682,0.00042397913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029655946,0.002239292,0.0016134776,0.0019729477,0.00057925825,0.0026502346,0.0036082475,0.0016944646,0.024818048],"category_scores_gemma":[0.08617147,0.0010290826,0.00221597,0.0010869949,0.0018419088,0.0032382188,0.0042659086,0.0048762574,0.0058334973],"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":[0.0011492025,0.0007311536,0.007751804,0.0035973063,0.001804344,0.0011231498,0.0021777353,0.14817834,0.014199711,0.29597336,0.028029567,0.4952844],"study_design_scores_gemma":[0.00031449838,0.0007720874,0.0034039726,0.00077562104,0.00021266015,0.0005976816,0.0004163386,0.5393282,0.0129388245,0.3864054,0.05461707,0.00021754592],"about_ca_topic_score_codex":0.0010842583,"about_ca_topic_score_gemma":0.0012561632,"teacher_disagreement_score":0.029655946,"about_ca_system_score_codex":0.0010107605,"about_ca_system_score_gemma":0.0027076325,"threshold_uncertainty_score":0.15683752},"labels":[],"label_agreement":null},{"id":"W2810697546","doi":"10.1016/j.foodqual.2018.07.001","title":"Effect of sugar and acid composition, aroma release, and assessment conditions on aroma enhancement by taste in model wines","year":2018,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Seed Growers' Association","keywords":"Aroma; Chemistry; Food science; Taste; Flavor; Sugar; Psychology","score_opus":0.1026755815762135,"score_gpt":0.37557841453199187,"score_spread":0.2729028329557784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810697546","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":[0.9992982,0.00013841869,0.00031305008,0.0000136612,0.0000122126985,0.000016069587,0.00005822837,0.000004416916,0.00014552628],"genre_scores_gemma":[0.99793786,0.00019897243,0.0013253727,0.000026953196,0.000008915013,0.000030182866,0.000091717055,0.00001352619,0.00036646661],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964714,0.00011569033,0.000044217148,0.000059760452,0.0000518609,0.00008140126],"domain_scores_gemma":[0.9993724,0.00027000916,0.00007208368,0.000038752914,0.00009454127,0.00015216229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006109102,0.00047869957,0.00048075253,0.00018628278,0.0003172991,0.00071219046,0.0002121949,0.0002761758,0.0011877596],"category_scores_gemma":[0.0014594203,0.00026545164,0.00048699384,0.00022908782,0.000433598,0.0005991263,0.00039992045,0.00048017045,0.0001038557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.027171623,0.0005333378,0.0015966027,0.00012567453,0.000057057357,0.00007244859,0.00014768154,0.0003067138,0.96673286,0.00005514037,0.000042915155,0.0031578806],"study_design_scores_gemma":[0.00021014013,0.008512812,0.0114039,0.000013439384,0.0002125866,0.00008266206,0.00020379254,0.0022140045,0.9766659,0.00007088662,0.0003710259,0.000038787475],"about_ca_topic_score_codex":0.0016100568,"about_ca_topic_score_gemma":0.0020403543,"teacher_disagreement_score":0.0016100568,"about_ca_system_score_codex":0.00025220765,"about_ca_system_score_gemma":0.000417622,"threshold_uncertainty_score":0.003973484},"labels":[],"label_agreement":null},{"id":"W2885923091","doi":"10.1108/bfj-02-2018-0112","title":"Exploring the visual appeal of food guide graphics","year":2018,"lang":"en","type":"article","venue":"British Food Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Usability; Graphics; Thematic analysis; Food choice; Originality; Computer science; Communication design; Qualitative research; Data science; Knowledge management; Multimedia; Human–computer interaction; Medicine; Sociology","score_opus":0.14356058403406727,"score_gpt":0.3047416468078687,"score_spread":0.16118106277380143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885923091","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":[0.9027069,0.0012549304,0.021117732,0.0021021443,0.00013714736,0.0003467275,0.0002259932,0.00015687267,0.0719517],"genre_scores_gemma":[0.98502594,0.00048395782,0.009891499,0.00042599582,0.000024875726,0.00022786741,0.00007968336,0.00008252716,0.0037576223],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9947136,0.0040120743,0.00010372118,0.00022234388,0.00077964267,0.00016857058],"domain_scores_gemma":[0.9722927,0.024256365,0.0009566121,0.0006938884,0.0015480211,0.00025238522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005845683,0.00044157336,0.00020576497,0.0017479606,0.0014394582,0.004871962,0.0006447389,0.00097175344,0.005639919],"category_scores_gemma":[0.038189594,0.0002283268,0.00035158888,0.0009191595,0.0039039594,0.003209229,0.0029680808,0.0009902352,0.00038451856],"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":[0.00077715877,0.00015918606,0.029440338,0.003237561,0.000053390824,0.0014184001,0.78076106,0.0011589193,0.012504844,0.036256008,0.005927626,0.12830557],"study_design_scores_gemma":[0.00007478905,0.000817381,0.063772686,0.003111209,0.0001759434,0.0024570297,0.73081326,0.0036499754,0.0058731087,0.024740223,0.16433565,0.00017867285],"about_ca_topic_score_codex":0.0032229836,"about_ca_topic_score_gemma":0.0037028824,"teacher_disagreement_score":0.005845683,"about_ca_system_score_codex":0.001531404,"about_ca_system_score_gemma":0.00079952565,"threshold_uncertainty_score":0.03091526},"labels":[],"label_agreement":null},{"id":"W2885938839","doi":"10.5539/jfr.v7n5p98","title":"Textural Hardness of Selected Ugandan Banana Cultivars under Different Processing Treatments","year":2018,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"African Development Bank Group","keywords":"Steaming; Food science; Chemistry; Mashing; Cultivar; Boiling; Horticulture; Biology","score_opus":0.19987216354079504,"score_gpt":0.422580498555205,"score_spread":0.22270833501440998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885938839","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":[0.99875414,0.00022415351,0.00017671735,0.0000066353396,0.0000030923654,0.000008390807,0.00015514345,0.0000071394024,0.00066454726],"genre_scores_gemma":[0.99637514,0.00015080711,0.00062947406,0.000022502649,0.0000024848835,0.000021376025,0.00040629558,0.000017112108,0.0023748346],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987876,0.000016201075,0.000009386664,0.000038741728,0.00003802966,0.00001886359],"domain_scores_gemma":[0.99968505,0.000054047887,0.00009398024,0.000017807573,0.000096759155,0.00005236293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015313951,0.0003443417,0.00029309132,0.00040232373,0.0002850414,0.000642061,0.000121921,0.00021771977,0.001517031],"category_scores_gemma":[0.00031542307,0.0002309555,0.00023322146,0.0004046514,0.00022851277,0.00034747118,0.0002815494,0.00053341384,0.00020179649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015183522,0.00007336885,0.0264818,0.00019041561,0.000071864735,0.00020975481,0.00070429867,0.00013539159,0.9652455,0.000045405162,0.00006840451,0.005255423],"study_design_scores_gemma":[0.000010036649,0.00081938313,0.89923227,0.000013446207,0.000069715476,0.00032474534,0.0008071984,0.000375472,0.096744284,0.000047965903,0.0015280306,0.000027494294],"about_ca_topic_score_codex":0.0013027244,"about_ca_topic_score_gemma":0.0030968823,"teacher_disagreement_score":0.001517031,"about_ca_system_score_codex":0.00019508248,"about_ca_system_score_gemma":0.00006988496,"threshold_uncertainty_score":0.005074978},"labels":[],"label_agreement":null},{"id":"W2886829222","doi":"10.1111/joss.12463","title":"Use of experienced panelists and the projective mapping task in comparison to trained panelists and naïve consumers","year":2018,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Psychology; Task (project management); Product (mathematics); Applied psychology; Population; Social psychology; Mathematics; Medicine; Engineering","score_opus":0.24732391016775584,"score_gpt":0.3796202335311885,"score_spread":0.13229632336343267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886829222","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":[0.9988727,0.000045740435,0.00014588403,0.000017418028,0.000008184167,0.000025602183,0.000021098218,0.000003760237,0.0008596501],"genre_scores_gemma":[0.99795246,0.00006591583,0.00047651405,0.00004678575,0.000011916589,0.000055378012,0.00009764537,0.000006418753,0.0012869966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968526,0.00126142,0.00026951334,0.0005563406,0.00071214826,0.0003479954],"domain_scores_gemma":[0.9785824,0.011182396,0.0036209945,0.0010588466,0.00362637,0.0019290296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004199894,0.0003705556,0.00040601188,0.0009176527,0.00059344195,0.0011186795,0.00044782294,0.0006491721,0.0033407088],"category_scores_gemma":[0.019511689,0.00031364275,0.0004228367,0.0003131432,0.0005663526,0.0008155241,0.00092253304,0.00062072306,0.0005063216],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004304387,0.0020049673,0.91733855,0.0002604168,0.00021158093,0.0008216818,0.024509849,0.00025153955,0.017213156,0.000107988315,0.00090902374,0.032066878],"study_design_scores_gemma":[0.000034474386,0.0022993786,0.98063344,0.00004413262,0.000055308057,0.0002798022,0.014207165,0.00046923384,0.0013319332,0.0000853986,0.0005222759,0.000037409176],"about_ca_topic_score_codex":0.0023052152,"about_ca_topic_score_gemma":0.004312847,"teacher_disagreement_score":0.004199894,"about_ca_system_score_codex":0.00029114183,"about_ca_system_score_gemma":0.00026921928,"threshold_uncertainty_score":0.022211432},"labels":[],"label_agreement":null},{"id":"W2887850400","doi":"10.24323/akademik-gida.449600","title":"Kırmızı ve Yeşil Mercimekten Elde Edilen Diyet Liflerinin Karakterizasyonu ve Fonksiyonel Özellikleri","year":2018,"lang":"tr","type":"article","venue":"Akademik Gıda","topic":"Sensory Analysis and Statistical Methods","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":"Stantec (Canada)","funders":"","keywords":"Medicine","score_opus":0.05785710588720603,"score_gpt":0.3106310891617134,"score_spread":0.25277398327450734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887850400","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":[0.72203815,0.025404707,0.12741455,0.005037123,0.0010004526,0.00047230616,0.0030687638,0.0031586778,0.11240535],"genre_scores_gemma":[0.9224123,0.007858641,0.026389007,0.00056401733,0.00014100628,0.00031877024,0.0015962113,0.0003181686,0.040401995],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987124,0.00016758044,0.000075179414,0.00026823857,0.00059718214,0.00017944121],"domain_scores_gemma":[0.9975442,0.0005560557,0.0004025009,0.00016106386,0.0011636646,0.00017235818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016395351,0.0011419985,0.0011622444,0.0015139544,0.0011799942,0.0036336866,0.00086121325,0.0016045413,0.017795477],"category_scores_gemma":[0.00377257,0.0004605758,0.0009821843,0.0012475353,0.0010130671,0.0024862753,0.0014769435,0.001752869,0.0039446107],"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":[0.0042893556,0.0005095985,0.0842602,0.0042595486,0.0004866986,0.0014721932,0.004996524,0.013985696,0.12816598,0.018973015,0.02034942,0.7182518],"study_design_scores_gemma":[0.0003173659,0.003103367,0.4278875,0.0023535842,0.0014364405,0.0025500325,0.022087738,0.04539885,0.15427282,0.03536968,0.30448177,0.0007407639],"about_ca_topic_score_codex":0.009501299,"about_ca_topic_score_gemma":0.009865355,"teacher_disagreement_score":0.017795477,"about_ca_system_score_codex":0.0017605091,"about_ca_system_score_gemma":0.0019323225,"threshold_uncertainty_score":0.059531808},"labels":[],"label_agreement":null},{"id":"W2896173061","doi":"10.1080/09571264.2018.1532879","title":"Investigating the use of partial napping with ultra-flash profiling to identify flavour differences in replicated, experimental wines","year":2018,"lang":"en","type":"article","venue":"Journal of Wine Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wine; Flavour; Replicate; Profiling (computer programming); Sensory analysis; Descriptive statistics; Food science; Sensory system; Statistics; Mathematics; Computer science; Chemistry; Psychology","score_opus":0.4205542444815173,"score_gpt":0.46277885780833744,"score_spread":0.04222461332682015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896173061","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":[0.9759587,0.0003578769,0.022183647,0.00003571469,0.000033463988,0.00025988603,0.00033575765,0.00009302764,0.0007419304],"genre_scores_gemma":[0.9572619,0.0002591799,0.04009429,0.00011355325,0.000018388348,0.0005137693,0.000664638,0.000065556575,0.0010086772],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988738,0.00026171273,0.00008339409,0.00039274097,0.00029383737,0.00009449121],"domain_scores_gemma":[0.9981371,0.00068423455,0.0002978747,0.00030925928,0.00043677894,0.00013473968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016095602,0.0005403537,0.0005733392,0.0005629722,0.0004719111,0.00078007526,0.0006013093,0.00048087054,0.00069659273],"category_scores_gemma":[0.0022892763,0.00031527726,0.0007151755,0.0005226466,0.0005839117,0.00073603285,0.00064765936,0.0007218933,0.00017549438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000336521,0.00012575135,0.00425077,0.00009910131,0.000055490953,0.00004207803,0.00017661009,0.0001662299,0.9870631,0.00005675961,0.000025985451,0.0076015433],"study_design_scores_gemma":[0.000025903468,0.0055989553,0.30764925,0.000031602645,0.00028759107,0.0003729091,0.00058218447,0.0045057437,0.67813694,0.0003745327,0.0023584163,0.00007601264],"about_ca_topic_score_codex":0.0015217041,"about_ca_topic_score_gemma":0.00312567,"teacher_disagreement_score":0.0016095602,"about_ca_system_score_codex":0.000386741,"about_ca_system_score_gemma":0.0003223535,"threshold_uncertainty_score":0.008512318},"labels":[],"label_agreement":null},{"id":"W2900872559","doi":"10.1016/j.foodqual.2018.11.016","title":"Application of TCATA to examine variation in beer perception due to thermal taste status","year":2018,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Taste; Variation (astronomy); Perception; Food science; Psychology; Advertising; Business; Chemistry; Physics","score_opus":0.17170560760184406,"score_gpt":0.3505041101435821,"score_spread":0.17879850254173807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900872559","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":[0.9422197,0.0002264984,0.051562786,0.00004598988,0.00014748782,0.0006228145,0.0015636671,0.00021607887,0.0033949742],"genre_scores_gemma":[0.9361717,0.0001601192,0.05467235,0.00013509003,0.00002381683,0.0025620419,0.0010764879,0.00013806878,0.0050602877],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990113,0.00033109324,0.00007802761,0.00022594127,0.00026547266,0.00008818023],"domain_scores_gemma":[0.99691236,0.0017046246,0.00023757399,0.00022674765,0.0007705815,0.0001481394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012728046,0.000475308,0.0005556192,0.0010395249,0.00055630226,0.0006771113,0.00035422033,0.00039930327,0.0028357801],"category_scores_gemma":[0.003993911,0.0002372244,0.00060742605,0.0017063073,0.0003236509,0.00026202438,0.00041661103,0.0009799117,0.00040342406],"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":[0.005884432,0.0012890829,0.06265506,0.0003629173,0.0004523687,0.000122068224,0.00083108933,0.00094488583,0.8525054,0.00074697274,0.00061674643,0.07358905],"study_design_scores_gemma":[0.00014278482,0.007771947,0.60171306,0.00003247038,0.0004079643,0.0006428213,0.0011800819,0.03055344,0.3522818,0.000601515,0.0045306897,0.0001414389],"about_ca_topic_score_codex":0.004686331,"about_ca_topic_score_gemma":0.0068293605,"teacher_disagreement_score":0.004686331,"about_ca_system_score_codex":0.0004210564,"about_ca_system_score_gemma":0.0006325994,"threshold_uncertainty_score":0.009486616},"labels":[],"label_agreement":null},{"id":"W2912392824","doi":"10.1002/0470013192.bsa584","title":"Scaling Asymmetric Matrices","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Asymmetry; Variety (cybernetics); Diagonal; Antisymmetric relation; Scaling; Multidimensional scaling; Mathematics; Statistical physics; Physics; Statistics; Geometry; Mathematical physics; Particle physics","score_opus":0.03002565984652082,"score_gpt":0.3293031580247486,"score_spread":0.29927749817822774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912392824","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":[0.07640117,0.0009054783,0.8357042,0.0010592351,0.00091073994,0.00022593314,0.0019736208,0.0010640594,0.08175559],"genre_scores_gemma":[0.7701884,0.0013391079,0.18127242,0.0011667549,0.0014051072,0.0005310727,0.0024334614,0.0004889094,0.041174687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973774,0.00062361587,0.00019752007,0.0007859365,0.0007805807,0.00023501451],"domain_scores_gemma":[0.9902956,0.0030375011,0.0016091036,0.0024278339,0.002101645,0.00052827183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013544997,0.0010134581,0.0007784717,0.0026653328,0.0010684462,0.0028209023,0.0010440148,0.00094330683,0.028638294],"category_scores_gemma":[0.016810203,0.00042433615,0.00070119876,0.0023631374,0.0023442016,0.0041103666,0.0017029997,0.0011882428,0.0052831606],"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":[0.00006761943,0.000030012809,0.0009259653,0.00010984177,0.000029348304,0.00022578966,0.00023514775,0.0065509877,0.0032847612,0.9281877,0.012060579,0.048292235],"study_design_scores_gemma":[0.000016957454,0.000034019133,0.00069140043,0.000038093338,0.000009463454,0.00031273213,0.0000985621,0.03596231,0.0013253517,0.94523937,0.016243247,0.000028499157],"about_ca_topic_score_codex":0.00086269993,"about_ca_topic_score_gemma":0.000866733,"teacher_disagreement_score":0.028638294,"about_ca_system_score_codex":0.0006529083,"about_ca_system_score_gemma":0.0005624131,"threshold_uncertainty_score":0.09580469},"labels":[],"label_agreement":null},{"id":"W2913402591","doi":"10.1002/9781118445112.stat06496","title":"Multitrait–Multimethod Analyses","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Statistical analysis; Linear discriminant analysis; Set (abstract data type); Mathematics; Computer science; Data mining; Statistics","score_opus":0.1688339833671556,"score_gpt":0.40892162324430786,"score_spread":0.24008763987715226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913402591","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":[0.017367458,0.0011805585,0.94804174,0.0008020225,0.001062449,0.0065289596,0.009989561,0.0029671725,0.012060073],"genre_scores_gemma":[0.105300866,0.00078189245,0.85277647,0.00052112195,0.0002969391,0.021720545,0.0054989667,0.002210278,0.010892876],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92232454,0.04827236,0.006118552,0.009207763,0.012767015,0.0013097514],"domain_scores_gemma":[0.846785,0.10104576,0.008211744,0.025640273,0.017597172,0.0007199978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060082074,0.0025349774,0.0030173454,0.007026159,0.0020695599,0.0049281167,0.0042942306,0.0014536371,0.036314704],"category_scores_gemma":[0.21661244,0.0010456523,0.0044417665,0.010878843,0.0022098066,0.0034578587,0.003828486,0.0036402505,0.006062284],"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":[0.0011697178,0.0014581667,0.037362747,0.008289725,0.006606309,0.00069801917,0.0068605123,0.0154420715,0.0045748698,0.15130435,0.10357836,0.66265523],"study_design_scores_gemma":[0.00041305728,0.0024616702,0.09804523,0.0040642526,0.0027030718,0.0009838796,0.0050912974,0.15042321,0.018546155,0.3219116,0.394629,0.0007276839],"about_ca_topic_score_codex":0.0039310004,"about_ca_topic_score_gemma":0.006296313,"teacher_disagreement_score":0.060082074,"about_ca_system_score_codex":0.001974007,"about_ca_system_score_gemma":0.0056501273,"threshold_uncertainty_score":0.3177482},"labels":[],"label_agreement":null},{"id":"W2915111008","doi":"10.1016/j.foodqual.2019.02.007","title":"Liking and consumption of vegetables with more appealing and less appealing sensory properties: Associations with attitudes, food neophobia and food choice motivations in European adolescents","year":2019,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Erzincan Üniversitesi","keywords":"Neophobia; Consumption (sociology); Food choice; Psychology; Food science; Sensory system; Environmental health; Medicine; Developmental psychology; Biology","score_opus":0.21131807844967704,"score_gpt":0.30534024264344306,"score_spread":0.09402216419376602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915111008","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":[0.99976164,0.000053239764,0.000015802209,0.000011169637,0.0000014319951,0.0000021187827,0.000028913153,5.185467e-7,0.0001251535],"genre_scores_gemma":[0.99963796,0.000040926774,0.000042063868,0.000014418668,0.0000015697034,0.0000036784631,0.000046383848,9.582867e-7,0.00021217298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99972767,0.000076404634,0.000028088858,0.0000616205,0.000047426336,0.00005878451],"domain_scores_gemma":[0.9988073,0.0002958981,0.00044952452,0.000044152875,0.000092475915,0.00031060437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006448077,0.00027900975,0.000281213,0.0006298882,0.0003453648,0.0009850365,0.00019609474,0.0007327152,0.001970478],"category_scores_gemma":[0.0014183708,0.0003395636,0.0006354543,0.0005022735,0.00044321595,0.00041032542,0.0004519657,0.0008231021,0.00020824773],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018371819,0.00026502486,0.9971873,0.000011002662,0.00005681313,0.000057765126,0.0005024068,0.00003402894,0.00052571954,0.000040905157,0.000029739052,0.0011056345],"study_design_scores_gemma":[0.0000036833965,0.000108189466,0.9988617,0.0000037331827,0.000015617969,0.00007702352,0.0006920337,0.00010488193,0.000055477747,0.000021150605,0.00005318915,0.0000032731764],"about_ca_topic_score_codex":0.0066164397,"about_ca_topic_score_gemma":0.0076527474,"teacher_disagreement_score":0.0066164397,"about_ca_system_score_codex":0.0002696192,"about_ca_system_score_gemma":0.00028780053,"threshold_uncertainty_score":0.013155878},"labels":[],"label_agreement":null},{"id":"W2922270433","doi":"10.1017/s1368980019000302","title":"Using multiple correspondence analysis to identify behaviour patterns associated with overweight and obesity in Vanuatu adults","year":2019,"lang":"en","type":"article","venue":"Public Health Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Université du Québec à Montréal","funders":"Binghamton University; Temple University; Wenner-Gren Foundation","keywords":"Overweight; Obesity; Percentile; Categorical variable; Demography; Modernization theory; Multiple correspondence analysis; Medicine; Scale (ratio); Gerontology; Geography; Statistics; Mathematics; Cartography; Internal medicine; Political science; Sociology","score_opus":0.07543773952732571,"score_gpt":0.34808772550731754,"score_spread":0.2726499859799918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922270433","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":[0.9970018,0.00007718984,0.0020287922,0.000033696862,0.0000059316203,0.00006498227,0.00015938803,0.000009306705,0.0006188777],"genre_scores_gemma":[0.9969903,0.000033642656,0.0026427135,0.0000063739335,0.0000043732475,0.00007317699,0.00012192891,0.0000023109021,0.00012505344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969934,0.001997578,0.00019215696,0.00033684692,0.00033810484,0.00014182515],"domain_scores_gemma":[0.99499977,0.0025473153,0.0014806932,0.0002743046,0.0005418598,0.00015613734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031576979,0.00036828968,0.00027758148,0.0026658892,0.0006743483,0.0007392157,0.0003108552,0.00024368806,0.0013545591],"category_scores_gemma":[0.013484149,0.00016861658,0.00047685736,0.0022737256,0.0003267216,0.00044976926,0.0009601333,0.00027837206,0.00012365879],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002666507,0.0001238214,0.97028244,0.00007971815,0.00020644606,0.0000429389,0.0020594778,0.0004203673,0.0006159533,0.00020728531,0.00020446593,0.025490481],"study_design_scores_gemma":[0.000015993923,0.00028076113,0.9891879,0.000034731172,0.000058806243,0.00009147103,0.0036127728,0.0055521987,0.0004448796,0.00030035936,0.00040338328,0.000016765307],"about_ca_topic_score_codex":0.015053888,"about_ca_topic_score_gemma":0.023243722,"teacher_disagreement_score":0.015053888,"about_ca_system_score_codex":0.00048963726,"about_ca_system_score_gemma":0.0007711117,"threshold_uncertainty_score":0.029932559},"labels":[],"label_agreement":null},{"id":"W2927267696","doi":"10.1101/598888","title":"A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Baycrest Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Partial least squares regression; Categorical variable; Multivariate statistics; Ordinal data; Partial correlation; Regression; Generalization; Regression analysis; Canonical correlation; Data mining; Computer science; Statistics; Mathematics; Artificial intelligence; Correlation","score_opus":0.06977155887095884,"score_gpt":0.2958783058451518,"score_spread":0.22610674697419295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927267696","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":[0.0042827846,0.00010198911,0.99274313,0.0003062796,0.000055440792,0.00008433306,0.0005572627,0.00157323,0.00029553156],"genre_scores_gemma":[0.05999474,0.0001226564,0.93585694,0.00021714566,0.00012443431,0.00040116112,0.0010511038,0.0008521788,0.0013796741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.985565,0.008908711,0.0005183292,0.0029265305,0.0018063245,0.0002751734],"domain_scores_gemma":[0.9833055,0.009886656,0.00095619354,0.0035141103,0.001989083,0.0003484654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019747099,0.0018837095,0.0015406221,0.0032594814,0.0014997707,0.0019174479,0.0028592001,0.0016340697,0.004972153],"category_scores_gemma":[0.040985383,0.0006737823,0.004519584,0.0051710266,0.0016306806,0.0019372645,0.0033271317,0.004077305,0.0026084238],"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":[0.0005289499,0.0005366951,0.013400869,0.00046440467,0.0016370056,0.0006638417,0.0007432229,0.19961627,0.008498531,0.094448105,0.036313448,0.64314854],"study_design_scores_gemma":[0.00006611246,0.00010603702,0.0028638314,0.000027540067,0.00007056849,0.00030673924,0.0000996481,0.9120989,0.0020414295,0.071451195,0.010786332,0.000081583894],"about_ca_topic_score_codex":0.00763433,"about_ca_topic_score_gemma":0.010071976,"teacher_disagreement_score":0.019747099,"about_ca_system_score_codex":0.0010568586,"about_ca_system_score_gemma":0.0037417712,"threshold_uncertainty_score":0.104433835},"labels":[],"label_agreement":null},{"id":"W2940670759","doi":"","title":"Happiness Ingredients Detection using Multi-Task Deep Learning.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Happiness; Task (project management); Computer science; Artificial intelligence; Deep learning; Machine learning; Psychology; Engineering; Social psychology","score_opus":0.22129384116215908,"score_gpt":0.37219621312787804,"score_spread":0.15090237196571896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940670759","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":[0.5908232,0.005242406,0.37473655,0.0008956066,0.0007985016,0.00028430833,0.0051971315,0.002668267,0.019354071],"genre_scores_gemma":[0.934161,0.00086352753,0.053178415,0.00030055226,0.00010836258,0.00014762265,0.0030059626,0.00010171474,0.008132858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998672,0.00002539637,0.000006084529,0.0000388319,0.000027650305,0.000034789908],"domain_scores_gemma":[0.99984646,0.00003587483,0.00002330287,0.000012628367,0.000062363375,0.000019267569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003554918,0.00064960134,0.00041804338,0.0007716818,0.00022383321,0.00047782678,0.0004333286,0.0006352388,0.0028696219],"category_scores_gemma":[0.00072732504,0.00017318709,0.00054000644,0.0003468111,0.0001236898,0.00053456397,0.00084412144,0.0005896127,0.00090749783],"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":[0.0037739328,0.0009855728,0.03243584,0.0005851336,0.0005432574,0.0005628146,0.00016786852,0.014999017,0.12792118,0.0019649519,0.040292222,0.7757682],"study_design_scores_gemma":[0.00009922175,0.0005847708,0.07961606,0.00012693886,0.0002971747,0.0004970526,0.00043271208,0.8372955,0.0628709,0.006187471,0.011891155,0.000101062025],"about_ca_topic_score_codex":0.0017154654,"about_ca_topic_score_gemma":0.003764693,"teacher_disagreement_score":0.0028696219,"about_ca_system_score_codex":0.0002380364,"about_ca_system_score_gemma":0.0001886312,"threshold_uncertainty_score":0.0095998645},"labels":[],"label_agreement":null},{"id":"W2941963750","doi":"10.1111/joss.12503","title":"Applying temporal check‐all‐that‐apply (TCATA) to mouthfeel and texture properties of red wines","year":2019,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Mouthfeel; Wine; Wine color; Texture (cosmology); Winemaking; Artificial intelligence; Mathematics; Food science; Computer science; Chemistry","score_opus":0.15378143345300177,"score_gpt":0.3188339890238269,"score_spread":0.16505255557082515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941963750","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":[0.98812056,0.00019453393,0.010648496,0.00001702757,0.000028686698,0.000099803234,0.00017315694,0.000060674916,0.0006570497],"genre_scores_gemma":[0.9846206,0.00019440398,0.013819446,0.00005329621,0.000016522996,0.00009814254,0.0002524908,0.00003142759,0.0009136927],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996099,0.00007659371,0.000025747764,0.000102948834,0.00014922846,0.000035715337],"domain_scores_gemma":[0.99850345,0.0005099131,0.0002359589,0.0001392305,0.00050860643,0.00010287127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073241536,0.00039152318,0.00034757584,0.00071609864,0.00028089923,0.0003727599,0.00020302723,0.00034218546,0.0013220607],"category_scores_gemma":[0.0019124812,0.00014944002,0.0004209623,0.0004103072,0.0002554164,0.00021575762,0.00041147709,0.00040785605,0.00021228372],"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":[0.0014762145,0.00027352732,0.030390214,0.0002472174,0.000089074005,0.00006968128,0.00031732747,0.0006958847,0.92663765,0.00006712152,0.00017663838,0.039559454],"study_design_scores_gemma":[0.000044306667,0.004602093,0.5630934,0.000034118308,0.0002348221,0.0007178762,0.0005846425,0.013701032,0.41477707,0.00021575858,0.0018853486,0.00010955656],"about_ca_topic_score_codex":0.0015673887,"about_ca_topic_score_gemma":0.0031681408,"teacher_disagreement_score":0.0015673887,"about_ca_system_score_codex":0.00014716433,"about_ca_system_score_gemma":0.00018389641,"threshold_uncertainty_score":0.0044227242},"labels":[],"label_agreement":null},{"id":"W2945261981","doi":"10.2427/13059","title":"Continuity correction of Pearson’s chi-square test in 2x2 Contingency Tables: A mini-review on recent development","year":2022,"lang":"en","type":"article","venue":"Epidemiology Biostatistics and Public Health","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"University of Alberta","funders":"","keywords":"Contingency table; Pearson's chi-squared test; Chi-square test; Pearson product-moment correlation coefficient; Statistic; Nonparametric statistics; Continuity correction; Statistics; Test (biology); Mathematics; Test statistic; Square (algebra); Statistical hypothesis testing; Calculus (dental); Poisson distribution; Medicine","score_opus":0.14208620616154058,"score_gpt":0.3718474289774695,"score_spread":0.2297612228159289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945261981","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":[0.0013128171,0.9319423,0.055616632,0.0047430317,0.0036515782,0.00011605177,0.0003674713,0.00036666964,0.0018834854],"genre_scores_gemma":[0.020592032,0.88703,0.07794136,0.0027366083,0.007537468,0.00039653046,0.0010599417,0.0004951637,0.0022108702],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98657507,0.005286668,0.002100197,0.001894538,0.003890613,0.000253006],"domain_scores_gemma":[0.8712689,0.10417696,0.0067101317,0.0032629748,0.013886621,0.00069435587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024262907,0.00122954,0.0029185556,0.005467856,0.00066603295,0.0032247794,0.0045168158,0.0018768642,0.006401616],"category_scores_gemma":[0.07482502,0.000793088,0.002682826,0.007022202,0.0035447513,0.003868496,0.0015469186,0.004142788,0.0024691077],"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":[0.00017169525,0.00005580288,0.0045307307,0.011241477,0.00039165278,0.0002732832,0.00032268476,0.0006830767,0.00041791698,0.013418402,0.025680454,0.9428128],"study_design_scores_gemma":[0.00013399482,0.00068718754,0.018188462,0.021647658,0.0016925348,0.007955586,0.0009405688,0.0045368373,0.0035656416,0.048734058,0.89144284,0.00047463493],"about_ca_topic_score_codex":0.003962082,"about_ca_topic_score_gemma":0.0028459611,"teacher_disagreement_score":0.024262907,"about_ca_system_score_codex":0.001582049,"about_ca_system_score_gemma":0.0039858157,"threshold_uncertainty_score":0.12831604},"labels":[],"label_agreement":null},{"id":"W2945785681","doi":"10.7717/peerj.6853","title":"Exploring perceptions of meaningfulness in visual representations of bivariate relationships","year":2019,"lang":"en","type":"article","venue":"PeerJ","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"York University","funders":"","keywords":"Bivariate analysis; Psychology; Correlation; Equivalence (formal languages); Perception; Cognitive psychology; Social psychology; Association (psychology); Context (archaeology); Statistics; Mathematics; Geography","score_opus":0.21928762006823788,"score_gpt":0.34489195174752474,"score_spread":0.12560433167928686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945785681","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":[0.74391705,0.0012351809,0.20736763,0.0021224064,0.0003560922,0.00044243547,0.00046976906,0.00092008023,0.04316943],"genre_scores_gemma":[0.9585771,0.0002618359,0.039560348,0.00018018401,0.00005494511,0.00019751843,0.00017680363,0.00016135827,0.00082984625],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99079037,0.005554923,0.00050237315,0.00087313476,0.0019280111,0.00035115375],"domain_scores_gemma":[0.90861607,0.07142889,0.0065676896,0.0054890774,0.006489122,0.0014091539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018964224,0.00081551983,0.0005150941,0.004189608,0.0012205022,0.006297032,0.0009520928,0.0014131328,0.007956935],"category_scores_gemma":[0.13880181,0.00057474565,0.00075387926,0.0013976237,0.004940582,0.008552773,0.004287469,0.0020687329,0.0004823996],"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":[0.002797333,0.00044091465,0.08762727,0.003524343,0.00046936437,0.0017058642,0.3976971,0.010659614,0.07493231,0.13743497,0.009394488,0.27331644],"study_design_scores_gemma":[0.00037126563,0.0035879186,0.25602004,0.003549913,0.0007218179,0.0041653225,0.28228733,0.0645815,0.016223997,0.2808832,0.086572744,0.0010349547],"about_ca_topic_score_codex":0.0014293281,"about_ca_topic_score_gemma":0.0018623299,"teacher_disagreement_score":0.018964224,"about_ca_system_score_codex":0.0012896721,"about_ca_system_score_gemma":0.00082527014,"threshold_uncertainty_score":0.10029358},"labels":[],"label_agreement":null},{"id":"W2946837779","doi":"10.3390/foods8060184","title":"Characterizing the Dynamic Textural Properties of Hydrocolloids in Pureed Foods—A Comparison Between TDS and TCATA","year":2019,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Food science; Chemistry","score_opus":0.04927070442766909,"score_gpt":0.2813451180766907,"score_spread":0.23207441364902162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946837779","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":[0.9977982,0.00016883484,0.0014209369,0.000004082339,0.000004202854,0.00005499413,0.00014995258,0.000007751862,0.00039104134],"genre_scores_gemma":[0.99211735,0.0002500188,0.00620984,0.000021567992,0.000009498386,0.00012993091,0.0005091485,0.000012409384,0.00074026047],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99933064,0.00012369963,0.00010045993,0.00013980093,0.00027278063,0.00003269622],"domain_scores_gemma":[0.9975508,0.0008595107,0.000407578,0.00019187038,0.00080367277,0.0001866288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010362634,0.00041994627,0.00036773467,0.0011514609,0.00026845778,0.00049837935,0.00020926884,0.00028476462,0.0009971315],"category_scores_gemma":[0.001783551,0.00023826599,0.00047860597,0.00076864555,0.0004086001,0.00041655623,0.00052684004,0.00035697946,0.00014123872],"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":[0.0022184115,0.00030516266,0.026130218,0.00029102058,0.00019401057,0.00007963924,0.0005865186,0.00032296372,0.95564014,0.000051702387,0.000042346048,0.014137957],"study_design_scores_gemma":[0.00007164507,0.010198679,0.6533607,0.000028998562,0.00039173343,0.0006629256,0.0014691406,0.003166333,0.3290125,0.0001609148,0.0014045401,0.00007184075],"about_ca_topic_score_codex":0.001076554,"about_ca_topic_score_gemma":0.0025871708,"teacher_disagreement_score":0.0011514609,"about_ca_system_score_codex":0.00023042496,"about_ca_system_score_gemma":0.00017703879,"threshold_uncertainty_score":0.0054802895},"labels":[],"label_agreement":null},{"id":"W2947077721","doi":"10.1016/j.foodres.2019.05.037","title":"A comparison of sensory attribute profiles and liking between regular and sodium-reduced food products","year":2019,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Food science; Taste; Sensory system; Sodium; Chemistry; Psychology","score_opus":0.2654444946648232,"score_gpt":0.42985248204339843,"score_spread":0.1644079873785752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947077721","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":[0.9993461,0.000036968147,0.00021638119,0.000007591678,0.0000039749393,0.00001021461,0.000096644835,0.0000043800455,0.00027766914],"genre_scores_gemma":[0.99900013,0.000035166155,0.0004196495,0.000011641032,0.000001872214,0.000009277554,0.00018099611,0.000003398723,0.00033782583],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997738,0.0000556305,0.000021712765,0.00004202736,0.00007825185,0.000028586712],"domain_scores_gemma":[0.9993563,0.00020521028,0.000099735385,0.000044920704,0.00017195495,0.000121914556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000648882,0.00021608206,0.00024478158,0.00049425423,0.00020006477,0.00038068157,0.00015312593,0.00021796278,0.001972839],"category_scores_gemma":[0.0011465652,0.00012760237,0.0005272658,0.0003354259,0.00023484456,0.0004231782,0.00030403738,0.00037660572,0.0001846111],"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":[0.058418017,0.0023506104,0.26917243,0.00050114107,0.0007537744,0.000298968,0.001930626,0.00054010417,0.5994972,0.00032080428,0.0005205857,0.06569573],"study_design_scores_gemma":[0.0001229401,0.014484392,0.9393678,0.000021162869,0.00023581895,0.00043488163,0.001968444,0.0014315377,0.040825713,0.00017194227,0.00088798674,0.000047344678],"about_ca_topic_score_codex":0.0013780528,"about_ca_topic_score_gemma":0.0012852388,"teacher_disagreement_score":0.001972839,"about_ca_system_score_codex":0.00012971973,"about_ca_system_score_gemma":0.00013930931,"threshold_uncertainty_score":0.006599784},"labels":[],"label_agreement":null},{"id":"W2947239006","doi":"10.1093/jn/nxz050","title":"Food Type and Lentil Variety Affect Satiety Responses but Not Food Intake in Healthy Adults When Lentils Are Substituted for Commonly Consumed Carbohydrates","year":2019,"lang":"en","type":"article","venue":"Journal of Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Agriculture and Agri-Food Canada; University of Guelph","funders":"Agriculture and Agri-Food Canada","keywords":"Appetite; Meal; Food science; Crossover study; Food intake; Medicine; Obesity; Animal science; Biology; Internal medicine","score_opus":0.05844239348911573,"score_gpt":0.292042626019586,"score_spread":0.2336002325304703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947239006","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":[0.99977356,0.000029867891,0.000026504118,0.0000073092992,0.0000032400967,0.000003136291,0.000041062052,0.0000020634272,0.00011317102],"genre_scores_gemma":[0.9992003,0.000030776133,0.00011579698,0.000034862154,0.0000030171273,0.000009072794,0.000088411754,0.0000022477197,0.0005156523],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999118,0.000020984524,0.0000066177627,0.000026811682,0.000017675782,0.000016094917],"domain_scores_gemma":[0.99970144,0.00007005619,0.000083378465,0.000020041063,0.000020818074,0.00010418492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023082847,0.00021587942,0.00024484555,0.0001594696,0.0001620934,0.00030797522,0.00010938387,0.00023929136,0.0017020297],"category_scores_gemma":[0.00054852525,0.00016873189,0.00020189118,0.00010805996,0.00022402301,0.00022505877,0.00018865026,0.0003168659,0.00015868885],"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":[0.100458205,0.0069044665,0.5833573,0.00021105715,0.00054713,0.00047990092,0.00083480764,0.00019767662,0.2794908,0.00010857497,0.0008002212,0.026609965],"study_design_scores_gemma":[0.00008690396,0.0034175185,0.9933808,0.0000050149274,0.00005877676,0.00008824926,0.00014669288,0.00024710334,0.0024353662,0.000020514566,0.00010628663,0.000006638627],"about_ca_topic_score_codex":0.0020994553,"about_ca_topic_score_gemma":0.0041560507,"teacher_disagreement_score":0.0020994553,"about_ca_system_score_codex":0.00013337396,"about_ca_system_score_gemma":0.000088491324,"threshold_uncertainty_score":0.0056939125},"labels":[],"label_agreement":null},{"id":"W2947323594","doi":"10.1111/joss.12524","title":"Development and validation of a color evaluation process for sweet potato preference characterization","year":2019,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vineland Research and Innovation Centre","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Hue; Mathematics; Species evenness; Quantitative Descriptive Analysis; Food science; Artificial intelligence; Flavor; Statistics; Psychology; Computer science; Chemistry; Biology","score_opus":0.239011424826396,"score_gpt":0.37967066641542474,"score_spread":0.14065924158902873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947323594","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":[0.35655823,0.0000898487,0.6337782,0.0001111753,0.000043096596,0.0034138027,0.000609726,0.0017888787,0.0036069804],"genre_scores_gemma":[0.40868992,0.00007776918,0.58574426,0.00006173418,0.000017776018,0.0024706183,0.001022753,0.0002654534,0.0016497615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9923152,0.0035114202,0.000634655,0.0009976944,0.0023097144,0.00023135073],"domain_scores_gemma":[0.9715218,0.00916106,0.0014699865,0.002891587,0.014324883,0.00063069706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018340908,0.0009386628,0.00062357937,0.0030261169,0.0009126863,0.0014305933,0.0010409747,0.0006066348,0.0019388489],"category_scores_gemma":[0.02815436,0.00041712268,0.00082676916,0.0009723164,0.00071909017,0.0010856217,0.0016713142,0.0007096562,0.00087180256],"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":[0.0014038247,0.0017694555,0.068960324,0.0005749835,0.00011908475,0.0002618215,0.007933576,0.009550268,0.32793793,0.0029261466,0.0019011342,0.57666147],"study_design_scores_gemma":[0.00029821496,0.005112226,0.23746523,0.00043123733,0.00033485593,0.00076951995,0.007073732,0.22332233,0.50017476,0.003881186,0.020695563,0.00044111582],"about_ca_topic_score_codex":0.0028019715,"about_ca_topic_score_gemma":0.003181074,"teacher_disagreement_score":0.018340908,"about_ca_system_score_codex":0.0009100976,"about_ca_system_score_gemma":0.00241808,"threshold_uncertainty_score":0.09699714},"labels":[],"label_agreement":null},{"id":"W2950680445","doi":"10.1111/joss.12526","title":"No rose without a thorn: Hedonic testing of live rose plants","year":2019,"lang":"en","type":"article","venue":"Journal of Sensory Studies","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":"Vineland Research and Innovation Centre","funders":"","keywords":"Rose (mathematics); Advertising; Psychology; Horticulture; Biology; Business","score_opus":0.1219909523658462,"score_gpt":0.33833103780821544,"score_spread":0.21634008544236924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950680445","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":[0.99818254,0.000012864876,0.00039940164,0.000013263697,0.0000067790866,0.00004089529,0.000035412384,0.000007629816,0.0013013036],"genre_scores_gemma":[0.99477994,0.000039577983,0.0025807915,0.000060323728,0.0000067002075,0.000046613186,0.00007634533,0.000009710738,0.0023999952],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997757,0.00006728871,0.000012150905,0.00004417866,0.000077714445,0.00002294839],"domain_scores_gemma":[0.9990891,0.0004265217,0.00011625534,0.000115595874,0.00013539812,0.00011705567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005254621,0.00024832928,0.00018227081,0.00013337778,0.00025194735,0.0004484306,0.00022465528,0.00027571514,0.006042935],"category_scores_gemma":[0.001669294,0.00012634223,0.0002305775,0.0001040236,0.00018500723,0.00045483588,0.0002980852,0.00052126776,0.00046716415],"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":[0.006384301,0.013296206,0.13192996,0.00075137656,0.00014396674,0.0013038781,0.008242764,0.00070046016,0.7200834,0.0005207091,0.002469293,0.11417376],"study_design_scores_gemma":[0.00015257948,0.03354435,0.81444055,0.000071748786,0.0001303191,0.0015238127,0.007987079,0.0034995058,0.13307132,0.0004761168,0.0050137304,0.00008885761],"about_ca_topic_score_codex":0.00076389627,"about_ca_topic_score_gemma":0.0020188054,"teacher_disagreement_score":0.006042935,"about_ca_system_score_codex":0.00013866165,"about_ca_system_score_gemma":0.00007983203,"threshold_uncertainty_score":0.02021563},"labels":[],"label_agreement":null},{"id":"W2953578726","doi":"10.1111/joss.12528","title":"Partial projective mapping and ultra‐flash profile with and without red light: A case study with white wine","year":2019,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Research Nova Scotia; Acadia University; Department of Agriculture, Nova Scotia","keywords":"Flavor; White Wine; Wine; Mouthfeel; Psychology; Sensory system; Categorization; Food science; Artificial intelligence; Cognitive psychology; Computer science; Chemistry","score_opus":0.06221904153438789,"score_gpt":0.30934913545240383,"score_spread":0.24713009391801594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953578726","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":[0.99627316,0.00029680596,0.0011680505,0.0004185545,0.000041196246,0.000056010384,0.000024862136,0.000009337895,0.0017120112],"genre_scores_gemma":[0.99651706,0.0003256863,0.0011588777,0.0002005443,0.00003420539,0.000018365241,0.000017840854,0.000019510024,0.0017077302],"study_design_codex":"case_report","study_design_gemma":"observational","domain_scores_codex":[0.9990127,0.00030268688,0.00006455673,0.00015702948,0.00023732305,0.00022566337],"domain_scores_gemma":[0.99887174,0.00048128117,0.00015039233,0.0000749891,0.00012165305,0.00029984713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014721209,0.0010945951,0.00074413174,0.0014672825,0.0031990982,0.0017421816,0.0008878321,0.0021651438,0.0017099833],"category_scores_gemma":[0.0036295196,0.0006481474,0.0010732781,0.0011049759,0.0017757034,0.0010521155,0.0013768728,0.0020990537,0.00032643235],"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":[0.00033317387,0.0016401074,0.053956997,0.00020149429,0.00006621424,0.89420414,0.030916631,0.00036762352,0.004485062,0.00091266626,0.0006880627,0.012227752],"study_design_scores_gemma":[0.000042803822,0.0014395784,0.050131686,0.00014612942,0.00009402291,0.8829739,0.050886218,0.0027372744,0.005564981,0.0011547976,0.0047249584,0.000103658036],"about_ca_topic_score_codex":0.0059099197,"about_ca_topic_score_gemma":0.01157109,"teacher_disagreement_score":0.0059099197,"about_ca_system_score_codex":0.0014024642,"about_ca_system_score_gemma":0.0006497728,"threshold_uncertainty_score":0.011751056},"labels":[],"label_agreement":null},{"id":"W2956411469","doi":"10.1016/j.jneb.2019.05.469","title":"P93 Utilizing Sensory Evaluation Methodology in Recipe Development for Expanded Food and Nutrition Education Program (EFNEP)","year":2019,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Recipe; Sensory system; Food science; Computer science; Psychology; Chemistry; Cognitive psychology","score_opus":0.21940025673990943,"score_gpt":0.4385711027164145,"score_spread":0.21917084597650505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956411469","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":[0.47089645,0.0002927742,0.46271542,0.00019470918,0.00015225791,0.010961289,0.0051382906,0.0019828682,0.04766596],"genre_scores_gemma":[0.28112927,0.00019505412,0.6974332,0.00010370634,0.000008763798,0.0045808908,0.0016693685,0.0002510133,0.014628716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790996,0.000935569,0.00015443022,0.00020685593,0.0006995977,0.000093537405],"domain_scores_gemma":[0.9974452,0.00066867686,0.00015833136,0.00020390614,0.0014570743,0.00006672006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060224426,0.00051300845,0.0002935466,0.001098595,0.0006258526,0.00096021383,0.00040879843,0.00026312642,0.009162775],"category_scores_gemma":[0.00497988,0.00032627775,0.00059184426,0.0011235875,0.000246732,0.00047371472,0.00084438646,0.00045103236,0.0013765543],"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":[0.0015809098,0.002121465,0.037568267,0.0008157232,0.000115161914,0.00015939807,0.0010874956,0.0024590157,0.14051646,0.002381603,0.0065397094,0.80465484],"study_design_scores_gemma":[0.000251693,0.0063414653,0.39076492,0.00041024113,0.00034761088,0.00058787,0.0022882102,0.05115264,0.48462877,0.0026620068,0.06036033,0.00020421353],"about_ca_topic_score_codex":0.0039454107,"about_ca_topic_score_gemma":0.012446352,"teacher_disagreement_score":0.009162775,"about_ca_system_score_codex":0.0004306006,"about_ca_system_score_gemma":0.0016141022,"threshold_uncertainty_score":0.0318501},"labels":[],"label_agreement":null},{"id":"W2968485389","doi":"10.5539/ijsp.v8n5p58","title":"A Bivariate Index for Visually Measuring Marginal Inhomogeneity in Square Tables","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Homogeneity (statistics); Bivariate analysis; Mathematics; Marginal distribution; Statistics; Standard deviation; Random variable","score_opus":0.042209170289468245,"score_gpt":0.30459698652407113,"score_spread":0.2623878162346029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968485389","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":[0.10605293,0.0005020019,0.88500357,0.0001308075,0.00006486392,0.00018200181,0.0009861216,0.0014089859,0.005668728],"genre_scores_gemma":[0.7847705,0.0002666867,0.21244149,0.00007521297,0.00006323904,0.00024181527,0.0009127353,0.00020252922,0.0010258177],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99698573,0.0009456628,0.00033669255,0.0004330084,0.0011223203,0.0001765401],"domain_scores_gemma":[0.9837173,0.009131253,0.002220477,0.0017237734,0.0027545597,0.0004526497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041469014,0.0006732973,0.00075074134,0.004226092,0.00057807105,0.0019684732,0.0009141145,0.00064975704,0.003929813],"category_scores_gemma":[0.028087411,0.00031510057,0.00078365003,0.003508813,0.0011704214,0.0028028695,0.0017791985,0.00097367686,0.00066536263],"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":[0.0023515965,0.00037680482,0.15383558,0.0011114307,0.0006356784,0.00056094676,0.002536644,0.09700695,0.053854216,0.06707167,0.0076292,0.61302936],"study_design_scores_gemma":[0.00015061126,0.0013804446,0.1810392,0.00027196854,0.00052674836,0.001849625,0.0026156097,0.6524508,0.04945127,0.09035897,0.01926861,0.0006361577],"about_ca_topic_score_codex":0.0019071992,"about_ca_topic_score_gemma":0.0013890127,"teacher_disagreement_score":0.004226092,"about_ca_system_score_codex":0.00082982995,"about_ca_system_score_gemma":0.00074905274,"threshold_uncertainty_score":0.021931171},"labels":[],"label_agreement":null},{"id":"W2969293129","doi":"10.1016/j.jneb.2019.07.012","title":"Using Sensory Science to Evaluate Consumer Acceptance of Recipes in a Nutrition Education Intervention for Limited Resource Populations","year":2019,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Georgia","keywords":"Psychological intervention; Curriculum; Intervention (counseling); Session (web analytics); Recipe; Nutrition Education; Scale (ratio); Psychology; Quality (philosophy); Descriptive statistics; Medical education; Medicine; Gerontology; Food science; Pedagogy; Advertising","score_opus":0.14492204823825297,"score_gpt":0.43412825450839143,"score_spread":0.2892062062701385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969293129","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":[0.9990602,0.000015668651,0.00021314803,0.000027888394,0.0000053067542,0.00013229426,0.00002955481,0.00000537919,0.0005104327],"genre_scores_gemma":[0.99702126,0.00005831428,0.0018736911,0.000055714445,0.000004645313,0.0003579373,0.0000662503,0.0000034957613,0.0005587471],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991586,0.00035770936,0.000054060038,0.00007923269,0.00027151266,0.00007878895],"domain_scores_gemma":[0.9985195,0.0006794925,0.00025938379,0.000055971483,0.00030098628,0.00018470547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002088667,0.0004696301,0.00039017736,0.0005721446,0.0005231692,0.0006053495,0.00026622647,0.0005350423,0.0021311317],"category_scores_gemma":[0.0045978012,0.00024926293,0.00067350594,0.00025978166,0.00032241654,0.0004767612,0.0004578775,0.0007094729,0.00019013557],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.025908845,0.1045375,0.6844517,0.00082036055,0.0009655458,0.00022110765,0.0055068345,0.0011818776,0.027751893,0.00029738806,0.0011161858,0.1472407],"study_design_scores_gemma":[0.00081741303,0.05734443,0.92756677,0.0000713282,0.00038174994,0.00010563282,0.0028651785,0.0041138763,0.0057627023,0.00023481056,0.0006734911,0.00006255029],"about_ca_topic_score_codex":0.0027733662,"about_ca_topic_score_gemma":0.006097045,"teacher_disagreement_score":0.0027733662,"about_ca_system_score_codex":0.00039719738,"about_ca_system_score_gemma":0.0006501578,"threshold_uncertainty_score":0.011046052},"labels":[],"label_agreement":null},{"id":"W2970735146","doi":"","title":"A comparative study of the rheological and sensory properties of a petroleum-free and a petroleum-based cosmetic cream.","year":2018,"lang":"en","type":"article","venue":"PubMed","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":"University of Guelph","funders":"","keywords":"Rheology; Petroleum; Viscoelasticity; Materials science; Food science; Chemistry; Composite material; Organic chemistry","score_opus":0.09775521283760015,"score_gpt":0.26386705529438287,"score_spread":0.16611184245678273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970735146","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":[0.99142236,0.002567653,0.003361806,0.00004680584,0.000053911324,0.0000797947,0.00023718178,0.000014798224,0.0022156013],"genre_scores_gemma":[0.99396163,0.0012078913,0.0028874155,0.000058911945,0.000013970813,0.000033395423,0.00017363453,0.000011996824,0.0016511845],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983203,0.000033878998,0.000009520661,0.000026465912,0.00008301269,0.000015076342],"domain_scores_gemma":[0.9996687,0.00009095803,0.000045140052,0.000018298613,0.00013521427,0.000041580202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003927557,0.00025069894,0.00023677338,0.0003542781,0.00018102342,0.0002395297,0.0001276917,0.0001905946,0.0014615706],"category_scores_gemma":[0.0006053613,0.00008149352,0.00027931985,0.0002266698,0.00014504082,0.00029390334,0.00023085202,0.0003260722,0.00019840353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069805724,0.00013865206,0.0011144263,0.00027901048,0.000026595591,0.0000890042,0.0000785323,0.00009767027,0.9861034,0.00007811647,0.000076112265,0.011220504],"study_design_scores_gemma":[0.000029116427,0.004514753,0.046872098,0.000053102813,0.00018574267,0.00093464256,0.0004705483,0.0012952586,0.9405643,0.0001382864,0.004906933,0.000035142624],"about_ca_topic_score_codex":0.00039324985,"about_ca_topic_score_gemma":0.00049200805,"teacher_disagreement_score":0.0014615706,"about_ca_system_score_codex":0.000120498575,"about_ca_system_score_gemma":0.00015197636,"threshold_uncertainty_score":0.0048894286},"labels":[],"label_agreement":null},{"id":"W2971022468","doi":"10.6000/1929-6029.2019.08.06","title":"Multivariate Analysis of Data on Migraine Treatment","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multidimensional scaling; Profiling (computer programming); Multivariate statistics; Multivariate analysis; Psychology; Cluster (spacecraft); Data mining; Computer science; Mathematics; Artificial intelligence; Econometrics; Machine learning","score_opus":0.25046714305381557,"score_gpt":0.5279397906446741,"score_spread":0.27747264759085855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971022468","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":[0.9158607,0.0006397357,0.07074346,0.00049868086,0.00012391615,0.00018265654,0.009222496,0.0004205159,0.002307835],"genre_scores_gemma":[0.9852207,0.00017154633,0.009307868,0.000026407097,0.000048222682,0.00015030685,0.0045479606,0.000035785473,0.0004911736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99326164,0.0041868626,0.00040482864,0.0007522939,0.0010569851,0.00033731826],"domain_scores_gemma":[0.98261833,0.011188677,0.0021033098,0.0027236121,0.0009887211,0.0003773221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005130104,0.00040447398,0.0007198852,0.0019766903,0.00032945754,0.00066874793,0.00039604589,0.0003777506,0.002989578],"category_scores_gemma":[0.022247387,0.00009790595,0.00072361075,0.0030146672,0.00047312022,0.0006042735,0.0008537421,0.00085333065,0.0002935351],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018149874,0.00039970834,0.74251664,0.00045434182,0.0021394226,0.0004040002,0.0012645426,0.016087955,0.009687132,0.004382404,0.0047584614,0.21609037],"study_design_scores_gemma":[0.000026278358,0.0010251729,0.93486184,0.000058777172,0.0002759781,0.00041284098,0.00095816905,0.047906063,0.0027360292,0.0045710406,0.007093232,0.00007450112],"about_ca_topic_score_codex":0.0023198184,"about_ca_topic_score_gemma":0.001666952,"teacher_disagreement_score":0.005130104,"about_ca_system_score_codex":0.0003932542,"about_ca_system_score_gemma":0.0005127413,"threshold_uncertainty_score":0.027130842},"labels":[],"label_agreement":null},{"id":"W2972125743","doi":"10.1007/978-3-030-28817-4_5","title":"Modeling of Food Choice","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Food choice; Affect (linguistics); Psychology; Social psychology; Medicine; Communication","score_opus":0.10585777552321628,"score_gpt":0.2819980025628673,"score_spread":0.176140227039651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972125743","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":[0.020252345,0.0032882988,0.90306336,0.0013266937,0.00040106312,0.000048455644,0.0011554944,0.00041024375,0.0700541],"genre_scores_gemma":[0.6118355,0.0057283994,0.15116449,0.0005731443,0.0005059329,0.00053768035,0.0016194655,0.00043421346,0.22760119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998504,0.000064390515,0.000004488399,0.000036366317,0.000030050049,0.000014249787],"domain_scores_gemma":[0.99973947,0.00016826742,0.000021576674,0.00002515501,0.000028554014,0.0000169819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040792607,0.00055513374,0.00084758387,0.0003973599,0.00029625156,0.00090539217,0.0016841504,0.001119359,0.008556421],"category_scores_gemma":[0.0012233865,0.0004080165,0.000989782,0.0005915023,0.0006106063,0.00088067335,0.0005789191,0.00077223644,0.0017826036],"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":[0.000023668783,0.000030254509,0.00046148832,0.000086837295,0.000055013887,0.00005967266,0.00004657348,0.6724226,0.0008047225,0.29747152,0.007066548,0.021471158],"study_design_scores_gemma":[0.0000032218793,0.000007754477,0.00017183063,0.0000088364695,0.0000078775765,0.000027344793,0.0000070851247,0.9014439,0.000093166,0.09393945,0.0042826394,0.000006907487],"about_ca_topic_score_codex":0.0077294167,"about_ca_topic_score_gemma":0.0053313104,"teacher_disagreement_score":0.008556421,"about_ca_system_score_codex":0.0007102804,"about_ca_system_score_gemma":0.0005058412,"threshold_uncertainty_score":0.028624117},"labels":[],"label_agreement":null},{"id":"W29736828","doi":"10.1007/978-1-4615-4397-8_4","title":"Improved Standard Errors of Standardized Parameters in Covariance Structure Models: Implications for Construct Explication","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Covariance matrix; Covariance; Confirmatory factor analysis; Exploratory factor analysis; Statistics; Factor analysis; Mathematics; Estimation of covariance matrices; Explication; Econometrics; Structural equation modeling","score_opus":0.07039184468337399,"score_gpt":0.2985792088172115,"score_spread":0.2281873641338375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W29736828","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":[0.0065716095,0.00067288394,0.98894775,0.0005965434,0.00026076654,0.000051368246,0.00033478902,0.0005726256,0.0019916235],"genre_scores_gemma":[0.12421859,0.0010145472,0.8702145,0.00033065447,0.00028697567,0.00038034492,0.0008254081,0.00092100946,0.0018079153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9567975,0.030139701,0.0031988707,0.00473905,0.0046729585,0.00045191747],"domain_scores_gemma":[0.6539038,0.28126308,0.0073424373,0.040304285,0.016670698,0.00051578507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06162977,0.0031558417,0.0026365584,0.0037416823,0.001157826,0.008098344,0.0056735086,0.0034366974,0.0053617116],"category_scores_gemma":[0.41847762,0.0023616136,0.0031673291,0.007559623,0.004115863,0.016907858,0.003860856,0.008520515,0.0017789037],"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":[0.0002821666,0.00026104262,0.009972817,0.0007581852,0.00092159805,0.00016151502,0.0033454937,0.076397195,0.0018223893,0.5164336,0.0119047845,0.3777392],"study_design_scores_gemma":[0.00012303988,0.000119971046,0.006064523,0.000437729,0.0003462507,0.0001426735,0.00049740466,0.14414974,0.0023156118,0.83831215,0.007306872,0.00018411328],"about_ca_topic_score_codex":0.0066386093,"about_ca_topic_score_gemma":0.009452192,"teacher_disagreement_score":0.06162977,"about_ca_system_score_codex":0.002175045,"about_ca_system_score_gemma":0.0030041616,"threshold_uncertainty_score":0.32593328},"labels":[],"label_agreement":null},{"id":"W29795318","doi":"10.1038/s41372-018-0099-6","title":"Utilización de suero de queso y bifidobacterias en una leche fermentada a partir de leche de búfala","year":2002,"lang":"es","type":"article","venue":"Alimentaria","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Canadian Institutes of Health Research","keywords":"Humanities; Mathematics; Philosophy","score_opus":0.05647513297247097,"score_gpt":0.3201543434915932,"score_spread":0.26367921051912224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W29795318","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":[0.99918085,0.00026819372,0.00013436665,0.00002850977,0.0000058260766,0.000007656656,0.00014363139,0.000004322275,0.0002265666],"genre_scores_gemma":[0.99918956,0.00017221607,0.0002000355,0.000025965823,0.00000675849,0.000012082613,0.00014263619,0.0000022671682,0.0002484852],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99968183,0.000101300815,0.000028069711,0.00006322495,0.00004729959,0.00007823189],"domain_scores_gemma":[0.99957603,0.00010114685,0.0001543634,0.000040695893,0.000074360774,0.000053418906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048305874,0.00032396667,0.00039039055,0.0006509269,0.00034130935,0.0005697417,0.000164221,0.0003000254,0.00082089606],"category_scores_gemma":[0.0015731421,0.00015612044,0.00029366667,0.0006034387,0.0003083469,0.0002247517,0.00023833303,0.00040859779,0.00011006302],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019379501,0.000098386714,0.9867164,0.000038494305,0.0000578057,0.0001350711,0.00027794408,0.000042590418,0.0034341617,0.000024321876,0.00006468247,0.0071720947],"study_design_scores_gemma":[0.000028713464,0.0013155572,0.9942648,0.000024894356,0.00008546894,0.00035025878,0.0006778893,0.00037387008,0.0023512256,0.00004785329,0.00047116983,0.000008288063],"about_ca_topic_score_codex":0.009192004,"about_ca_topic_score_gemma":0.011563478,"teacher_disagreement_score":0.009192004,"about_ca_system_score_codex":0.0004686295,"about_ca_system_score_gemma":0.00030414842,"threshold_uncertainty_score":0.01827699},"labels":[],"label_agreement":null},{"id":"W2980458746","doi":"10.11606/s1518-8787.2019053001316","title":"Development and evaluation of food environment audit instrument: AUDITNOVA","year":2019,"lang":"en","type":"article","venue":"Revista de Saúde Pública","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":41,"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":"IC Design Education Center; Fundação de Amparo à Pesquisa do Estado de São Paulo; International Development Research Centre","keywords":"Reliability (semiconductor); Audit; Kappa; Cohen's kappa; Statistics; Test (biology); Index (typography); Pearson product-moment correlation coefficient; Population; Content validity; Medicine; Psychology; Mathematics; Environmental health; Business; Computer science; Psychometrics; Accounting","score_opus":0.07405251752701544,"score_gpt":0.28156977822112733,"score_spread":0.2075172606941119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980458746","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7852916,0.0022654613,0.11234606,0.0012981577,0.00082162797,0.052994456,0.015608743,0.0025141635,0.026859747],"genre_scores_gemma":[0.5955866,0.0018393522,0.3272204,0.00036924283,0.0001520657,0.058661025,0.010166066,0.0003750139,0.005630216],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9752924,0.012303467,0.0049458556,0.0011544382,0.0055168793,0.00078698527],"domain_scores_gemma":[0.93346286,0.01727314,0.008259586,0.004597436,0.034394518,0.0020124842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034709327,0.00085752393,0.0015535208,0.005021169,0.0011318787,0.0018882522,0.0012601274,0.0005201237,0.0039026518],"category_scores_gemma":[0.057311047,0.0008432801,0.0018633921,0.0036707562,0.00087613275,0.0013193092,0.0023937775,0.00097355025,0.0012146484],"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":[0.0022788339,0.002289783,0.37216365,0.0029561261,0.00030216246,0.00017602387,0.0032810501,0.0034294669,0.0051671597,0.0024671215,0.012573628,0.592915],"study_design_scores_gemma":[0.0006318973,0.005242044,0.897719,0.0027108649,0.00055073504,0.00067907094,0.0070746606,0.020770049,0.010276758,0.0027812805,0.051276613,0.00028697375],"about_ca_topic_score_codex":0.003156349,"about_ca_topic_score_gemma":0.00445038,"teacher_disagreement_score":0.034709327,"about_ca_system_score_codex":0.0020658672,"about_ca_system_score_gemma":0.007850446,"threshold_uncertainty_score":0.18356264},"labels":[],"label_agreement":null},{"id":"W2986162612","doi":"10.1016/j.foodres.2019.108761","title":"Investigating caloric values and consumers’ perceptions of Nova Scotia rosé wines","year":2019,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"Department of Agriculture, Nova Scotia","keywords":"Nova scotia; Caloric theory; Perception; Nova (rocket); Food science; Psychology; Chemistry; Geography; Medicine; Endocrinology; Neuroscience; Engineering; Archaeology; Aeronautics","score_opus":0.15637783455626802,"score_gpt":0.4077673096323488,"score_spread":0.2513894750760808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986162612","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":[0.9993057,0.000013499724,0.000006688229,0.000013581879,0.0000012506529,0.0000030669078,0.00001893653,2.7753018e-7,0.000637043],"genre_scores_gemma":[0.9991622,0.000029121507,0.000047158603,0.000021871698,0.000001066069,0.0000030324043,0.000037885413,5.935265e-7,0.00069708156],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982834,0.00004235994,0.000008181645,0.000023453918,0.00006376932,0.000033884528],"domain_scores_gemma":[0.9990238,0.00024580443,0.00026123834,0.000039225775,0.00022793347,0.00020189032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044254801,0.00013482606,0.00010519879,0.00020647426,0.0004710199,0.0007360767,0.00012552063,0.00017516557,0.0014133232],"category_scores_gemma":[0.0014998649,0.00009247694,0.00020512224,0.00019649566,0.00029497908,0.00014890748,0.00035471152,0.00035909197,0.000107332984],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032764932,0.0005285846,0.9565027,0.00007404005,0.0001417384,0.00047153496,0.010491209,0.000119948934,0.01623396,0.00015278308,0.00042956907,0.011577528],"study_design_scores_gemma":[0.000007697845,0.00013969532,0.995046,0.0000065624813,0.000012757373,0.00002926403,0.004073924,0.00007142223,0.00030038267,0.000011638074,0.0002969412,0.0000036630604],"about_ca_topic_score_codex":0.27950713,"about_ca_topic_score_gemma":0.4907305,"teacher_disagreement_score":0.72049284,"about_ca_system_score_codex":0.0011410748,"about_ca_system_score_gemma":0.0005310848,"threshold_uncertainty_score":0.55576044},"labels":[],"label_agreement":null},{"id":"W2990763132","doi":"10.1080/00273171.2019.1694477","title":"Sparse Extended Redundancy Analysis: Variable Selection via the Exclusive LASSO","year":2019,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"York University","funders":"","keywords":"Latent variable; Redundancy (engineering); Computer science; Lasso (programming language); Feature selection; Set (abstract data type); Selection (genetic algorithm); Econometrics; Variables; Data mining; Artificial intelligence; Mathematics; Machine learning; Statistics","score_opus":0.19290077209158177,"score_gpt":0.4418091805023224,"score_spread":0.2489084084107406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990763132","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":[0.0044654324,0.00026822175,0.9941532,0.00027566342,0.000037306523,0.000073120274,0.00012752085,0.0002756408,0.0003239626],"genre_scores_gemma":[0.23788854,0.00075754494,0.7543848,0.00071384246,0.0004594718,0.0013547757,0.0017679199,0.0003475687,0.0023255695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9882957,0.009004233,0.0003383242,0.0010229609,0.0009991496,0.000339559],"domain_scores_gemma":[0.9884998,0.008383969,0.0009495956,0.00089822727,0.0010380818,0.00023045593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012542599,0.0027723997,0.003004493,0.0017789367,0.0012638922,0.0018856585,0.002942761,0.00176825,0.0021632193],"category_scores_gemma":[0.023096059,0.0007476214,0.0022183498,0.0023800102,0.0014891026,0.0015877767,0.0034460612,0.00329305,0.00081445556],"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":[0.0006306819,0.00040467418,0.006007516,0.0007775615,0.0011943788,0.00068755716,0.00050465885,0.6205053,0.007740062,0.043879095,0.015329677,0.30233887],"study_design_scores_gemma":[0.000047181016,0.00008338089,0.00049840147,0.000033791828,0.000049669725,0.00006623784,0.00003359085,0.9801323,0.0008687585,0.01667338,0.0014820824,0.0000313229],"about_ca_topic_score_codex":0.0024041736,"about_ca_topic_score_gemma":0.0024797511,"teacher_disagreement_score":0.012542599,"about_ca_system_score_codex":0.0005702891,"about_ca_system_score_gemma":0.0024385718,"threshold_uncertainty_score":0.06633246},"labels":[],"label_agreement":null},{"id":"W2997261114","doi":"10.71781/18350","title":"Identifier, mesurer et évaluer l’efficacité des techniques de rétroaction dans un contexte d’évaluation de potentiel","year":2019,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Humanities; Library science; Computer science; Philosophy","score_opus":0.08809226908347,"score_gpt":0.3798673025165333,"score_spread":0.29177503343306327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997261114","genre_codex":"review","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":[0.280077,0.550406,0.06624093,0.015823158,0.003284039,0.010562742,0.0014633499,0.0005210875,0.07162175],"genre_scores_gemma":[0.67530966,0.13931048,0.15587324,0.006539347,0.00079603586,0.008758457,0.0009030631,0.00030834725,0.012201362],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9095872,0.05960576,0.006413727,0.0038242873,0.019270666,0.0012983878],"domain_scores_gemma":[0.8332867,0.11762727,0.018866163,0.007366005,0.020787371,0.0020664725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.085426025,0.0014971002,0.0023775117,0.004071974,0.0016468695,0.00772336,0.0027866063,0.002217493,0.008617947],"category_scores_gemma":[0.13957962,0.000820722,0.0044750497,0.0035581326,0.002320271,0.0059097125,0.003296252,0.003396901,0.0013313263],"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":[0.004065753,0.0017539898,0.023467826,0.058842614,0.0057739723,0.00019029803,0.013343435,0.0011377479,0.0027602646,0.0076382305,0.006944922,0.87408096],"study_design_scores_gemma":[0.005291024,0.037482895,0.1874377,0.20218238,0.045347303,0.0026432513,0.038280036,0.0060822414,0.028904263,0.028670354,0.41659167,0.0010869591],"about_ca_topic_score_codex":0.0076167393,"about_ca_topic_score_gemma":0.018775946,"teacher_disagreement_score":0.085426025,"about_ca_system_score_codex":0.0058220155,"about_ca_system_score_gemma":0.013951138,"threshold_uncertainty_score":0.4517814},"labels":[],"label_agreement":null},{"id":"W2999350999","doi":"10.3390/foods9010063","title":"Consumers’ Perceptions and Sensory Properties of Beef Patty Analogues","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":39,"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":"Institute of Population and Public Health; King Saud University","keywords":"Food science; Flavor; Health benefits; Business; Chemistry; Medicine; Traditional medicine","score_opus":0.11682092118719206,"score_gpt":0.26959621751871454,"score_spread":0.15277529633152248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999350999","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":[0.9994881,0.0000650852,0.00011283399,0.00002050422,0.0000021498045,0.0000069632015,0.000015498177,0.0000016888948,0.00028714308],"genre_scores_gemma":[0.9990055,0.00010516067,0.0003483725,0.00004790296,0.000004416629,0.000007902537,0.000044556844,0.0000018899643,0.00043441562],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99963033,0.00011666448,0.000019870662,0.000041865365,0.00015226817,0.00003889734],"domain_scores_gemma":[0.999146,0.00038661624,0.00022316408,0.000025945235,0.00015376581,0.0000644471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088946073,0.00026885967,0.00021615047,0.00028990203,0.00021280312,0.0007605626,0.00012058604,0.00040856155,0.0025509985],"category_scores_gemma":[0.0014636081,0.00017298161,0.0003365887,0.0002543285,0.0002612411,0.0004671608,0.00035816766,0.00055031985,0.00017118084],"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":[0.013384355,0.0023968455,0.2827276,0.0018511039,0.00055579154,0.0019508695,0.023159806,0.0007721473,0.5945549,0.00040050072,0.00058518635,0.07766084],"study_design_scores_gemma":[0.000097673925,0.014494484,0.93230104,0.00012501146,0.00026131817,0.0010903678,0.017685931,0.0012721217,0.029960938,0.00018595309,0.0024494654,0.000075624186],"about_ca_topic_score_codex":0.0013084636,"about_ca_topic_score_gemma":0.0010851274,"teacher_disagreement_score":0.0025509985,"about_ca_system_score_codex":0.00013313863,"about_ca_system_score_gemma":0.000075410986,"threshold_uncertainty_score":0.008533895},"labels":[],"label_agreement":null},{"id":"W3007678714","doi":"10.1111/joss.12568","title":"Investigating the effect of extrinsic cues on consumers' evaluation of red wine using a projective mapping task","year":2020,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Department of Agriculture, Nova Scotia","keywords":"Wine; Bottle; Perception; Psychology; Food science; Affect (linguistics); Advertising; Communication; Chemistry; Business; Geography","score_opus":0.3014584364813877,"score_gpt":0.39480082744527234,"score_spread":0.09334239096388464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007678714","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":[0.9986106,0.000023068094,0.0005551246,0.000008911329,0.000003862738,0.000023530903,0.00003699252,0.000008458531,0.0007295185],"genre_scores_gemma":[0.99735427,0.000038659316,0.0018313079,0.000030138013,0.000005545436,0.000075008684,0.00008093459,0.000008789903,0.00057527825],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99927765,0.00027027182,0.000040751314,0.0001579184,0.00019510681,0.00005835063],"domain_scores_gemma":[0.99593985,0.0020728826,0.0011193269,0.000267989,0.00036083395,0.00023911614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010318658,0.00049077155,0.00027564287,0.00024403936,0.00018233462,0.0007797832,0.00022948679,0.00043601153,0.0029108312],"category_scores_gemma":[0.008048469,0.00025608577,0.0002867004,0.00015156854,0.00021962059,0.0004914468,0.0006741404,0.00041732873,0.000201613],"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":[0.012334827,0.0028917054,0.2532095,0.0009969146,0.00045069645,0.0005078686,0.009096014,0.0010644901,0.65115213,0.0005854762,0.0008902429,0.06682019],"study_design_scores_gemma":[0.0001410601,0.0030354871,0.9734064,0.000035704546,0.00010691285,0.00015493101,0.0015252089,0.003333624,0.017280212,0.00029742514,0.00063086435,0.000052132047],"about_ca_topic_score_codex":0.00069371646,"about_ca_topic_score_gemma":0.00097243505,"teacher_disagreement_score":0.0029108312,"about_ca_system_score_codex":0.00014105756,"about_ca_system_score_gemma":0.00012389023,"threshold_uncertainty_score":0.00973767},"labels":[],"label_agreement":null},{"id":"W3008622210","doi":"10.5539/jfr.v9n2p7","title":"Quantitative Descriptive Analysis and Acceptance Testing of Yogurt with no Added Sugar","year":2020,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Sucralose; Aroma; Food science; Quantitative Descriptive Analysis; Chemistry; Flavor; Mathematics","score_opus":0.3519422418656727,"score_gpt":0.40323750390864005,"score_spread":0.05129526204296736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008622210","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":[0.9892238,0.0001913055,0.0049584755,0.000055135508,0.000038442035,0.00058862806,0.0017000814,0.00004283222,0.0032013592],"genre_scores_gemma":[0.9903674,0.00014185232,0.0055636354,0.00004997744,0.000022941995,0.0011314794,0.0011524057,0.000015097257,0.0015551707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965102,0.0008413271,0.00031181402,0.00037810396,0.0017782976,0.00018023499],"domain_scores_gemma":[0.99086434,0.0040323753,0.001378595,0.00036956303,0.0031797362,0.00017549614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038718306,0.0004341094,0.0005373652,0.0013311191,0.00032975167,0.0006814786,0.00037363742,0.00026476692,0.0025866602],"category_scores_gemma":[0.0069387434,0.00010204361,0.0006724102,0.0014878298,0.0005841943,0.00033524848,0.00041759314,0.0005150786,0.00025969275],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005395072,0.0026751491,0.5555433,0.0038070574,0.0008264487,0.0011827318,0.028433783,0.002529587,0.17350025,0.0022497764,0.0039278953,0.2199289],"study_design_scores_gemma":[0.00003424692,0.006064108,0.9416931,0.00016304888,0.00016449264,0.00043128425,0.018462008,0.005497141,0.020129196,0.0009104285,0.0063343765,0.00011653234],"about_ca_topic_score_codex":0.0008851274,"about_ca_topic_score_gemma":0.0011028653,"teacher_disagreement_score":0.0038718306,"about_ca_system_score_codex":0.00034705704,"about_ca_system_score_gemma":0.00028672937,"threshold_uncertainty_score":0.0204764},"labels":[],"label_agreement":null},{"id":"W3008678529","doi":"10.4236/ojs.2020.101008","title":"Mean Absolute Deviations about the Mean, the Cut Norm and Taxicab Correspondence Analysis","year":2020,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Université de Moncton","funders":"","keywords":"Mathematics; Standard deviation; Maximization; Least absolute deviations; Statistics; Absolute deviation; Minification; Large deviations theory; Norm (philosophy); Combinatorics; Mathematical optimization; Estimator","score_opus":0.09323538382250615,"score_gpt":0.32679371224766535,"score_spread":0.2335583284251592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008678529","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":[0.030093288,0.00036975875,0.9669452,0.00022997695,0.000037168717,0.000057580008,0.00019557597,0.0002269552,0.0018445521],"genre_scores_gemma":[0.4423535,0.00042426144,0.55242574,0.00019383949,0.00014785932,0.00055489014,0.00082084874,0.0004231797,0.0026559292],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99467987,0.0017744965,0.00031244245,0.0013387137,0.0016842883,0.00021023658],"domain_scores_gemma":[0.98487526,0.009636068,0.0018556877,0.001957304,0.0013187012,0.00035693988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006759031,0.0008608629,0.0013909659,0.0033204043,0.0010345555,0.0029103996,0.0012128443,0.0014488099,0.001999495],"category_scores_gemma":[0.031544957,0.000490991,0.000944413,0.0031935635,0.0038314534,0.0026003788,0.0022079465,0.0021417039,0.000718625],"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":[0.0007246987,0.00019788941,0.013344331,0.00045220315,0.0003314348,0.00027487657,0.00065496453,0.2953115,0.018139783,0.32790455,0.0050077774,0.33765596],"study_design_scores_gemma":[0.000030056839,0.00021608753,0.008441633,0.00007460282,0.000048694194,0.00036497944,0.00020777466,0.6997429,0.007698188,0.27790555,0.0051756483,0.00009401657],"about_ca_topic_score_codex":0.0013419642,"about_ca_topic_score_gemma":0.0012916974,"teacher_disagreement_score":0.006759031,"about_ca_system_score_codex":0.0013221344,"about_ca_system_score_gemma":0.0016447486,"threshold_uncertainty_score":0.03574562},"labels":[],"label_agreement":null},{"id":"W3009180810","doi":"10.48550/arxiv.2003.02906","title":"Mean absolute deviations about the mean, the cut norm and taxicab correspondence analysis","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Mathematics; Standard deviation; Maximization; Least absolute deviations; Absolute deviation; Minification; Statistics; Norm (philosophy); Combinatorics; Mathematical optimization; Estimator","score_opus":0.14959629668054303,"score_gpt":0.23131774068501054,"score_spread":0.08172144400446751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009180810","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":[0.026037483,0.00033577072,0.9713417,0.0002526788,0.000028838762,0.000030128662,0.00014782163,0.0002055726,0.0016199605],"genre_scores_gemma":[0.4761828,0.000584737,0.5171312,0.0002330538,0.00016418335,0.0004117184,0.0009049353,0.0005200513,0.0038673035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963188,0.0012248649,0.000190608,0.0009540185,0.0011491625,0.00016258817],"domain_scores_gemma":[0.9895616,0.0062794387,0.0014300729,0.0013214395,0.0010686178,0.0003387185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049478495,0.0008114277,0.0011146633,0.0029724226,0.0009017935,0.0026233518,0.0011049632,0.0014376896,0.0021447388],"category_scores_gemma":[0.025745647,0.0004892745,0.000770614,0.0026684464,0.0036804022,0.0027931996,0.002281767,0.0021021364,0.0008542796],"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":[0.0004877738,0.000116705894,0.007417521,0.00029108202,0.000203252,0.00019452437,0.00043199823,0.28508437,0.0136417,0.4244027,0.0046156556,0.26311272],"study_design_scores_gemma":[0.000017628845,0.000119331715,0.0035608886,0.00005055305,0.0000262148,0.00019592482,0.00011245846,0.60733485,0.0050609536,0.3800365,0.0034291598,0.000055554447],"about_ca_topic_score_codex":0.0012668921,"about_ca_topic_score_gemma":0.0012329774,"teacher_disagreement_score":0.0049478495,"about_ca_system_score_codex":0.0013232947,"about_ca_system_score_gemma":0.0012651122,"threshold_uncertainty_score":0.026167095},"labels":[],"label_agreement":null},{"id":"W3019652755","doi":"10.1016/j.appet.2020.104720","title":"Food-based social comparisons influence liking and consumption.","year":2020,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Meal; Psychology; Food consumption; Consumption (sociology); Social psychology; Food science; Economics; Biology","score_opus":0.09427034224703204,"score_gpt":0.28674722972236616,"score_spread":0.19247688747533412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019652755","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":[0.9951444,0.00014298201,0.0007905658,0.00005664518,0.00002717425,0.000022001006,0.00017300213,0.000023233837,0.00361999],"genre_scores_gemma":[0.99875987,0.000049698316,0.00048750197,0.000030869865,0.000010755931,0.000026218311,0.00010160486,0.000014951114,0.0005186201],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990031,0.0005811939,0.000029357328,0.00014339235,0.00018667088,0.000056268458],"domain_scores_gemma":[0.9938798,0.0039423187,0.0010105809,0.0004116834,0.00031670998,0.0004388585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012819421,0.00027294405,0.0002627837,0.0005069091,0.0002664225,0.0009883591,0.00022618665,0.00031591122,0.0069016023],"category_scores_gemma":[0.010339139,0.00016908799,0.00033916757,0.00042275948,0.0005424682,0.00045459752,0.00049142505,0.00053322455,0.00034687226],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008218136,0.002401911,0.82291794,0.00045219623,0.0018038083,0.00029518845,0.0033927315,0.0008257983,0.06288793,0.0017104389,0.002343415,0.09275052],"study_design_scores_gemma":[0.00002343363,0.0006636831,0.9954352,0.000013605496,0.00010552201,0.000058416896,0.0004896582,0.0010288701,0.0012795292,0.0005654462,0.00031969562,0.000017109405],"about_ca_topic_score_codex":0.0015280872,"about_ca_topic_score_gemma":0.0033933779,"teacher_disagreement_score":0.0069016023,"about_ca_system_score_codex":0.00022106411,"about_ca_system_score_gemma":0.00015861564,"threshold_uncertainty_score":0.023088098},"labels":[],"label_agreement":null},{"id":"W3039345580","doi":"10.1007/s12529-020-09916-2","title":"Variety Salience and Enjoyment of Repetitiously Consumed Foods: a Field Experiment","year":2020,"lang":"en","type":"article","venue":"International Journal of Behavioral Medicine","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Aeronautics and Space Administration","keywords":"Salience (neuroscience); Psychology; Variety (cybernetics); Environmental health; Social psychology; Developmental psychology; Medicine","score_opus":0.08654525507383326,"score_gpt":0.36959016694188357,"score_spread":0.2830449118680503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039345580","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":[0.999035,0.000010648264,0.0002189547,0.00003461097,0.000020119593,0.00022974459,0.000108821194,0.000015151838,0.00032708485],"genre_scores_gemma":[0.9800889,0.00008763242,0.0060238643,0.00044295384,0.000091022455,0.003416332,0.0006676403,0.00006672377,0.009114994],"study_design_codex":"nonrandomized_trial","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9982381,0.00034275232,0.00009985406,0.0007620969,0.00028089853,0.0002762281],"domain_scores_gemma":[0.9904767,0.0040308675,0.001241852,0.001264663,0.00045006032,0.0025357613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002320281,0.0022288298,0.001684354,0.0008549006,0.0026767408,0.002148521,0.0024123793,0.002332042,0.010160935],"category_scores_gemma":[0.0055772085,0.002261298,0.0007825196,0.00052858214,0.003869422,0.0015941452,0.0014478237,0.0044660857,0.0011693225],"study_design_candidate":"nonrandomized_trial","study_design_consensus":"nonrandomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.17226261,0.56653255,0.02105676,0.0003280832,0.00028283944,0.00026821077,0.0039180913,0.0005665095,0.22434051,0.00084124855,0.0011614047,0.008441168],"study_design_scores_gemma":[0.02789326,0.6124187,0.30562344,0.00006451647,0.00080761197,0.00035636537,0.0034793625,0.0053717713,0.038646866,0.0023341775,0.0026629956,0.00034088618],"about_ca_topic_score_codex":0.0047200127,"about_ca_topic_score_gemma":0.008015394,"teacher_disagreement_score":0.010160935,"about_ca_system_score_codex":0.0008589653,"about_ca_system_score_gemma":0.0015771444,"threshold_uncertainty_score":0.033991694},"labels":[],"label_agreement":null},{"id":"W3039621746","doi":"10.3390/foods9070873","title":"Multiple Correspondence and Hierarchical Cluster Analyses for the Profiling of Fresh Apple Customers Using Data from Two Marketplaces","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Cultivar; Profiling (computer programming); Marketing; Market segmentation; Dimension (graph theory); Advertising; Business; Mathematics; Biology; Computer science; Horticulture","score_opus":0.309842443548665,"score_gpt":0.4083396929827565,"score_spread":0.0984972494340915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039621746","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":[0.96415764,0.000062442254,0.029079877,0.00011969429,0.00005796904,0.0014825482,0.0017399413,0.00026712156,0.0030328697],"genre_scores_gemma":[0.955033,0.000023616052,0.04039851,0.000026366723,0.0000316934,0.002296203,0.0014270031,0.00007230792,0.0006912484],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9862518,0.0071463864,0.0011155052,0.0017041753,0.0030818188,0.0007003202],"domain_scores_gemma":[0.9653367,0.023080075,0.002966341,0.0033433756,0.0045336024,0.00073991064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009421793,0.0007718219,0.0009948437,0.0075553632,0.0026105102,0.0017279637,0.0010358662,0.00061843335,0.0043033888],"category_scores_gemma":[0.04211592,0.0004346483,0.0017919034,0.008196887,0.00091995724,0.0014220732,0.0024177902,0.0010837038,0.00067696103],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003596731,0.0019039956,0.75888896,0.00051036704,0.001590414,0.0006861467,0.027119765,0.0047516758,0.013261549,0.005673801,0.006625386,0.17539132],"study_design_scores_gemma":[0.00016717233,0.0017486959,0.8859832,0.000055936904,0.00022260402,0.00038875328,0.015954208,0.083535366,0.0036702512,0.0034499774,0.0046288776,0.00019483795],"about_ca_topic_score_codex":0.015322951,"about_ca_topic_score_gemma":0.012027692,"teacher_disagreement_score":0.015322951,"about_ca_system_score_codex":0.0014530289,"about_ca_system_score_gemma":0.002131802,"threshold_uncertainty_score":0.049827814},"labels":[],"label_agreement":null},{"id":"W3045800323","doi":"10.1007/s11947-020-02508-8","title":"Predictive Consumer Acceptance Models and Quality Attributes for Cookies Enriched with Potato Protein Isolate and Concentrate","year":2020,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Potato starch; Food science; Gluten; Wheat flour; Mathematics; Starch; Chemistry","score_opus":0.06950192871404832,"score_gpt":0.28264547472704016,"score_spread":0.21314354601299185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045800323","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":[0.99250984,0.00004481064,0.0069579696,0.000035248057,0.000004521318,0.000019057092,0.0002085269,0.000041189945,0.00017880257],"genre_scores_gemma":[0.99755496,0.000022227867,0.0016803198,0.000009524368,0.0000025033073,0.000030439372,0.0004280929,0.000009208429,0.00026276728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995198,0.00024072175,0.000017233713,0.00011330319,0.000049698676,0.000059209862],"domain_scores_gemma":[0.994358,0.004849895,0.0002400626,0.00015116017,0.00028549493,0.00011549888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031954101,0.0009493257,0.00048568123,0.00071696175,0.0002230426,0.0012306126,0.00057489437,0.00058803655,0.0020521043],"category_scores_gemma":[0.0057528694,0.00030435668,0.0015509769,0.0004148206,0.0003580219,0.0006445303,0.00042871633,0.0011397027,0.00036806066],"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":[0.007080601,0.003797911,0.41098818,0.0001983659,0.001446024,0.0002329163,0.0008984319,0.4801761,0.010948107,0.0015549315,0.0011536739,0.0815248],"study_design_scores_gemma":[0.000036337504,0.0008623822,0.078600064,0.000016368102,0.00016073076,0.000046955884,0.00022247918,0.9176769,0.0014487775,0.0007897176,0.00010900072,0.000030182306],"about_ca_topic_score_codex":0.0076213586,"about_ca_topic_score_gemma":0.0046226634,"teacher_disagreement_score":0.0076213586,"about_ca_system_score_codex":0.0008254927,"about_ca_system_score_gemma":0.00043712877,"threshold_uncertainty_score":0.016899109},"labels":[],"label_agreement":null},{"id":"W3081025914","doi":"10.1016/bs.afnr.2020.06.006","title":"Sensory methods applied to the development of probiotic and prebiotic foods","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in food and nutrition research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Prebiotic; Food science; Functional food; Probiotic; Sensory system; Food products; Product (mathematics); Biotechnology; Food industry; New product development; Sensory analysis; Biology; Business; Marketing; Mathematics","score_opus":0.15736108203336763,"score_gpt":0.40902551604911014,"score_spread":0.2516644340157425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081025914","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":[0.018654592,0.092615224,0.82658803,0.00072223623,0.0021636453,0.0004052321,0.0014797548,0.0013222089,0.05604906],"genre_scores_gemma":[0.12399977,0.12324898,0.6378818,0.0014267449,0.0006498254,0.001005217,0.0017158581,0.0009509065,0.10912092],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989667,0.0001882593,0.000045536297,0.00014431203,0.0006130674,0.000042195075],"domain_scores_gemma":[0.99916196,0.0004664922,0.000039570463,0.000040000272,0.00026655468,0.000025396228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016452466,0.0011325625,0.00067635416,0.0020288783,0.00032739918,0.0013953296,0.0009863024,0.00072781695,0.006467898],"category_scores_gemma":[0.0018308728,0.00062673277,0.0008239088,0.0023355158,0.00083068915,0.0009099008,0.0008661403,0.001865274,0.0019695302],"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":[0.00021993704,0.00016099335,0.0004983331,0.0027263816,0.000084569016,0.00018964731,0.00028450924,0.0043651653,0.2741614,0.017249135,0.012022755,0.6880373],"study_design_scores_gemma":[0.00005162315,0.001283509,0.010013238,0.0013300822,0.00019227357,0.0018902116,0.0006632562,0.04209195,0.50413126,0.063311234,0.37467986,0.00036151693],"about_ca_topic_score_codex":0.0012656974,"about_ca_topic_score_gemma":0.002802635,"teacher_disagreement_score":0.006467898,"about_ca_system_score_codex":0.0005182447,"about_ca_system_score_gemma":0.0006287181,"threshold_uncertainty_score":0.02163732},"labels":[],"label_agreement":null},{"id":"W3085693773","doi":"10.1039/9781788016155","title":"Handbook of Food Structure Development","year":2019,"lang":"en","type":"book","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Food processing; Macro; Data science; Taste; Scale (ratio); Computer science; Field (mathematics); Food science; Geography; Mathematics; Cartography; Chemistry","score_opus":0.03748638380259402,"score_gpt":0.2370203568422686,"score_spread":0.19953397303967457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085693773","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013491765,0.09469492,0.019715078,0.004500838,0.0076046376,0.00022681536,0.0028951438,0.001734311,0.86727893],"genre_scores_gemma":[0.0037740066,0.049548488,0.017103698,0.0015163084,0.0005134972,0.00014269163,0.0024673874,0.0006630777,0.9242708],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994906,0.00004704386,0.00002764817,0.000074274125,0.0003314379,0.000028903636],"domain_scores_gemma":[0.99967194,0.00008487843,0.000017325083,0.00003841697,0.0001545068,0.000032963504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004191953,0.0009237767,0.0007672657,0.0021652088,0.0010238454,0.0032566169,0.001542791,0.0013034833,0.11737518],"category_scores_gemma":[0.0009298358,0.0005629832,0.0005840854,0.002447692,0.0006349062,0.0030145035,0.001472017,0.002336047,0.08222876],"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":[0.000025940468,0.000059980706,0.00012104147,0.0008877463,0.000007794902,0.00013035449,0.00036069308,0.00054330507,0.0020747122,0.047326487,0.5958823,0.35257965],"study_design_scores_gemma":[7.0508264e-7,0.0000048019974,0.00005541403,0.000073066556,7.341075e-7,0.00009277864,0.000019908244,0.00003851736,0.00011472892,0.0018329347,0.9977639,0.000002529288],"about_ca_topic_score_codex":0.0016269006,"about_ca_topic_score_gemma":0.0039389585,"teacher_disagreement_score":0.11737518,"about_ca_system_score_codex":0.0013761052,"about_ca_system_score_gemma":0.0015530274,"threshold_uncertainty_score":0.39265907},"labels":[],"label_agreement":null},{"id":"W3087978613","doi":"10.1007/978-981-15-3311-2_13","title":"From Joint Graphical Display to Bi-Modal Clustering: [1] A Giant Leap in Quantification Theory","year":2020,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cluster analysis; Set (abstract data type); Graphical model; Multidimensional scaling; Scaling; Graphical display; Cluster (spacecraft); Theoretical computer science; Artificial intelligence; Mathematics; Machine learning; Computer graphics (images); Geometry","score_opus":0.19749490679735485,"score_gpt":0.3631668787169121,"score_spread":0.16567197191955726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087978613","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":[0.0008306109,0.004327424,0.9809651,0.003027092,0.0004196267,0.00002171097,0.0003427285,0.002147752,0.00791796],"genre_scores_gemma":[0.05338027,0.0064925845,0.91851366,0.0020513735,0.0012446707,0.00015931299,0.0009729375,0.0026644524,0.014520705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981578,0.00053114403,0.00007995571,0.00040564104,0.0007618532,0.00006354982],"domain_scores_gemma":[0.99741983,0.001447875,0.000076382756,0.0005099301,0.00047254824,0.000073570365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002283319,0.0013959489,0.0011782701,0.002691333,0.0010586678,0.0050080004,0.0024437448,0.001715084,0.013575592],"category_scores_gemma":[0.0072112456,0.0008555317,0.0009858288,0.0054879417,0.005788193,0.009182039,0.003538606,0.0038087436,0.0049399445],"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":[0.00006194472,0.000025574236,0.00014881123,0.00054228434,0.000049173457,0.00003676689,0.0006114964,0.008045558,0.0023919267,0.5878742,0.088545546,0.31166673],"study_design_scores_gemma":[0.0000065366403,0.00001300663,0.00018666254,0.000098477634,0.00001715039,0.00009624568,0.00006923251,0.04354858,0.0015907101,0.89225113,0.06207,0.00005236736],"about_ca_topic_score_codex":0.003362072,"about_ca_topic_score_gemma":0.003412385,"teacher_disagreement_score":0.013575592,"about_ca_system_score_codex":0.001270568,"about_ca_system_score_gemma":0.0008357729,"threshold_uncertainty_score":0.045414865},"labels":[],"label_agreement":null},{"id":"W3089022142","doi":"10.1101/2020.09.23.310045","title":"Evenness-richness scatter plots: a graphical, intuitive approach to alpha diversity analysis","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Species evenness; Species richness; Alpha diversity; Mathematics; Statistics; Rank abundance curve; Diversity index; Scatter plot; Metric (unit); Gamma diversity; Multivariate statistics; Multivariate analysis of variance; Ecology; Biology; Engineering","score_opus":0.04227963935279471,"score_gpt":0.2453017004574013,"score_spread":0.20302206110460658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089022142","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":[0.0072901417,0.00025450313,0.9751569,0.00033030458,0.0001224987,0.00015011661,0.0026996934,0.011247758,0.0027479706],"genre_scores_gemma":[0.10128585,0.00034807573,0.88946384,0.00026638235,0.00020328828,0.00074404344,0.00274494,0.0031242191,0.0018193418],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968803,0.0016633789,0.00025432566,0.00041560267,0.0006905513,0.000095872674],"domain_scores_gemma":[0.9793858,0.014315507,0.0018645844,0.002002921,0.0019861443,0.0004450044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072533055,0.002163976,0.0012220285,0.010649833,0.0008896976,0.0049499115,0.0015328191,0.0009581635,0.020643106],"category_scores_gemma":[0.030026756,0.0010272401,0.0013818996,0.0062779114,0.0015920306,0.0033701425,0.0026744162,0.0030383333,0.0037853692],"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":[0.000982757,0.0007812546,0.02330564,0.0021961443,0.00078400836,0.001393142,0.006480434,0.046106767,0.040170465,0.37559122,0.09815514,0.40405303],"study_design_scores_gemma":[0.00022175805,0.00033431913,0.018723827,0.00071673794,0.00016454892,0.0013163945,0.0015741814,0.2911997,0.011031575,0.5765304,0.097831294,0.00035536976],"about_ca_topic_score_codex":0.0012065092,"about_ca_topic_score_gemma":0.0011795913,"teacher_disagreement_score":0.020643106,"about_ca_system_score_codex":0.00067040604,"about_ca_system_score_gemma":0.0007595718,"threshold_uncertainty_score":0.06905812},"labels":[],"label_agreement":null},{"id":"W3106713601","doi":"10.1111/joss.12624","title":"A preliminary investigation into participants' reactions to a sensory trial investigating a cannabis edible","year":2020,"lang":"en","type":"article","venue":"Journal of Sensory Studies","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":"Acadia University","funders":"","keywords":"Cannabis; Product (mathematics); Cosmetics; Sensory system; Consumption (sociology); Food preparation; Psychology; Food science; Medicine; Food processing; Psychiatry; Mathematics","score_opus":0.26669588348573414,"score_gpt":0.3799919280497863,"score_spread":0.11329604456405218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106713601","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":[0.9924846,0.0001832809,0.003004894,0.0009930673,0.00012979704,0.00087245763,0.00007233497,0.000049200233,0.0022103365],"genre_scores_gemma":[0.9844737,0.0002939022,0.007058313,0.0020370635,0.000056534744,0.0019888447,0.0000624209,0.000036471163,0.0039927918],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99281234,0.0048246738,0.00045089578,0.00049564353,0.0008357263,0.0005807617],"domain_scores_gemma":[0.97052795,0.01940468,0.0020758496,0.0011835052,0.0053150035,0.0014930326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012813666,0.00071801234,0.0007395662,0.0005607498,0.0029611895,0.0017786718,0.00080330705,0.0025777675,0.006272033],"category_scores_gemma":[0.040482815,0.00049783045,0.00085172505,0.00022846574,0.0019413751,0.0011656649,0.0022843122,0.0026998266,0.0010726874],"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":[0.0033169487,0.0021154005,0.03580465,0.0014300848,0.00006718842,0.004616146,0.863711,0.00020319526,0.05266807,0.0007502461,0.0024646083,0.032852486],"study_design_scores_gemma":[0.00040000968,0.020298665,0.040030796,0.0008823256,0.0001424927,0.0025644398,0.89073807,0.0015105786,0.013030511,0.001394013,0.028680088,0.00032801964],"about_ca_topic_score_codex":0.0012853318,"about_ca_topic_score_gemma":0.0015892644,"teacher_disagreement_score":0.012813666,"about_ca_system_score_codex":0.00082039204,"about_ca_system_score_gemma":0.0010101959,"threshold_uncertainty_score":0.06776601},"labels":[],"label_agreement":null},{"id":"W3113640607","doi":"10.3390/foods10010060","title":"Nutrition in Disguise: Effects of Food Neophobia, Healthy Eating Interests and Provision of Health Information on Liking and Perceptions of Nutrient-Dense Foods in Older Adults","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Institute for Aging; University of Waterloo; University of Guelph","funders":"","keywords":"Neophobia; Perception; Food choice; Environmental health; Population; Psychology; Gerontology; Medicine; Developmental psychology","score_opus":0.01850105356424199,"score_gpt":0.28336777302793553,"score_spread":0.26486671946369356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113640607","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":[0.99968815,0.00003475609,0.000011552093,0.000019009489,0.0000023582174,0.000005038983,0.000013842564,0.0000011324199,0.00022417812],"genre_scores_gemma":[0.9996307,0.00002359865,0.000047848946,0.000024813384,0.0000032339567,0.000006787718,0.000024528534,5.647245e-7,0.00023800244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994954,0.0001995734,0.000044296194,0.000074737894,0.00011682975,0.00006908132],"domain_scores_gemma":[0.99740595,0.0010654525,0.00065872865,0.000112986396,0.00017724793,0.0005796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013936808,0.00026842474,0.00025411302,0.00026206486,0.00021867634,0.00047214684,0.0001671305,0.00041538032,0.002387893],"category_scores_gemma":[0.003935998,0.00017331693,0.0006473273,0.00011319713,0.00032332857,0.00038709142,0.0005747257,0.0005622486,0.0001517059],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011658571,0.004166219,0.9549199,0.00014291347,0.00045664413,0.00012511847,0.0024679909,0.00012065745,0.0053649377,0.000061561244,0.00027668432,0.020238742],"study_design_scores_gemma":[0.00002877078,0.0022095747,0.996757,0.0000066292237,0.00005182792,0.000026888918,0.00048638682,0.0001321239,0.00018623965,0.000017640543,0.00009328403,0.0000036822876],"about_ca_topic_score_codex":0.0027383687,"about_ca_topic_score_gemma":0.005269046,"teacher_disagreement_score":0.0027383687,"about_ca_system_score_codex":0.00027012275,"about_ca_system_score_gemma":0.00023904761,"threshold_uncertainty_score":0.007988274},"labels":[],"label_agreement":null},{"id":"W3117411205","doi":"10.4236/fns.2020.1112077","title":"The Use of Ripe Plantain (&amp;lt;i&amp;gt;Musa spp&amp;lt;/i&amp;gt;.) and Peppercorns for Beverage Processing as a Pro-Vitamin A Carotenoid and Nutrition Catalyst","year":2020,"lang":"en","type":"article","venue":"Food and Nutrition Sciences","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Food science; Carotenoid; Musaceae; Vitamin; Food processing; Biology; Chemistry; Biotechnology; Horticulture; Biochemistry","score_opus":0.1505501074924113,"score_gpt":0.3149140714902981,"score_spread":0.1643639639978868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117411205","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":[0.9851751,0.0011407186,0.005974898,0.00014570414,0.00003233708,0.0003807804,0.0012886071,0.00007101182,0.005790843],"genre_scores_gemma":[0.96183044,0.0011164574,0.029173987,0.00015355542,0.00001778827,0.00037926872,0.0012113507,0.000037359227,0.0060797576],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994709,0.00012787912,0.000035743968,0.00012551149,0.00020434751,0.000035685825],"domain_scores_gemma":[0.99916315,0.00020770276,0.00030999994,0.000052703424,0.00020561507,0.000060778595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069150334,0.00034034596,0.00023130854,0.0009574431,0.00050386693,0.0006847937,0.0003579084,0.00031022605,0.0032090184],"category_scores_gemma":[0.00082493405,0.0001411947,0.0004040181,0.0008944209,0.00035015208,0.00045962585,0.00047055478,0.00045605,0.0005184435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018783695,0.0011357234,0.115672074,0.0027268336,0.00029776306,0.00092595123,0.0021448103,0.00057818356,0.65097237,0.0009695546,0.0015055608,0.22119279],"study_design_scores_gemma":[0.000028365925,0.0052183974,0.7577172,0.0002578857,0.00032243555,0.0014253174,0.0036106126,0.002092049,0.19801354,0.0009842758,0.030237185,0.00009270932],"about_ca_topic_score_codex":0.0017562982,"about_ca_topic_score_gemma":0.006132905,"teacher_disagreement_score":0.0032090184,"about_ca_system_score_codex":0.00030684366,"about_ca_system_score_gemma":0.00038378156,"threshold_uncertainty_score":0.010735273},"labels":[],"label_agreement":null},{"id":"W3121383538","doi":"10.4236/ojs.2021.111010","title":"Uncovering and Displaying the Coherent Groups of Rank Data by Exploratory Riffle Shuffling","year":2021,"lang":"en","type":"preprint","venue":"Open Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shuffling; Riffle; Rank (graph theory); Set (abstract data type); Contingency table; Combinatorics; Mathematics; Computer science; Statistics","score_opus":0.127473884003913,"score_gpt":0.34213104145804285,"score_spread":0.21465715745412986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121383538","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59070605,0.00042320858,0.39910832,0.00061485975,0.000027418084,0.00028371954,0.0034043668,0.0009324131,0.004499725],"genre_scores_gemma":[0.89418644,0.000099229175,0.10169764,0.000076451935,0.00003284296,0.00021709556,0.0028702072,0.000062581625,0.00075741974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964818,0.0020498272,0.00015848389,0.00053194084,0.000520073,0.00025791215],"domain_scores_gemma":[0.98083514,0.011065407,0.0022010345,0.0042023705,0.0011826621,0.00051341514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004602412,0.00054416765,0.0008933239,0.0045728264,0.0009277862,0.0018381324,0.00081296684,0.0008594741,0.0025109546],"category_scores_gemma":[0.021543304,0.00036303123,0.00093815173,0.003537678,0.0016647681,0.0019866752,0.0020456742,0.0011210317,0.000601815],"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":[0.0026753168,0.00067320885,0.21509977,0.0007214182,0.000522875,0.0016262712,0.009947794,0.09628099,0.026612276,0.19499838,0.013403486,0.4374382],"study_design_scores_gemma":[0.0001631584,0.00080036005,0.091009505,0.00013130979,0.00008992058,0.0009936348,0.0041305893,0.5671432,0.013222901,0.30791694,0.0141503215,0.00024824578],"about_ca_topic_score_codex":0.0022861029,"about_ca_topic_score_gemma":0.0026373183,"teacher_disagreement_score":0.004602412,"about_ca_system_score_codex":0.00094032363,"about_ca_system_score_gemma":0.0008323217,"threshold_uncertainty_score":0.024340212},"labels":[],"label_agreement":null},{"id":"W3137275681","doi":"10.18697/ajfand.97.20210","title":"Consumer intentions to buy nutrient-rich precooked bean snacks: Does sensory evaluation matter?","year":2021,"lang":"en","type":"article","venue":"African Journal of Food Agriculture Nutrition and Development","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Centre for International Agricultural Research; International Development Research Centre","keywords":"Sweetness; Taste; Food science; Flavour; Business; Consumption (sociology); Advertising; Marketing; Chemistry","score_opus":0.040771151245844316,"score_gpt":0.2752620122543605,"score_spread":0.23449086100851615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137275681","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":[0.99897027,0.000103879516,0.00003293977,0.00013314323,0.0000029653597,0.000007549317,0.00003235325,8.661019e-7,0.0007160892],"genre_scores_gemma":[0.99953735,0.00012455929,0.000050285387,0.000046709512,0.0000025631548,0.0000032166674,0.000030269266,6.30004e-7,0.00020435643],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956006,0.00015851052,0.000029691388,0.00005224496,0.000111867594,0.00008762102],"domain_scores_gemma":[0.99748296,0.0009890267,0.0008852952,0.00007792563,0.00032357918,0.0002413265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012814086,0.00017441624,0.00018717012,0.0002635075,0.00034495068,0.0007933548,0.00020345878,0.0005257379,0.0019788954],"category_scores_gemma":[0.0041183596,0.00026366432,0.00042439645,0.00030335668,0.00048226787,0.0004598967,0.0002704072,0.00065368594,0.00024985516],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013283241,0.00021412085,0.99305665,0.00003193029,0.000038937484,0.00007395811,0.0012031143,0.000024165032,0.00027593583,0.000038555998,0.000094318675,0.0048154187],"study_design_scores_gemma":[0.000002950764,0.00014713909,0.99724555,0.00001558454,0.000023063245,0.00003980858,0.0020800212,0.00020463453,0.00006665894,0.000031873056,0.00013751739,0.0000050447284],"about_ca_topic_score_codex":0.013213792,"about_ca_topic_score_gemma":0.026159888,"teacher_disagreement_score":0.013213792,"about_ca_system_score_codex":0.00027935233,"about_ca_system_score_gemma":0.00037080492,"threshold_uncertainty_score":0.026273727},"labels":[],"label_agreement":null},{"id":"W3139266469","doi":"10.1002/cche.10424","title":"Influence of premilling thermal treatments of yellow peas, navy beans, and fava beans on the flavor and end‐product quality of tortillas and pitas","year":2021,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Business Development Bank of Canada; Canadian International Grains Institute","funders":"","keywords":"Flavor; Food science; Roasting; Chemistry; Organoleptic; Sensory analysis","score_opus":0.03369110131478119,"score_gpt":0.27268564688955566,"score_spread":0.23899454557477445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139266469","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":[0.9991942,0.0002061158,0.00029049584,0.0000089822715,0.0000055762616,0.00001522795,0.000043800675,0.000009660772,0.00022583485],"genre_scores_gemma":[0.99806625,0.00015104478,0.0010887884,0.0000313683,0.000002598113,0.000017699529,0.000105525214,0.0000119941715,0.00052466313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998116,0.000043657954,0.000017418644,0.000048989707,0.00003939453,0.00003892733],"domain_scores_gemma":[0.9996092,0.00008541372,0.00012227043,0.000025484447,0.0000713981,0.00008631051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027425928,0.0003142761,0.0002616856,0.00014857516,0.00019019576,0.0004483686,0.00015194404,0.0001706346,0.0013048098],"category_scores_gemma":[0.00043988862,0.00019440566,0.00028549696,0.00014262728,0.0002682096,0.00021455668,0.0002020485,0.0005081308,0.00015354717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089864346,0.000082323815,0.0009416638,0.000052301955,0.000015991498,0.00002736855,0.00005158767,0.000048164675,0.9964605,0.000012303415,0.000011519386,0.0013975615],"study_design_scores_gemma":[0.0000290091,0.0057433364,0.081751294,0.000016945678,0.000093141345,0.00013650203,0.00028982927,0.00059802405,0.91045725,0.000020996884,0.00084497896,0.000018806],"about_ca_topic_score_codex":0.002524089,"about_ca_topic_score_gemma":0.0051355436,"teacher_disagreement_score":0.002524089,"about_ca_system_score_codex":0.00027765904,"about_ca_system_score_gemma":0.00020698634,"threshold_uncertainty_score":0.0050187707},"labels":[],"label_agreement":null},{"id":"W3147659269","doi":"10.1111/bmsp.12240","title":"Model‐based recursive partitioning of extended redundancy analysis with an application to nicotine dependence among US adults","year":2021,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Manitoba","funders":"","keywords":"Covariate; Recursive partitioning; Partition (number theory); Econometrics; Statistics; Mathematics; Hierarchy; Set (abstract data type); Redundancy (engineering); Computer science; Combinatorics","score_opus":0.02750667536499382,"score_gpt":0.324438908424291,"score_spread":0.29693223305929717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147659269","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":[0.037229236,0.00022947631,0.9612537,0.00015234126,0.000025596575,0.00013731816,0.00014494368,0.00047951192,0.00034784377],"genre_scores_gemma":[0.26248455,0.0002084041,0.73529404,0.00007427979,0.000040210743,0.00057165115,0.00052223325,0.00024383272,0.00056082435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936958,0.00515192,0.00018881932,0.000462571,0.0003243956,0.00017650964],"domain_scores_gemma":[0.99140817,0.0061090295,0.00049293635,0.0010698709,0.00079431816,0.00012560078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008938575,0.0011225861,0.0014356578,0.0019967526,0.000935524,0.00081520557,0.0013234724,0.0006115303,0.0015000227],"category_scores_gemma":[0.025769403,0.0005931618,0.002985468,0.0018553725,0.00069076166,0.00085562875,0.002458476,0.0012613648,0.00040630187],"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":[0.00065810804,0.0005596532,0.031946152,0.000729012,0.0024807372,0.0008201765,0.0029288882,0.41435227,0.010885663,0.057629026,0.0052562053,0.47175404],"study_design_scores_gemma":[0.000051630795,0.00019482545,0.006696838,0.000036123798,0.0001379734,0.00013101622,0.0001332299,0.9606264,0.0008929824,0.029143935,0.0018950349,0.00005994911],"about_ca_topic_score_codex":0.010069333,"about_ca_topic_score_gemma":0.012165734,"teacher_disagreement_score":0.010069333,"about_ca_system_score_codex":0.0007498322,"about_ca_system_score_gemma":0.001645921,"threshold_uncertainty_score":0.047272325},"labels":[],"label_agreement":null},{"id":"W3160804602","doi":"10.3390/beverages7020024","title":"The Use of Temporal Check-All-That-Apply and Category Scaling by Experienced Panellists to Evaluate Sweet and Dry Ciders","year":2021,"lang":"en","type":"article","venue":"Beverages","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Aftertaste; Food science; Chemistry; Taste","score_opus":0.12452912845499059,"score_gpt":0.31986704964535084,"score_spread":0.19533792119036025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160804602","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":[0.9097926,0.0010417912,0.066880606,0.00022485798,0.00050009787,0.004416252,0.0015235597,0.0004157616,0.015204362],"genre_scores_gemma":[0.78115934,0.0013166735,0.19998346,0.00084232545,0.00022282758,0.0054551647,0.0020019724,0.00030113352,0.008717037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9949367,0.0016879075,0.00040337568,0.00080070266,0.001987622,0.00018371393],"domain_scores_gemma":[0.98670083,0.003778576,0.00154598,0.0010896816,0.006328171,0.00055681664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008544132,0.00085095485,0.00064181816,0.0016859664,0.0007587871,0.0009881915,0.00061351404,0.00063224265,0.004340931],"category_scores_gemma":[0.013453238,0.00024003308,0.000842919,0.0010813907,0.0005985387,0.0009805439,0.0010195922,0.0009218547,0.0011895156],"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":[0.0079721585,0.0026402667,0.26084322,0.0022935471,0.00074269326,0.0005219058,0.009485604,0.0016051426,0.32291943,0.0015850266,0.009809533,0.37958148],"study_design_scores_gemma":[0.00027411385,0.008838568,0.8922111,0.00042143432,0.00036505438,0.00094374164,0.0075979885,0.007907084,0.055690017,0.0019852442,0.023398856,0.00036675832],"about_ca_topic_score_codex":0.0011854446,"about_ca_topic_score_gemma":0.004139207,"teacher_disagreement_score":0.008544132,"about_ca_system_score_codex":0.00038686785,"about_ca_system_score_gemma":0.00044003062,"threshold_uncertainty_score":0.04518622},"labels":[],"label_agreement":null},{"id":"W3164559651","doi":"10.1002/jsfa.11346","title":"Identification of sensory properties driving consumers' liking of commercially available kale and arugula","year":2021,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Aroma; Quantitative Descriptive Analysis; Taste; Food science; Leafy; Leafy vegetables; Mathematics; Biology; Horticulture","score_opus":0.03384006067610539,"score_gpt":0.24225259156117576,"score_spread":0.20841253088507036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164559651","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":[0.9991373,0.000049102367,0.00009061659,0.000012222884,0.0000014963034,0.0000085599995,0.000052188854,0.0000030641368,0.00064543047],"genre_scores_gemma":[0.99890554,0.000040036448,0.00043388692,0.000012101697,0.0000016577625,0.000009471747,0.000102714635,0.0000021869323,0.00049243204],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997843,0.00006126438,0.00001740092,0.00004012138,0.00006966091,0.00002727971],"domain_scores_gemma":[0.99895966,0.000434253,0.00027193743,0.000045457007,0.0002170919,0.00007152502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062034605,0.00018310835,0.00020578127,0.00025131303,0.00025398028,0.00068611454,0.00014160323,0.00024042361,0.0047311927],"category_scores_gemma":[0.0011029236,0.000084296626,0.0003307822,0.0002082652,0.000206282,0.00041245235,0.00030158,0.00036553913,0.00036685454],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003104355,0.00095917383,0.8421799,0.00071244536,0.00019134417,0.0004951186,0.0058872225,0.00019519811,0.1062651,0.00020772609,0.0005125831,0.039289836],"study_design_scores_gemma":[0.000008400131,0.0012457,0.991698,0.0000259776,0.00004912604,0.0002300732,0.002193439,0.0003877771,0.0035397327,0.00003976918,0.0005687933,0.000013208458],"about_ca_topic_score_codex":0.0018142281,"about_ca_topic_score_gemma":0.0026837601,"teacher_disagreement_score":0.0047311927,"about_ca_system_score_codex":0.0001530274,"about_ca_system_score_gemma":0.00010956499,"threshold_uncertainty_score":0.015827358},"labels":[],"label_agreement":null},{"id":"W3168771965","doi":"10.1111/1750-3841.15776","title":"Acceptance of oat‐based beverages tailored for patients with cancer","year":2021,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Food science; Avena; Cancer; Health benefits; Food products; Traditional medicine; Biology; Internal medicine; Agronomy","score_opus":0.040907530345540184,"score_gpt":0.30347576055320313,"score_spread":0.26256823020766296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168771965","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":[0.998181,0.00030838547,0.0001759044,0.00011584843,0.000009298883,0.000025479316,0.00009108392,0.0000064941682,0.0010864666],"genre_scores_gemma":[0.99861896,0.00026098356,0.00036857656,0.00017482185,0.0000072811176,0.000018720577,0.00009158363,0.0000028591962,0.00045628645],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99960595,0.0001482764,0.000036006368,0.00003999836,0.00012572228,0.000044072945],"domain_scores_gemma":[0.99884415,0.00035893678,0.00036667162,0.00004348218,0.0002563628,0.00013038008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007423614,0.00017576116,0.00026415903,0.00018242592,0.00023771958,0.000531476,0.00011964158,0.00033338173,0.0033649474],"category_scores_gemma":[0.0026555513,0.000059075835,0.0004729074,0.00014440836,0.00014093173,0.000372349,0.00035502747,0.0004433803,0.00026508063],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051001003,0.0019085628,0.7691825,0.0014291592,0.00071339787,0.00048453486,0.010226251,0.00029486534,0.04305825,0.00020612596,0.0018808539,0.16551547],"study_design_scores_gemma":[0.00015020218,0.0075753154,0.9684244,0.00018848146,0.00044320105,0.0008060283,0.009614837,0.0006471017,0.006220808,0.00022643738,0.005624064,0.000079076366],"about_ca_topic_score_codex":0.00092635304,"about_ca_topic_score_gemma":0.0012615835,"teacher_disagreement_score":0.0033649474,"about_ca_system_score_codex":0.00013257565,"about_ca_system_score_gemma":0.00017018968,"threshold_uncertainty_score":0.011256874},"labels":[],"label_agreement":null},{"id":"W3168844598","doi":"10.1080/07373937.2021.1933514","title":"Food quality evaluation in drying: Structuring of measurable food attributes into multi-dimensional fuzzy sets","year":2021,"lang":"en","type":"article","venue":"Drying Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy logic; Data mining; Defuzzification; Neuro-fuzzy; Computer science; Fuzzy classification; Artificial intelligence; Fuzzy clustering; Fuzzy set operations; Fuzzy number; Adaptive neuro fuzzy inference system; Mathematics; Fuzzy set; Pattern recognition (psychology); Machine learning; Fuzzy control system","score_opus":0.15188552421265644,"score_gpt":0.35623183836471284,"score_spread":0.2043463141520564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168844598","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":[0.2923078,0.00066160056,0.7016353,0.0001644009,0.00004935692,0.00020477435,0.00023107426,0.00040340793,0.004342289],"genre_scores_gemma":[0.7885021,0.00031425146,0.21000047,0.00005040611,0.000013627151,0.0001040007,0.00020604965,0.000024510095,0.0007845255],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989666,0.00022480045,0.00007829858,0.00019815161,0.0004846229,0.00004756682],"domain_scores_gemma":[0.99918586,0.000246043,0.00019085302,0.00009122293,0.00025788648,0.000028203116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014335444,0.00047108834,0.00047729665,0.0013713235,0.00040334385,0.0011602616,0.00049229915,0.0004183109,0.0007215603],"category_scores_gemma":[0.0027516023,0.0002181599,0.000626961,0.0010138042,0.00066080096,0.0008994095,0.00060497224,0.0004937725,0.00013565422],"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":[0.0008373511,0.0004544641,0.04058944,0.00073039584,0.00023546985,0.00038134295,0.0014030536,0.15773772,0.26712736,0.009838972,0.0012465048,0.51941794],"study_design_scores_gemma":[0.000029892606,0.0007599014,0.09689678,0.0001427242,0.00010516549,0.0003658607,0.0008891131,0.7721285,0.110061325,0.013799336,0.004640015,0.00018136324],"about_ca_topic_score_codex":0.0029772017,"about_ca_topic_score_gemma":0.0028032146,"teacher_disagreement_score":0.0029772017,"about_ca_system_score_codex":0.0006603672,"about_ca_system_score_gemma":0.0004541765,"threshold_uncertainty_score":0.007581413},"labels":[],"label_agreement":null},{"id":"W3169520658","doi":"10.1016/j.foodres.2021.110534","title":"Carbonated emotions: Consumers’ sensory perception and emotional response to carbonated and still fruit juices","year":2021,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Carbonation; Flavour; Mouthfeel; Food science; Perception; Psychology; Taste; Flavor; Chemistry","score_opus":0.13309595635171403,"score_gpt":0.386358644788319,"score_spread":0.25326268843660493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169520658","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":[0.99899226,0.000098297154,0.0000826527,0.000028276281,0.000008282271,0.000018349892,0.00006990509,0.0000021632748,0.00069976796],"genre_scores_gemma":[0.99829656,0.00011596001,0.00041603617,0.00006104768,0.000011381408,0.000032962475,0.0001280831,0.000003581305,0.0009344291],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998599,0.000030763546,0.000009003114,0.000028054958,0.000047280322,0.00002484214],"domain_scores_gemma":[0.9996093,0.00012855533,0.00008852786,0.000017816348,0.00008207565,0.00007368027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028332797,0.00022697063,0.00021042893,0.00016788326,0.0002543421,0.0006661291,0.0001206003,0.0004462224,0.004596738],"category_scores_gemma":[0.0014108011,0.0001460676,0.00027724795,0.00022182739,0.0003169499,0.00047522556,0.0005180997,0.00050919823,0.00014356714],"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":[0.07864291,0.0028741076,0.32614756,0.0012625292,0.00059143314,0.0006028279,0.01199962,0.0005146307,0.4976899,0.0006783305,0.0016939586,0.07730214],"study_design_scores_gemma":[0.00013686177,0.0023579337,0.9846182,0.000024965844,0.00010506623,0.00018775722,0.002874867,0.000739276,0.007953778,0.00011212896,0.00085915666,0.000029908086],"about_ca_topic_score_codex":0.0028933727,"about_ca_topic_score_gemma":0.0040639937,"teacher_disagreement_score":0.004596738,"about_ca_system_score_codex":0.00023635026,"about_ca_system_score_gemma":0.0001579231,"threshold_uncertainty_score":0.015377641},"labels":[],"label_agreement":null},{"id":"W3180370339","doi":"10.21203/rs.3.rs-1631332/v1","title":"Comparison of Canonical Correlation and Partial Least Squares analyses of simulated and empirical data","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Baycrest Hospital; Simon Fraser University","funders":"","keywords":"Statistics; Canonical correlation; Sample size determination; Principal component analysis; Partial least squares regression; Mathematics; Multivariate statistics; Correlation; Sample (material); Reproducibility; Partial correlation; Block (permutation group theory); Block design; Chemistry; Chromatography; Combinatorics","score_opus":0.6493061486081897,"score_gpt":0.6110466207834215,"score_spread":0.03825952782476816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180370339","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":[0.38595232,0.0009864173,0.6042133,0.00069900916,0.00022819801,0.0003124629,0.0010439266,0.0030548237,0.003509607],"genre_scores_gemma":[0.7503842,0.00035490468,0.2451179,0.00015199067,0.000055350854,0.00045114508,0.0021257617,0.0006519092,0.0007068617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9784275,0.017170267,0.00068117597,0.0017610601,0.0016972102,0.00026280538],"domain_scores_gemma":[0.79981595,0.16668752,0.0041490532,0.013310248,0.015314596,0.0007226226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031649236,0.0010166958,0.00077094126,0.0026179848,0.00064988807,0.0018394749,0.0011278356,0.0006988455,0.0018645888],"category_scores_gemma":[0.16973583,0.0005246756,0.001876273,0.0025117968,0.0014650061,0.0017779921,0.0012237774,0.0012391225,0.00070455],"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":[0.002242198,0.00040486723,0.08313541,0.001457524,0.0034414174,0.000316296,0.0018821234,0.57011783,0.0063762018,0.023797672,0.00780763,0.29902083],"study_design_scores_gemma":[0.000069571885,0.00019635382,0.01814515,0.0000729926,0.00011041406,0.0001244261,0.00024126357,0.96817994,0.0031377298,0.0077879354,0.0018556346,0.00007868927],"about_ca_topic_score_codex":0.0060066665,"about_ca_topic_score_gemma":0.006410457,"teacher_disagreement_score":0.031649236,"about_ca_system_score_codex":0.0010385172,"about_ca_system_score_gemma":0.0023316958,"threshold_uncertainty_score":0.16737914},"labels":[],"label_agreement":null},{"id":"W3183114291","doi":"10.1016/j.foodres.2021.110608","title":"Free word association perceptions of red meats; beef is ‘yummy’, bison is ‘lean game meat’, horse is ‘off limits’","year":2021,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Word Association; Red meat; Perception; Consumption (sociology); Food science; Biology; Psychology","score_opus":0.15727236286703608,"score_gpt":0.39920360851091946,"score_spread":0.24193124564388338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183114291","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":[0.9850103,0.00021334959,0.0007597561,0.0002448497,0.00019115981,0.000039885854,0.00022120897,0.000021566046,0.013297923],"genre_scores_gemma":[0.9930674,0.00014941835,0.00095341227,0.00046303167,0.000069224654,0.00007780058,0.0003625204,0.00003058458,0.0048266198],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988576,0.00037105472,0.00012278238,0.000113779526,0.00044153817,0.00009323472],"domain_scores_gemma":[0.99083847,0.0048049577,0.0018207497,0.00031695215,0.0014063613,0.00081248843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016218139,0.000389295,0.00041566492,0.0008453397,0.00091550825,0.0018999641,0.00022267291,0.0010109517,0.022104578],"category_scores_gemma":[0.012694899,0.00019916642,0.0003839823,0.00045657525,0.00088631565,0.0020756095,0.0016641731,0.0013544468,0.0013969224],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015411755,0.0028315801,0.5605639,0.002295071,0.00091258145,0.0010579509,0.2012063,0.0008492423,0.08427541,0.01083132,0.0161185,0.10364638],"study_design_scores_gemma":[0.00016258997,0.0016512952,0.8726985,0.00031611876,0.00025614,0.0006168604,0.0976392,0.0013247529,0.0029566477,0.004781404,0.017390594,0.00020592225],"about_ca_topic_score_codex":0.0036414894,"about_ca_topic_score_gemma":0.005356295,"teacher_disagreement_score":0.022104578,"about_ca_system_score_codex":0.00041001162,"about_ca_system_score_gemma":0.00024788975,"threshold_uncertainty_score":0.07394725},"labels":[],"label_agreement":null},{"id":"W3186109931","doi":"10.1111/joss.12688","title":"Projective mapping and ultra‐flash profile studies should include a list of descriptors and definitions: An investigation into descriptors used by untrained panelists","year":2021,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Department of Agriculture, Nova Scotia","keywords":"Task (project management); Psychology; Mouthfeel; Computer science; Debiasing; Information retrieval; Artificial intelligence; Social psychology","score_opus":0.32437847643611223,"score_gpt":0.36501386540156944,"score_spread":0.04063538896545721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186109931","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":[0.9892061,0.00026981364,0.0066203657,0.00023522008,0.000044900033,0.0002982085,0.00004473701,0.00002173685,0.0032589082],"genre_scores_gemma":[0.98979795,0.00027116362,0.0076668076,0.00031546192,0.000023807306,0.0005940806,0.00009737751,0.00001822145,0.0012150676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99113643,0.0056368154,0.0005876543,0.00071715034,0.0015014323,0.0004204605],"domain_scores_gemma":[0.97402376,0.013972203,0.003256695,0.0024079054,0.0055130012,0.0008264046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018534018,0.00047449727,0.0004222343,0.0010972928,0.0013857558,0.0022824123,0.0009741496,0.00085638,0.002468933],"category_scores_gemma":[0.040421296,0.00031949254,0.000508204,0.0007299369,0.0015071162,0.002976385,0.0026573946,0.0011028007,0.0005307154],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012015386,0.0013677155,0.3848644,0.001831917,0.0001392821,0.0007496804,0.36492902,0.00040068993,0.052682094,0.0037457366,0.0036284674,0.18445948],"study_design_scores_gemma":[0.0001124166,0.0041918294,0.49547705,0.0010808448,0.00009788613,0.0015281696,0.443704,0.003004807,0.01531109,0.0040842462,0.031089034,0.00031857946],"about_ca_topic_score_codex":0.00094820117,"about_ca_topic_score_gemma":0.0016452176,"teacher_disagreement_score":0.018534018,"about_ca_system_score_codex":0.0005213808,"about_ca_system_score_gemma":0.00067912607,"threshold_uncertainty_score":0.09801841},"labels":[],"label_agreement":null},{"id":"W3195567601","doi":"10.1002/sam.11543","title":"Parallel coordinate order for<scp>high‐dimensional</scp>data","year":2021,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parallel coordinates; Computer science; Visualization; Coordinate descent; Data mining; Dimension (graph theory); Data visualization; Data structure; Theoretical computer science; Algorithm; Mathematics","score_opus":0.13471235951114346,"score_gpt":0.3734952579050034,"score_spread":0.2387828983938599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195567601","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":[0.020303715,0.0001645806,0.975043,0.00046097528,0.000045842156,0.00013863308,0.0006891608,0.0012956767,0.0018583861],"genre_scores_gemma":[0.16667588,0.00027407138,0.82911915,0.000090853755,0.00006078669,0.00027869048,0.0013557719,0.00040696596,0.0017378277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983063,0.0006229086,0.00014058113,0.00026013938,0.00055324385,0.00011686038],"domain_scores_gemma":[0.9963748,0.0013779459,0.0005104963,0.0007085486,0.0008554025,0.000172822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017078967,0.0009527612,0.0009561814,0.0023572105,0.00088458526,0.002576103,0.00083881064,0.0006479461,0.0046051242],"category_scores_gemma":[0.006837036,0.0003470752,0.0008801071,0.003989013,0.0013099012,0.0022442662,0.0015067274,0.0015685814,0.0009851087],"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":[0.0005588702,0.00030103995,0.0073921154,0.0009248366,0.00011578022,0.0007745127,0.00092965807,0.24271841,0.031753786,0.2654188,0.027377605,0.42173454],"study_design_scores_gemma":[0.000051493134,0.00018203903,0.002510826,0.00006360895,0.000024559151,0.00037198592,0.00032168254,0.83915323,0.014541459,0.120902635,0.021799909,0.000076612756],"about_ca_topic_score_codex":0.004329857,"about_ca_topic_score_gemma":0.0055644023,"teacher_disagreement_score":0.0046051242,"about_ca_system_score_codex":0.001183424,"about_ca_system_score_gemma":0.0020537993,"threshold_uncertainty_score":0.015405655},"labels":[],"label_agreement":null},{"id":"W3196416080","doi":"10.3390/foods10092082","title":"Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates","year":2021,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":33,"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":"Alberta Innovates","keywords":"Sweetness; Sugar; Dark chocolate; Food science; Milk Chocolate; Sensory system; Perception; Added sugar; Chemistry; Psychology; Cognitive psychology","score_opus":0.05002006097998545,"score_gpt":0.30965816930180917,"score_spread":0.2596381083218237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196416080","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":[0.9989287,0.000038246762,0.0007735187,0.0000058096666,0.0000028375957,0.0000069628827,0.000027769682,0.000007661311,0.00020839799],"genre_scores_gemma":[0.99692,0.0001012394,0.0019915677,0.000032405984,0.0000026391708,0.00001812049,0.000086383414,0.000011150335,0.000836522],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998802,0.000023603277,0.0000072898133,0.000015231865,0.000057900466,0.000015799864],"domain_scores_gemma":[0.9997439,0.000067318666,0.0000535407,0.000019263001,0.000089583205,0.000026446925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001771021,0.00022473068,0.00021336162,0.00019879133,0.00008766494,0.00035186234,0.00009234792,0.00018108646,0.0017012844],"category_scores_gemma":[0.00067837245,0.00013140342,0.00029696955,0.0001589119,0.00015236784,0.00028135942,0.0002939507,0.00032397592,0.00012169416],"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":[0.00083835976,0.00007901679,0.0034171704,0.00007703363,0.00002937674,0.0001152622,0.00026088316,0.0001979481,0.98674136,0.000028824978,0.00004565864,0.008169185],"study_design_scores_gemma":[0.00006331263,0.0049390993,0.39810932,0.00003860856,0.0001915115,0.0008726538,0.0020415527,0.0045846184,0.5868499,0.00015695288,0.0020567426,0.000095715164],"about_ca_topic_score_codex":0.0007416539,"about_ca_topic_score_gemma":0.0009575511,"teacher_disagreement_score":0.0017012844,"about_ca_system_score_codex":0.000112401285,"about_ca_system_score_gemma":0.000070122616,"threshold_uncertainty_score":0.005691409},"labels":[],"label_agreement":null},{"id":"W3205273015","doi":"10.3390/foods10102393","title":"Effect of Rosemary (Rosmarinus officinalis L.) Supplementation on Probiotic Yoghurt: Physicochemical Properties, Microbial Content, and Sensory Attributes","year":2021,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rosmarinus; Food science; Officinalis; Flavour; Lactobacillus rhamnosus; Streptococcus thermophilus; Chemistry; Lactobacillus; Lactic acid; Probiotic; Bacteria; Biology; Essential oil; Fermentation; Botany","score_opus":0.06329278784841855,"score_gpt":0.27479573882827185,"score_spread":0.2115029509798533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205273015","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":[0.9952636,0.0036302323,0.0002056038,0.00012468324,0.00004014725,0.000042803782,0.00012649414,0.000026290894,0.0005401695],"genre_scores_gemma":[0.9941024,0.0019614156,0.0015014992,0.00017197034,0.000025472942,0.000047540958,0.00024774167,0.000018106019,0.0019237755],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980265,0.000057410645,0.000019893298,0.000042786287,0.00003760396,0.00003957402],"domain_scores_gemma":[0.99979466,0.000029159233,0.00005981523,0.00001205385,0.000043399676,0.000060920338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022118872,0.00067492336,0.0010793647,0.00035744807,0.00025778168,0.00055614323,0.000309569,0.00048809784,0.0012055798],"category_scores_gemma":[0.00037460896,0.0002213098,0.0005783348,0.00027660982,0.00022663285,0.0004459443,0.00043500998,0.000829998,0.00017154848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006771755,0.0018822569,0.0006719732,0.00067708857,0.00010371535,0.00017619472,0.00015900933,0.000121173915,0.9790082,0.000052524,0.00012788804,0.010248137],"study_design_scores_gemma":[0.00066260266,0.050958082,0.05201989,0.00031730474,0.00095607154,0.00044110994,0.00070589106,0.0016546445,0.88030976,0.00015640203,0.011721427,0.00009687581],"about_ca_topic_score_codex":0.001343925,"about_ca_topic_score_gemma":0.0023335745,"teacher_disagreement_score":0.001343925,"about_ca_system_score_codex":0.00017317322,"about_ca_system_score_gemma":0.00026273084,"threshold_uncertainty_score":0.0040330887},"labels":[],"label_agreement":null},{"id":"W3207415914","doi":"10.1111/joss.12720","title":"Using check‐all‐that‐apply to evaluate wine and food pairings: An investigation with white wines","year":2021,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Wine; White Wine; Psychology; Sweetness; Perception; Food science; Wine tasting; Social psychology; Taste; Chemistry","score_opus":0.31539837277924004,"score_gpt":0.3795914808569483,"score_spread":0.06419310807770828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207415914","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":[0.99855596,0.000015438854,0.0004651013,0.0000269234,0.000007682197,0.00014563302,0.000020025718,0.000004418099,0.00075880566],"genre_scores_gemma":[0.99257565,0.00007579028,0.0051112953,0.0001440325,0.000012151312,0.0004915571,0.000086146276,0.0000111398085,0.0014923054],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970458,0.0016474152,0.00015970363,0.00028583128,0.0006900395,0.00017111866],"domain_scores_gemma":[0.9850545,0.009122414,0.0014760365,0.0011002544,0.002607263,0.0006395317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077554267,0.00039023336,0.000400847,0.0005445604,0.0013039229,0.0012191329,0.0004902388,0.0006908308,0.0021820748],"category_scores_gemma":[0.01920184,0.00038370598,0.0006013686,0.0004292189,0.00066367077,0.0009971168,0.0011374565,0.0008743919,0.0005083256],"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":[0.0035016465,0.02212077,0.6377086,0.0009267342,0.00019027494,0.001954339,0.1559795,0.00041884588,0.041854106,0.0011235224,0.0019268749,0.13229474],"study_design_scores_gemma":[0.00024185628,0.01752784,0.84001404,0.00023014504,0.00018286973,0.0011843247,0.10062271,0.004209198,0.019877743,0.001219031,0.014487656,0.00020245464],"about_ca_topic_score_codex":0.0020801448,"about_ca_topic_score_gemma":0.0045880303,"teacher_disagreement_score":0.0077554267,"about_ca_system_score_codex":0.00045633764,"about_ca_system_score_gemma":0.0005647684,"threshold_uncertainty_score":0.04101509},"labels":[],"label_agreement":null},{"id":"W3208681303","doi":"10.6000/1927-520x.2021.10.12","title":"Sensory Properties and Consumer Liking of Buffalo Stracchino Cheese","year":2021,"lang":"en","type":"article","venue":"Journal of Buffalo Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taste; Food science; Flavor; Sensory system; Sensory analysis; Psychology; Chemistry; Cognitive psychology","score_opus":0.09469979909894705,"score_gpt":0.2948460033927048,"score_spread":0.20014620429375773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208681303","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":[0.99967,0.00003526401,0.000058300324,0.0000056126123,0.00000120604,0.000003907689,0.000029709952,0.0000018117863,0.000194174],"genre_scores_gemma":[0.9993455,0.000058737496,0.00015505421,0.000016783106,0.0000046190044,0.00000637267,0.00012209627,0.0000028973657,0.0002878909],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998776,0.000022650962,0.000010170669,0.000024313707,0.00004826246,0.000017005592],"domain_scores_gemma":[0.9997216,0.00007597708,0.00008316112,0.000016473323,0.000053213622,0.00004951052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029413152,0.0002140683,0.0002736069,0.00035694477,0.00014393684,0.00030434827,0.000078877885,0.00025133963,0.0020749231],"category_scores_gemma":[0.0007307425,0.00010603087,0.00028387742,0.000147951,0.00016244495,0.0001993722,0.00022840626,0.00021423289,0.00017073484],"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":[0.005120298,0.0004544705,0.2741547,0.00030245,0.00018953533,0.00075179973,0.001459527,0.00023258393,0.7010369,0.000066759494,0.00018403748,0.016046975],"study_design_scores_gemma":[0.000012845912,0.0012743947,0.9885389,0.00000864946,0.00003703596,0.00035455448,0.00047808333,0.0002940749,0.008750775,0.000024070152,0.0002152245,0.000011362434],"about_ca_topic_score_codex":0.0009588777,"about_ca_topic_score_gemma":0.001143296,"teacher_disagreement_score":0.0020749231,"about_ca_system_score_codex":0.00008863827,"about_ca_system_score_gemma":0.00006365335,"threshold_uncertainty_score":0.0069413185},"labels":[],"label_agreement":null},{"id":"W3211956384","doi":"10.1016/j.foodqual.2021.104456","title":"An analysis of the sensory properties, emotional responses and social settings associated with non-alcoholic beer","year":2021,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Perception; Psychology; Focus group; Consumption (sociology); Medicine; Clinical psychology; Marketing","score_opus":0.22885854642460218,"score_gpt":0.33085004375753096,"score_spread":0.10199149733292878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211956384","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":[0.99947184,0.00003110656,0.00019500521,0.0000031081988,9.729698e-7,0.000012174814,0.00009841095,0.000002310161,0.00018497863],"genre_scores_gemma":[0.9988695,0.000038867845,0.0004909199,0.0000067140354,0.0000021968638,0.000020586784,0.00017242067,0.000002922161,0.00039588608],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998147,0.00005586117,0.000014039821,0.000023811264,0.000055444587,0.00003620225],"domain_scores_gemma":[0.9993862,0.00023634461,0.000110671324,0.000032492313,0.00015326717,0.000081022175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038401192,0.00024468917,0.00023256573,0.0007411495,0.0003659945,0.0003789425,0.00014529178,0.00015876605,0.0015088997],"category_scores_gemma":[0.0008683938,0.00011140699,0.00047302712,0.0007569596,0.00023640253,0.00016382452,0.00029762145,0.00024567568,0.000093191244],"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":[0.015805937,0.0018421486,0.3836948,0.0004498272,0.0005832232,0.0007265751,0.0015857959,0.0007369582,0.5444382,0.0002732045,0.00024866464,0.049614742],"study_design_scores_gemma":[0.000011199134,0.0016681482,0.9814922,0.0000044368066,0.00008813985,0.00026504556,0.00063476316,0.0007489159,0.014824979,0.000056326448,0.00019213914,0.000013742419],"about_ca_topic_score_codex":0.0037233534,"about_ca_topic_score_gemma":0.005531373,"teacher_disagreement_score":0.0037233534,"about_ca_system_score_codex":0.00025206726,"about_ca_system_score_gemma":0.00022529408,"threshold_uncertainty_score":0.0074033737},"labels":[],"label_agreement":null},{"id":"W4200179880","doi":"10.3390/beverages7040080","title":"Consumer Perception of Milk and Plant-Based Alternatives Added to Coffee","year":2021,"lang":"en","type":"article","venue":"Beverages","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Sweetness; Food science; Soy milk; Business; Flavor; Biology","score_opus":0.04003553397287628,"score_gpt":0.2847784590946583,"score_spread":0.24474292512178203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200179880","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":[0.99938774,0.00007751348,0.000051411003,0.00002476235,0.000003070215,0.0000066049274,0.000020053621,0.0000018395771,0.00042698582],"genre_scores_gemma":[0.99925405,0.00008913895,0.00023180629,0.000046795823,0.0000053047024,0.000005862532,0.000036749276,0.000001588972,0.00032870902],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956185,0.00016088711,0.00002559401,0.000043851694,0.0001643795,0.000043391057],"domain_scores_gemma":[0.9989543,0.00036296374,0.0003004367,0.000037832582,0.0002267216,0.00011763878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069870666,0.0001769205,0.00025017292,0.00034237016,0.00029896977,0.000679283,0.00010861742,0.00037810876,0.0018074207],"category_scores_gemma":[0.0018343874,0.00011251303,0.00042437259,0.00021658122,0.0002095228,0.00032981203,0.0003611985,0.00033638693,0.00009530716],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011849415,0.001376388,0.79433024,0.0010694222,0.00077413226,0.0015004956,0.02112761,0.0003784578,0.107865945,0.0003146089,0.000920821,0.0584925],"study_design_scores_gemma":[0.000047268335,0.0035259286,0.9745165,0.000069859605,0.00020690172,0.0006079822,0.014895831,0.0006079588,0.003559749,0.00009260668,0.0018209992,0.00004839412],"about_ca_topic_score_codex":0.002430189,"about_ca_topic_score_gemma":0.0031082723,"teacher_disagreement_score":0.002430189,"about_ca_system_score_codex":0.00017128189,"about_ca_system_score_gemma":0.000083470084,"threshold_uncertainty_score":0.0060464144},"labels":[],"label_agreement":null},{"id":"W4200286567","doi":"10.1111/jtxs.12656","title":"Understanding relations between rheology, tribology, and sensory perception of modified texture foods","year":2021,"lang":"en","type":"article","venue":"Journal of Texture Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rheology; Tribology; Texture (cosmology); Sensory system; Perception; Materials science; Food science; Artificial intelligence; Computer science; Psychology; Composite material; Cognitive psychology; Chemistry","score_opus":0.2747867501482459,"score_gpt":0.36791454948636637,"score_spread":0.09312779933812049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200286567","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":[0.9987643,0.00038338514,0.00060033955,0.000013314377,0.0000022071592,0.0000066420166,0.000025184481,0.0000028933675,0.00020166201],"genre_scores_gemma":[0.9991072,0.00015294699,0.00056014844,0.00001664862,0.0000027408876,0.000006110188,0.000031426127,0.0000016806496,0.00012115545],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998777,0.000027500068,0.000009579953,0.000029545923,0.000035286415,0.000020379603],"domain_scores_gemma":[0.9996166,0.00012242139,0.000132388,0.000016865262,0.00007929592,0.00003242526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038587057,0.00020535025,0.0002222737,0.00038804614,0.0001065558,0.0005326259,0.0001226837,0.0003147178,0.0008522462],"category_scores_gemma":[0.0007460485,0.00018389711,0.00018197908,0.00016699175,0.00020830214,0.00049469824,0.00022532481,0.0002875227,0.000092349954],"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":[0.00059853407,0.0000796908,0.02461253,0.00011231208,0.00003260874,0.00008461727,0.00021374214,0.00011133321,0.966404,0.000023803865,0.000020319147,0.0077065597],"study_design_scores_gemma":[0.000009637372,0.0017169706,0.84049904,0.000025032967,0.00007055767,0.0004851099,0.001364779,0.0017788077,0.15335894,0.00012872141,0.0005384764,0.000023945651],"about_ca_topic_score_codex":0.00043455843,"about_ca_topic_score_gemma":0.00043398797,"teacher_disagreement_score":0.0008522462,"about_ca_system_score_codex":0.00009877958,"about_ca_system_score_gemma":0.000073883784,"threshold_uncertainty_score":0.002851069},"labels":[],"label_agreement":null},{"id":"W4210633483","doi":"10.3390/foods11030457","title":"Temporal Sensory Profiles of Regular and Sodium-Reduced Foods Elicited by Temporal Dominance of Sensations (TDS) and Temporal Check-All-That-Apply (TCATA)","year":2022,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Food science; Sensory system; Sodium; Dominance (genetics); Chemistry; Psychology; Biochemistry; Cognitive psychology","score_opus":0.04739721129223304,"score_gpt":0.2795016764154787,"score_spread":0.23210446512324565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210633483","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":[0.9923947,0.00020733675,0.005357965,0.000021090082,0.000022388718,0.00013370413,0.00032819412,0.000033553562,0.0015009976],"genre_scores_gemma":[0.9848822,0.0003527526,0.012481729,0.00008909775,0.00001350651,0.00022850456,0.00036389605,0.00002540589,0.0015629585],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997062,0.000050720355,0.000020328398,0.00005588793,0.0001413732,0.00002542766],"domain_scores_gemma":[0.99935454,0.00016798462,0.00017382471,0.000048880906,0.00019521192,0.000059483715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000617823,0.0002978843,0.00025234043,0.00042575912,0.0001458915,0.00032135475,0.00015541508,0.00026438048,0.0021979297],"category_scores_gemma":[0.0014637469,0.0001325357,0.000372761,0.00035682486,0.00022727641,0.00033212075,0.0004360762,0.000421755,0.00018180479],"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":[0.0098713515,0.0006985935,0.05735611,0.0009238524,0.00021694534,0.00016747777,0.0011844226,0.0005226598,0.84858894,0.00033696363,0.00051934994,0.079613306],"study_design_scores_gemma":[0.00011888059,0.010595969,0.8634598,0.000083848594,0.00024632184,0.0008127881,0.0016691642,0.0033281052,0.11649705,0.00063243945,0.0024565572,0.000098970275],"about_ca_topic_score_codex":0.0010497315,"about_ca_topic_score_gemma":0.0026631965,"teacher_disagreement_score":0.0021979297,"about_ca_system_score_codex":0.00014461244,"about_ca_system_score_gemma":0.00018554782,"threshold_uncertainty_score":0.0073527694},"labels":[],"label_agreement":null},{"id":"W4211095815","doi":"10.1111/joss.12735","title":"Apple flavor and its effects on sensory characteristics and consumer preference","year":2022,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Vineland Research and Innovation Centre","funders":"","keywords":"Flavor; Preference; Taste; Perception; Sensory system; Variety (cybernetics); Sensory analysis; Food science; Marketing; Psychology; Mathematics; Business; Cognitive psychology; Biology; Statistics","score_opus":0.11819010740269319,"score_gpt":0.3143182515973139,"score_spread":0.1961281441946207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211095815","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":[0.99897575,0.00007740605,0.000108928434,0.000008561988,0.0000022308477,0.00001010132,0.000070012014,0.000002948046,0.0007440306],"genre_scores_gemma":[0.99906236,0.000051132665,0.00023115393,0.000011615608,0.0000015615781,0.000005588297,0.00007365568,0.0000022679997,0.0005607463],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975175,0.00007091457,0.00001295484,0.000034503664,0.00010042044,0.000029378376],"domain_scores_gemma":[0.9989889,0.00036660235,0.0002109346,0.000045990808,0.0002879161,0.000099663455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007449481,0.00018184449,0.00014934786,0.00040918228,0.00026482122,0.0007064891,0.00010712746,0.00015543346,0.002732349],"category_scores_gemma":[0.001475735,0.00010278074,0.0002554169,0.00038827647,0.00028347093,0.0002251004,0.00028302532,0.0002908394,0.000113271744],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0053408076,0.0007446998,0.71973747,0.00027409144,0.00024314775,0.00033695422,0.0014251255,0.00041601944,0.24004133,0.0001805336,0.0003682204,0.030891525],"study_design_scores_gemma":[0.0000049628843,0.00040258415,0.99563086,0.000004700208,0.000024960818,0.000045379697,0.00033911542,0.00023159881,0.003131898,0.00003164743,0.00014686749,0.0000055207124],"about_ca_topic_score_codex":0.009206012,"about_ca_topic_score_gemma":0.020190062,"teacher_disagreement_score":0.009206012,"about_ca_system_score_codex":0.00044373656,"about_ca_system_score_gemma":0.00023840189,"threshold_uncertainty_score":0.018304825},"labels":[],"label_agreement":null},{"id":"W4211176391","doi":"10.1002/9781118165409.refs","title":"References","year":2006,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science","score_opus":0.03080171086096967,"score_gpt":0.2556497236819543,"score_spread":0.22484801282098466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211176391","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056518754,0.023009967,0.011032569,0.0060274857,0.017754963,0.00026438397,0.033016767,0.002594749,0.9057338],"genre_scores_gemma":[0.0020425648,0.009188391,0.005996083,0.0022949094,0.0014149811,0.00011206115,0.018863931,0.0011738914,0.95891327],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915814,0.00009534177,0.000044848708,0.000097182725,0.00055223046,0.000052288637],"domain_scores_gemma":[0.9983929,0.00029126086,0.00005413547,0.00017301193,0.0009810518,0.00010762848],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008405914,0.0009943055,0.0008723626,0.0073778564,0.0010982575,0.0021198178,0.001896587,0.0010663047,0.47428942],"category_scores_gemma":[0.0042715124,0.00026584498,0.00071189046,0.0060147378,0.0003356884,0.0018440236,0.001426442,0.0011549267,0.45230457],"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":[0.000011072019,0.000017700377,0.00007055636,0.00017618471,0.0000031323514,0.00003505561,0.000023977998,0.00012411615,0.00008796269,0.0031536797,0.901463,0.09483348],"study_design_scores_gemma":[0.0000015183671,0.0000022322508,0.00014692596,0.0001312398,0.0000033547528,0.00003942559,0.00001664128,0.00003880018,0.00010315039,0.0015921543,0.9979201,0.0000044956505],"about_ca_topic_score_codex":0.014312051,"about_ca_topic_score_gemma":0.02871658,"teacher_disagreement_score":0.5257106,"about_ca_system_score_codex":0.001515805,"about_ca_system_score_gemma":0.0017326428,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4213098399","doi":"10.5539/ijsp.v11n2p52","title":"Inferences About a Quantile Shift Measure of Effect Size When There Is a Covariate","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Measure (data warehouse); Quantile; Econometrics; Statistics; Mathematics; Extant taxon; Percentile; Computer science; Data mining","score_opus":0.043616041787198245,"score_gpt":0.30728485878786244,"score_spread":0.2636688170006642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213098399","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":[0.034370765,0.0011965885,0.9585229,0.0012993299,0.00033588544,0.00047890135,0.00077299715,0.00044734802,0.0025753698],"genre_scores_gemma":[0.57433194,0.0006008485,0.41803014,0.0019303409,0.0007196089,0.0019350335,0.00095608656,0.0002523508,0.0012435613],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90281886,0.07138799,0.0041663763,0.010493355,0.01019463,0.0009389154],"domain_scores_gemma":[0.5288975,0.41314164,0.015636614,0.034766905,0.006468231,0.0010891384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14703393,0.0010640505,0.0028840415,0.0040603844,0.0013905235,0.0027061158,0.0036289713,0.0035600439,0.0043088025],"category_scores_gemma":[0.44846684,0.00070632953,0.0033648228,0.0035158512,0.0066617206,0.0056942515,0.003471334,0.0050815796,0.00066439126],"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":[0.0037024298,0.00061679364,0.16928583,0.0035053836,0.008186617,0.0014532473,0.0067783603,0.045677114,0.0136272665,0.23405705,0.014379564,0.49873036],"study_design_scores_gemma":[0.0005310795,0.0032445756,0.1870196,0.001402838,0.0017966152,0.0020309158,0.0023239464,0.11897871,0.013483764,0.6455642,0.023111679,0.0005121379],"about_ca_topic_score_codex":0.0023131336,"about_ca_topic_score_gemma":0.0013787892,"teacher_disagreement_score":0.14703393,"about_ca_system_score_codex":0.0011205694,"about_ca_system_score_gemma":0.0010805288,"threshold_uncertainty_score":0.77759904},"labels":[],"label_agreement":null},{"id":"W4220728075","doi":"10.1111/1541-4337.12893","title":"Sensory descriptors for pulses and pulse‐derived ingredients: Toward a standardized lexicon and sensory wheel","year":2022,"lang":"en","type":"review","venue":"Comprehensive Reviews in Food Science and Food Safety","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"","keywords":"Aroma; Organoleptic; Flavor; Taste; Quantitative Descriptive Analysis; Sensory system; Food science; Sensory analysis; Descriptive statistics; Mathematics; Lexicon; Computer science; Psychology; Biology; Statistics; Natural language processing; Cognitive psychology","score_opus":0.26571142149002214,"score_gpt":0.377594728626752,"score_spread":0.11188330713672984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220728075","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":[0.02892607,0.6999649,0.16847572,0.0252579,0.0062165437,0.0026130965,0.006920845,0.0013896035,0.060235407],"genre_scores_gemma":[0.11033836,0.5839699,0.26587722,0.012238149,0.002087543,0.004036578,0.009278549,0.00044903078,0.011724747],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653995,0.0011625407,0.0008713882,0.00036133968,0.00095052994,0.000114320326],"domain_scores_gemma":[0.9955753,0.0017946956,0.0005006968,0.00023142296,0.0017648136,0.00013310494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068086116,0.0017912937,0.0023222452,0.010660937,0.0009752779,0.005735495,0.0028820839,0.0015696222,0.0032507898],"category_scores_gemma":[0.008433592,0.0005888461,0.0015423512,0.007018582,0.00423559,0.009518565,0.0033422236,0.003636329,0.0016432843],"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":[0.00021725748,0.00019263205,0.0028074628,0.05089146,0.00019393099,0.000646449,0.012142271,0.00096253725,0.009040209,0.13314328,0.04427258,0.74548995],"study_design_scores_gemma":[0.000040056144,0.00028902123,0.010504954,0.038516395,0.00034137,0.0020917852,0.010444662,0.0014236751,0.0017833964,0.04705675,0.8872881,0.0002198129],"about_ca_topic_score_codex":0.005682862,"about_ca_topic_score_gemma":0.0053738044,"teacher_disagreement_score":0.010660937,"about_ca_system_score_codex":0.0034744232,"about_ca_system_score_gemma":0.007948365,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4221004679","doi":"10.1111/joss.12743","title":"Projective mapping as a versatile sensory profiling tool: A review of recent studies on different food products","year":2022,"lang":"en","type":"review","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Perception; Computer science; Psychology; Sensory system; Projective test; Cognitive psychology","score_opus":0.4762734069482244,"score_gpt":0.43924251708050077,"score_spread":0.037030889867723626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221004679","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":[0.00039927848,0.9988973,0.00014508981,0.00010992564,0.000049383296,0.000013766788,0.000037531518,0.0000032392672,0.0003444769],"genre_scores_gemma":[0.0029270311,0.9962728,0.00046768627,0.00014056075,0.00004120322,0.000027797381,0.00003936997,0.0000034915688,0.000080092075],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99741155,0.00074730907,0.0007241621,0.0004012161,0.00063072913,0.00008504164],"domain_scores_gemma":[0.9830256,0.013425836,0.0014714806,0.00022455544,0.0017153991,0.00013714458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053473352,0.0012246087,0.0028436643,0.01026727,0.00056649005,0.0029422936,0.001494009,0.0014244397,0.0040624077],"category_scores_gemma":[0.01218684,0.00074735284,0.0028372325,0.011008741,0.0012139435,0.0033186374,0.0012387843,0.0013114143,0.00056016777],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019100035,0.00010747029,0.0012072815,0.44185317,0.0011418266,0.00022356605,0.00089664635,0.00022186783,0.00094506145,0.0019166281,0.0040822085,0.5472133],"study_design_scores_gemma":[0.000059398164,0.00068000157,0.01962356,0.45575884,0.009440843,0.002902796,0.0029962633,0.0003580006,0.002075947,0.0026651516,0.5032431,0.00019610267],"about_ca_topic_score_codex":0.004146779,"about_ca_topic_score_gemma":0.0069699646,"teacher_disagreement_score":0.01026727,"about_ca_system_score_codex":0.0011541162,"about_ca_system_score_gemma":0.0036377765,"threshold_uncertainty_score":0.028279781},"labels":[],"label_agreement":null},{"id":"W4232598815","doi":"10.5539/jfr.v4n3p182","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 4, No. 3","year":2015,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Library science; Computer science","score_opus":0.4793660646241916,"score_gpt":0.49221563266157203,"score_spread":0.01284956803738041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232598815","genre_codex":"editorial","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":[0.00021130219,0.003016631,0.0011538177,0.20843476,0.7830242,0.0006748724,0.0007552495,0.00041702122,0.002312188],"genre_scores_gemma":[0.007493088,0.008582463,0.0048211156,0.29592282,0.6270292,0.0037875327,0.00143276,0.0013128235,0.049618207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9536235,0.009933378,0.009729653,0.0028741262,0.021855604,0.0019837874],"domain_scores_gemma":[0.18944977,0.035021596,0.013091613,0.005959301,0.7474517,0.009026049],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.048342023,0.002394716,0.005914107,0.0066741295,0.0040168255,0.007712079,0.004796189,0.014448042,0.059862543],"category_scores_gemma":[0.4590383,0.0014217788,0.0045469985,0.003249148,0.0027350802,0.005301603,0.0033475573,0.011104934,0.032899823],"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":[0.00005194298,0.0000054509155,0.00012350084,0.00055597955,0.000013017934,0.00009479908,0.00006673835,0.000009634912,0.00006438672,0.00012285345,0.99497557,0.003916124],"study_design_scores_gemma":[0.00023161799,0.000052202864,0.0013683245,0.0039790184,0.00012265622,0.0011920497,0.00061200827,0.0003021531,0.0004301331,0.0016647052,0.98989046,0.00015465856],"about_ca_topic_score_codex":0.004780242,"about_ca_topic_score_gemma":0.007161591,"teacher_disagreement_score":0.94013745,"about_ca_system_score_codex":0.005640673,"about_ca_system_score_gemma":0.012879045,"threshold_uncertainty_score":0.25566012},"labels":[],"label_agreement":null},{"id":"W4234063897","doi":"10.5539/jfr.v3n6p202","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 3, No. 6","year":2014,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Library science; Psychology; Computer science","score_opus":0.3037164384656304,"score_gpt":0.46490164277787766,"score_spread":0.16118520431224725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234063897","genre_codex":"editorial","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":[0.00020220553,0.0029638421,0.0010629263,0.20662756,0.785096,0.0005871117,0.00080727943,0.00038963984,0.0022634265],"genre_scores_gemma":[0.0071768262,0.008714864,0.0047675227,0.29509553,0.62857896,0.0033862072,0.0016197856,0.0012337641,0.049426556],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.95229787,0.009772782,0.010365717,0.0028550616,0.022856731,0.001851761],"domain_scores_gemma":[0.19489191,0.035118643,0.013245235,0.005672301,0.7420833,0.008988516],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.04673715,0.0023182682,0.0055537526,0.006444593,0.0037088718,0.007220188,0.0047558183,0.01439078,0.059581608],"category_scores_gemma":[0.46755198,0.0013905063,0.004458517,0.0029854763,0.0024724032,0.005137098,0.003301407,0.011256389,0.03381475],"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":[0.000053284588,0.000005568645,0.00012980579,0.0005761773,0.000012766099,0.00009834851,0.000062301995,0.000010083532,0.000066057284,0.00011908071,0.9947642,0.004102279],"study_design_scores_gemma":[0.00021841841,0.000053832115,0.0013682523,0.0041840477,0.00011603909,0.0012702073,0.00055086036,0.00029247857,0.00043714207,0.0014457243,0.9899157,0.00014721142],"about_ca_topic_score_codex":0.004490763,"about_ca_topic_score_gemma":0.00686436,"teacher_disagreement_score":0.94041836,"about_ca_system_score_codex":0.0051794425,"about_ca_system_score_gemma":0.011523836,"threshold_uncertainty_score":0.24717265},"labels":[],"label_agreement":null},{"id":"W4236534883","doi":"10.5539/jfr.v4n1p200","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 4, No. 1","year":2015,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.4850125824500144,"score_gpt":0.4929597857417981,"score_spread":0.007947203291783722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236534883","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002027985,0.003607496,0.0009980456,0.2309854,0.7601342,0.0006615944,0.0008392363,0.00037076938,0.0022004198],"genre_scores_gemma":[0.006916659,0.009562223,0.0047350433,0.29411098,0.63182616,0.0040236292,0.0016337006,0.0012720015,0.045919593],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9425667,0.013378429,0.012244132,0.0037428509,0.025708297,0.0023595623],"domain_scores_gemma":[0.16058905,0.04215271,0.014494853,0.0064067747,0.7673279,0.009028777],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.057538476,0.0024621296,0.006389862,0.008038232,0.0044294777,0.008270307,0.0051911506,0.014692779,0.06329376],"category_scores_gemma":[0.5244802,0.0015602242,0.004388616,0.00364629,0.0027940392,0.0061183465,0.003817066,0.011592766,0.033358615],"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":[0.00005291766,0.000006143302,0.00013394828,0.0006921598,0.000013804015,0.000083586805,0.000080653255,0.000009917478,0.000057498084,0.00013590774,0.9944629,0.004270573],"study_design_scores_gemma":[0.00022366771,0.000055293312,0.0014218983,0.0048137116,0.00012480508,0.0010170806,0.0006824353,0.00025525683,0.00035996674,0.0016079169,0.98928213,0.00015584679],"about_ca_topic_score_codex":0.0045928587,"about_ca_topic_score_gemma":0.0073413732,"teacher_disagreement_score":0.94246155,"about_ca_system_score_codex":0.0061956174,"about_ca_system_score_gemma":0.014135272,"threshold_uncertainty_score":0.3042962},"labels":[],"label_agreement":null},{"id":"W4236972431","doi":"10.5539/jfr.v4n2p176","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 4, No. 2","year":2015,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Library science; Psychology; Sociology; Computer science","score_opus":0.4748955883461905,"score_gpt":0.4921207943144094,"score_spread":0.017225205968218915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236972431","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018127708,0.003020445,0.0009646978,0.19939454,0.7925999,0.0005930297,0.00069982384,0.00036842586,0.0021778604],"genre_scores_gemma":[0.0061561475,0.007755269,0.004036311,0.27820867,0.64811,0.0035099494,0.0013742046,0.001196499,0.04965298],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9490242,0.011345383,0.010476271,0.0032683788,0.023751274,0.0021344624],"domain_scores_gemma":[0.18375297,0.036156792,0.012707425,0.006172632,0.7519071,0.009303115],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05083057,0.0026237995,0.006165572,0.0074721985,0.0041882857,0.008354831,0.0049944394,0.015119653,0.064182244],"category_scores_gemma":[0.4781181,0.0015610221,0.0043753725,0.0033616263,0.0027139334,0.0060868273,0.0036853154,0.011344512,0.034189828],"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":[0.00005070045,0.000005604757,0.00010993563,0.0005608556,0.000012658346,0.00008572523,0.00006274077,0.000008884474,0.00006040883,0.00011341259,0.99540186,0.0035271733],"study_design_scores_gemma":[0.00024143711,0.00005690273,0.0013232975,0.004027409,0.00011655056,0.0011164929,0.000607455,0.00027953906,0.00039529128,0.0014477215,0.9902355,0.00015239946],"about_ca_topic_score_codex":0.004394226,"about_ca_topic_score_gemma":0.0067620673,"teacher_disagreement_score":0.94916946,"about_ca_system_score_codex":0.005604828,"about_ca_system_score_gemma":0.012563475,"threshold_uncertainty_score":0.268821},"labels":[],"label_agreement":null},{"id":"W4239216898","doi":"10.5539/jfr.v3n5p129","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 3, No. 5","year":2014,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology","score_opus":0.303612788975707,"score_gpt":0.4648923134148599,"score_spread":0.16127952443915294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239216898","genre_codex":"editorial","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":[0.0002122581,0.0030816188,0.0011412326,0.20601884,0.78541356,0.0006431042,0.000772693,0.0004041784,0.0023124465],"genre_scores_gemma":[0.008400911,0.008093105,0.0047949143,0.31477916,0.60985684,0.0034713214,0.0015543806,0.0012069596,0.047842488],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9475789,0.010844211,0.011434812,0.0032151225,0.024849512,0.0020773183],"domain_scores_gemma":[0.19355285,0.0354109,0.013527042,0.005648221,0.74306,0.008801026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049457356,0.0023251527,0.005558975,0.006883262,0.00388315,0.007825246,0.0046992665,0.015090981,0.0605353],"category_scores_gemma":[0.47139415,0.0014085304,0.004648431,0.0030606815,0.0025481551,0.005099794,0.0033378429,0.010584072,0.031573173],"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":[0.000054509066,0.000005574185,0.00014287457,0.0005895076,0.000014843909,0.000103379585,0.000068883906,0.000010244686,0.00006403771,0.0001283456,0.99451435,0.004303498],"study_design_scores_gemma":[0.00023551875,0.000055860266,0.0015792262,0.004628906,0.0001403952,0.0012410032,0.0006186264,0.0003160013,0.00045094083,0.0014866495,0.9890877,0.00015910939],"about_ca_topic_score_codex":0.004953527,"about_ca_topic_score_gemma":0.007504204,"teacher_disagreement_score":0.0605353,"about_ca_system_score_codex":0.0059036706,"about_ca_system_score_gemma":0.0124692675,"threshold_uncertainty_score":0.26155865},"labels":[],"label_agreement":null},{"id":"W4239271187","doi":"10.1093/med/9780199685219.003.0004","title":"Scaling responses","year":2014,"lang":"en","type":"book","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McMaster University","funders":"","keywords":"Guttman scale; Respondent; Likert scale; Scaling; Scale (ratio); Multidimensional scaling; Econometrics; Statistics; Computer science; Psychology; Mathematics; Geography; Cartography","score_opus":0.05607350764102947,"score_gpt":0.2808243489995673,"score_spread":0.22475084135853785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239271187","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023616593,0.0020808317,0.19442055,0.008008046,0.0055466094,0.0047794157,0.005878919,0.0024378737,0.75323117],"genre_scores_gemma":[0.26795924,0.007653054,0.30456012,0.0071104662,0.0013969381,0.009462267,0.008661359,0.0025922777,0.3906042],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98137283,0.009173591,0.0011639461,0.0016150994,0.006127023,0.0005475182],"domain_scores_gemma":[0.9747756,0.010019821,0.00085447397,0.0026711076,0.011198181,0.00048083012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008933263,0.0012566312,0.0008424938,0.003281216,0.0015301723,0.004557472,0.0016019035,0.0015794685,0.118286744],"category_scores_gemma":[0.06177293,0.00040921778,0.0008145704,0.004187615,0.0016964879,0.0033822593,0.0033586351,0.0026547736,0.03941579],"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":[0.0002567951,0.00015720235,0.0021186313,0.0017102791,0.000036042547,0.00019390324,0.014853654,0.0011892149,0.005875562,0.1325661,0.24957052,0.591472],"study_design_scores_gemma":[0.000029210287,0.00020911626,0.003638078,0.0014593512,0.000023960773,0.00033079475,0.010702716,0.0016151635,0.0034867027,0.038127277,0.94028693,0.00009090091],"about_ca_topic_score_codex":0.0008302021,"about_ca_topic_score_gemma":0.0008916618,"teacher_disagreement_score":0.118286744,"about_ca_system_score_codex":0.0022881909,"about_ca_system_score_gemma":0.0015087395,"threshold_uncertainty_score":0.39570856},"labels":[],"label_agreement":null},{"id":"W4240524721","doi":"10.1016/b978-0-08-100596-5.00134-7","title":"The Application of Sensory Science to the Evaluation of Grain-Based Foods","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sensory system; Perception; Sensory analysis; Presentation (obstetrics); Product testing; Quantitative sensory testing; Test (biology); Quantitative Descriptive Analysis; Psychology; Computer science; Cognitive psychology; Mathematics; Statistics; Engineering; Medicine; Biology; Neuroscience; Operations management","score_opus":0.11695137598958337,"score_gpt":0.35255911771412834,"score_spread":0.23560774172454496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240524721","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":[0.021494843,0.2814147,0.5836247,0.0032136494,0.0020803884,0.0003356395,0.0027255504,0.0020156135,0.10309489],"genre_scores_gemma":[0.11755634,0.24830566,0.50517964,0.0030161839,0.0013444906,0.0005296117,0.0020009645,0.0010111162,0.12105603],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892944,0.00023152755,0.000060383576,0.00012603142,0.0006247215,0.000027959119],"domain_scores_gemma":[0.9985172,0.0010977682,0.00004661345,0.00006993058,0.00023832527,0.00003008569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002077217,0.0010954625,0.0009657601,0.0021029226,0.0002819327,0.0023204,0.0009505176,0.00086266414,0.008783384],"category_scores_gemma":[0.0026218027,0.00044970732,0.00074669014,0.0022351183,0.0014595601,0.0014179925,0.0010588393,0.0017665396,0.0025463703],"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":[0.00012141953,0.00011905915,0.000921991,0.002110736,0.00008431639,0.00012597069,0.0002380478,0.0030960813,0.045417685,0.012263056,0.01953429,0.91596717],"study_design_scores_gemma":[0.000043109514,0.001215212,0.035563134,0.0022674047,0.00019965676,0.0024140715,0.0012159516,0.03677477,0.077843115,0.27615765,0.5658939,0.00041210442],"about_ca_topic_score_codex":0.002092941,"about_ca_topic_score_gemma":0.0034534743,"teacher_disagreement_score":0.008783384,"about_ca_system_score_codex":0.00065485516,"about_ca_system_score_gemma":0.00090067496,"threshold_uncertainty_score":0.029383302},"labels":[],"label_agreement":null},{"id":"W4243345331","doi":"10.1002/9781118445112.stat02498.pub2","title":"Redundancy Analysis","year":2016,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McGill University","funders":"","keywords":"Canonical correlation; Redundancy (engineering); Canonical analysis; Mathematics; Correlation; Statistics; Econometrics; Computer science","score_opus":0.056046734663667566,"score_gpt":0.33917498116526235,"score_spread":0.2831282465015948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243345331","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":[0.057412878,0.008558163,0.78766656,0.0021452843,0.0033393842,0.0066859922,0.07422292,0.0099174,0.05005144],"genre_scores_gemma":[0.28327194,0.004185397,0.6116457,0.00088671345,0.0007440206,0.007738291,0.073886365,0.0021038866,0.015537652],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9744946,0.008646912,0.003250188,0.004962024,0.008022787,0.0006235744],"domain_scores_gemma":[0.96086776,0.012172817,0.0031063068,0.009468345,0.013911714,0.00047301603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015001814,0.0024983482,0.0029377202,0.012095194,0.0025337792,0.004525664,0.002580332,0.0007783712,0.026357599],"category_scores_gemma":[0.060825787,0.00074355653,0.0039133327,0.014537259,0.0012745502,0.0023832447,0.0030007958,0.0015963765,0.009889758],"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":[0.0011566367,0.00039629504,0.025395606,0.010185497,0.0040676896,0.00086865504,0.002480849,0.010304322,0.018908037,0.061055727,0.11073713,0.7544435],"study_design_scores_gemma":[0.00030142473,0.0012046564,0.07844056,0.0032663306,0.0037019784,0.0027366483,0.002721187,0.058877446,0.031585593,0.16475253,0.6516338,0.00077791815],"about_ca_topic_score_codex":0.002053,"about_ca_topic_score_gemma":0.0023812265,"teacher_disagreement_score":0.026357599,"about_ca_system_score_codex":0.001959453,"about_ca_system_score_gemma":0.004707325,"threshold_uncertainty_score":0.08817494},"labels":[],"label_agreement":null},{"id":"W4245998256","doi":"10.1111/1750-3841.12687","title":"Industrial Applications of Selected <i>JFS</i> Articles","year":2014,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ice cream; Food science; Chemistry","score_opus":0.06272343321586653,"score_gpt":0.28525290075909215,"score_spread":0.2225294675432256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245998256","genre_codex":"other","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":[0.003944355,0.045170285,0.008836866,0.022766596,0.12439736,0.00055360317,0.0058866474,0.0038134924,0.78463084],"genre_scores_gemma":[0.028939642,0.04626807,0.008291753,0.010200421,0.088673234,0.00042635037,0.008253426,0.0016452727,0.8073017],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988971,0.000076063196,0.0000670643,0.0000873888,0.000756058,0.000116335126],"domain_scores_gemma":[0.9949181,0.00064775307,0.0002490434,0.00036764654,0.0031696605,0.00064775156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013757901,0.0010706031,0.00047240595,0.0065864017,0.0018227423,0.0040683597,0.0012924843,0.002030589,0.23125196],"category_scores_gemma":[0.0037092613,0.0003031566,0.0010546197,0.0044583175,0.0006511078,0.0018761569,0.0012663967,0.0010442676,0.08081967],"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":[0.00006537764,0.00006403624,0.0002760627,0.0007947145,0.000013999459,0.00021369226,0.00006169045,0.000114317096,0.00366085,0.0021350994,0.84272546,0.14987467],"study_design_scores_gemma":[0.000007221238,0.00005308066,0.0006987416,0.00028736622,0.000008397827,0.00015904156,0.00008002857,0.00009435103,0.0014609249,0.0007087796,0.9964317,0.000010335928],"about_ca_topic_score_codex":0.0027844661,"about_ca_topic_score_gemma":0.005217316,"teacher_disagreement_score":0.23125196,"about_ca_system_score_codex":0.001549115,"about_ca_system_score_gemma":0.001296255,"threshold_uncertainty_score":0.7736149},"labels":[],"label_agreement":null},{"id":"W4247434114","doi":"10.31234/osf.io/qauct","title":"A note on the implications of factorial invariance for common factor variable equivalence","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Equivalence (formal languages); Factorial; Mathematics; Measurement invariance; Random variable; Set (abstract data type); Variable (mathematics); Factor analysis; Statistics; Econometrics; Variables; Confirmatory factor analysis; Discrete mathematics; Computer science; Structural equation modeling; Mathematical analysis","score_opus":0.1491137688186467,"score_gpt":0.35945453033728814,"score_spread":0.21034076151864142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247434114","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":[0.039645776,0.0051929704,0.8166135,0.07471047,0.0032173349,0.0002518111,0.000455192,0.0004495963,0.059463356],"genre_scores_gemma":[0.7473941,0.0027265344,0.21799776,0.017457977,0.0062002516,0.0010379403,0.00035396576,0.0003626402,0.0064688586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9483353,0.032248944,0.00345124,0.006723026,0.008216064,0.0010254334],"domain_scores_gemma":[0.6412828,0.3031199,0.006743987,0.03682905,0.010047376,0.001976852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08713058,0.0011141459,0.0019568847,0.001746905,0.00348669,0.0046822266,0.003532882,0.0032907468,0.0138457455],"category_scores_gemma":[0.301773,0.00061679696,0.0026082627,0.0020684425,0.030754777,0.01319065,0.006683997,0.012061085,0.0012960773],"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":[0.00016776308,0.000050330607,0.0023013626,0.00021838571,0.0000944267,0.00029369752,0.002548398,0.001267669,0.00063934823,0.9557342,0.0052182325,0.031466212],"study_design_scores_gemma":[0.00002625354,0.00007567891,0.0016625775,0.00006056681,0.000020619418,0.000101880425,0.00025144377,0.0016051461,0.00026548866,0.99132967,0.004570822,0.00002993132],"about_ca_topic_score_codex":0.0027614436,"about_ca_topic_score_gemma":0.0016206446,"teacher_disagreement_score":0.08713058,"about_ca_system_score_codex":0.0017675591,"about_ca_system_score_gemma":0.0030227106,"threshold_uncertainty_score":0.46079606},"labels":[],"label_agreement":null},{"id":"W4248492751","doi":"10.5539/jfr.v3n3p164","title":"Reviewer Acknowledgements for Journal of Food Research, Vol. 3, No. 3","year":2014,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Library science; Computer science","score_opus":0.3038138921065818,"score_gpt":0.46479253798037634,"score_spread":0.16097864587379457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248492751","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030599613,0.0025471752,0.0018662788,0.25268295,0.7343236,0.00074267515,0.0013932666,0.00073946797,0.005398632],"genre_scores_gemma":[0.015045591,0.0077733975,0.0093580475,0.34550357,0.46653578,0.0047455984,0.0029677588,0.003216793,0.14485349],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9701867,0.005876784,0.0053340443,0.0017518295,0.015577321,0.0012732755],"domain_scores_gemma":[0.26637143,0.02723978,0.009337623,0.005378452,0.68336564,0.008307074],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03318829,0.0016550039,0.003382836,0.0053008865,0.003197949,0.00546636,0.0035689063,0.008333646,0.07755463],"category_scores_gemma":[0.36770234,0.0009411461,0.002676821,0.0025823738,0.0020444794,0.004148928,0.0024283482,0.0074738185,0.041826747],"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":[0.000025690326,0.0000028467925,0.00008131484,0.0002189738,0.000004834146,0.00005054706,0.000047247013,0.000005782124,0.00004934667,0.00011558889,0.99616796,0.0032298053],"study_design_scores_gemma":[0.00011991336,0.000029502846,0.0011696668,0.001775733,0.000065469474,0.00061162194,0.0005096372,0.00024879063,0.0004807342,0.0014224749,0.99347055,0.000095879186],"about_ca_topic_score_codex":0.0058005988,"about_ca_topic_score_gemma":0.009844138,"teacher_disagreement_score":0.9668117,"about_ca_system_score_codex":0.004782779,"about_ca_system_score_gemma":0.01106633,"threshold_uncertainty_score":0.25944608},"labels":[],"label_agreement":null},{"id":"W4252458897","doi":"10.3410/f.732199987.793571324","title":"Faculty Opinions recommendation of Elucidating the underlying components of food valuation in the human orbitofrontal cortex.","year":2020,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Orbitofrontal cortex; Valuation (finance); Multivariate statistics; Value (mathematics); Psychology; Cognitive psychology; Computer science; Neuroscience; Prefrontal cortex; Cognition; Machine learning; Business","score_opus":0.17007362589191855,"score_gpt":0.4050144015733027,"score_spread":0.23494077568138416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252458897","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013987059,0.00023580076,0.0002676214,0.00034313864,0.00008217435,0.000024674722,0.99496835,0.0002998578,0.00237972],"genre_scores_gemma":[0.003504483,0.00010856796,0.00078995875,0.000116125826,0.000020989433,0.00009882388,0.99320316,0.000066164546,0.0020917803],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994313,0.000121285404,0.00005443771,0.0001808104,0.000121891266,0.000090251386],"domain_scores_gemma":[0.99806637,0.00059561967,0.00022839187,0.00039353888,0.00052031176,0.0001957125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001350473,0.0011162731,0.00062919734,0.0019185077,0.0005838959,0.0017037132,0.0013284853,0.0012815289,0.05621261],"category_scores_gemma":[0.0068438295,0.0002881116,0.0011372182,0.0018001864,0.00028335123,0.0007381306,0.0012612526,0.0010154868,0.052611705],"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":[0.00018873495,0.000045643774,0.007297107,0.00091562496,0.00010993548,0.000058639293,0.00004820493,0.00032226281,0.00040494546,0.0007235037,0.97930956,0.010575831],"study_design_scores_gemma":[0.00039940796,0.000040258183,0.03147171,0.000423164,0.00013460242,0.00018336541,0.00016203622,0.0011401286,0.0007632509,0.0020855647,0.96316016,0.000036416986],"about_ca_topic_score_codex":0.017364254,"about_ca_topic_score_gemma":0.046088427,"teacher_disagreement_score":0.05621261,"about_ca_system_score_codex":0.0008057807,"about_ca_system_score_gemma":0.0017239001,"threshold_uncertainty_score":0.18804991},"labels":[],"label_agreement":null},{"id":"W4253515722","doi":"10.1002/9781118445112.stat00425.pub2","title":"Mosaic Displays","year":2015,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"York University","funders":"","keywords":"Mosaic; Categorical variable; Log-linear model; Statistics; Independence (probability theory); Conditional independence; Mathematics; Shading; Econometrics; Computer science; Geography; Computer graphics (images); Linear model","score_opus":0.08539752937857514,"score_gpt":0.329974455486878,"score_spread":0.24457692610830284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253515722","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048933383,0.00048909144,0.17696284,0.001158569,0.0009029836,0.00084579823,0.13565493,0.34123242,0.33786014],"genre_scores_gemma":[0.06926563,0.00091957673,0.17031308,0.0018808467,0.0005692701,0.0023262375,0.1828675,0.15417954,0.41767827],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989203,0.00016960198,0.00007614531,0.00022632204,0.00048810392,0.000119615295],"domain_scores_gemma":[0.9954508,0.0014934301,0.00015166741,0.0010954621,0.0013796792,0.00042900362],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020445415,0.0011789836,0.00095768756,0.0022627132,0.0010815875,0.0035160715,0.0028789737,0.0011684268,0.49771535],"category_scores_gemma":[0.009686835,0.0009011784,0.0010561268,0.0022075525,0.00037034618,0.0030186453,0.003290872,0.0011304052,0.18463254],"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":[0.0005118388,0.000058046244,0.00060055294,0.0003079005,0.000037029688,0.00013948375,0.00027371413,0.0006667537,0.0027531895,0.01119565,0.9151112,0.06834474],"study_design_scores_gemma":[0.00017140544,0.000053277327,0.001739501,0.00012736823,0.00004068589,0.00016944631,0.0001184999,0.004856826,0.0035194138,0.011599775,0.9775354,0.000068447225],"about_ca_topic_score_codex":0.0041405656,"about_ca_topic_score_gemma":0.0056301607,"teacher_disagreement_score":0.49771535,"about_ca_system_score_codex":0.00061522203,"about_ca_system_score_gemma":0.0010221257,"threshold_uncertainty_score":0.71644807},"labels":[],"label_agreement":null},{"id":"W4254126932","doi":"10.1201/b11534-15","title":"Omics Approaches to Meat Quality Management","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Government; National Cancer Research Institute; Government of South Australia; Alberta Water Research Institute","keywords":"Quality (philosophy); Data science; Computer science; Biotechnology; Business; Biology; Philosophy","score_opus":0.45014117391399566,"score_gpt":0.3202136394241287,"score_spread":0.12992753448986694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254126932","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":[0.0030484383,0.14446971,0.6991635,0.003667089,0.004087583,0.00014675113,0.0019588775,0.0016613825,0.14179662],"genre_scores_gemma":[0.014601272,0.14597122,0.4712897,0.005001121,0.0024708805,0.00042100993,0.0026339688,0.0012775946,0.35633326],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995371,0.00007596769,0.000025053303,0.000096150376,0.00024092113,0.000024801813],"domain_scores_gemma":[0.9996953,0.00016860888,0.000015670152,0.000032579763,0.00007332285,0.000014642031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000976743,0.0014028783,0.001093708,0.0019831199,0.0003507526,0.0021756357,0.0011614913,0.0009820912,0.01210343],"category_scores_gemma":[0.00086507545,0.00051018153,0.00071234524,0.0024345755,0.0008953051,0.0017771578,0.0011944398,0.0015469268,0.0078298235],"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":[0.00004087663,0.00008132219,0.00035160128,0.0011336502,0.000071557115,0.00013010378,0.00018708449,0.0021365893,0.015536508,0.08577035,0.07350025,0.8210601],"study_design_scores_gemma":[0.0000049428445,0.000037749687,0.0011430036,0.0004107669,0.000035284465,0.00041628725,0.00011835131,0.0029159295,0.007794016,0.19107315,0.7960143,0.000036306737],"about_ca_topic_score_codex":0.0011492168,"about_ca_topic_score_gemma":0.0029421372,"teacher_disagreement_score":0.01210343,"about_ca_system_score_codex":0.00079595635,"about_ca_system_score_gemma":0.00067833305,"threshold_uncertainty_score":0.04049003},"labels":[],"label_agreement":null},{"id":"W4254982077","doi":"10.1007/978-2-8178-0115-5_11","title":"Statistique descriptive","year":2011,"lang":"fr","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Humanities; Art","score_opus":0.1538449066076354,"score_gpt":0.2890281678149122,"score_spread":0.1351832612072768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254982077","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":[0.0019064939,0.010453976,0.8484487,0.004790469,0.0022600505,0.00026347872,0.008140893,0.0056509427,0.118084915],"genre_scores_gemma":[0.08241538,0.012238845,0.657049,0.0041086143,0.0068186,0.0035408032,0.014431581,0.006763103,0.21263401],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9909542,0.0043229447,0.00059289916,0.0013268831,0.0025998217,0.00020324855],"domain_scores_gemma":[0.97847885,0.016075378,0.00059015834,0.002887973,0.0018301355,0.00013748504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008673124,0.0014844362,0.0020694595,0.0030497494,0.0008290246,0.0055935443,0.0013741879,0.0013447829,0.0668722],"category_scores_gemma":[0.026740737,0.0010088822,0.0013720662,0.0031877323,0.0028925282,0.0030716092,0.0011529948,0.0071060066,0.02888127],"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":[0.00010211334,0.00007274031,0.0006225605,0.0005797945,0.00010588701,0.0000887777,0.0003503867,0.0026045644,0.00095916865,0.54976445,0.122822605,0.321927],"study_design_scores_gemma":[0.000054466538,0.000075253476,0.002546584,0.00032881548,0.000055878376,0.00044458092,0.00021446963,0.013333562,0.001498861,0.4842187,0.49717093,0.000057890597],"about_ca_topic_score_codex":0.0033220174,"about_ca_topic_score_gemma":0.0025961045,"teacher_disagreement_score":0.0668722,"about_ca_system_score_codex":0.002527375,"about_ca_system_score_gemma":0.0034816253,"threshold_uncertainty_score":0.22370982},"labels":[],"label_agreement":null},{"id":"W4256518833","doi":"10.1002/9781118445112.stat05745","title":"Dummy Variables","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Cancer Care Ontario","funders":"","keywords":"Categorical variable; Statistics; Variables; Mathematics; Regression analysis; Linear regression; Variable (mathematics); Econometrics; Multicollinearity; Referent; Regression","score_opus":0.05535846204422474,"score_gpt":0.30651418525453245,"score_spread":0.2511557232103077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256518833","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06568553,0.006381734,0.28674796,0.008105255,0.00788851,0.0058918027,0.43405664,0.005981099,0.17926162],"genre_scores_gemma":[0.372476,0.0046110805,0.14215446,0.0046426565,0.002421102,0.0136126885,0.23566337,0.0036103122,0.2208084],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9857669,0.007883089,0.0012736516,0.0016858466,0.0024064977,0.0009841009],"domain_scores_gemma":[0.9400505,0.04110457,0.0063703326,0.006477328,0.0047934838,0.001203836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010957815,0.0015218699,0.0015098504,0.0037900312,0.0009075942,0.0033782576,0.0041520046,0.0020764384,0.22659132],"category_scores_gemma":[0.08091251,0.0007158806,0.0021606344,0.008036228,0.0014102873,0.0032928665,0.0029553557,0.004440234,0.050736096],"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":[0.002638534,0.0006874349,0.0521898,0.0039019373,0.00074940023,0.0007395743,0.0014419369,0.017777305,0.001036942,0.19578278,0.52095073,0.20210356],"study_design_scores_gemma":[0.00047341583,0.00073757156,0.029503867,0.002282391,0.00041237616,0.00057485857,0.0013943788,0.019197274,0.00156677,0.09782478,0.8457942,0.00023813947],"about_ca_topic_score_codex":0.0042482205,"about_ca_topic_score_gemma":0.0037773547,"teacher_disagreement_score":0.22659132,"about_ca_system_score_codex":0.0017191791,"about_ca_system_score_gemma":0.0029161759,"threshold_uncertainty_score":0.7580235},"labels":[],"label_agreement":null},{"id":"W4280529555","doi":"10.1038/s41467-022-30187-w","title":"Large-scale GWAS of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":106,"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 Global Health Research","funders":"FP7 Health; National Institute on Drug Abuse; National Institute of Mental Health; Avera Institute for Human Genetics; Norwegian Biodiversity Information Centre; National Institutes of Health; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; National Institute of Diabetes and Digestive and Kidney Diseases; ZonMw; University of Bristol; Medical Research Council; Koninklijke Nederlandse Akademie van Wetenschappen; European Commission; King's College London; National Institute for Health and Care Research; Horizon 2020 Framework Programme; Cancer Research UK; Chronic Disease Research Foundation; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust; British Heart Foundation","keywords":"Heritability; Genome-wide association study; Genetic correlation; Biobank; Correlation; Scale (ratio); Structural equation modeling; Twin study; Psychology; Evolutionary biology; Biology; Genetic variation; Genetics; Statistics; Gene; Mathematics; Single-nucleotide polymorphism","score_opus":0.03664345978946882,"score_gpt":0.2903897054708038,"score_spread":0.25374624568133497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280529555","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":[0.9944886,0.00024650985,0.0031068414,0.00008681546,0.000013369388,0.000011086149,0.0016541093,0.00003744438,0.0003552336],"genre_scores_gemma":[0.9968652,0.00009109407,0.0017647439,0.00003756556,0.00000988723,0.00001887388,0.0008748823,0.000017550296,0.00032028777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992223,0.00026992636,0.000066481334,0.0002866618,0.00008265628,0.00007196221],"domain_scores_gemma":[0.9979936,0.0010020374,0.0004064889,0.00035615024,0.000103101534,0.00013863326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000919827,0.0005071561,0.0004474553,0.0006525721,0.00035659445,0.00061010895,0.00029520833,0.00052264536,0.0039341627],"category_scores_gemma":[0.0026599574,0.00023666077,0.00076800166,0.0012810836,0.0003729531,0.00022314898,0.0006326977,0.00049769523,0.0002670728],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014691292,0.00007321936,0.93261844,0.00017366851,0.0016466047,0.0015618298,0.00058235053,0.0010416176,0.043410543,0.0004668255,0.0009102799,0.016045544],"study_design_scores_gemma":[0.00003136067,0.000106798,0.9973304,0.000011044888,0.00019201377,0.00044078572,0.0000805913,0.0005682289,0.0005691522,0.00023927743,0.00041971196,0.0000106423695],"about_ca_topic_score_codex":0.0042853993,"about_ca_topic_score_gemma":0.007377658,"teacher_disagreement_score":0.0042853993,"about_ca_system_score_codex":0.00017792606,"about_ca_system_score_gemma":0.00022992307,"threshold_uncertainty_score":0.013161123},"labels":[],"label_agreement":null},{"id":"W4281398725","doi":"10.1111/joss.12751","title":"Temporal ranking for characterization and improved discrimination of protein beverages","year":2022,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Ranking (information retrieval); Sucralose; Rank (graph theory); Computer science; Sensory system; Food science; Paired comparison; Mathematics; Artificial intelligence; Statistics; Psychology; Chemistry; Cognitive psychology","score_opus":0.08184492740902317,"score_gpt":0.318233171556971,"score_spread":0.23638824414794785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281398725","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":[0.62464505,0.001323585,0.3634148,0.0002664261,0.00027006684,0.000759235,0.0007021552,0.001133235,0.007485431],"genre_scores_gemma":[0.7329434,0.00080411526,0.2608772,0.00025658318,0.000117196505,0.000443602,0.0004184591,0.00015592416,0.0039835265],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983985,0.0005727802,0.00009055874,0.00023612902,0.00062054803,0.00008147322],"domain_scores_gemma":[0.9971323,0.0007527175,0.00064966135,0.00027964302,0.0010246813,0.00016111007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002680741,0.00059902837,0.00045240897,0.0010757046,0.00023614799,0.0006231085,0.0005955217,0.00051628606,0.0035539626],"category_scores_gemma":[0.0046933955,0.00025816736,0.0005699024,0.0006271139,0.0002836388,0.0009004724,0.0006903994,0.0006095826,0.000623083],"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":[0.0025389446,0.0006331307,0.019971501,0.0008375103,0.00014614109,0.00007839583,0.00036385804,0.0030486332,0.6102808,0.0008344385,0.0016501411,0.35961655],"study_design_scores_gemma":[0.00031442798,0.012970923,0.32551092,0.00019976989,0.00058123935,0.001374537,0.0012758049,0.17215036,0.46715975,0.0028522494,0.015044389,0.00056562125],"about_ca_topic_score_codex":0.0010979192,"about_ca_topic_score_gemma":0.002690258,"teacher_disagreement_score":0.0035539626,"about_ca_system_score_codex":0.0002222207,"about_ca_system_score_gemma":0.00041139565,"threshold_uncertainty_score":0.014177263},"labels":[],"label_agreement":null},{"id":"W4281634251","doi":"10.1111/1750-3841.16200","title":"Creating foods for older adults: Emotional responses and liking of microwave‐assisted thermal sterilization processed meals","year":2022,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Meal; Flavor; Sterilization (economics); Meal preparation; Food science; Psychology; Chemistry","score_opus":0.054849583172911345,"score_gpt":0.31161803571663643,"score_spread":0.2567684525437251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281634251","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":[0.9997124,0.00005125066,0.000040457177,0.0000073145634,0.0000022753895,0.0000068289264,0.000014777585,0.0000010423862,0.00016369368],"genre_scores_gemma":[0.99909985,0.00010944703,0.0002542791,0.000054489767,0.0000070483497,0.000017277362,0.000041535623,9.757703e-7,0.00041502126],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988997,0.000029177841,0.000009954906,0.000016050317,0.000038238904,0.000016465025],"domain_scores_gemma":[0.99962234,0.00007178165,0.0001459271,0.000018077742,0.00008269711,0.00005910242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036786194,0.00025632148,0.00021097175,0.00014455839,0.00017918013,0.0002524285,0.0000707912,0.0003414527,0.0013403862],"category_scores_gemma":[0.0011859525,0.000112186906,0.0002755099,0.000067804904,0.00010956561,0.00018049982,0.0002307598,0.00029301224,0.00018637373],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00475019,0.0025513778,0.7951754,0.00042637475,0.000281611,0.0007238475,0.00872547,0.0001570789,0.13630821,0.000036660003,0.0005540217,0.05030971],"study_design_scores_gemma":[0.000026292955,0.0029717346,0.99187535,0.0000105576055,0.00005254493,0.00041446404,0.0019647586,0.00011224528,0.002260505,0.000011617242,0.0002909398,0.000008846128],"about_ca_topic_score_codex":0.00058503903,"about_ca_topic_score_gemma":0.0010195848,"teacher_disagreement_score":0.0013403862,"about_ca_system_score_codex":0.000071426024,"about_ca_system_score_gemma":0.000055301396,"threshold_uncertainty_score":0.0044840574},"labels":[],"label_agreement":null},{"id":"W4281680983","doi":"10.1111/joss.12766","title":"Effect of geographical origin on consumers' emotional response to alcoholic beverages: A study with wine and cider","year":2022,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"","keywords":"Wine; Purchasing; Affect (linguistics); Consumer behaviour; Psychology; Marketing; Country of origin; Advertising; Geography; Food science; Business; Communication","score_opus":0.05090738770949359,"score_gpt":0.34957136223696345,"score_spread":0.29866397452746984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281680983","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":[0.9998338,0.000009683878,0.000011562727,0.000006282021,0.000001761046,0.000014887513,0.00000593225,2.5699333e-7,0.000115761395],"genre_scores_gemma":[0.9993352,0.00003699928,0.00015768566,0.00006648448,0.0000068373542,0.000047338144,0.000024331619,0.0000010474788,0.00032403631],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993224,0.00035299387,0.000040078718,0.00008317224,0.00013722728,0.00006421355],"domain_scores_gemma":[0.997661,0.0012599647,0.00035409266,0.00012342185,0.00030161443,0.00029994274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018101997,0.00027205082,0.00050305115,0.00026081505,0.00080797775,0.00085797877,0.0001800002,0.0005840534,0.0020789248],"category_scores_gemma":[0.0043307864,0.00032764612,0.0004511517,0.00027447063,0.0005107253,0.00054098293,0.00043517424,0.00079506746,0.00017724773],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03552326,0.07640398,0.7440991,0.000711887,0.00057930814,0.0011525538,0.06751928,0.0003078794,0.043004755,0.00028621507,0.0006973254,0.02971451],"study_design_scores_gemma":[0.001018246,0.03872561,0.93461627,0.000034377164,0.0002285031,0.00024099457,0.020781392,0.0005991142,0.0024859419,0.00011199606,0.0010945527,0.0000630401],"about_ca_topic_score_codex":0.0029326086,"about_ca_topic_score_gemma":0.0050847917,"teacher_disagreement_score":0.0029326086,"about_ca_system_score_codex":0.00028260527,"about_ca_system_score_gemma":0.0001823873,"threshold_uncertainty_score":0.00957334},"labels":[],"label_agreement":null},{"id":"W4281759924","doi":"10.1016/j.meatsci.2022.108869","title":"Effect of age on sensory perception of beef patties with varying firmness","year":2022,"lang":"en","type":"article","venue":"Meat Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Melbourne","keywords":"Flavour; Taste; Food science; Perception; Age groups; Psychology; Chemistry; Demography","score_opus":0.03412010473646945,"score_gpt":0.29188310085468355,"score_spread":0.25776299611821407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281759924","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":[0.9994412,0.000095643205,0.00014098934,0.000013771012,0.00001518953,0.000005231809,0.00008081364,0.0000036010854,0.00020363333],"genre_scores_gemma":[0.99861217,0.00006746605,0.00020836336,0.000032313073,0.00001146369,0.0000081927765,0.00008530656,0.000007318564,0.00096745003],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996138,0.00008523656,0.000039511942,0.000110962465,0.000062327585,0.00008813083],"domain_scores_gemma":[0.9958682,0.0023622436,0.0005439558,0.00020047565,0.0004236952,0.000601392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007283117,0.00037800404,0.00039900764,0.00027489883,0.00014951843,0.00045486604,0.00013990636,0.00032442564,0.0037773277],"category_scores_gemma":[0.00334993,0.00025631025,0.00034160347,0.00023235581,0.00035625332,0.0004745821,0.00032432453,0.0005541629,0.00023887561],"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":[0.17893076,0.0034478072,0.17225498,0.00021186365,0.00039172714,0.0007915453,0.0012802768,0.00066193886,0.61662406,0.00017039795,0.00035975967,0.024874877],"study_design_scores_gemma":[0.00015316771,0.019142458,0.9338486,0.000014812381,0.00027632783,0.000321209,0.0005456097,0.0010406671,0.04401215,0.00010053235,0.0005001828,0.000044325596],"about_ca_topic_score_codex":0.00135129,"about_ca_topic_score_gemma":0.0022198723,"teacher_disagreement_score":0.0037773277,"about_ca_system_score_codex":0.00017345812,"about_ca_system_score_gemma":0.00016652347,"threshold_uncertainty_score":0.012636423},"labels":[],"label_agreement":null},{"id":"W4282939069","doi":"10.1016/j.foodres.2022.111513","title":"Consumer perception and emotional responses to plant-based cheeses","year":2022,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Flavour; Perception; Food science; Medicine; Mathematics; Materials science; Psychology; Biology","score_opus":0.21898066745016972,"score_gpt":0.4112996549334169,"score_spread":0.1923189874832472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282939069","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":[0.99939764,0.000033940225,0.00011639009,0.000019726578,0.0000070062865,0.0000068800236,0.000024645973,0.0000023823802,0.00039135083],"genre_scores_gemma":[0.9991296,0.00004810004,0.00024011428,0.000039454746,0.000007706943,0.000012954805,0.000076933764,0.000005527415,0.0004396287],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985814,0.000046018053,0.000006549625,0.000021653985,0.000043071897,0.000024556659],"domain_scores_gemma":[0.99952006,0.0002212479,0.00009936956,0.000023991677,0.00006691881,0.00006839392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038216438,0.0002540757,0.00020827672,0.0001696372,0.00019705307,0.0006085061,0.00009182763,0.00033904507,0.002494705],"category_scores_gemma":[0.001858917,0.00012513736,0.00031423508,0.00012690319,0.00033151623,0.00023119476,0.00038214054,0.0006065007,0.00010748088],"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":[0.03425496,0.00076550053,0.04471076,0.00040860733,0.00025555003,0.00051387143,0.003185928,0.0005127431,0.9004456,0.00030757763,0.00034808018,0.01429083],"study_design_scores_gemma":[0.0003031752,0.007551932,0.92821413,0.000044044875,0.0002949613,0.00045025538,0.003489043,0.002457576,0.055808187,0.00040116417,0.000933143,0.000052442185],"about_ca_topic_score_codex":0.0010454352,"about_ca_topic_score_gemma":0.0011972791,"teacher_disagreement_score":0.002494705,"about_ca_system_score_codex":0.00021701532,"about_ca_system_score_gemma":0.00010093022,"threshold_uncertainty_score":0.008345604},"labels":[],"label_agreement":null},{"id":"W4284971561","doi":"10.1016/j.foodres.2022.111648","title":"An investigation into consumer perception and attitudes towards plant-based alternatives to milk","year":2022,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Aftertaste; Perception; Food science; Psychology; Biology; Flavor","score_opus":0.18195289425695924,"score_gpt":0.42673283905600295,"score_spread":0.24477994479904372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284971561","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":[0.99972636,0.0000110530655,0.000025806376,0.000014439402,0.0000011588191,0.000006328915,0.000011580111,4.7666407e-7,0.00020271745],"genre_scores_gemma":[0.9990978,0.000046598096,0.0002266033,0.000032736083,0.0000022422337,0.000010269557,0.000037515285,8.740334e-7,0.00054521835],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996791,0.00010878165,0.000019607876,0.000032325424,0.00012571231,0.000034451183],"domain_scores_gemma":[0.9985917,0.0008019161,0.00027957384,0.000042334348,0.00017356932,0.000110869514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010119153,0.0002129243,0.00015130774,0.0002640039,0.00039077277,0.00057127565,0.00014477939,0.00036656964,0.0027480754],"category_scores_gemma":[0.0022168453,0.00015027566,0.00040027438,0.0002935463,0.00031327517,0.00053406536,0.00029798632,0.0006902138,0.00017141309],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.031951584,0.019488256,0.67115176,0.0008679249,0.00048296887,0.0012533711,0.03287539,0.00050515315,0.15218846,0.001312928,0.00076577085,0.08715646],"study_design_scores_gemma":[0.0002676795,0.033098105,0.92750156,0.000059096925,0.00024816205,0.0005224621,0.020008817,0.0013999224,0.014144286,0.00031938154,0.0023815583,0.000048828508],"about_ca_topic_score_codex":0.0019258546,"about_ca_topic_score_gemma":0.0023524773,"teacher_disagreement_score":0.0027480754,"about_ca_system_score_codex":0.00026742188,"about_ca_system_score_gemma":0.00023282619,"threshold_uncertainty_score":0.009193242},"labels":[],"label_agreement":null},{"id":"W4287373583","doi":"10.48550/arxiv.2101.08188","title":"Uncovering and Displaying the Coherent Groups of Rank Data by\\n Exploratory Riffle Shuffling","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Shuffling; Riffle; Rank (graph theory); Set (abstract data type); Contingency table; Combinatorics; Mathematics; Seriation (archaeology); Computer science; Statistics; Artificial intelligence; Geography","score_opus":0.17727865154452996,"score_gpt":0.22649833422431495,"score_spread":0.04921968267978499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287373583","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":[0.34393898,0.00020007275,0.6493456,0.0003936001,0.000020409541,0.00021557666,0.0010816219,0.0010998348,0.0037042897],"genre_scores_gemma":[0.7157446,0.00012245546,0.2804967,0.00008444758,0.00003529348,0.0002518181,0.0018747195,0.000092204355,0.0012977162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984432,0.000755159,0.000084408566,0.0002905614,0.00027505995,0.00015171144],"domain_scores_gemma":[0.99203616,0.0041910694,0.000914709,0.0019627546,0.00054477487,0.00035048684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027522433,0.00043945192,0.0006117311,0.0035349606,0.0007892174,0.0018203381,0.00080853916,0.00066520413,0.0033081467],"category_scores_gemma":[0.011119522,0.00039623224,0.00086244714,0.0019356341,0.0013859688,0.0019150157,0.0020622322,0.00080935087,0.0006381706],"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":[0.001869661,0.00046397228,0.06849021,0.00052650657,0.00028965168,0.001170941,0.006621344,0.07601766,0.04899537,0.21167572,0.0073625203,0.5765164],"study_design_scores_gemma":[0.00010397059,0.000554573,0.037210904,0.00008523783,0.00006707035,0.00076504075,0.0023331123,0.6491663,0.025049783,0.2730401,0.011439106,0.00018486253],"about_ca_topic_score_codex":0.0015355393,"about_ca_topic_score_gemma":0.0019040075,"teacher_disagreement_score":0.0035349606,"about_ca_system_score_codex":0.0007309396,"about_ca_system_score_gemma":0.0007950409,"threshold_uncertainty_score":0.014555454},"labels":[],"label_agreement":null},{"id":"W4297361061","doi":"10.1016/j.metip.2022.100100","title":"Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data","year":2022,"lang":"en","type":"article","venue":"Methods in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"St. Michael's Hospital; Centre for Addiction and Mental Health","funders":"City University of New York","keywords":"Categorical variable; Correspondence analysis; Linear discriminant analysis; Psychology; Group (periodic table); Inference; Typology; Artificial intelligence; Computer science; Natural language processing; Cognitive psychology; Mathematics; Machine learning; Geography","score_opus":0.3452071259071408,"score_gpt":0.45364120296253835,"score_spread":0.10843407705539754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297361061","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":[0.16197744,0.00030341317,0.82467264,0.00032995266,0.00017083032,0.002002386,0.0025901876,0.0018055338,0.0061475253],"genre_scores_gemma":[0.471213,0.00014020131,0.5213587,0.00011558995,0.00004707819,0.0037084874,0.0018309493,0.00041932074,0.0011665168],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98622906,0.008412087,0.0007699048,0.0025112308,0.0016894938,0.00038817187],"domain_scores_gemma":[0.9271274,0.058874078,0.0026754525,0.0058086016,0.004832519,0.00068206206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015981445,0.0013331637,0.0011715965,0.005666702,0.0020379878,0.0023742544,0.0013276702,0.00079309475,0.008638982],"category_scores_gemma":[0.071143255,0.00041582147,0.0016479879,0.0055169156,0.0011933834,0.0022312975,0.003084456,0.0015711854,0.0009420382],"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":[0.0027080576,0.0011038074,0.1874772,0.002226516,0.0032523246,0.00045622684,0.013168144,0.01080168,0.028241627,0.054338414,0.01608525,0.68014085],"study_design_scores_gemma":[0.00050429004,0.0035918092,0.38865995,0.00057979254,0.0011107504,0.0013839466,0.012386944,0.36144114,0.0329449,0.14995812,0.046741832,0.000696583],"about_ca_topic_score_codex":0.0028364705,"about_ca_topic_score_gemma":0.003467173,"teacher_disagreement_score":0.015981445,"about_ca_system_score_codex":0.0009979568,"about_ca_system_score_gemma":0.0020333196,"threshold_uncertainty_score":0.08451903},"labels":[],"label_agreement":null},{"id":"W4298147657","doi":"10.15439/2022f57","title":"On Multiplicative, Additive and Qualitative Pairwise Comparisons","year":2022,"lang":"en","type":"article","venue":"Annals of Computer Science and Information Systems","topic":"Sensory Analysis and Statistical Methods","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":"IBM (Canada); McMaster University","funders":"","keywords":"Pairwise comparison; Multiplicative function; Computer science; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.11292710939438423,"score_gpt":0.3620555644116679,"score_spread":0.2491284550172837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298147657","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":[0.0046701552,0.0033363127,0.97410417,0.0010455989,0.00056549825,0.00018738957,0.00027066935,0.000102286554,0.015717944],"genre_scores_gemma":[0.1564193,0.004260292,0.82917494,0.0017275645,0.0016335191,0.0016216104,0.0005112228,0.00021847179,0.004433033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9145676,0.0633722,0.0029385113,0.00686638,0.011566096,0.00068910257],"domain_scores_gemma":[0.8388525,0.13517334,0.0052255713,0.012202577,0.0076625487,0.00088350166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053717103,0.002260784,0.0021057352,0.006385512,0.0020848906,0.0056312853,0.0040219966,0.001921912,0.0070858262],"category_scores_gemma":[0.14569342,0.0009203118,0.002357743,0.0076822736,0.015002771,0.01111991,0.0077909753,0.004627135,0.0012293058],"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":[0.00007078523,0.000039451676,0.0006742207,0.00050151173,0.00018508369,0.00014024125,0.0006466115,0.009181101,0.00045480827,0.918326,0.002643785,0.06713642],"study_design_scores_gemma":[0.0000061747214,0.00004612512,0.00026122812,0.000088445224,0.000023197146,0.00013567324,0.00009745922,0.008638141,0.00016954677,0.98566693,0.0048455577,0.000021456923],"about_ca_topic_score_codex":0.0011797239,"about_ca_topic_score_gemma":0.0013106645,"teacher_disagreement_score":0.053717103,"about_ca_system_score_codex":0.002841884,"about_ca_system_score_gemma":0.0019751631,"threshold_uncertainty_score":0.28408659},"labels":[],"label_agreement":null},{"id":"W4302761498","doi":"10.5539/jfr.v12n1p1","title":"Culinary Treatments Affect Sensory Attributes and Consumer Preference for Sweet Potato Cultivars","year":2022,"lang":"en","type":"article","venue":"Journal of Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Assiniboine Community College","funders":"","keywords":"Sweetness; Taste; Cultivar; Food science; Significant difference; Horticulture; Chemistry; Mathematics; Biology","score_opus":0.34219571888846023,"score_gpt":0.4249081541394051,"score_spread":0.08271243525094485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302761498","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":[0.9992951,0.00022010512,0.000067101646,0.0000104657565,0.000002558668,0.0000062664926,0.00007264751,0.0000034871034,0.0003223946],"genre_scores_gemma":[0.9975418,0.0004185311,0.0005596985,0.00008075318,0.00000244916,0.000018176857,0.0003618632,0.0000089822415,0.0010077509],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99972695,0.000065660664,0.000025601705,0.00007197191,0.00006586488,0.000043852375],"domain_scores_gemma":[0.99958116,0.00008982533,0.00012828497,0.000026439417,0.00009797427,0.0000762854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024539357,0.00023281177,0.00032373326,0.00021374198,0.00021296227,0.0005569384,0.00014099086,0.00021024943,0.0016349537],"category_scores_gemma":[0.0005664693,0.00013438848,0.00037199882,0.00031980305,0.00017776225,0.00024978205,0.0002879419,0.0004520126,0.00019232283],"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":[0.0044920095,0.0007261322,0.052733082,0.0004346211,0.00028284435,0.00045771495,0.00091933063,0.00013141446,0.9260708,0.000037989554,0.00024588197,0.01346814],"study_design_scores_gemma":[0.000019938007,0.0024591114,0.9461699,0.000026909598,0.00014631996,0.00017201157,0.0010479713,0.00023294082,0.048715763,0.000025861314,0.0009630865,0.000020162066],"about_ca_topic_score_codex":0.0034935027,"about_ca_topic_score_gemma":0.009693913,"teacher_disagreement_score":0.0034935027,"about_ca_system_score_codex":0.00027100212,"about_ca_system_score_gemma":0.00017163488,"threshold_uncertainty_score":0.0069463253},"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":"W4303614963","doi":"10.1016/b978-0-323-85263-0.00005-3","title":"Introduction","year":2022,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Wine tasting; Wine; Sensory system; Quality (philosophy); Psychology; Sensory analysis; Cognitive psychology; Food science; Chemistry; Epistemology; Philosophy","score_opus":0.03060121788802218,"score_gpt":0.2527239794137385,"score_spread":0.2221227615257163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303614963","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037655485,0.0029646507,0.0059114313,0.001885696,0.0035514857,0.00014605804,0.0031229835,0.0015471387,0.98049396],"genre_scores_gemma":[0.00077614916,0.0014744666,0.0020612224,0.00067068683,0.00037425768,0.00006505249,0.0018382552,0.0003327257,0.99240714],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994118,0.000050649065,0.000022465785,0.000120971956,0.00033685457,0.000057293055],"domain_scores_gemma":[0.99941504,0.000088512854,0.000019466439,0.00008556604,0.00028665285,0.000104837905],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006655529,0.0011404016,0.0006706122,0.001808289,0.0011918406,0.0038493157,0.0015944957,0.001709578,0.60850304],"category_scores_gemma":[0.0017712817,0.00037234137,0.00058049074,0.001570819,0.00044875132,0.0026593679,0.0021136105,0.0015455245,0.5362022],"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":[0.000023446832,0.000040309005,0.00009095616,0.00019644939,0.0000024185588,0.000060936392,0.000101774596,0.00013048714,0.0005480814,0.018997414,0.715158,0.26464972],"study_design_scores_gemma":[0.0000016167695,0.0000051115294,0.000072846626,0.0000627107,9.532092e-7,0.000041862626,0.000026550139,0.000023849307,0.000087203705,0.002580563,0.9970943,0.0000025119107],"about_ca_topic_score_codex":0.0033102669,"about_ca_topic_score_gemma":0.0054196646,"teacher_disagreement_score":0.60850304,"about_ca_system_score_codex":0.0010831754,"about_ca_system_score_gemma":0.0017733544,"threshold_uncertainty_score":0.55842286},"labels":[],"label_agreement":null},{"id":"W4304779986","doi":"10.51412/psnnjp.2022.29","title":"Evaluation of cosmetic lipsticks for hazardous heavy metals and determination of antimicrobial potency","year":2022,"lang":"en","type":"article","venue":"Nigerian Journal of Pharmacy","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Lipstick; Acetone; Chemistry; Distilled water; Chromatography; Preservative; Diluent; Agar; Food science; Nuclear chemistry; Biology; Biochemistry","score_opus":0.09082639851625793,"score_gpt":0.3633362156298487,"score_spread":0.2725098171135908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304779986","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":[0.9884115,0.006104724,0.0023391077,0.00008678291,0.000056446916,0.00013218347,0.00041369704,0.000027517666,0.0024280725],"genre_scores_gemma":[0.9822527,0.0037871052,0.009577005,0.00015116872,0.000031405696,0.00010674357,0.0005669176,0.000027051825,0.0034998886],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991841,0.00014912692,0.00005602535,0.00010475083,0.00042797066,0.00007805748],"domain_scores_gemma":[0.99940395,0.00010142953,0.00015577873,0.000024945974,0.0002680876,0.000045782792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006841456,0.000667304,0.0005640677,0.0012189101,0.0003484622,0.0004543157,0.00025178358,0.00045058376,0.001442096],"category_scores_gemma":[0.0005459574,0.0001736567,0.0005746996,0.0007532559,0.0003587554,0.00035762636,0.00037934695,0.00038732027,0.00029915254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050007843,0.0002835086,0.0075118826,0.0007745177,0.0000476025,0.00015631126,0.00014568033,0.00018893903,0.97764593,0.000065756634,0.00010605961,0.01257381],"study_design_scores_gemma":[0.000027836268,0.006506644,0.0481925,0.00016067442,0.0002239124,0.00087039295,0.0007794395,0.00086654164,0.93735415,0.000113899805,0.0048661204,0.000037886828],"about_ca_topic_score_codex":0.00054825685,"about_ca_topic_score_gemma":0.0012975272,"teacher_disagreement_score":0.001442096,"about_ca_system_score_codex":0.00025936935,"about_ca_system_score_gemma":0.00025075368,"threshold_uncertainty_score":0.0048243403},"labels":[],"label_agreement":null},{"id":"W4306147209","doi":"10.31542/muse.v6i1.2283","title":"Measuring the activity of Saccharomyces cerevisiae in relation to home-based additives by measured net weight loss","year":2022,"lang":"en","type":"article","venue":"MacEwan University Student eJournal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Saccharomyces cerevisiae; Yeast; Fermentation; Saccharomyces; Food science; Chemistry; Biochemistry","score_opus":0.03201254066878473,"score_gpt":0.24382494291175752,"score_spread":0.2118124022429728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306147209","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":[0.99367404,0.00040116344,0.0040688342,0.000018320758,0.000033021362,0.000057795827,0.00041931594,0.00004245213,0.0012849882],"genre_scores_gemma":[0.9868716,0.00049536955,0.008487905,0.000055492303,0.000011632616,0.00014113462,0.0008289129,0.000032519645,0.0030753093],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944633,0.00007927743,0.000037894777,0.00012773275,0.00027245836,0.00003628187],"domain_scores_gemma":[0.9996406,0.00008151283,0.00009188021,0.000043210868,0.000106195424,0.000036635116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037145856,0.00050545513,0.000360861,0.000511447,0.00029916622,0.0006157215,0.00023009282,0.00027503568,0.0014381689],"category_scores_gemma":[0.00052384933,0.00012204023,0.00040241829,0.00072531606,0.00025652198,0.00023312075,0.00032711835,0.00073325,0.0002944233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009083283,0.00065787754,0.026315995,0.00015969633,0.00007508938,0.00004708619,0.0003092044,0.00020769556,0.9554293,0.00007918913,0.00010049479,0.015710035],"study_design_scores_gemma":[0.000015490661,0.0047349264,0.18392876,0.000019137666,0.00012402785,0.00016158087,0.00042118586,0.0012641319,0.8080028,0.00008528668,0.0012149343,0.000027603766],"about_ca_topic_score_codex":0.0010698375,"about_ca_topic_score_gemma":0.0019157823,"teacher_disagreement_score":0.0014381689,"about_ca_system_score_codex":0.00021072589,"about_ca_system_score_gemma":0.00016474232,"threshold_uncertainty_score":0.0048111677},"labels":[],"label_agreement":null},{"id":"W4307501639","doi":"10.1111/1750-3841.16380","title":"Sensory characterization of yellow pea and ground chicken hybrid meat burgers using static and dynamic methodologies","year":2022,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Research Nova Scotia; Canada Foundation for Innovation","keywords":"Food science; Processed meat; Sensory system; Livestock; Food products; Consumption (sociology); Mathematics; Biology; Psychology; Ecology; Cognitive psychology","score_opus":0.10018756992652171,"score_gpt":0.3251157930795288,"score_spread":0.2249282231530071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307501639","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":[0.9988721,0.000162237,0.0005184279,0.000010069552,0.000004299917,0.000016140773,0.00007334092,0.0000041705684,0.00033914892],"genre_scores_gemma":[0.99589807,0.0002829049,0.0021463905,0.00007443822,0.000004230405,0.00003320763,0.0002884492,0.0000080924665,0.0012641139],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998418,0.000026120337,0.000010915651,0.0000352464,0.00006498968,0.000020878999],"domain_scores_gemma":[0.99973744,0.000051286126,0.000048017166,0.000009933484,0.000111729416,0.000041479885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039713754,0.00027343008,0.00017005489,0.00045447357,0.00018842483,0.00028662142,0.00012982183,0.00026942906,0.0014693779],"category_scores_gemma":[0.00033146725,0.00015253093,0.00036413677,0.00026882865,0.00022918373,0.00026355172,0.00027876726,0.00039969594,0.00016100082],"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":[0.0008075035,0.00015173345,0.008484365,0.00014300685,0.000030005267,0.00013429498,0.00033181824,0.00013148779,0.9850706,0.00003498129,0.00007313102,0.0046070623],"study_design_scores_gemma":[0.000054960612,0.012450343,0.6064535,0.00008008278,0.00018975313,0.0010955177,0.0029606929,0.002429308,0.37176275,0.000113034155,0.0023337398,0.00007628809],"about_ca_topic_score_codex":0.0013883184,"about_ca_topic_score_gemma":0.0023467757,"teacher_disagreement_score":0.0014693779,"about_ca_system_score_codex":0.00011561357,"about_ca_system_score_gemma":0.000081136444,"threshold_uncertainty_score":0.0049155354},"labels":[],"label_agreement":null},{"id":"W4308564112","doi":"10.3389/fnut.2022.1008577","title":"Hedonic response sensitivity to variations in the evaluation task and culinary preparation in a natural consumption context","year":2022,"lang":"en","type":"article","venue":"Frontiers in Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Cafeteria; Context (archaeology); Task (project management); Consumption (sociology); Test (biology); Natural (archaeology); Psychology; Social psychology; Medicine; Engineering; Aesthetics","score_opus":0.041325270036207756,"score_gpt":0.3246238952577586,"score_spread":0.2832986252215508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308564112","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":[0.9974497,0.00008785293,0.0013675065,0.000012650297,0.000028235527,0.00006487997,0.0000635034,0.000012070641,0.0009136258],"genre_scores_gemma":[0.9961061,0.000070536466,0.002349612,0.00009028457,0.000027440577,0.00019917435,0.00035389882,0.000032525822,0.00077040447],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99529546,0.0020185623,0.00039531387,0.00086963124,0.0011489055,0.00027210443],"domain_scores_gemma":[0.9895257,0.006222543,0.0013695314,0.001664414,0.0007568751,0.00046098136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036235803,0.00062890357,0.00056953647,0.00032696215,0.00024477293,0.0009066197,0.00038454987,0.00064171385,0.0019032303],"category_scores_gemma":[0.01519494,0.00036022018,0.00054726953,0.0003156583,0.00054207596,0.00035462074,0.0010799952,0.00077479496,0.00030995143],"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":[0.024235172,0.0032316304,0.11965267,0.00057229714,0.0005196879,0.00035710473,0.002603712,0.0018971723,0.81200904,0.00025885695,0.00039758455,0.03426506],"study_design_scores_gemma":[0.0001781444,0.015861535,0.9272186,0.00003126369,0.00017129348,0.00037121575,0.0006149326,0.0018250243,0.052458618,0.00031183605,0.0008753693,0.0000821029],"about_ca_topic_score_codex":0.0006937849,"about_ca_topic_score_gemma":0.00068450073,"teacher_disagreement_score":0.0036235803,"about_ca_system_score_codex":0.00026212985,"about_ca_system_score_gemma":0.00018020558,"threshold_uncertainty_score":0.019163549},"labels":[],"label_agreement":null},{"id":"W4309404989","doi":"","title":"Tribute To Tobler : Création et visualisation de champs vectoriels pour l'analyse de matrice Origine Destination","year":2021,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Ministère des Transports","funders":"","keywords":"Humanities; Tribute; Visualization; Mathematics; Computer science; Art; Artificial intelligence; Art history","score_opus":0.03582035005434275,"score_gpt":0.30105058872966434,"score_spread":0.2652302386753216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309404989","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":[0.014143071,0.010716716,0.65236473,0.06243768,0.05944259,0.0015015837,0.044693876,0.10691038,0.047789413],"genre_scores_gemma":[0.103880726,0.0065678866,0.23090981,0.0070336782,0.014710874,0.0030646112,0.06663168,0.077030234,0.49017048],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997182,0.0009556859,0.00009907984,0.00055715645,0.0010080636,0.00019813502],"domain_scores_gemma":[0.9906114,0.0044475636,0.00013000568,0.0007785313,0.0029322205,0.001100295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055897417,0.0019449624,0.0026772092,0.0028080307,0.0021059867,0.0037475885,0.0017129579,0.0024060658,0.2179043],"category_scores_gemma":[0.021599209,0.00080083683,0.0015703562,0.002343081,0.0009492248,0.0031793308,0.002476895,0.0025798918,0.069855414],"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":[0.00029422206,0.0000803633,0.00054361,0.00027067785,0.000058337446,0.0001406676,0.0005277345,0.0012854597,0.004142082,0.006354097,0.9035336,0.082769126],"study_design_scores_gemma":[0.00031393152,0.00018734716,0.0040662386,0.00016832718,0.0000417489,0.00032302458,0.0005042169,0.015405586,0.0062071164,0.01227937,0.9603526,0.00015049663],"about_ca_topic_score_codex":0.022459617,"about_ca_topic_score_gemma":0.009843645,"teacher_disagreement_score":0.2179043,"about_ca_system_score_codex":0.0010277036,"about_ca_system_score_gemma":0.0023010373,"threshold_uncertainty_score":0.72896254},"labels":[],"label_agreement":null},{"id":"W4312070532","doi":"10.1002/env.2784","title":"Estimating functional single index models with compact support","year":2022,"lang":"en","type":"article","venue":"Environmetrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Function (biology); Index (typography); Single-index model; Scalar (mathematics); Mathematics; Applied mathematics; Computer science; Statistics; Mathematical optimization; Physics","score_opus":0.08326067718766723,"score_gpt":0.23956543106604128,"score_spread":0.15630475387837406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312070532","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":[0.0773174,0.0001649851,0.92156965,0.00014473358,0.000016899727,0.00002862498,0.000115108436,0.00017635128,0.0004662341],"genre_scores_gemma":[0.9043748,0.00016818919,0.09324777,0.00008320261,0.00005079128,0.00013522543,0.0004935366,0.000062854,0.0013835828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99765694,0.0012460123,0.00009463071,0.0004910416,0.0003585375,0.00015277175],"domain_scores_gemma":[0.9729657,0.021832852,0.0020656055,0.0015010233,0.0013369445,0.00029777485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066077104,0.0011781186,0.0019364817,0.0015079431,0.00046066364,0.0017138867,0.0024550648,0.0018807127,0.0011709593],"category_scores_gemma":[0.027577559,0.00092856004,0.00125514,0.0010409089,0.0018651579,0.002450953,0.0019310298,0.0017868341,0.00025817053],"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":[0.00008170482,0.000046757123,0.0024708523,0.00005355508,0.000049293798,0.00009071273,0.00004361611,0.9739281,0.0008370276,0.01188597,0.00023477136,0.010277683],"study_design_scores_gemma":[0.0000014205593,0.0000063246875,0.00013271233,0.0000018604428,0.0000018155563,0.000003899289,0.000003028888,0.997701,0.000065614404,0.0020557211,0.000023818278,0.000002879884],"about_ca_topic_score_codex":0.008475893,"about_ca_topic_score_gemma":0.004113151,"teacher_disagreement_score":0.008475893,"about_ca_system_score_codex":0.0011558667,"about_ca_system_score_gemma":0.0009168416,"threshold_uncertainty_score":0.03494531},"labels":[],"label_agreement":null},{"id":"W4312593026","doi":"10.1007/978-3-030-95864-0_15","title":"Chapter 15: Cluster Analysis and Correspondence Analysis","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Contingency table; Categorical variable; Hierarchical clustering; Correspondence analysis; Statistic; Cluster (spacecraft); Similarity (geometry); Cluster analysis; Set (abstract data type); Multivariate statistics; Statistics; Descriptive statistics; Mathematics; Computer science; Data mining; Artificial intelligence","score_opus":0.04137087385385163,"score_gpt":0.27044217624616235,"score_spread":0.22907130239231072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312593026","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":[0.0028679196,0.03181947,0.765435,0.005051343,0.0035034704,0.0011044911,0.0028709653,0.0024329491,0.18491434],"genre_scores_gemma":[0.028576083,0.0327613,0.7913668,0.0012950754,0.0023162328,0.0016221749,0.004125947,0.0016978597,0.13623843],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99644536,0.0011022917,0.00017223152,0.0006461119,0.0015433251,0.0000906197],"domain_scores_gemma":[0.9963148,0.0019554642,0.00015873245,0.00036863575,0.0011297516,0.000072731105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032916325,0.0012317835,0.0010441194,0.0037081388,0.0017375842,0.00427373,0.0016429722,0.000937835,0.04220031],"category_scores_gemma":[0.008906163,0.00064887584,0.0010763684,0.0052377437,0.0014540497,0.0020008103,0.0017500153,0.0022230612,0.014400997],"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":[0.00003287579,0.0000756564,0.00061648875,0.0014695212,0.00011324017,0.00015406127,0.0014411838,0.0051678424,0.0013970931,0.20479688,0.2572357,0.52749944],"study_design_scores_gemma":[0.0000070825586,0.00002934998,0.0017360746,0.000953794,0.00003263201,0.00025394803,0.0005124061,0.008077087,0.0020431872,0.23048219,0.75582117,0.000051187566],"about_ca_topic_score_codex":0.0024758384,"about_ca_topic_score_gemma":0.0030912093,"teacher_disagreement_score":0.04220031,"about_ca_system_score_codex":0.0021290267,"about_ca_system_score_gemma":0.0022174793,"threshold_uncertainty_score":0.14117408},"labels":[],"label_agreement":null},{"id":"W4312871235","doi":"10.56530/lcgc.eu.gi5670v6","title":"Multivariate Optimization Procedure for Dynamic Headspace Extractions Coupled to GC(×GC)","year":2022,"lang":"en","type":"article","venue":"LCGC Europe","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":"University of Alberta","funders":"","keywords":"Extraction (chemistry); Reproducibility; Multivariate statistics; Water content; Moisture; Sample (material); Sample preparation; Gas chromatography; Chromatography; Process engineering; Computer science; Environmental science; Chemistry; Engineering; Machine learning; Organic chemistry","score_opus":0.0408127661287583,"score_gpt":0.3137113509735177,"score_spread":0.27289858484475943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312871235","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":[0.049554363,0.0018149747,0.9329714,0.00017741039,0.00016297195,0.0018945741,0.0029979746,0.0059688664,0.0044574775],"genre_scores_gemma":[0.09601685,0.002105151,0.88128257,0.00040574494,0.00007044466,0.009178091,0.0037818886,0.0013795295,0.0057798447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967507,0.00043761305,0.00022117062,0.00086047244,0.0015571279,0.00017297758],"domain_scores_gemma":[0.99902165,0.0003523921,0.000105599545,0.00011421288,0.00038069166,0.000025413085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023366106,0.0029258693,0.0013344688,0.0017688688,0.001321313,0.000851768,0.0013319852,0.0010177342,0.006845564],"category_scores_gemma":[0.0031750393,0.00075855764,0.0012022285,0.0021801714,0.00080981804,0.00073802366,0.001118436,0.002651952,0.002940052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005981221,0.0005405645,0.0017669525,0.0014752434,0.00025238874,0.00017212646,0.00038189127,0.008527788,0.79586047,0.0036542967,0.0040525445,0.18271768],"study_design_scores_gemma":[0.00006144806,0.0010502043,0.009442865,0.00009552143,0.00020032587,0.0003385437,0.00017535282,0.05828458,0.8800704,0.0016165756,0.048415523,0.00024875146],"about_ca_topic_score_codex":0.004068157,"about_ca_topic_score_gemma":0.006786939,"teacher_disagreement_score":0.006845564,"about_ca_system_score_codex":0.00080639287,"about_ca_system_score_gemma":0.0026343775,"threshold_uncertainty_score":0.0229007},"labels":[],"label_agreement":null},{"id":"W4313441591","doi":"10.13033/isahp.y2022.064","title":"2022 ISAHP Book of Abstracts/Schedule","year":2022,"lang":"en","type":"article","venue":"ISAHP proceedings","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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 Northern British Columbia","funders":"","keywords":"Schedule; Computer science; Operating system","score_opus":0.023159670784714582,"score_gpt":0.2476005113299567,"score_spread":0.2244408405452421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313441591","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051023846,0.0013285921,0.009407335,0.0015679668,0.005281802,0.0007529471,0.013070105,0.003289296,0.9647918],"genre_scores_gemma":[0.001986337,0.0009054361,0.0037805778,0.0002523826,0.00094184134,0.00022823164,0.00646755,0.0009613132,0.9844764],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992662,0.0000949558,0.00005640864,0.000109122964,0.0004171243,0.00005605588],"domain_scores_gemma":[0.99763024,0.00036930025,0.00010245933,0.0003848219,0.0010998044,0.00041330667],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010185271,0.0011280301,0.000879745,0.0028430033,0.0010391016,0.0037475368,0.0015018514,0.0011809102,0.8661837],"category_scores_gemma":[0.003709564,0.0007168186,0.0006951824,0.0038890305,0.00045261285,0.0018929718,0.0019783492,0.0015911942,0.77531534],"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":[0.000029192088,0.00003920246,0.000045690267,0.00011232052,0.0000029601374,0.00002665504,0.000012901969,0.00018889892,0.00045728433,0.0023187727,0.91088074,0.08588538],"study_design_scores_gemma":[0.000007080995,0.00001686895,0.00027753442,0.000057814486,0.0000013448912,0.000029474619,0.000016171998,0.00021054689,0.00012247066,0.0009928454,0.99826306,0.0000047847434],"about_ca_topic_score_codex":0.0029358757,"about_ca_topic_score_gemma":0.004236217,"teacher_disagreement_score":0.1338163,"about_ca_system_score_codex":0.0012637314,"about_ca_system_score_gemma":0.0017281619,"threshold_uncertainty_score":0.19087273},"labels":[],"label_agreement":null},{"id":"W4318068174","doi":"10.1002/jsfa.12473","title":"Consumer perception of salt‐reduced bread with the addition of brown seaweed evaluated under blinded and informed conditions","year":2023,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; Canada Foundation for Innovation","keywords":"Aftertaste; Ingredient; Food science; Perception; Psychology; Chemistry; Flavor","score_opus":0.04901205369061668,"score_gpt":0.3072729607665432,"score_spread":0.2582609070759265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318068174","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":[0.99918824,0.00004242416,0.00025392382,0.000029213337,0.000012405679,0.00010042952,0.00005404334,0.000006294277,0.00031299537],"genre_scores_gemma":[0.9954726,0.000110364905,0.003002856,0.00012899555,0.00001740371,0.0002635583,0.00013797535,0.0000070435403,0.00085922284],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998769,0.00040314693,0.00011788209,0.00020716632,0.0004251535,0.000077601035],"domain_scores_gemma":[0.99757165,0.00088726473,0.0006594051,0.00019713018,0.0004892466,0.00019531928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022185906,0.00045602935,0.00056111737,0.00015289438,0.00039763952,0.00062201556,0.00020825567,0.0008133968,0.0041502505],"category_scores_gemma":[0.005240927,0.0002473029,0.00073355954,0.00013107504,0.0006455468,0.0005069739,0.000550646,0.0008053239,0.00028453965],"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":[0.19014677,0.020848973,0.11755051,0.00275085,0.00086729345,0.00074489095,0.015683902,0.0008872797,0.5902095,0.0006242379,0.0014689078,0.05821682],"study_design_scores_gemma":[0.0065864366,0.22775814,0.6266389,0.0003486124,0.001726504,0.00076162285,0.008766994,0.0036413842,0.11684367,0.0009762174,0.005499577,0.00045202015],"about_ca_topic_score_codex":0.0010854469,"about_ca_topic_score_gemma":0.001331412,"teacher_disagreement_score":0.0041502505,"about_ca_system_score_codex":0.00022343703,"about_ca_system_score_gemma":0.00022985008,"threshold_uncertainty_score":0.013883948},"labels":[],"label_agreement":null},{"id":"W4327741079","doi":"10.1002/pts.2725","title":"Understanding the packaging colour on consumer perception of plant‐based hamburgers: A preliminary study","year":2023,"lang":"en","type":"article","venue":"Packaging Technology and Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Agriculture and Agri-Food Canada","funders":"Universidade Federal de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Advertising; Purchasing; Business; Marketing; Perception; Quality (philosophy); Food packaging; Package design; Food science; Psychology; Engineering; Chemistry","score_opus":0.1363321723962479,"score_gpt":0.31959212826366373,"score_spread":0.18325995586741584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327741079","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":[0.99946123,0.000039170223,0.00004040422,0.000018827124,0.000002108991,0.000012021261,0.000012887082,4.3007933e-7,0.00041299884],"genre_scores_gemma":[0.99920636,0.00008207707,0.00016707896,0.00004421127,0.000004837292,0.000017026912,0.00002189863,9.390764e-7,0.00045555085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951565,0.00020351706,0.000025749414,0.00005432043,0.00012270098,0.00007799229],"domain_scores_gemma":[0.99726117,0.0014904418,0.0005229175,0.000074065625,0.0004048156,0.00024655295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016679801,0.00022802241,0.00020688729,0.00036050173,0.00039637106,0.0010393298,0.00016971535,0.00044276813,0.003464355],"category_scores_gemma":[0.0028077357,0.00019784813,0.00046190803,0.00024325629,0.0004572026,0.00068763486,0.0004900584,0.0006727909,0.0002835542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022340165,0.0063381093,0.8185726,0.0007838516,0.0001368818,0.0007004733,0.12165801,0.000107636464,0.015528695,0.00043675973,0.0006165623,0.03288654],"study_design_scores_gemma":[0.000026643553,0.0032660807,0.94760436,0.000072958,0.00007488756,0.00015428998,0.0464093,0.00028980238,0.00089616026,0.0001136287,0.0010710624,0.00002080578],"about_ca_topic_score_codex":0.002085467,"about_ca_topic_score_gemma":0.0024537528,"teacher_disagreement_score":0.003464355,"about_ca_system_score_codex":0.0002910663,"about_ca_system_score_gemma":0.00021819961,"threshold_uncertainty_score":0.011589348},"labels":[],"label_agreement":null},{"id":"W4366598734","doi":"10.3390/foods12081709","title":"A Prospective Review of the Sensory Properties of Plant-Based Dairy and Meat Alternatives with a Focus on Texture","year":2023,"lang":"en","type":"review","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Acadia University","funders":"","keywords":"Variety (cybernetics); Product (mathematics); Sensory system; Computer science; Food products; Sensory analysis; Biochemical engineering; Business; Biotechnology; Food science; Mathematics; Artificial intelligence; Engineering; Psychology; Biology","score_opus":0.13307062510158948,"score_gpt":0.3267009294066888,"score_spread":0.19363030430509934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366598734","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":[0.00032529118,0.9979494,0.00015153363,0.00011493535,0.00012914202,0.000010246373,0.00006305583,0.0000073283964,0.0012490472],"genre_scores_gemma":[0.0010609983,0.99776626,0.0003239416,0.00013208852,0.000057220543,0.000011102604,0.000064666834,0.0000023364946,0.0005814745],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970526,0.000050574003,0.00005965175,0.00005729064,0.00010623492,0.000021012429],"domain_scores_gemma":[0.9992986,0.0003746104,0.000107731874,0.00001781047,0.00017361782,0.000027726368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006122587,0.0009949551,0.0013543603,0.004113067,0.0002973628,0.001142485,0.00073725014,0.00081751554,0.006262426],"category_scores_gemma":[0.0013077671,0.00034277298,0.0010969433,0.004280675,0.00033707588,0.0015208247,0.00051418605,0.0010403948,0.0014822284],"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":[0.00011154186,0.00011759516,0.0002797535,0.14812958,0.0002645962,0.00019273792,0.0001362092,0.0003599982,0.004380361,0.0031884497,0.023089373,0.8197497],"study_design_scores_gemma":[0.000017981021,0.00024976855,0.0030727012,0.029692933,0.00070415024,0.0014957672,0.00021212669,0.00013199211,0.0015125944,0.0015746157,0.9612867,0.000048637827],"about_ca_topic_score_codex":0.0014888636,"about_ca_topic_score_gemma":0.0024675427,"teacher_disagreement_score":0.006262426,"about_ca_system_score_codex":0.0004675749,"about_ca_system_score_gemma":0.0012428926,"threshold_uncertainty_score":0.0209499},"labels":[],"label_agreement":null},{"id":"W4376288698","doi":"10.1108/ejm-07-2022-0508","title":"Rejoinder: fractures in the edifice of PLS","year":2023,"lang":"en","type":"article","venue":"European Journal of Marketing","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Employment and Social Development Canada; Memorial University of Newfoundland","funders":"","keywords":"Argumentation theory; Originality; Argument (complex analysis); Value (mathematics); Marketing research; Marketing; Positive economics; Partial least squares regression; Epistemology; Psychology; Sociology; Economics; Computer science; Social psychology; Philosophy; Business; Medicine; Creativity","score_opus":0.04908655893666944,"score_gpt":0.29268916848525506,"score_spread":0.24360260954858562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376288698","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034344213,0.0058036973,0.057235528,0.87576663,0.043253645,0.00014778538,0.00018077195,0.0003560232,0.013821512],"genre_scores_gemma":[0.23452851,0.0047569387,0.09399429,0.58401304,0.055725858,0.001751561,0.00017487744,0.0018415255,0.023213495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.6774872,0.21503326,0.01449237,0.02529504,0.064965844,0.0027262832],"domain_scores_gemma":[0.39969474,0.4953379,0.010532126,0.031523354,0.060721003,0.002190897],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18975951,0.001976978,0.0026056515,0.0040863366,0.00857258,0.016677992,0.006470693,0.017002795,0.0046487204],"category_scores_gemma":[0.48058745,0.0013100823,0.0023615102,0.003323115,0.054109596,0.015390591,0.010520093,0.04958179,0.003467196],"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":[0.00025300615,0.00003900428,0.00091539515,0.0007776308,0.00018667942,0.00032154174,0.030274695,0.0005332669,0.00036700425,0.6158208,0.29669556,0.053815387],"study_design_scores_gemma":[0.0000756691,0.000079168,0.0006530122,0.0018501171,0.00009347917,0.0002543939,0.008566399,0.0031100095,0.0013285682,0.51202536,0.47180557,0.00015820596],"about_ca_topic_score_codex":0.0040645283,"about_ca_topic_score_gemma":0.0025961576,"teacher_disagreement_score":0.18975951,"about_ca_system_score_codex":0.008888973,"about_ca_system_score_gemma":0.009766074,"threshold_uncertainty_score":0.9991708},"labels":[],"label_agreement":null},{"id":"W4383617202","doi":"10.1007/978-3-031-35702-2_7","title":"UX Calculator: An Online Tool to Support User Testing","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Usability; Calculator; User experience design; Software deployment; Process (computing); Software engineering; Human–computer interaction; Operating system","score_opus":0.12559028439537617,"score_gpt":0.3238550414641978,"score_spread":0.19826475706882163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383617202","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011729353,0.00043374454,0.52293175,0.00024861327,0.0002572311,0.0008908751,0.0049247406,0.43645185,0.02213188],"genre_scores_gemma":[0.12704058,0.00072001235,0.6483243,0.000593281,0.00026937772,0.0031645908,0.015460248,0.105841555,0.09858597],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850523,0.00035213504,0.00014220475,0.00016766589,0.00074914197,0.000083528925],"domain_scores_gemma":[0.98615384,0.009760287,0.00041379398,0.0015016282,0.0017196472,0.00045080975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033188455,0.0023446688,0.001337198,0.0038250613,0.00060684537,0.002287762,0.002871344,0.0012826865,0.11605507],"category_scores_gemma":[0.016022274,0.0010765017,0.00075505226,0.0017049506,0.00042611282,0.0028310998,0.0028132428,0.0012904548,0.032308035],"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":[0.0007132855,0.0004901942,0.0028578,0.00076555245,0.00006507046,0.00048082898,0.0008618854,0.0011168803,0.016582143,0.0036641848,0.19424748,0.7781546],"study_design_scores_gemma":[0.0011126845,0.0016652977,0.029490912,0.0014069643,0.0003834691,0.004306222,0.001000555,0.15487689,0.13413335,0.027197575,0.6436422,0.00078382046],"about_ca_topic_score_codex":0.0006911215,"about_ca_topic_score_gemma":0.0008376465,"teacher_disagreement_score":0.11605507,"about_ca_system_score_codex":0.00035480162,"about_ca_system_score_gemma":0.000687508,"threshold_uncertainty_score":0.3882429},"labels":[],"label_agreement":null},{"id":"W4384563362","doi":"10.1016/j.afres.2023.100320","title":"An investigation into consumer perception of the aftertaste of plant-based dairy alternatives using a word association task","year":2023,"lang":"en","type":"article","venue":"Applied Food Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Research Nova Scotia; Canada Foundation for Innovation","keywords":"Aftertaste; Perception; Task (project management); Association (psychology); Word Association; Psychology; Food science; Computer science; Chemistry; Artificial intelligence; Engineering","score_opus":0.14793267471152055,"score_gpt":0.3804731001004671,"score_spread":0.23254042538894654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384563362","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":[0.999456,0.000012154238,0.00017065709,0.000006416272,0.000002327275,0.000029267741,0.00002694448,0.0000022051117,0.0002939405],"genre_scores_gemma":[0.99574524,0.00007428115,0.0027218692,0.00007593717,0.000007577667,0.00019494402,0.00017346471,0.0000067632955,0.0009998025],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.9997354,0.00007198708,0.000026530013,0.00005513517,0.000083461564,0.000027442293],"domain_scores_gemma":[0.99801135,0.0012304861,0.00038184194,0.000083011415,0.00019980839,0.00009343475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009485034,0.00032050873,0.0003955637,0.00021215017,0.00022724239,0.0005990161,0.00022505384,0.00046421614,0.0022053039],"category_scores_gemma":[0.0033800995,0.00021533856,0.00032164776,0.00015447808,0.00023432476,0.000567989,0.0003894453,0.00045693183,0.0001891306],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.024864752,0.008006241,0.23807776,0.0017804462,0.000398038,0.0009901485,0.03038964,0.0007319337,0.643617,0.0007581154,0.0009855487,0.049400415],"study_design_scores_gemma":[0.0005993606,0.0137925735,0.9468922,0.00006505207,0.00020858989,0.00049531285,0.009930297,0.0045386604,0.021444943,0.00036121276,0.001570694,0.00010122499],"about_ca_topic_score_codex":0.000903625,"about_ca_topic_score_gemma":0.0013212237,"teacher_disagreement_score":0.0022053039,"about_ca_system_score_codex":0.00012559761,"about_ca_system_score_gemma":0.000112676,"threshold_uncertainty_score":0.0073775053},"labels":[],"label_agreement":null},{"id":"W4384938832","doi":"10.1177/25152459231162567","title":"Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses","year":2023,"lang":"en","type":"article","venue":"Advances in Methods and Practices in Psychological Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Carleton University; Université Laval; University of Alberta; University of Calgary","funders":"Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; National Research University Higher School of Economics; Agence Nationale de la Recherche; Syracuse University; National Science Foundation","keywords":"Flexibility (engineering); Set (abstract data type); Construct (python library); Computer science; Variety (cybernetics); Bayesian probability; Certainty; Quality (philosophy); Econometrics; Interpretation (philosophy); Data science; Psychology; Statistics; Artificial intelligence; Mathematics; Epistemology","score_opus":0.26152394787874805,"score_gpt":0.6074150001455154,"score_spread":0.34589105226676736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384938832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050135676,0.0035562105,0.94046307,0.0018298314,0.00019171559,0.0005850091,0.00018277272,0.00022755265,0.0028281908],"genre_scores_gemma":[0.5717053,0.0009283856,0.4245032,0.0004375363,0.00016636847,0.0017419561,0.00018923548,0.00014347522,0.0001846249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.5327247,0.4039968,0.018543808,0.020303179,0.023207473,0.0012240985],"domain_scores_gemma":[0.14868407,0.76183057,0.026524736,0.053836323,0.008345466,0.0007788053],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.34368166,0.0023762092,0.0024972302,0.011323856,0.0029587245,0.01052126,0.0035940157,0.0023724276,0.0019932778],"category_scores_gemma":[0.70360094,0.0019845653,0.004691745,0.010068506,0.016255554,0.012735424,0.010160921,0.0061019245,0.00022963915],"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":[0.0015572348,0.00034397157,0.13362554,0.0061243265,0.015755259,0.00068225403,0.05485704,0.0454545,0.0047625895,0.31928286,0.0031039861,0.41445044],"study_design_scores_gemma":[0.0002036209,0.00058209104,0.045267016,0.0023372264,0.0015792693,0.000659471,0.0060417885,0.07648991,0.0034769913,0.8539767,0.008699726,0.00068614725],"about_ca_topic_score_codex":0.003236917,"about_ca_topic_score_gemma":0.0033398105,"teacher_disagreement_score":0.6563183,"about_ca_system_score_codex":0.0034903318,"about_ca_system_score_gemma":0.0033678769,"threshold_uncertainty_score":0.80935735},"labels":[],"label_agreement":null},{"id":"W4385066663","doi":"10.1111/joss.12860","title":"An approach for clustering consumers by their top‐box and top‐choice responses","year":2023,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","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":"Ontario Universities’ Application Centre","funders":"Norges Forskningsråd","keywords":"Cluster analysis; Cluster (spacecraft); Coding (social sciences); Computer science; Product (mathematics); Psychology; Data science; Data mining; Machine learning; Statistics; Mathematics","score_opus":0.16709594111227455,"score_gpt":0.379099989925928,"score_spread":0.21200404881365348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385066663","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":[0.04776883,0.00005593022,0.94706184,0.00048684768,0.00006719939,0.0006662566,0.00022753455,0.00056130334,0.0031042593],"genre_scores_gemma":[0.18997252,0.00003851149,0.8067319,0.00013535749,0.000021252918,0.0008949387,0.00028871387,0.0001598269,0.0017569757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99257576,0.0038777108,0.00035609896,0.0014406407,0.0014389352,0.0003108125],"domain_scores_gemma":[0.985451,0.006720149,0.0010005175,0.0018677462,0.0045432183,0.0004173479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009511945,0.0008814487,0.00091451127,0.004289202,0.0017755596,0.0032294623,0.0018505404,0.0014318104,0.006655728],"category_scores_gemma":[0.028722689,0.0005483462,0.0011664918,0.003520781,0.0023826645,0.0023849378,0.0024360048,0.0017509618,0.0014250957],"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":[0.0017121215,0.0008614755,0.02793462,0.001048575,0.0005563953,0.00024567344,0.026644835,0.050345093,0.027872784,0.14815521,0.014011754,0.70061153],"study_design_scores_gemma":[0.00023245167,0.00037892823,0.025239028,0.0002655284,0.00015754618,0.0002943945,0.0102320975,0.73854226,0.012500341,0.19197284,0.019818777,0.00036578605],"about_ca_topic_score_codex":0.007825297,"about_ca_topic_score_gemma":0.008528136,"teacher_disagreement_score":0.009511945,"about_ca_system_score_codex":0.0033416408,"about_ca_system_score_gemma":0.0030849632,"threshold_uncertainty_score":0.05030459},"labels":[],"label_agreement":null},{"id":"W4385587624","doi":"10.1016/b978-0-323-95225-5.00002-x","title":"The emergence of digital technologies and their impact on sensory science","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sensory system; Psychology; Computer science; Cognitive psychology","score_opus":0.047086868682082186,"score_gpt":0.2913659631761316,"score_spread":0.24427909449404944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385587624","genre_codex":"other","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":[0.00976969,0.18660623,0.12004847,0.0064790347,0.002260934,0.00006337482,0.00089820265,0.00063622533,0.67323786],"genre_scores_gemma":[0.09975201,0.1981159,0.110783726,0.0042728065,0.002365984,0.00014988158,0.000780987,0.0006962159,0.58308244],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99965274,0.000056997767,0.0000148675235,0.000053413733,0.00020398572,0.00001796094],"domain_scores_gemma":[0.9986292,0.0010513314,0.00003729327,0.000068753405,0.00017576935,0.000037811835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073663983,0.0006215421,0.00037661596,0.0016072782,0.0003790041,0.0052395076,0.0006839255,0.0010848944,0.03282464],"category_scores_gemma":[0.0016064659,0.0003143103,0.00029209765,0.002601565,0.0020040502,0.003526782,0.0010661477,0.0015848193,0.0057395534],"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":[0.00006455443,0.000045956112,0.00059067406,0.0010297806,0.000026095258,0.00018556947,0.00071228034,0.001527604,0.0077730916,0.23835623,0.0453464,0.70434177],"study_design_scores_gemma":[0.0000071841087,0.0000568167,0.0031554664,0.0007294025,0.000015078074,0.0005806827,0.00073106965,0.003365574,0.0018223745,0.26547673,0.7240225,0.000037102054],"about_ca_topic_score_codex":0.001992965,"about_ca_topic_score_gemma":0.0035975797,"teacher_disagreement_score":0.03282464,"about_ca_system_score_codex":0.0008233524,"about_ca_system_score_gemma":0.00067588926,"threshold_uncertainty_score":0.10980934},"labels":[],"label_agreement":null},{"id":"W4385681511","doi":"10.48550/arxiv.2308.03079","title":"Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Visualization; Contingency table; Correspondence analysis; Dimensionality reduction; Computer science; Dimension (graph theory); Set (abstract data type); Data set; Table (database); Data mining; Information retrieval; Artificial intelligence; Mathematics; Machine learning; Combinatorics","score_opus":0.3319689067478558,"score_gpt":0.2929399133416865,"score_spread":0.03902899340616928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385681511","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":[0.20077834,0.0033158478,0.7315771,0.0026664585,0.00033714343,0.0003340922,0.025184106,0.02034299,0.01546397],"genre_scores_gemma":[0.4632649,0.0012146358,0.5186352,0.0002556685,0.00012942647,0.00030192503,0.010233756,0.0022802325,0.0036842418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942017,0.00018665213,0.00004509613,0.000096385804,0.00019949008,0.00005216317],"domain_scores_gemma":[0.99607974,0.0026548333,0.00023282626,0.00038706596,0.0005008703,0.00014468098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007540761,0.0006228835,0.0005585395,0.003911998,0.00077538955,0.002036896,0.0004923398,0.0005354408,0.010339056],"category_scores_gemma":[0.004640571,0.00023852487,0.00062354695,0.0034509923,0.0005242472,0.0011279994,0.0013224782,0.00082540617,0.0013584826],"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":[0.0016306294,0.00034339877,0.029477866,0.0028048786,0.00044641085,0.004961999,0.011066787,0.049341474,0.060093366,0.09795258,0.11102933,0.6308514],"study_design_scores_gemma":[0.00018319738,0.00026970473,0.039637357,0.00068475935,0.00020102362,0.003969116,0.006448237,0.4523052,0.049838074,0.1795352,0.2666273,0.00030081393],"about_ca_topic_score_codex":0.004155216,"about_ca_topic_score_gemma":0.004951001,"teacher_disagreement_score":0.010339056,"about_ca_system_score_codex":0.0004196283,"about_ca_system_score_gemma":0.0006720197,"threshold_uncertainty_score":0.034587562},"labels":[],"label_agreement":null},{"id":"W4386103834","doi":"10.7554/elife.82566.sa2","title":"Author response: Statistical inference on representational geometries","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Inference; Statistical inference; Computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.243044035888829,"score_gpt":0.4601538733027522,"score_spread":0.2171098374139232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386103834","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017419602,0.0013126414,0.014521263,0.66713506,0.25549045,0.00034732165,0.003784267,0.0014147707,0.054252278],"genre_scores_gemma":[0.048890132,0.0038659815,0.015472162,0.22851983,0.09646963,0.0009701278,0.0047491137,0.0034774346,0.5975856],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98506945,0.00600634,0.0011290886,0.0011486288,0.0060906652,0.0005557558],"domain_scores_gemma":[0.72561264,0.13118286,0.0039601615,0.012780034,0.12204413,0.004420184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015319641,0.0007159123,0.0013583046,0.0022114222,0.0022410485,0.0035625787,0.0016064377,0.0071814633,0.15589677],"category_scores_gemma":[0.31005362,0.0004885768,0.001029774,0.0015635556,0.0027345677,0.0026496525,0.0030565436,0.0076914537,0.06579591],"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":[0.00006195041,0.000008627725,0.00013111113,0.00014768209,0.00001272186,0.00009028367,0.000121110985,0.00012729997,0.00012805905,0.0045908005,0.9870869,0.007493471],"study_design_scores_gemma":[0.000075355325,0.000023959145,0.0008669925,0.00035807843,0.00001999095,0.0002858307,0.00048239436,0.0012260621,0.00070526905,0.025677737,0.97022253,0.00005582549],"about_ca_topic_score_codex":0.0047966302,"about_ca_topic_score_gemma":0.006651004,"teacher_disagreement_score":0.15589677,"about_ca_system_score_codex":0.002186473,"about_ca_system_score_gemma":0.006833605,"threshold_uncertainty_score":0.52152663},"labels":[],"label_agreement":null},{"id":"W4386565713","doi":"10.1016/j.foodqual.2023.104985","title":"Particle perception: Defining sensory thresholds for grittiness of upcycled apple pomace powders","year":2023,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vineland Research and Innovation Centre","funders":"Agriculture and Agri-Food Canada","keywords":"Population; Particle size; Food science; Pomace; Perception; Particle (ecology); Detection threshold; Mathematics; Statistics; Computer science; Psychology; Chemistry; Engineering; Biology; Medicine; Chemical engineering; Environmental health; Ecology","score_opus":0.31780600270338033,"score_gpt":0.37684256752953554,"score_spread":0.05903656482615521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386565713","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":[0.9957969,0.00017109937,0.0032785854,0.0000125914175,0.0000074229024,0.000034594002,0.0001239295,0.000013818986,0.00056105707],"genre_scores_gemma":[0.9971419,0.000082742765,0.002033585,0.000032015017,0.000003947448,0.000023390097,0.00016354768,0.00000924526,0.0005096183],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997662,0.00003317176,0.000011335052,0.00006275732,0.00009003134,0.000036597532],"domain_scores_gemma":[0.9996327,0.00016440525,0.000049449263,0.000015876449,0.000069930036,0.00006759802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058161333,0.000280382,0.00018636095,0.0003589364,0.00016631672,0.00057560025,0.00021480312,0.00036236632,0.0016536438],"category_scores_gemma":[0.0012653911,0.00014312974,0.0002796961,0.00019869943,0.00027195463,0.00043624354,0.00032295918,0.0004682029,0.00014577345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024902443,0.000116769785,0.011256414,0.00012268826,0.000023382414,0.00007304549,0.00018497939,0.000180051,0.97618437,0.00010122936,0.000079702855,0.009187091],"study_design_scores_gemma":[0.000045482524,0.002508131,0.5782977,0.000016250766,0.000059362617,0.00044691656,0.00048095323,0.0030385384,0.41441554,0.00015406274,0.00050917786,0.000027909173],"about_ca_topic_score_codex":0.0025304975,"about_ca_topic_score_gemma":0.0026390497,"teacher_disagreement_score":0.0025304975,"about_ca_system_score_codex":0.00023642665,"about_ca_system_score_gemma":0.00014981127,"threshold_uncertainty_score":0.0055319667},"labels":[],"label_agreement":null},{"id":"W4387135103","doi":"10.1016/j.disc.2023.113708","title":"Binary color-coded magic squares: A study of uniqueness under rotation/reflection, PCA, and LDA analysis","year":2023,"lang":"en","type":"article","venue":"Discrete Mathematics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Mount Saint Vincent University; Université Laval","keywords":"Magic square; Uniqueness; Mathematics; Combinatorics; MAGIC (telescope); Binary number; Arithmetic; Mathematical analysis; Physics","score_opus":0.0726810977640017,"score_gpt":0.3436417287089666,"score_spread":0.2709606309449649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387135103","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":[0.19384189,0.0004008025,0.8014026,0.00021931084,0.000044818305,0.0000434292,0.000087496956,0.00013136702,0.0038282634],"genre_scores_gemma":[0.799127,0.0004883658,0.1973515,0.000058807153,0.00009383581,0.0000504633,0.00017766995,0.00018152664,0.0024708575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826306,0.0008154468,0.000048549293,0.0002905062,0.000500519,0.00008188971],"domain_scores_gemma":[0.9866969,0.008642138,0.0012551436,0.0014222213,0.0017334234,0.00025018543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033611646,0.00052563293,0.00056951476,0.0014692834,0.00076940114,0.0014890168,0.0012126461,0.0006744839,0.0016526128],"category_scores_gemma":[0.020476846,0.0003663122,0.0005804371,0.0015791579,0.0029620584,0.0023041056,0.00114808,0.0010933299,0.00025445974],"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":[0.0011734532,0.00017139952,0.0122554,0.000549545,0.00024595307,0.0004295235,0.001207383,0.10123136,0.082499675,0.56387675,0.0020748158,0.23428471],"study_design_scores_gemma":[0.000024305868,0.00015365503,0.004947875,0.000031642714,0.00005478211,0.000605872,0.00022419062,0.8822608,0.018713359,0.09147942,0.0014213194,0.00008275158],"about_ca_topic_score_codex":0.0011356008,"about_ca_topic_score_gemma":0.0008800917,"teacher_disagreement_score":0.0033611646,"about_ca_system_score_codex":0.00038417376,"about_ca_system_score_gemma":0.0007032268,"threshold_uncertainty_score":0.017775774},"labels":[],"label_agreement":null},{"id":"W4387190608","doi":"10.1016/j.foohum.2023.09.028","title":"Sensory perception of ice cream and plant-based alternatives evaluated blinded and with ingredient lists","year":2023,"lang":"en","type":"article","venue":"Food and Humanity","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Ingredient; Ice cream; Flavour; Food science; Aftertaste; Perception; Flavor; Psychology; Chemistry","score_opus":0.11745500623594646,"score_gpt":0.3143340354017445,"score_spread":0.19687902916579803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387190608","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":[0.9977695,0.0001367241,0.0009822258,0.00002140543,0.000070322254,0.00008518849,0.00014994155,0.000010274248,0.00077441987],"genre_scores_gemma":[0.99516195,0.0002098494,0.0020526415,0.00010669575,0.000027759368,0.000097246026,0.000267528,0.000017412702,0.0020588161],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991725,0.00019223127,0.000055497134,0.00017243416,0.0002762423,0.0001310677],"domain_scores_gemma":[0.99831176,0.00068119523,0.00018388711,0.00009871214,0.0005492573,0.00017521784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009342408,0.0006449798,0.00056479714,0.0002840298,0.00040005657,0.00081445434,0.0003143076,0.00072295795,0.004022849],"category_scores_gemma":[0.0030343295,0.0002738271,0.00054421247,0.0003404639,0.00073128834,0.00085151003,0.00056976965,0.0011822297,0.00023226546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.07529921,0.0016299495,0.003409684,0.00039984833,0.00011540214,0.00014547686,0.0006923488,0.0006547048,0.90788966,0.00028310638,0.00022369005,0.00925703],"study_design_scores_gemma":[0.0015007509,0.08522645,0.115027644,0.00015342931,0.0007250561,0.00040520928,0.0036328062,0.0066932747,0.78329825,0.0008444087,0.00231565,0.0001771326],"about_ca_topic_score_codex":0.0034405817,"about_ca_topic_score_gemma":0.002223026,"teacher_disagreement_score":0.004022849,"about_ca_system_score_codex":0.0003878568,"about_ca_system_score_gemma":0.0005656263,"threshold_uncertainty_score":0.0134577155},"labels":[],"label_agreement":null},{"id":"W4388343167","doi":"10.1016/j.jneb.2023.09.006","title":"Nudging Toward Sustainable Food Consumption at University Canteens: A Systematic Review and Meta-Analysis","year":2023,"lang":"en","type":"review","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Horizon 2020; HORIZON EUROPE Framework Programme; Horizon 2020 Framework Programme; European Commission","keywords":"Nudge theory; Context (archaeology); Psychological intervention; Meta-analysis; Scopus; Consumption (sociology); Food choice; Intervention (counseling); Systematic review; Psychology; MEDLINE; Medicine; Social psychology; Sociology; Political science; Geography; Social science","score_opus":0.23032369570617048,"score_gpt":0.39343196015161047,"score_spread":0.16310826444544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388343167","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":[0.0023306692,0.9960795,0.00036726784,0.0002015881,0.00012444594,0.00040348156,0.00024164745,0.000014720079,0.00023667724],"genre_scores_gemma":[0.059416875,0.93480873,0.0025339453,0.00063262775,0.00017708565,0.0018698167,0.000356856,0.000015010923,0.00018901759],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.98711824,0.0063141645,0.0034100846,0.000982227,0.0017529755,0.00042229187],"domain_scores_gemma":[0.9637516,0.02799243,0.0047128513,0.0007494738,0.002458716,0.00033487985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022233443,0.0025375905,0.013731028,0.008903983,0.0008323661,0.0036581096,0.0021423644,0.0021616204,0.004393598],"category_scores_gemma":[0.05588574,0.00122836,0.022790654,0.008108014,0.00094611425,0.0024407033,0.0017227755,0.0016972465,0.00027626404],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007940065,0.00005094624,0.0015881411,0.888726,0.07598636,0.000068737725,0.00014581656,0.00019282658,0.00019023962,0.00018992419,0.00058111676,0.031485908],"study_design_scores_gemma":[0.00076547416,0.00075933716,0.0069644065,0.3923124,0.59016347,0.00018188402,0.00029793597,0.00017861053,0.00045190012,0.0004525265,0.007416411,0.000055609155],"about_ca_topic_score_codex":0.007332327,"about_ca_topic_score_gemma":0.016595013,"teacher_disagreement_score":0.022233443,"about_ca_system_score_codex":0.0038401443,"about_ca_system_score_gemma":0.009535857,"threshold_uncertainty_score":0.117583096},"labels":[],"label_agreement":null},{"id":"W4388490914","doi":"10.1016/j.jneb.2023.07.011","title":"Sensory-Informed Evaluation Method for Use With Peer Educators of Nutrition Education Programs","year":2023,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Georgia; U.S. Department of Agriculture","keywords":"Recipe; Wine tasting; Nutrition Education; Food preparation; Test (biology); Medical education; Psychology; Computer science; Serving size; Medicine; Food science; Gerontology; Environmental health","score_opus":0.11403464700910494,"score_gpt":0.421736135220267,"score_spread":0.30770148821116206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388490914","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50059044,0.00048378579,0.41787618,0.00035832686,0.0005929447,0.035585113,0.00459992,0.0049021216,0.035011165],"genre_scores_gemma":[0.40997592,0.00030737653,0.5466388,0.00022205495,0.000062780346,0.030996848,0.0011657844,0.00037545015,0.010254962],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9900051,0.0053500067,0.0008331912,0.00070538785,0.002803388,0.0003029077],"domain_scores_gemma":[0.96702653,0.013675933,0.0014418864,0.0019243218,0.015201364,0.0007299745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015845368,0.0010077889,0.0010045412,0.002641477,0.0012305715,0.001387156,0.0011422188,0.000604108,0.016491013],"category_scores_gemma":[0.033410482,0.00055531535,0.0008128285,0.0017673932,0.0004195607,0.0009960401,0.0015531537,0.0008132015,0.0017988519],"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":[0.0069989706,0.004990691,0.051843286,0.0015340622,0.00023648041,0.00021964297,0.005639411,0.0015933873,0.03450259,0.001579122,0.009029646,0.8818328],"study_design_scores_gemma":[0.002884183,0.021272236,0.6074784,0.0022111775,0.00140909,0.00165135,0.02789781,0.09573272,0.15861237,0.007224214,0.07237842,0.0012480852],"about_ca_topic_score_codex":0.002809091,"about_ca_topic_score_gemma":0.010759643,"teacher_disagreement_score":0.016491013,"about_ca_system_score_codex":0.00071609,"about_ca_system_score_gemma":0.0026591618,"threshold_uncertainty_score":0.08379936},"labels":[],"label_agreement":null},{"id":"W4388638053","doi":"10.3390/foods12224097","title":"Consumer Perception and Sensory Drivers of Liking of Fortified Oat Milks","year":2023,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Results Driven Agriculture Research","keywords":"Fortification; Avena; Health benefits; Food science; Glucan; Sensory analysis; Chemistry; Biology; Medicine; Traditional medicine; Biochemistry; Agronomy","score_opus":0.06972986135676247,"score_gpt":0.2951300509694179,"score_spread":0.22540018961265545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388638053","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":[0.9997081,0.000023639648,0.000033344466,0.000010572053,9.529413e-7,0.000004003937,0.000018859631,9.210697e-7,0.0001995742],"genre_scores_gemma":[0.9996296,0.00003369587,0.000120657234,0.000014368322,0.0000014683625,0.0000035297696,0.000036253794,7.7153265e-7,0.00015973925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997249,0.00007794861,0.000022069964,0.00003823799,0.00010456592,0.000032363954],"domain_scores_gemma":[0.9989767,0.00033954898,0.0003568269,0.000041659347,0.00019391964,0.00009148905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065041624,0.00014310695,0.000166092,0.00029345788,0.00015128826,0.0005799416,0.00009365994,0.0002761895,0.0015572713],"category_scores_gemma":[0.0017685335,0.00009022996,0.00031291132,0.00020627826,0.00020538372,0.00030754658,0.00028312978,0.00032030162,0.000081202],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030108446,0.0005995713,0.9253868,0.00018483012,0.00023210011,0.0003351108,0.0043411977,0.00018918906,0.046573095,0.0001280415,0.0001789577,0.01884027],"study_design_scores_gemma":[0.0000121207595,0.00097565586,0.9929053,0.00001444574,0.000052232434,0.00015570929,0.0032676547,0.00041115936,0.0018688838,0.000050104845,0.00027503894,0.0000116421115],"about_ca_topic_score_codex":0.0024466678,"about_ca_topic_score_gemma":0.0022626154,"teacher_disagreement_score":0.0024466678,"about_ca_system_score_codex":0.00016118084,"about_ca_system_score_gemma":0.000090277725,"threshold_uncertainty_score":0.005209565},"labels":[],"label_agreement":null},{"id":"W4389795459","doi":"10.1016/j.fochx.2023.101072","title":"Baked sweetpotato textures and sweetness: An investigation into relationships between physicochemical and cooked attributes","year":2023,"lang":"en","type":"article","venue":"Food Chemistry X","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute of Planners; Agricultural Research Service; Centro Internacional de la Papa; Bill and Melinda Gates Foundation; U.S. Department of Agriculture","keywords":"Sweetness; Food science; Mouthfeel; Sugar; Starch; Chemistry; Granule (geology); Amylase; Raw material; Materials science; Biochemistry; Organic chemistry; Composite material; Enzyme","score_opus":0.08201676388421053,"score_gpt":0.2824258537830877,"score_spread":0.20040908989887718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389795459","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":[0.999652,0.000020227531,0.00020980525,0.000002339181,4.1684524e-7,0.0000020747746,0.00004340333,0.0000028625034,0.00006681661],"genre_scores_gemma":[0.99929476,0.000018418326,0.00038334393,0.0000059151525,5.948498e-7,0.0000031295076,0.00012934236,0.000005475659,0.00015886565],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99987316,0.000031821368,0.000011106446,0.00003938663,0.000030230789,0.00001425737],"domain_scores_gemma":[0.99941754,0.00020339387,0.00016126664,0.00004370676,0.00009710437,0.00007696018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003273199,0.00020787655,0.00021468561,0.00034336478,0.00014655295,0.0004098063,0.000086549655,0.00016399223,0.00084712164],"category_scores_gemma":[0.0006523218,0.00014348776,0.00021522628,0.00024698526,0.00017613628,0.0003199897,0.0002598369,0.00028582706,0.00009424721],"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":[0.0011182058,0.00016114791,0.23725845,0.00007481391,0.00013205875,0.00013742589,0.0004566769,0.00019740168,0.75312304,0.000058042737,0.000033961904,0.0072488836],"study_design_scores_gemma":[0.0000034319223,0.00038011296,0.98480254,0.0000016859159,0.000023896282,0.00009316215,0.0001327753,0.0004498568,0.0139950225,0.000020697536,0.00009036314,0.0000064240753],"about_ca_topic_score_codex":0.0009539606,"about_ca_topic_score_gemma":0.0021187277,"teacher_disagreement_score":0.0009539606,"about_ca_system_score_codex":0.00013775994,"about_ca_system_score_gemma":0.00007555012,"threshold_uncertainty_score":0.0028339624},"labels":[],"label_agreement":null},{"id":"W4391177927","doi":"10.1007/978-1-0716-3650-3_12","title":"Rapid and Cost-Effective Methods for Wine Sensory Profiling: Napping and Sorting","year":2024,"lang":"en","type":"book-chapter","venue":"Methods and protocols in food science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Profiling (computer programming); Wine; Sensory system; Computer science; Sorting; Psychology; Food science; Biology; Neuroscience; Algorithm","score_opus":0.1880831660532069,"score_gpt":0.4686746244339346,"score_spread":0.2805914583807277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391177927","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":[0.012912705,0.024345925,0.9198456,0.0012673164,0.0016717381,0.0009343456,0.005224837,0.006178027,0.027619533],"genre_scores_gemma":[0.025937324,0.026044639,0.78354657,0.002392256,0.00039876963,0.0017093789,0.0078888,0.002262405,0.1498198],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877864,0.00010318579,0.00004497577,0.00022721676,0.00077296403,0.00007293566],"domain_scores_gemma":[0.99932694,0.00024871045,0.000044079472,0.00007212303,0.00026235663,0.00004586929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001221417,0.001741273,0.0013396766,0.0013842916,0.0006756297,0.0017732473,0.0018062828,0.0010962256,0.011411705],"category_scores_gemma":[0.0011082994,0.001074074,0.0008977802,0.0015858798,0.0006582594,0.001700818,0.0014215324,0.0030459187,0.016487911],"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":[0.00010465248,0.00012925974,0.00020006245,0.00072662486,0.000036072273,0.000068055815,0.000104073035,0.00045643462,0.69542146,0.0030158448,0.02616817,0.2735694],"study_design_scores_gemma":[0.00002073451,0.00033498893,0.002708631,0.00017367647,0.00008178523,0.0007532975,0.00016335229,0.0046644863,0.6998399,0.0074335504,0.2836715,0.00015411642],"about_ca_topic_score_codex":0.0011771584,"about_ca_topic_score_gemma":0.004940582,"teacher_disagreement_score":0.011411705,"about_ca_system_score_codex":0.0005413938,"about_ca_system_score_gemma":0.0010860142,"threshold_uncertainty_score":0.03817594},"labels":[],"label_agreement":null},{"id":"W4391219408","doi":"10.1007/978-981-99-5329-5_5","title":"Contrasts for Neyman’s Modified Chi-Square Statistic in One-Way Contingency Tables","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Contingency table; Statistic; Statistics; Chi-square test; Mathematics; Square (algebra); Econometrics","score_opus":0.2104091816099263,"score_gpt":0.32510760476295697,"score_spread":0.11469842315303067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391219408","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":[0.0036913855,0.0007394976,0.9796337,0.0003256392,0.0009969292,0.00023392946,0.0012216708,0.0024907326,0.010666547],"genre_scores_gemma":[0.03779998,0.00036344794,0.9470905,0.00030050168,0.00038377542,0.0013812997,0.0011256253,0.002107423,0.009447403],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99208367,0.004107079,0.00048502203,0.001387905,0.001757112,0.00017925445],"domain_scores_gemma":[0.96775436,0.027395668,0.0007549798,0.0021556027,0.0017334503,0.00020589658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007888663,0.0014400183,0.0015954305,0.0022621427,0.0009210654,0.0018109393,0.002840768,0.0012547636,0.04669276],"category_scores_gemma":[0.03901864,0.0009473911,0.001555199,0.002680569,0.0015079452,0.0033786832,0.0011414113,0.0042212526,0.008578317],"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":[0.00066557404,0.0002925345,0.0027501148,0.0015025723,0.00027350764,0.000867275,0.0010332284,0.0044523226,0.010582698,0.405768,0.11082301,0.46098912],"study_design_scores_gemma":[0.00021721459,0.001016642,0.012907174,0.0004617254,0.0002012932,0.0024506846,0.00046866832,0.04805858,0.013175599,0.6497314,0.27101234,0.00029864558],"about_ca_topic_score_codex":0.0006686797,"about_ca_topic_score_gemma":0.0013646822,"teacher_disagreement_score":0.04669276,"about_ca_system_score_codex":0.0011282595,"about_ca_system_score_gemma":0.001076351,"threshold_uncertainty_score":0.15620285},"labels":[],"label_agreement":null},{"id":"W4391219482","doi":"10.1007/978-981-99-5329-5_20","title":"Correspondence Analysis with Pre-Specified Marginals and Goodman’s Marginal-Free Correspondence Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Correspondence analysis; Mathematics; Statistics","score_opus":0.06724618542776363,"score_gpt":0.2799040350476047,"score_spread":0.21265784961984108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391219482","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":[0.001294331,0.00009962081,0.9963404,0.000042213414,0.000025289008,0.000024724755,0.00006527257,0.00036904885,0.0017390655],"genre_scores_gemma":[0.061410196,0.0002455198,0.92733145,0.00012067062,0.00012410792,0.00027917704,0.0005463082,0.001358472,0.008583973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99249506,0.0026655279,0.00048490264,0.001608834,0.0022864267,0.00045923298],"domain_scores_gemma":[0.9906254,0.004924581,0.00041777774,0.002199619,0.0016623727,0.00017022312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070231496,0.0012704609,0.0020879216,0.004230125,0.0020466635,0.0033128792,0.0033213827,0.0018263637,0.014242609],"category_scores_gemma":[0.023219343,0.001543234,0.0035042812,0.0059312833,0.0032848825,0.005801927,0.0038983335,0.0041078357,0.005242585],"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":[0.00020857294,0.00012218239,0.0009920582,0.0002995855,0.00014379769,0.00016147915,0.00046348345,0.02236018,0.006140981,0.5083635,0.010096319,0.45064777],"study_design_scores_gemma":[0.000024209796,0.000100838944,0.0019191429,0.000054966797,0.00009414582,0.0004879009,0.00016897543,0.3317293,0.01312462,0.6202699,0.03188025,0.00014579795],"about_ca_topic_score_codex":0.0022413917,"about_ca_topic_score_gemma":0.0022800711,"teacher_disagreement_score":0.014242609,"about_ca_system_score_codex":0.0014163421,"about_ca_system_score_gemma":0.0021387108,"threshold_uncertainty_score":0.047646284},"labels":[],"label_agreement":null},{"id":"W4391689805","doi":"10.58430/jib.v130i1.43","title":"Barley variety interacts positively with floor malting to produce different malts and beers","year":2024,"lang":"en","type":"article","venue":"Journal of the Institute of Brewing","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Canada Malting (Canada)","funders":"","keywords":"Food science; Variety (cybernetics); Chemistry; Mathematics; Statistics","score_opus":0.01576510746434692,"score_gpt":0.24125870435420899,"score_spread":0.22549359688986206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391689805","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":[0.9986375,0.0001642809,0.00047342305,0.00004942981,0.000012928473,0.000013352574,0.00010074271,0.000029660108,0.00051870814],"genre_scores_gemma":[0.9966973,0.00016646976,0.0013052212,0.00010621803,0.0000067820265,0.000026428539,0.0001762574,0.000058029418,0.0014573063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993124,0.0001755762,0.000040961306,0.0002356313,0.0001392802,0.000096166725],"domain_scores_gemma":[0.99923253,0.00026523965,0.00019432108,0.00008509166,0.00007858592,0.0001442731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039501954,0.00033659645,0.0006267946,0.00020653172,0.00032872526,0.0013922898,0.0002183401,0.00025548242,0.002383958],"category_scores_gemma":[0.00080088037,0.0002757627,0.0005500516,0.0003080228,0.0004476323,0.0003950227,0.0005775531,0.0007663264,0.00035099112],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031517146,0.0005037052,0.028512906,0.00013676622,0.000193008,0.00022518101,0.00035619826,0.0001819555,0.9592316,0.0001404656,0.00020653073,0.0071599926],"study_design_scores_gemma":[0.00004115988,0.0032513991,0.82736015,0.000028272754,0.00031439165,0.0002018624,0.0010023571,0.0018091345,0.16322318,0.00035029737,0.002363814,0.000054002612],"about_ca_topic_score_codex":0.0021205633,"about_ca_topic_score_gemma":0.003584786,"teacher_disagreement_score":0.002383958,"about_ca_system_score_codex":0.00048470768,"about_ca_system_score_gemma":0.00032343433,"threshold_uncertainty_score":0.0079751015},"labels":[],"label_agreement":null},{"id":"W4391763322","doi":"10.1007/978-3-031-17299-1_3674","title":"Cultural or Ethnic Homogeneity Preference Index","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Homogeneity (statistics); Ethnic group; Index (typography); Psychology; Preference; Social psychology; Anthropology; Sociology; Mathematics; Statistics; Computer science; World Wide Web","score_opus":0.3542337797531496,"score_gpt":0.353532822617405,"score_spread":0.0007009571357446398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391763322","genre_codex":"empirical","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":[0.4952134,0.0017946439,0.005943189,0.00026843185,0.00022199232,0.00016186351,0.005349454,0.00013587212,0.49091122],"genre_scores_gemma":[0.93369395,0.000918099,0.0054459954,0.00012416422,0.00008074722,0.000110254434,0.0020707583,0.000042319472,0.057513744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999838,0.00003370736,0.000014740746,0.000015177139,0.00007342458,0.000024892179],"domain_scores_gemma":[0.9996605,0.00011593188,0.000067557885,0.000024961779,0.00008063119,0.00005039212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031185526,0.00025593516,0.00018652555,0.0012449417,0.00021425924,0.00058961805,0.00019515373,0.00012679845,0.025874877],"category_scores_gemma":[0.0010511536,0.000033545533,0.0002722792,0.0012845192,0.00016011225,0.0002878387,0.0003770343,0.00018072507,0.002797271],"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":[0.0016857712,0.00050005014,0.32908738,0.00070958387,0.00042694208,0.000737701,0.0020596986,0.0013960163,0.019366724,0.020799955,0.0482813,0.5749489],"study_design_scores_gemma":[0.00003218386,0.00062257354,0.87897694,0.00023231193,0.00022436769,0.0019028678,0.005940717,0.0024627605,0.00720974,0.009238541,0.09309264,0.00006441632],"about_ca_topic_score_codex":0.0018291614,"about_ca_topic_score_gemma":0.0049853176,"teacher_disagreement_score":0.025874877,"about_ca_system_score_codex":0.00023343123,"about_ca_system_score_gemma":0.000110331595,"threshold_uncertainty_score":0.08656013},"labels":[],"label_agreement":null},{"id":"W4391880811","doi":"10.1007/s11336-023-09944-3","title":"Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes","year":2024,"lang":"en","type":"article","venue":"Psychometrika","topic":"Sensory Analysis and Statistical Methods","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":"McGill University","funders":"","keywords":"Multivariate statistics; Component (thermodynamics); Multivariate analysis; Mathematics; Statistics; Composite number; Regular polygon; Econometrics; Computer science; Algorithm; Geometry","score_opus":0.04822188114810244,"score_gpt":0.3439971462648933,"score_spread":0.29577526511679086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391880811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027037107,0.00006022895,0.9964998,0.00004666795,0.000014406201,0.000053497264,0.00005683832,0.00021977806,0.00034516834],"genre_scores_gemma":[0.111049645,0.0002138673,0.8863976,0.000081498874,0.000065249114,0.00050141313,0.0005030762,0.00021537619,0.00097229105],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962607,0.0019885127,0.00017257956,0.000813306,0.0006001906,0.00016466808],"domain_scores_gemma":[0.99504,0.002628436,0.00038107196,0.000793515,0.0010213071,0.00013569501],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004237014,0.0019821725,0.0014866501,0.0037860258,0.00090677396,0.0024561149,0.0017229927,0.0010477807,0.002997431],"category_scores_gemma":[0.018188538,0.0004566873,0.0020178892,0.0047994284,0.0017279777,0.002452062,0.002664552,0.0020866771,0.00074224826],"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":[0.00023452585,0.00017115264,0.004560361,0.00039413205,0.0005425825,0.00019839518,0.00075355725,0.14521268,0.00749022,0.1031678,0.005511776,0.73176277],"study_design_scores_gemma":[0.000024312938,0.000078807585,0.002476292,0.000043457392,0.00009057774,0.00007978119,0.00011170393,0.90771544,0.0017853188,0.083758555,0.0037433172,0.00009247836],"about_ca_topic_score_codex":0.004401657,"about_ca_topic_score_gemma":0.0037393863,"teacher_disagreement_score":0.995763,"about_ca_system_score_codex":0.0008154825,"about_ca_system_score_gemma":0.0026018275,"threshold_uncertainty_score":0.02240771},"labels":[],"label_agreement":null},{"id":"W4391971909","doi":"10.3390/foods13040631","title":"Sensory Perception and Food-Evoked Emotions of Older Adults Assessing Microwave-Processed Meals with Different Salt Concentrations","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Neophobia; Meal; Aroma; Perception; Food science; Flavor; Sensory system; Psychology; Taste; Developmental psychology; Chemistry","score_opus":0.035599336203701544,"score_gpt":0.289525938729059,"score_spread":0.2539266025253575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391971909","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":[0.9996754,0.000026685002,0.00010174223,0.000004051751,0.0000019593588,0.000009461282,0.000017584302,0.0000014747437,0.00016171872],"genre_scores_gemma":[0.99897975,0.000049975824,0.0004528111,0.000028089884,0.0000043958075,0.000020876541,0.00004721213,0.0000012514782,0.00041556166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987996,0.000026067555,0.000010652443,0.000022129407,0.000039507202,0.000021803804],"domain_scores_gemma":[0.99969923,0.000054382974,0.00008256524,0.000014940564,0.00007937981,0.00006956895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003457547,0.00030541996,0.00025590832,0.00019747422,0.00014736908,0.00026324837,0.000080019374,0.000279493,0.0014812795],"category_scores_gemma":[0.0008282168,0.00012248756,0.00027269643,0.000089486275,0.00014115551,0.00017839119,0.00026632322,0.00027717793,0.00017541676],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072339443,0.0025108485,0.50848234,0.00033114196,0.00021313643,0.00057290273,0.0052471203,0.00013050296,0.43785462,0.000047437494,0.000303506,0.037072513],"study_design_scores_gemma":[0.000047974503,0.0051067746,0.9820571,0.000008291603,0.000055465844,0.00040805832,0.0017522282,0.0001785317,0.010089852,0.000021198706,0.00026051354,0.000014040583],"about_ca_topic_score_codex":0.00042702386,"about_ca_topic_score_gemma":0.00078764866,"teacher_disagreement_score":0.0014812795,"about_ca_system_score_codex":0.0000659722,"about_ca_system_score_gemma":0.000052846244,"threshold_uncertainty_score":0.0049553514},"labels":[],"label_agreement":null},{"id":"W4392289028","doi":"10.18280/ijdne.190108","title":"Assessment of Pliek-U Sensory Attributes: A Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method Application","year":2024,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Direktorat Riset dan Pengabdian Masyarakat; Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi; Direktorat Jenderal Pendidikan Tinggi; Universitas Syiah Kuala","keywords":"Aftertaste; Taste; Sensory system; Odor; Mathematics; Product (mathematics); Preference; Food science; Statistics; Psychology; Cognitive psychology; Chemistry","score_opus":0.035146898664988976,"score_gpt":0.34506695108683944,"score_spread":0.30992005242185044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392289028","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":[0.15237713,0.0008390986,0.84142506,0.00010295685,0.00002679535,0.00037683913,0.00014236313,0.00032883964,0.004380997],"genre_scores_gemma":[0.57065475,0.00048772545,0.4264053,0.00004925199,0.000013333971,0.0006513923,0.0001906132,0.000057389647,0.0014902303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99867296,0.0005795426,0.000092179005,0.0001776077,0.000393513,0.00008428294],"domain_scores_gemma":[0.999283,0.0004102854,0.00010352914,0.000020319676,0.00016594744,0.000016895447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002202411,0.0010676541,0.0010804572,0.0014732623,0.00038856288,0.0009779115,0.00063067453,0.00061576144,0.0015815377],"category_scores_gemma":[0.0026688995,0.0003672314,0.00149948,0.0009265994,0.00029053676,0.0005891459,0.0007183234,0.0005556279,0.00017722577],"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":[0.0008293804,0.00048041958,0.011151772,0.0018980515,0.0006348427,0.00035863896,0.0003878463,0.593744,0.064343944,0.0039056144,0.001086762,0.3211787],"study_design_scores_gemma":[0.000050106304,0.0009786473,0.007252182,0.00006619775,0.0001436014,0.00011916482,0.00018321798,0.9744194,0.013585112,0.001396584,0.0017369137,0.000068867266],"about_ca_topic_score_codex":0.0022138213,"about_ca_topic_score_gemma":0.0018780589,"teacher_disagreement_score":0.0022138213,"about_ca_system_score_codex":0.00035700056,"about_ca_system_score_gemma":0.0007264644,"threshold_uncertainty_score":0.011647582},"labels":[],"label_agreement":null},{"id":"W4392775866","doi":"10.3917/dm.043.0207","title":"Calibration internationale des échelles sémantiques","year":2006,"lang":"fr","type":"article","venue":"Décisions Marketing","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Humanities; Political science; Art","score_opus":0.04580734846864752,"score_gpt":0.29713688122854504,"score_spread":0.2513295327598975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392775866","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":[0.15980627,0.0007114114,0.81530255,0.0005423567,0.00017464423,0.0005776348,0.0025553708,0.0033682522,0.016961474],"genre_scores_gemma":[0.5711819,0.00024905833,0.42158473,0.00015088916,0.0000443827,0.0012762557,0.0026817592,0.0008278897,0.0020031254],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.95520484,0.023028756,0.0042817714,0.009227913,0.0073398342,0.00091688277],"domain_scores_gemma":[0.8527442,0.10912807,0.005494827,0.01565252,0.016576191,0.00040412508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03375854,0.0014140361,0.0010008147,0.005924531,0.00083015463,0.0047156,0.0013933053,0.0012385118,0.0067933546],"category_scores_gemma":[0.14718272,0.0007704147,0.0022038228,0.00520081,0.0017062553,0.0041225255,0.0032917964,0.0025690102,0.0019513671],"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":[0.0011141807,0.00032192786,0.14642912,0.0013078627,0.0018841412,0.0002122724,0.008550504,0.036976796,0.029590202,0.03609288,0.005305166,0.73221487],"study_design_scores_gemma":[0.0002101014,0.0007376607,0.35640883,0.0008331135,0.00081620837,0.00085632503,0.009201306,0.3918242,0.06824095,0.105020955,0.065420665,0.00042976715],"about_ca_topic_score_codex":0.0061556636,"about_ca_topic_score_gemma":0.0045408625,"teacher_disagreement_score":0.03375854,"about_ca_system_score_codex":0.0019432738,"about_ca_system_score_gemma":0.0013580201,"threshold_uncertainty_score":0.17853433},"labels":[],"label_agreement":null},{"id":"W4393323294","doi":"10.1016/b978-0-323-91747-6.00003-2","title":"Functional foods: Sensory, instrumental, and statistical analysis","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Boniface Hospital; University of Manitoba","funders":"","keywords":"Sensory system; Psychology; Cognitive psychology; Computer science","score_opus":0.041738851803820864,"score_gpt":0.2678051219795135,"score_spread":0.22606627017569264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393323294","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":[0.010404545,0.12207681,0.69367146,0.0023781525,0.002991917,0.0005576328,0.007998749,0.008540664,0.15138012],"genre_scores_gemma":[0.03722514,0.07075609,0.5593718,0.0017481509,0.0014244898,0.0010418879,0.006384304,0.003613684,0.3184345],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986859,0.0002959056,0.000085438405,0.00017135472,0.0007278447,0.000033522167],"domain_scores_gemma":[0.9977984,0.0013492017,0.00007216919,0.00017600373,0.0005543553,0.000049739385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030139864,0.0016183817,0.0010809746,0.0027622643,0.00039671452,0.002176467,0.0010373376,0.00087115564,0.039074764],"category_scores_gemma":[0.004137006,0.00077666366,0.0005989813,0.003095527,0.00088481075,0.0014718182,0.00093934353,0.0016439704,0.016867174],"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":[0.00011568681,0.00009305649,0.00073131727,0.0013072934,0.000047493082,0.00013001797,0.00015988137,0.0013065557,0.015965603,0.0073254546,0.073492445,0.89932525],"study_design_scores_gemma":[0.000029907844,0.00052203005,0.019419221,0.0015481015,0.00014401194,0.0027146668,0.0005853843,0.016967947,0.032363582,0.07673118,0.84877414,0.00019984641],"about_ca_topic_score_codex":0.0016543516,"about_ca_topic_score_gemma":0.003706885,"teacher_disagreement_score":0.039074764,"about_ca_system_score_codex":0.0005352961,"about_ca_system_score_gemma":0.000874651,"threshold_uncertainty_score":0.13071805},"labels":[],"label_agreement":null},{"id":"W4394401598","doi":"10.6084/m9.figshare.22623034","title":"Additional file 1 of A scoping review on two-stage randomized preference trial in the field of mental health and addiction","year":2023,"lang":"en","type":"review","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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; Centre for Addiction and Mental Health","funders":"","keywords":"Addiction; Randomized controlled trial; Mental health; Field trial; Field (mathematics); Psychology; Medicine; Psychiatry; Mathematics; Internal medicine; Biology","score_opus":0.3293958318155868,"score_gpt":0.44731907732310805,"score_spread":0.11792324550752126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394401598","genre_codex":"dataset","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003581327,0.0018299945,0.0007984371,0.0006407875,0.00016227207,0.009806336,0.98155576,0.0005018713,0.0043463735],"genre_scores_gemma":[0.018128803,0.015549532,0.03039865,0.0043385443,0.0009029782,0.28614655,0.57570136,0.0014831072,0.06735056],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99654216,0.0009402835,0.001307993,0.0003610253,0.0005909039,0.0002576544],"domain_scores_gemma":[0.9230643,0.060146704,0.008248593,0.0013303428,0.006477656,0.00073239964],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007447784,0.002243315,0.004963053,0.0077409823,0.001065265,0.0029227491,0.002517556,0.0022175142,0.87453973],"category_scores_gemma":[0.08048082,0.0015143385,0.003444632,0.012625287,0.0005862081,0.004203942,0.001866355,0.0014733823,0.07332219],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017155489,0.00010071073,0.00041629974,0.6189834,0.0005786947,0.00009436298,0.00021886257,0.00039115237,0.00024832232,0.0030874256,0.3481609,0.026004348],"study_design_scores_gemma":[0.03680389,0.0010121437,0.01221641,0.282708,0.00459313,0.0006119789,0.00067300576,0.0012955006,0.00091055804,0.015758226,0.6430655,0.0003516683],"about_ca_topic_score_codex":0.0065082116,"about_ca_topic_score_gemma":0.017893473,"teacher_disagreement_score":0.87453973,"about_ca_system_score_codex":0.0039728736,"about_ca_system_score_gemma":0.008932892,"threshold_uncertainty_score":0.17895377},"labels":[],"label_agreement":null},{"id":"W4394750765","doi":"10.1111/joss.12913","title":"Complex choices: Pole selection for polarized projective mapping applied to a complex product set","year":2024,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Vineland Research and Innovation Centre","funders":"","keywords":"Usability; Set (abstract data type); Product (mathematics); Simple (philosophy); Computer science; Projective test; Robustness (evolution); Projective space; Mathematics; Pure mathematics; Human–computer interaction; Geometry; Chemistry","score_opus":0.27665516876269935,"score_gpt":0.3978469609750081,"score_spread":0.12119179221230875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394750765","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5755971,0.00016970107,0.41299197,0.000115400515,0.00007059676,0.0006640854,0.0002577007,0.00069534424,0.009438142],"genre_scores_gemma":[0.73756844,0.00013132411,0.2599698,0.000055185865,0.000016027443,0.00065416354,0.00020735012,0.000116899915,0.0012807907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820435,0.0010318441,0.000053392934,0.00034875853,0.00028412766,0.00007740872],"domain_scores_gemma":[0.99540675,0.0028316188,0.00032587428,0.0005161681,0.0007654005,0.00015425216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029513931,0.00067101495,0.00046741733,0.000715882,0.00054879877,0.0012237098,0.0005816117,0.0004562211,0.0065724202],"category_scores_gemma":[0.014726179,0.00033088753,0.00054220925,0.0008257858,0.0006203259,0.001065191,0.0013616879,0.00066160824,0.0010431778],"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":[0.0028351063,0.0006798657,0.045662053,0.0009394457,0.00013473575,0.00034255144,0.0057320744,0.012823905,0.15371598,0.0070934766,0.0026981647,0.7673426],"study_design_scores_gemma":[0.0002928538,0.005139105,0.33582544,0.00044131727,0.000300418,0.0011978276,0.007701501,0.46915776,0.11927648,0.0368822,0.023366842,0.00041840298],"about_ca_topic_score_codex":0.0010686824,"about_ca_topic_score_gemma":0.0017761134,"teacher_disagreement_score":0.0065724202,"about_ca_system_score_codex":0.00031707864,"about_ca_system_score_gemma":0.00046225113,"threshold_uncertainty_score":0.021986961},"labels":[],"label_agreement":null},{"id":"W4396518286","doi":"10.12944/crnfsj.12.1.21","title":"Digital Image Analysis to Evaluate Sensory Attributes of Protein-Enriched Whole-Wheat Bread","year":2024,"lang":"en","type":"article","venue":"Current Research in Nutrition and Food Science Journal","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Digital image analysis; Food science; Sensory system; Sensory analysis; Wheat flour; Computer science; Computer vision; Artificial intelligence; Biology; Neuroscience","score_opus":0.22119147682464513,"score_gpt":0.4529115109050145,"score_spread":0.2317200340803694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396518286","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":[0.95807856,0.0007368304,0.038834468,0.00003498806,0.000023317656,0.00012018737,0.00047807302,0.00025124897,0.0014424218],"genre_scores_gemma":[0.9509223,0.000529304,0.047051083,0.000053807707,0.000010370268,0.000087565546,0.00042096069,0.000036163874,0.00088852306],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997086,0.0000541897,0.000024113799,0.000059714104,0.00012693834,0.000026492062],"domain_scores_gemma":[0.9994661,0.00013008456,0.000101369595,0.000036081005,0.00023826594,0.000028053459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087683095,0.000419311,0.0002252361,0.0014329652,0.00008348139,0.00041653423,0.00018443374,0.0002563743,0.000935844],"category_scores_gemma":[0.0010017648,0.00015269319,0.00041144094,0.000719867,0.0001773424,0.00030443765,0.00022804634,0.00029626937,0.00019947335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057699956,0.00011809116,0.01833054,0.0004184829,0.00012921874,0.0001697159,0.00023775124,0.0022263327,0.9062555,0.00013515507,0.00030844947,0.0710937],"study_design_scores_gemma":[0.000040077983,0.0014095084,0.4679916,0.00006157737,0.00031701406,0.0010640422,0.00079897814,0.05907527,0.4666899,0.00026171762,0.0022093519,0.000080961916],"about_ca_topic_score_codex":0.0012376691,"about_ca_topic_score_gemma":0.001572308,"teacher_disagreement_score":0.0014329652,"about_ca_system_score_codex":0.0001642506,"about_ca_system_score_gemma":0.00011722009,"threshold_uncertainty_score":0.0046371818},"labels":[],"label_agreement":null},{"id":"W4396672475","doi":"10.1017/s1368980024000983","title":"Development and testing of two tools to assess point-of-sale food and beverage marketing to children in restaurants","year":2024,"lang":"en","type":"article","venue":"Public Health Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Health Canada","keywords":"Business; Point of sale; Marketing; Food marketing; Point (geometry); Advertising; Food science; Computer science; Mathematics; Chemistry","score_opus":0.17690052424873945,"score_gpt":0.3627144918381947,"score_spread":0.18581396758945526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396672475","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":[0.6946563,0.003637571,0.19439636,0.002455391,0.0008733601,0.0658911,0.0042167436,0.0022911548,0.031581968],"genre_scores_gemma":[0.25901902,0.0018313286,0.6947763,0.00043468323,0.000054016724,0.037750073,0.002639871,0.00014908452,0.0033457198],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.93918115,0.027923979,0.008030881,0.002958325,0.019889507,0.0020161802],"domain_scores_gemma":[0.8351341,0.06799117,0.01042627,0.0057610613,0.07753801,0.0031494054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06991012,0.0018665503,0.0013499625,0.0078054313,0.0012399914,0.0037615218,0.0032820266,0.0014915407,0.0019893537],"category_scores_gemma":[0.13800547,0.0011679266,0.0031960206,0.0036395746,0.0016693114,0.0034630285,0.004327613,0.0017754566,0.0007142731],"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":[0.0007558409,0.0022690326,0.17442359,0.0054290094,0.00039543258,0.000512017,0.031674344,0.0023435121,0.0055641662,0.002290891,0.010191589,0.7641506],"study_design_scores_gemma":[0.0011318073,0.01029408,0.72362113,0.014410535,0.0014550723,0.0017897756,0.056540173,0.022579903,0.028639207,0.0053532156,0.13334647,0.00083874166],"about_ca_topic_score_codex":0.020669706,"about_ca_topic_score_gemma":0.04661554,"teacher_disagreement_score":0.06991012,"about_ca_system_score_codex":0.0060737096,"about_ca_system_score_gemma":0.023659635,"threshold_uncertainty_score":0.36972445},"labels":[],"label_agreement":null},{"id":"W4396702007","doi":"10.1002/fft2.411","title":"Preliminary exploration of acceptance and emotional responses to the key floral volatile compounds of Pu'er crude tea","year":2024,"lang":"en","type":"article","venue":"Food Frontiers","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Ministry of Education and Child Care","funders":"","keywords":"Key (lock); Psychology; Chemistry; Computer science; Computer security","score_opus":0.060668919761285654,"score_gpt":0.28862140963628735,"score_spread":0.2279524898750017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396702007","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":[0.99908304,0.00005758101,0.00035641066,0.00001892852,0.0000041065337,0.000015650297,0.0000772362,0.0000026833375,0.00038446402],"genre_scores_gemma":[0.9983719,0.00007058886,0.0006261244,0.000034234035,0.0000052763216,0.00003461004,0.0000942671,0.000002480284,0.0007606781],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998857,0.000030852156,0.0000057105653,0.000022766295,0.000028122355,0.000026874313],"domain_scores_gemma":[0.9998454,0.0000423198,0.000028965782,0.000011209001,0.000044723605,0.000027451122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002912115,0.00019020919,0.00019152605,0.00017458662,0.00033423555,0.00039088976,0.0000940681,0.00022242886,0.0027237297],"category_scores_gemma":[0.00045161252,0.000075261196,0.00034343437,0.00015313237,0.000206725,0.00022992287,0.0003054715,0.00037436013,0.00020503152],"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":[0.004627,0.00095546024,0.07754069,0.0003548769,0.00010943958,0.0003103029,0.0030938482,0.00012692332,0.8805905,0.00023927157,0.0002171403,0.031834573],"study_design_scores_gemma":[0.00006061616,0.0076461225,0.8674515,0.000026010512,0.00015556854,0.0005181342,0.0070974915,0.0010885046,0.11362109,0.0003387333,0.0019523865,0.00004377638],"about_ca_topic_score_codex":0.0006285255,"about_ca_topic_score_gemma":0.0008482586,"teacher_disagreement_score":0.0027237297,"about_ca_system_score_codex":0.00010466577,"about_ca_system_score_gemma":0.00010523101,"threshold_uncertainty_score":0.009111762},"labels":[],"label_agreement":null},{"id":"W4396750431","doi":"10.3390/foods13101454","title":"Investigation into the Sensory Properties of Plant-Based Eggs, as Well as Acceptance, Emotional Response, and Use","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Research Nova Scotia; Canada Foundation for Innovation","keywords":"Sensory system; Psychology; Cognitive psychology; Biology","score_opus":0.07584556742886497,"score_gpt":0.29035637016519483,"score_spread":0.21451080273632986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396750431","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":[0.99931324,0.00010437613,0.000093729606,0.000016270158,0.0000016356292,0.000005292721,0.000013411333,7.4318535e-7,0.00045143176],"genre_scores_gemma":[0.99864286,0.00020678529,0.0005496806,0.000046138317,0.0000030098329,0.000010409141,0.000039700764,0.0000015646215,0.0004998752],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998518,0.000045894343,0.000009354579,0.00002849419,0.000048569786,0.000015989388],"domain_scores_gemma":[0.99901307,0.0004530994,0.0002842852,0.000032674143,0.00016671036,0.000050150757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005021038,0.00014405423,0.00012805568,0.00016052302,0.00014133923,0.0003701561,0.00008918777,0.00023797381,0.0017432749],"category_scores_gemma":[0.0019158634,0.00008058012,0.0001736105,0.00012837748,0.00017499577,0.00041307503,0.00021036713,0.0002797417,0.00013039004],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043093455,0.0015208094,0.5109059,0.0020592932,0.00028152342,0.001237586,0.016455783,0.00022436593,0.32746258,0.0007697145,0.00067086343,0.13410218],"study_design_scores_gemma":[0.000028990107,0.0023333083,0.97438353,0.000075853066,0.000099205514,0.00073574553,0.008843756,0.00032754138,0.010967889,0.00026855012,0.0019100402,0.00002555113],"about_ca_topic_score_codex":0.0004980697,"about_ca_topic_score_gemma":0.0011799723,"teacher_disagreement_score":0.0017432749,"about_ca_system_score_codex":0.00010165135,"about_ca_system_score_gemma":0.00008733861,"threshold_uncertainty_score":0.0058318973},"labels":[],"label_agreement":null},{"id":"W4400409765","doi":"10.1111/ijfs.17338","title":"Consumers' sensory perception and emotional response towards animal and plant-based soups (familiar food items) with the addition of shio-koji (an unfamiliar ingredient)","year":2024,"lang":"en","type":"article","venue":"International Journal of Food Science & Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Ingredient; Perception; Psychology; Food science; Sensory system; Communication; Cognitive psychology; Biology; Neuroscience","score_opus":0.02570054222663201,"score_gpt":0.2830824041343805,"score_spread":0.2573818619077485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400409765","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":[0.9996489,0.00001550567,0.00004204941,0.000010126149,0.000003062994,0.000007791953,0.000026931853,0.0000020441257,0.00024342728],"genre_scores_gemma":[0.9988944,0.00004720548,0.00026275322,0.000042307864,0.000004616827,0.000024031096,0.00006489337,0.0000020453158,0.0006576821],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998323,0.000054000404,0.000013151816,0.000025162577,0.00004906385,0.00002636789],"domain_scores_gemma":[0.9991179,0.00031349668,0.0002673349,0.000049959577,0.00013707626,0.000114136456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043667038,0.00024832974,0.00022942314,0.00015299297,0.00018635881,0.00038661348,0.00008783238,0.00038237777,0.00401269],"category_scores_gemma":[0.0017960046,0.00013405547,0.00034973375,0.00012150863,0.00021875289,0.00029508962,0.00046627392,0.0004506372,0.0002730643],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019842474,0.002971616,0.46679077,0.0010768445,0.00037136133,0.0010569203,0.028303042,0.0004047829,0.44007593,0.00019669392,0.0013130899,0.03759645],"study_design_scores_gemma":[0.00012827438,0.005149628,0.97045046,0.00003296662,0.00012283995,0.00030406806,0.011899991,0.00052359083,0.010470309,0.00009510875,0.0007693766,0.00005329031],"about_ca_topic_score_codex":0.00072701805,"about_ca_topic_score_gemma":0.00073565694,"teacher_disagreement_score":0.00401269,"about_ca_system_score_codex":0.00010034301,"about_ca_system_score_gemma":0.000046089182,"threshold_uncertainty_score":0.01342386},"labels":[],"label_agreement":null},{"id":"W4400477576","doi":"10.1016/j.appet.2024.107601","title":"Self-reported food liking and wanting: A factor analytic study of ratings across 49 consecutive days","year":2024,"lang":"en","type":"article","venue":"Appetite","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Mental Health; National Science Foundation","keywords":"Psychology; Clinical psychology; Statistics; Social psychology; Mathematics","score_opus":0.05206083337685643,"score_gpt":0.32130967873992616,"score_spread":0.26924884536306976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400477576","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":[0.9993969,0.00001624338,0.00019817648,0.0000033639433,0.0000032987152,0.00003509708,0.00015530549,0.0000042450565,0.0001872513],"genre_scores_gemma":[0.9985091,0.000028058286,0.0005661107,0.00001041013,0.000006559122,0.00009156098,0.0004549129,0.0000057829034,0.0003274028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945456,0.00014671455,0.000052710657,0.00010450604,0.00019044447,0.00005116528],"domain_scores_gemma":[0.9977533,0.0007389692,0.00051558664,0.00020707371,0.0004806249,0.0003044254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010777577,0.00027046577,0.0005758165,0.0005480037,0.00046209872,0.00052800885,0.00019587985,0.00028421945,0.0009876837],"category_scores_gemma":[0.0033380839,0.00023403316,0.0005010907,0.000716863,0.00038136006,0.00041179822,0.00043622858,0.00049209344,0.00024795547],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049705375,0.0011086516,0.9554197,0.0000801583,0.00036067268,0.00011864087,0.0027713387,0.00023684006,0.018495107,0.000044182216,0.00026981105,0.016124336],"study_design_scores_gemma":[0.000006802199,0.0005112004,0.9986966,0.0000011324917,0.000014425396,0.000029254932,0.00028951146,0.00010604691,0.00027979576,0.0000049699124,0.000055220622,0.0000050642634],"about_ca_topic_score_codex":0.002822916,"about_ca_topic_score_gemma":0.006261198,"teacher_disagreement_score":0.002822916,"about_ca_system_score_codex":0.00022611872,"about_ca_system_score_gemma":0.00021191972,"threshold_uncertainty_score":0.0056998134},"labels":[],"label_agreement":null},{"id":"W4400478342","doi":"10.1017/9781108983624.005","title":"Cross-Variety Comparisons","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variety (cybernetics); Computer science; Artificial intelligence","score_opus":0.061460276133233877,"score_gpt":0.26054828528715546,"score_spread":0.19908800915392158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400478342","genre_codex":"empirical","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":[0.7131998,0.00472212,0.0077095125,0.00020859223,0.0003875516,0.00010934753,0.0035775204,0.00033915188,0.26974645],"genre_scores_gemma":[0.96877253,0.0012806193,0.00287039,0.000100914745,0.00012446403,0.00012003861,0.00531046,0.000302566,0.021118067],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782264,0.00063114555,0.00014985376,0.0006616093,0.0005265034,0.0002082401],"domain_scores_gemma":[0.9937302,0.0038270133,0.00027885853,0.001057166,0.0009141422,0.00019256557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114726,0.0003067255,0.0004233519,0.003672476,0.00081360264,0.002210771,0.0004698419,0.00029846994,0.05094052],"category_scores_gemma":[0.008694169,0.00015781652,0.00053643534,0.0036993409,0.00055444665,0.0023049433,0.0029430855,0.00058779813,0.0041277986],"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":[0.0021570108,0.00045083137,0.23804091,0.0010909574,0.0008712507,0.001569059,0.062337816,0.0014294405,0.0129508935,0.08288428,0.021071926,0.57514566],"study_design_scores_gemma":[0.00004132211,0.00078810536,0.730387,0.00023034547,0.00031484404,0.0017813534,0.031590298,0.0012295417,0.004329139,0.018520927,0.21071447,0.000072869334],"about_ca_topic_score_codex":0.0008602738,"about_ca_topic_score_gemma":0.001455334,"teacher_disagreement_score":0.05094052,"about_ca_system_score_codex":0.00041789457,"about_ca_system_score_gemma":0.00015075416,"threshold_uncertainty_score":0.17041302},"labels":[],"label_agreement":null},{"id":"W4400618358","doi":"10.1016/j.foodres.2024.114746","title":"An investigation into the sensory properties of luffa (Luffa cylindrica (L.)) seeds and a comparison to other seeds (flax, sunflower, chia, and hemp)","year":2024,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Flavour; Sunflower; Roasting; Food science; Sensory analysis; Horticulture; Mathematics; Chemistry; Biology","score_opus":0.15784629141491768,"score_gpt":0.3939434372613245,"score_spread":0.23609714584640684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400618358","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":[0.9984865,0.00060736196,0.00024959625,0.000026699365,0.0000055382684,0.000022697262,0.000095170704,0.000005520639,0.0005008457],"genre_scores_gemma":[0.99674326,0.0006297574,0.0011535168,0.000094754716,0.0000057798875,0.000022991408,0.00028183273,0.000004638432,0.0010634776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999205,0.000014297149,0.000004539058,0.000015724361,0.000031620188,0.0000134126],"domain_scores_gemma":[0.999845,0.00003478532,0.0000364667,0.0000056365334,0.00005466125,0.000023313904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002112324,0.00030807007,0.000273057,0.00038226112,0.0002583535,0.0002392875,0.00009650087,0.0002373244,0.00091776985],"category_scores_gemma":[0.00020341417,0.000070297225,0.00041361467,0.00031074736,0.00014336905,0.0002726278,0.00014726317,0.00036765842,0.000118649405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009988862,0.00040378855,0.010944738,0.00034336693,0.000045835117,0.00031819337,0.0002128552,0.000062948566,0.9733975,0.000048456888,0.00010706288,0.013116456],"study_design_scores_gemma":[0.000044305176,0.011491648,0.7760038,0.00007300169,0.00023842716,0.0018371132,0.0010246413,0.0009319466,0.20494066,0.00008721657,0.0032673154,0.000059875307],"about_ca_topic_score_codex":0.0024589286,"about_ca_topic_score_gemma":0.0054779314,"teacher_disagreement_score":0.0024589286,"about_ca_system_score_codex":0.00018032778,"about_ca_system_score_gemma":0.00013220336,"threshold_uncertainty_score":0.00488925},"labels":[],"label_agreement":null},{"id":"W4401734348","doi":"10.1111/ijfs.17483","title":"An analysis of consumer perception, emotional responses, and beliefs about mead","year":2024,"lang":"en","type":"article","venue":"International Journal of Food Science & Technology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Psychology; Perception; Wine; Social psychology; Content analysis; Food science","score_opus":0.030386293147620168,"score_gpt":0.3476068098789052,"score_spread":0.31722051673128504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401734348","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":[0.9995328,0.000009716333,0.00006970035,0.000011033571,8.3003437e-7,0.000009285848,0.00001880249,0.0000012723365,0.00034645197],"genre_scores_gemma":[0.9993198,0.00002367714,0.00027782074,0.00002044594,0.0000016627939,0.000019568628,0.00004397025,7.480012e-7,0.00029233756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971336,0.000110595916,0.000022420676,0.000028700037,0.00009226703,0.000032642867],"domain_scores_gemma":[0.9982987,0.0007383627,0.00047234353,0.00006848405,0.0002920628,0.00013013415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006827234,0.0001599139,0.000120404155,0.00030669625,0.0003120078,0.00051210873,0.0000955657,0.000260417,0.0022670513],"category_scores_gemma":[0.0028889272,0.00009320546,0.0002547799,0.00017337776,0.00025399288,0.00024764525,0.00038785028,0.00029385235,0.00012188159],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019604713,0.0012387772,0.89241916,0.00027598973,0.00018115167,0.0005247124,0.027487688,0.00027655752,0.040986806,0.0002003803,0.00050989055,0.0339384],"study_design_scores_gemma":[0.000020739068,0.0009626635,0.97292364,0.000026890408,0.000048511054,0.0002062175,0.022110589,0.000737502,0.0020474975,0.00010819445,0.00078542286,0.000022029619],"about_ca_topic_score_codex":0.001371995,"about_ca_topic_score_gemma":0.002562322,"teacher_disagreement_score":0.0022670513,"about_ca_system_score_codex":0.00021549177,"about_ca_system_score_gemma":0.00010997877,"threshold_uncertainty_score":0.007584095},"labels":[],"label_agreement":null},{"id":"W4401971219","doi":"10.1016/j.psychsport.2024.102724","title":"The pleasure of moving: A compositional data analysis of the association between replacing sedentary time with physical activity on affective valence in daily life","year":2024,"lang":"en","type":"article","venue":"Psychology of sport and exercise","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":true,"ca_institutions":"University of Waterloo; London Health Sciences Centre; Western University","funders":"","keywords":"Psychology; Pleasure; Association (psychology); Valence (chemistry); Physical activity; Sedentary behavior; Emotional valence; Developmental psychology; Cognitive psychology; Social psychology; Cognition; Chemistry; Psychotherapist; Psychiatry; Physical medicine and rehabilitation","score_opus":0.03617687125617853,"score_gpt":0.3208599840095509,"score_spread":0.2846831127533724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401971219","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":[0.971555,0.0001965018,0.024544397,0.00007794432,0.000028984514,0.0004336147,0.0015597416,0.00011388432,0.0014898822],"genre_scores_gemma":[0.97278714,0.00012519382,0.023484988,0.000026680376,0.000013815762,0.0005369773,0.0022788122,0.000041082192,0.0007054474],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99814177,0.00079529284,0.00014388625,0.000341688,0.00044544158,0.00013185147],"domain_scores_gemma":[0.9950517,0.0028481963,0.00060877757,0.0005457554,0.00082291715,0.00012270195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038736204,0.00063656876,0.00063121726,0.0018760079,0.0006521956,0.001335951,0.00052160496,0.00033865045,0.0026882016],"category_scores_gemma":[0.013469587,0.00024553752,0.0017910899,0.0022268624,0.00064404705,0.0006020646,0.0013967382,0.00067142246,0.00034565845],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038992013,0.00056103314,0.8058422,0.00083378644,0.0014556806,0.00030129653,0.0069673937,0.0050309063,0.022697054,0.001977049,0.0014206863,0.14901374],"study_design_scores_gemma":[0.000044918586,0.0006442909,0.96294373,0.00006807706,0.00042187973,0.00019203566,0.0024683748,0.027641496,0.002183789,0.0013159161,0.002004333,0.000071214105],"about_ca_topic_score_codex":0.016866028,"about_ca_topic_score_gemma":0.021066936,"teacher_disagreement_score":0.016866028,"about_ca_system_score_codex":0.00068079925,"about_ca_system_score_gemma":0.0008968983,"threshold_uncertainty_score":0.03353572},"labels":[],"label_agreement":null},{"id":"W4402117387","doi":"10.1111/1750-3841.17323","title":"Consumers’ familiarity with wine increased their acceptance of pasta sauce containing grape pomace","year":2024,"lang":"en","type":"article","venue":"Journal of Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Acadia University","keywords":"Pomace; Wine; Food science; Grape wine; Chemistry; Business","score_opus":0.0409955616334612,"score_gpt":0.2856931887023747,"score_spread":0.24469762706891351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402117387","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":[0.9993185,0.00011815162,0.00008717289,0.000036273803,0.000008277543,0.000008007437,0.000020113173,0.000004422363,0.0003990693],"genre_scores_gemma":[0.9986689,0.00011655661,0.00023648916,0.000050601957,0.000009757779,0.000007775767,0.000036457408,0.000002335742,0.00087113696],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998363,0.000045426666,0.00000901469,0.000041497242,0.000042959542,0.000024726023],"domain_scores_gemma":[0.9995403,0.0001404469,0.00012333083,0.000031543812,0.00008659981,0.000077779114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003174631,0.00019320413,0.00026712375,0.00009580979,0.0002131333,0.00042382072,0.00008202892,0.0002924429,0.0041137566],"category_scores_gemma":[0.00094363495,0.00011415635,0.0003515758,0.000082524544,0.0001531474,0.00026235986,0.00026389948,0.00063054264,0.00017485033],"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":[0.013261392,0.005962187,0.37784094,0.001336011,0.0006184343,0.0013079324,0.008302933,0.00025561554,0.49182773,0.00031364197,0.0016275682,0.0973456],"study_design_scores_gemma":[0.000069637754,0.0067161587,0.9736086,0.0000448612,0.00020853373,0.00032883423,0.0021942363,0.0007248245,0.013841601,0.00009969659,0.0021312137,0.000031620013],"about_ca_topic_score_codex":0.0018313243,"about_ca_topic_score_gemma":0.0017882677,"teacher_disagreement_score":0.0041137566,"about_ca_system_score_codex":0.00012244598,"about_ca_system_score_gemma":0.00009600367,"threshold_uncertainty_score":0.013761938},"labels":[],"label_agreement":null},{"id":"W4402126120","doi":"10.70099/bj/2024.09.01.40","title":"10.70099/BJ/2024.09.01.40","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shelf life; Sensory system; Food science; Sensory analysis; Environmental science; Statistics; Psychology; Mathematics; Biology; Cognitive psychology","score_opus":0.023681174712197942,"score_gpt":0.23233324419028284,"score_spread":0.2086520694780849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402126120","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009642202,0.00032064944,0.002678083,0.0005863347,0.0005392968,0.000102676975,0.0017397153,0.002015051,0.991054],"genre_scores_gemma":[0.0018779361,0.00019540222,0.0017093045,0.00029865035,0.00010441169,0.00004922623,0.000982911,0.00051757705,0.99426466],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996749,0.000036788184,0.000030275105,0.000064107866,0.00012686715,0.00006702838],"domain_scores_gemma":[0.99800664,0.00045432636,0.00007986557,0.0003720406,0.00047426464,0.0006129181],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001043298,0.0010404211,0.00071547314,0.0025515459,0.0009108038,0.002107614,0.0013474661,0.002140401,0.97211367],"category_scores_gemma":[0.0021511917,0.0005454495,0.0008170023,0.0017702105,0.0007818927,0.0020998737,0.0018243238,0.001301121,0.97447294],"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":[0.0002840518,0.00026384438,0.00068742153,0.0002537057,0.000015635474,0.000108414395,0.00005897121,0.00023273949,0.0025448063,0.0036339376,0.33844453,0.65347195],"study_design_scores_gemma":[0.000046341283,0.00007825982,0.0019089084,0.00027170673,0.0000139834565,0.0003120393,0.000090958376,0.00046503186,0.0009655783,0.0019248546,0.9938996,0.000022688517],"about_ca_topic_score_codex":0.0038761548,"about_ca_topic_score_gemma":0.0037232982,"teacher_disagreement_score":0.027886331,"about_ca_system_score_codex":0.0006538598,"about_ca_system_score_gemma":0.0007091981,"threshold_uncertainty_score":0.039776444},"labels":[],"label_agreement":null},{"id":"W4402799879","doi":"10.3390/foods13182912","title":"Atlantic Canadians’ Sensory Perception of Couscous Made with Sugar Kelp (Saccharina latissma)","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"Acadia University","keywords":"Kelp; Sugar; Food science; Perception; Biology; Botany","score_opus":0.03442443044857975,"score_gpt":0.2687735152685708,"score_spread":0.23434908481999106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402799879","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":[0.9980768,0.000035619538,0.000036828034,0.000051716215,0.0000035325916,0.0000052065416,0.000110649715,0.0000025852682,0.0016768476],"genre_scores_gemma":[0.9949234,0.00012930119,0.00020182088,0.00014424796,0.0000022484385,0.000004852835,0.00022249897,0.0000042383726,0.0043673627],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998535,0.0000070857595,0.0000026693665,0.000021438726,0.00007185215,0.000043503467],"domain_scores_gemma":[0.9997148,0.000026630885,0.000039326907,0.000009202774,0.00012848899,0.000081569124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016428929,0.00020856009,0.00020796477,0.00021512288,0.0014966512,0.00064984005,0.00017572129,0.0002618356,0.0051547256],"category_scores_gemma":[0.0004242138,0.00013059423,0.00023061268,0.00034627737,0.0005929738,0.00021588396,0.0003459357,0.00052002806,0.0003324375],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054982323,0.0012112942,0.5505705,0.0004002871,0.00015746999,0.0018313157,0.034464445,0.00023000917,0.32639343,0.0006231451,0.0060001425,0.07261965],"study_design_scores_gemma":[0.000015923311,0.00045650214,0.98111975,0.000027043723,0.00003558755,0.00016069802,0.011895375,0.00015439857,0.0024343054,0.000037977337,0.003629153,0.00003328205],"about_ca_topic_score_codex":0.8601764,"about_ca_topic_score_gemma":0.93904674,"teacher_disagreement_score":0.13982362,"about_ca_system_score_codex":0.0021823717,"about_ca_system_score_gemma":0.0015720617,"threshold_uncertainty_score":0.2812941},"labels":[],"label_agreement":null},{"id":"W4403360071","doi":"10.1002/jsfa.13964","title":"How do consumers discuss the texture of frozen blueberries? An investigation using word association, hedonic scales and rate‐all‐that‐apply","year":2024,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Texture (cosmology); Perception; Advertising; Food science; Mathematics; Psychology; Computer science; Business; Chemistry; Artificial intelligence","score_opus":0.03410711792529668,"score_gpt":0.2651835059215569,"score_spread":0.23107638799626018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403360071","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":[0.99886584,0.00004068106,0.00012219069,0.00004022794,0.000003970953,0.00002645451,0.000056453082,0.0000024804176,0.00084181817],"genre_scores_gemma":[0.99823916,0.00008877065,0.00053408195,0.000081422,0.000008452926,0.00007195231,0.00013285599,0.0000035619978,0.0008396232],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994267,0.00019797689,0.000044598502,0.000083675164,0.00020262015,0.000044437722],"domain_scores_gemma":[0.99521434,0.002091355,0.0015573055,0.00015638083,0.00077543437,0.0002052829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020586094,0.00021205877,0.00023318543,0.0004053585,0.00026799794,0.0010388735,0.00018116934,0.00038508713,0.0024527397],"category_scores_gemma":[0.0068026166,0.00014820657,0.00033415973,0.0003781812,0.00035671704,0.00073214463,0.0003585947,0.0005560141,0.00027843897],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019278945,0.0013916483,0.9075519,0.0003878076,0.00020439494,0.00023033159,0.02845258,0.00011880506,0.008738868,0.00033282323,0.000924427,0.049738485],"study_design_scores_gemma":[0.00003294009,0.0007035652,0.98521394,0.000029105786,0.00007354917,0.00009333422,0.011152702,0.0006564112,0.00077646784,0.00015856455,0.0010840934,0.000025230478],"about_ca_topic_score_codex":0.0014498436,"about_ca_topic_score_gemma":0.0020770866,"teacher_disagreement_score":0.0024527397,"about_ca_system_score_codex":0.00026912944,"about_ca_system_score_gemma":0.00016790551,"threshold_uncertainty_score":0.010887086},"labels":[],"label_agreement":null},{"id":"W4403935499","doi":"10.1016/j.sctalk.2024.100403","title":"Exploring fundamentals of immersive environment setups on food sensory perception in space contexts","year":2024,"lang":"en","type":"article","venue":"Science Talks","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Perception; Space (punctuation); Sensory system; Human–computer interaction; Computer science; Psychology; Cognitive psychology; Neuroscience","score_opus":0.15419610277321386,"score_gpt":0.30768856357570756,"score_spread":0.1534924608024937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403935499","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":[0.9798375,0.00081414747,0.015354036,0.00009909884,0.00005399855,0.00013198487,0.00009809957,0.00006949511,0.0035417555],"genre_scores_gemma":[0.98038894,0.00082274724,0.017848784,0.00006649096,0.000022898113,0.00016942086,0.00007641948,0.000034287314,0.0005699553],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99951947,0.00021995202,0.000025391895,0.00007401139,0.000090009205,0.00007121273],"domain_scores_gemma":[0.9985739,0.0009854032,0.00012515942,0.000096663134,0.00012707597,0.00009180808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007266984,0.0005268493,0.00020014454,0.00024408988,0.0002906958,0.0009901187,0.00036593957,0.0003149387,0.00316076],"category_scores_gemma":[0.0032867298,0.0003121402,0.0005026723,0.00012915494,0.0005768475,0.0007912521,0.0012119735,0.00034127146,0.00019069514],"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":[0.0022578423,0.0014124885,0.025826063,0.001944138,0.00020226768,0.0008223841,0.014881589,0.0038365093,0.80455476,0.0025422405,0.00078148325,0.14093834],"study_design_scores_gemma":[0.00042168435,0.03380919,0.5931833,0.001057367,0.0010712618,0.0032356125,0.020969253,0.018781116,0.2809113,0.005374853,0.040752567,0.00043258604],"about_ca_topic_score_codex":0.00059249956,"about_ca_topic_score_gemma":0.0012887126,"teacher_disagreement_score":0.00316076,"about_ca_system_score_codex":0.00019766674,"about_ca_system_score_gemma":0.0002081336,"threshold_uncertainty_score":0.010573864},"labels":[],"label_agreement":null},{"id":"W4404360754","doi":"10.1016/j.cofs.2024.101244","title":"Editorial overview: Sensory science and consumer perception 2024","year":2024,"lang":"en","type":"editorial","venue":"Current Opinion in Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Perception; Sensory system; Cognitive science; Psychology; Neuroscience","score_opus":0.10202411294144925,"score_gpt":0.3935750798343941,"score_spread":0.29155096689294485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404360754","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":[0.000014156089,0.01185184,0.000096855074,0.025092816,0.9619929,0.000017703875,0.00005828574,0.00003062206,0.00084477203],"genre_scores_gemma":[0.00014769592,0.0051878095,0.00007562477,0.017447574,0.9727404,0.000025896421,0.000036010682,0.000031615626,0.004307322],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99249,0.0013693041,0.0008186339,0.0009811248,0.0038967778,0.00044420405],"domain_scores_gemma":[0.96322685,0.010474175,0.0025902826,0.00081125245,0.01843577,0.0044616736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009719454,0.004960807,0.0053019477,0.007690485,0.0036230674,0.009391064,0.004359539,0.019961907,0.022442628],"category_scores_gemma":[0.031392455,0.0015366144,0.0037167156,0.0032527589,0.0027796465,0.004676777,0.0028233833,0.017100403,0.015533027],"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":[0.000029593106,0.000006802611,0.0000107914,0.00020465038,0.000014532652,0.000036795234,0.000004625954,0.000008001367,0.000037626483,0.00008247344,0.9961669,0.0033972568],"study_design_scores_gemma":[0.000096159376,0.00003183763,0.0003277134,0.0010267429,0.00010847637,0.00019674913,0.00003868781,0.00011369148,0.00008674328,0.0008560398,0.99709046,0.00002684405],"about_ca_topic_score_codex":0.0036647094,"about_ca_topic_score_gemma":0.009194197,"teacher_disagreement_score":0.022442628,"about_ca_system_score_codex":0.003872664,"about_ca_system_score_gemma":0.0044924924,"threshold_uncertainty_score":0.07507813},"labels":[],"label_agreement":null},{"id":"W4404998224","doi":"10.1016/j.ijgfs.2024.101078","title":"Sensory characterization of conifer-based extracts for culinary uses","year":2024,"lang":"en","type":"article","venue":"International Journal of Gastronomy and Food Science","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Mitacs","keywords":"Sensory system; Characterization (materials science); Food science; Biological system; Mathematics; Chemistry; Biology; Materials science; Nanotechnology; Neuroscience","score_opus":0.049945192548662566,"score_gpt":0.3171565087384792,"score_spread":0.26721131618981664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404998224","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":[0.9913816,0.0011713069,0.0027878443,0.000062304505,0.000032020143,0.00011191115,0.0013395882,0.000026471233,0.0030869707],"genre_scores_gemma":[0.9866109,0.001641174,0.0048046024,0.00019275953,0.0000183512,0.00012706396,0.0019942727,0.000030414321,0.0045804274],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99971336,0.00004040493,0.00001745518,0.000047341877,0.00011329261,0.00006810654],"domain_scores_gemma":[0.99960023,0.00009084439,0.00003969648,0.0000143875895,0.00019884805,0.000056043074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003690499,0.00082251604,0.00037628404,0.0008212278,0.00068237184,0.0004717312,0.00018633196,0.0002634294,0.003851573],"category_scores_gemma":[0.00048257987,0.00013989382,0.00040259055,0.00103661,0.00029745948,0.00047634466,0.00037218077,0.0009291166,0.00035521403],"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":[0.0008151723,0.00020503116,0.0031075282,0.00027554564,0.000031783762,0.00012323585,0.00016157945,0.00017554712,0.98597264,0.00006811665,0.00014185361,0.0089221],"study_design_scores_gemma":[0.000035664103,0.0042739096,0.16321164,0.00013267792,0.00026741525,0.0005563731,0.0013882082,0.0010309983,0.8225648,0.00017024002,0.006306867,0.00006120802],"about_ca_topic_score_codex":0.0032199335,"about_ca_topic_score_gemma":0.008965549,"teacher_disagreement_score":0.003851573,"about_ca_system_score_codex":0.0002533761,"about_ca_system_score_gemma":0.0006120416,"threshold_uncertainty_score":0.012884736},"labels":[],"label_agreement":null},{"id":"W4405197636","doi":"10.1007/978-3-662-70074-7_7","title":"Hypothesis Testing","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Continental (Canada)","funders":"","keywords":"Computer science","score_opus":0.1695921160832169,"score_gpt":0.2712092443073616,"score_spread":0.1016171282241447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405197636","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":[0.00793917,0.0018683912,0.7655365,0.0052702907,0.0030932561,0.003079801,0.003590069,0.0030368788,0.20658565],"genre_scores_gemma":[0.24384043,0.002107332,0.588937,0.006729682,0.0025284546,0.01012979,0.009418633,0.0011927803,0.13511589],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.979529,0.011977432,0.0008219652,0.002956594,0.0042205295,0.0004945221],"domain_scores_gemma":[0.9411527,0.04657609,0.0013104827,0.0061355494,0.004220599,0.0006046129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019980576,0.0012976916,0.001225857,0.001723206,0.0010599828,0.002717472,0.0024771811,0.0012896212,0.11098027],"category_scores_gemma":[0.073970996,0.00041865796,0.0015435838,0.0009131391,0.0027106549,0.0021567382,0.0022565757,0.002178508,0.033107784],"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":[0.00069609,0.00030186624,0.002615183,0.0013388229,0.00022239209,0.00027395185,0.00049034983,0.0021923538,0.0021779,0.2557399,0.110559046,0.62339216],"study_design_scores_gemma":[0.00038503052,0.0010816887,0.0032701152,0.0010278962,0.00019670386,0.0009796289,0.00080615276,0.018682167,0.005941149,0.73980033,0.22775497,0.00007417429],"about_ca_topic_score_codex":0.00056913,"about_ca_topic_score_gemma":0.0005374497,"teacher_disagreement_score":0.11098027,"about_ca_system_score_codex":0.000973018,"about_ca_system_score_gemma":0.0029801275,"threshold_uncertainty_score":0.371266},"labels":[],"label_agreement":null},{"id":"W4406063855","doi":"10.1016/j.foodres.2025.115670","title":"The espresso protocol as a tool for sensory quality evaluation","year":2025,"lang":"en","type":"article","venue":"Food Research International","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Protocol (science); Sensory system; Quality (philosophy); Computer science; Computational biology; Biology; Medicine; Neuroscience","score_opus":0.37603978960529416,"score_gpt":0.5834585401468899,"score_spread":0.20741875054159575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406063855","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39188245,0.0018133414,0.34968102,0.00064446754,0.0007921388,0.16413571,0.015534897,0.0038974846,0.07161851],"genre_scores_gemma":[0.1833339,0.00187305,0.63038456,0.0012497187,0.00015887964,0.13108422,0.012909762,0.0014506358,0.037555266],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944734,0.0017778339,0.00060221605,0.0006091044,0.002278708,0.00025874644],"domain_scores_gemma":[0.9922083,0.0011013785,0.00038135287,0.0009435006,0.0051289927,0.00023651679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010177586,0.0017501743,0.00093942863,0.002372553,0.0014272267,0.0010752579,0.0012146316,0.0009887706,0.022295654],"category_scores_gemma":[0.006502948,0.0005759507,0.0010140685,0.0016464917,0.001120965,0.00087498734,0.0023776768,0.0017424056,0.004575493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006656688,0.0033262975,0.016417244,0.004912261,0.0001760934,0.0014234954,0.007382318,0.0017848469,0.6397333,0.004795856,0.043592848,0.2697987],"study_design_scores_gemma":[0.001349548,0.018395504,0.31294018,0.0018529742,0.00039757768,0.002415804,0.0065615247,0.009969878,0.24349345,0.005286123,0.3964595,0.00087782845],"about_ca_topic_score_codex":0.0031946567,"about_ca_topic_score_gemma":0.00874483,"teacher_disagreement_score":0.022295654,"about_ca_system_score_codex":0.00075036444,"about_ca_system_score_gemma":0.0027193076,"threshold_uncertainty_score":0.07458639},"labels":[],"label_agreement":null},{"id":"W4406548573","doi":"10.1016/s1155-1941(23)45187-6","title":"10.1016/s1155-1941(23)45187-6","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Psychology; Philosophy","score_opus":0.01717049771910895,"score_gpt":0.20805532849441694,"score_spread":0.190884830775308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406548573","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046096015,0.0006733255,0.0018399078,0.00044064416,0.0005226432,0.00014204011,0.001180841,0.0017742504,0.99296546],"genre_scores_gemma":[0.00056748034,0.0002825829,0.000692702,0.00018560627,0.0000925738,0.00007258859,0.00051203516,0.00021834149,0.99737597],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991429,0.0000661679,0.0000797947,0.00028366674,0.00026575514,0.00016170983],"domain_scores_gemma":[0.9971673,0.00073487265,0.0001854521,0.00037705994,0.0005935633,0.0009418044],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015814389,0.0028826569,0.0020489048,0.0031332537,0.002067008,0.00440183,0.0039448524,0.005165969,0.990475],"category_scores_gemma":[0.0022173505,0.0011662068,0.0019381441,0.0030847008,0.0020451215,0.005561261,0.0035502769,0.0027797457,0.9934145],"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":[0.00036243777,0.00020629195,0.0006977379,0.0006758981,0.000051567382,0.00020507521,0.00008737367,0.00050273555,0.0024820822,0.0041548987,0.296141,0.6944328],"study_design_scores_gemma":[0.00005714473,0.00014665088,0.0008000791,0.00036133325,0.000021954553,0.00040289748,0.00012412554,0.00030682483,0.0005669305,0.00085388904,0.9963236,0.00003468728],"about_ca_topic_score_codex":0.0039743925,"about_ca_topic_score_gemma":0.0032939012,"teacher_disagreement_score":0.009525001,"about_ca_system_score_codex":0.00113794,"about_ca_system_score_gemma":0.0013844188,"threshold_uncertainty_score":0.013586283},"labels":[],"label_agreement":null},{"id":"W4407141636","doi":"10.1111/joss.70014","title":"Emotional Projective Mapping (EmoMap): A Pilot Study Examining a Tool for Collecting Emotional Responses to Food","year":2025,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Psychology; Projective test; Emotional regulation; Social psychology; Computer science; Developmental psychology","score_opus":0.3415484036514481,"score_gpt":0.4000462042733276,"score_spread":0.05849780062187948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407141636","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":[0.9888377,0.000021830929,0.007897825,0.00005375673,0.000017743696,0.0020194643,0.00019067778,0.00007061635,0.0008903409],"genre_scores_gemma":[0.9511674,0.000116156625,0.037969574,0.00017734572,0.000023606937,0.008133456,0.00039730815,0.00007382893,0.0019413169],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982993,0.0011478676,0.00006605834,0.0001663336,0.00018199364,0.00013850324],"domain_scores_gemma":[0.9932474,0.0042450344,0.00021800994,0.0006052708,0.001238074,0.0004462936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005838854,0.00065378414,0.0005060881,0.000541099,0.0008783064,0.00068824855,0.000804897,0.00062021153,0.003280868],"category_scores_gemma":[0.00926101,0.00042355742,0.000476048,0.00041994703,0.000757106,0.0008969821,0.0009378475,0.0008668619,0.00066034676],"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":[0.008420862,0.059531722,0.18104763,0.0033127456,0.00026136724,0.0050946865,0.1413576,0.002176941,0.2049055,0.0012325803,0.0064513,0.3862071],"study_design_scores_gemma":[0.0017879428,0.17337923,0.6211328,0.00046832394,0.0004425245,0.0030150167,0.09133759,0.016118947,0.06833173,0.0017132164,0.021948744,0.00032402968],"about_ca_topic_score_codex":0.001038929,"about_ca_topic_score_gemma":0.0018599295,"teacher_disagreement_score":0.005838854,"about_ca_system_score_codex":0.0002796569,"about_ca_system_score_gemma":0.0006837894,"threshold_uncertainty_score":0.0308792},"labels":[],"label_agreement":null},{"id":"W4407259294","doi":"10.1111/joss.70015","title":"A Preliminary Investigation Into the Use of <scp>AI</scp>‐Generated Food Images in a Survey Asking About Consumer Perception of Appeal, Naturalness, Healthiness, and Willingness to Consume","year":2025,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Naturalness; Perception; Psychology; Appeal; Product (mathematics); Social psychology; Advertising; Mathematics; Business","score_opus":0.12381470611296688,"score_gpt":0.348594038599614,"score_spread":0.22477933248664714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407259294","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":[0.99930406,0.000010667335,0.00014229164,0.000022332897,0.0000019407532,0.000032912652,0.000028585737,0.0000018134077,0.00045540274],"genre_scores_gemma":[0.99862266,0.000032401393,0.000798082,0.000058441266,0.000003213013,0.000071651484,0.0000380659,0.0000019028322,0.00037356868],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990439,0.00052998576,0.0000641861,0.00006671015,0.00020288143,0.0000922443],"domain_scores_gemma":[0.99234736,0.0043725385,0.001251182,0.0002823399,0.0014646617,0.00028190724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00286058,0.00017525979,0.00015858041,0.0003688373,0.00043467715,0.0008715667,0.00017405041,0.00039587717,0.002136885],"category_scores_gemma":[0.008658205,0.00015734322,0.00031463528,0.00024534678,0.0004230554,0.00044456383,0.00043179965,0.00047345742,0.00019725498],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012488004,0.001627094,0.7864254,0.0008204044,0.00011317343,0.00080847053,0.12985557,0.00018556823,0.033334516,0.00032693712,0.0009450347,0.044309046],"study_design_scores_gemma":[0.000028034647,0.0024667578,0.89444447,0.00008840971,0.000056971523,0.00037170228,0.095197216,0.0008335519,0.0037337209,0.00013126468,0.0026036282,0.000044304557],"about_ca_topic_score_codex":0.0035482715,"about_ca_topic_score_gemma":0.0060276682,"teacher_disagreement_score":0.0035482715,"about_ca_system_score_codex":0.00035492648,"about_ca_system_score_gemma":0.0002855773,"threshold_uncertainty_score":0.015128374},"labels":[],"label_agreement":null},{"id":"W4408245051","doi":"10.1111/1750-3841.70104","title":"Consumer perception of meal replacement beverages: A comparison between younger adults and older adults","year":2025,"lang":"en","type":"article","venue":"Journal of Food Science","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":"Acadia University","funders":"","keywords":"Perception; Meal; Food science; Environmental health; Consumption (sociology); Medicine; Psychology; Chemistry","score_opus":0.03239348773177994,"score_gpt":0.3203329502082218,"score_spread":0.28793946247644187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408245051","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":[0.99934036,0.0001832128,0.000025679155,0.000023542012,0.000004269346,0.0000055651713,0.00005523251,8.374936e-7,0.00036128267],"genre_scores_gemma":[0.99920386,0.0001997446,0.00006633559,0.00008505637,0.000010004914,0.000010444072,0.00012105848,0.000001045808,0.00030249316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977034,0.00003551912,0.000036145655,0.000040114162,0.00007899124,0.000038896145],"domain_scores_gemma":[0.99911696,0.00011083203,0.0003071137,0.000022390937,0.00024021522,0.0002024081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005445952,0.00018248644,0.00034822593,0.000578302,0.00029301085,0.00050182774,0.0001005013,0.00044236434,0.00233728],"category_scores_gemma":[0.0015254137,0.00013216291,0.00047604486,0.00033864175,0.00016703492,0.0005398973,0.00044600788,0.00030381745,0.0003556947],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006554583,0.00021100057,0.97857314,0.00010386738,0.00009644928,0.00020238927,0.00576682,0.000016291717,0.0024902401,0.000025834966,0.00025434032,0.011604118],"study_design_scores_gemma":[0.000008263529,0.0004426383,0.9949386,0.000013639088,0.000025330131,0.00017910103,0.003920617,0.000033462504,0.00008836996,0.000012628527,0.00033248373,0.0000049579207],"about_ca_topic_score_codex":0.0042725876,"about_ca_topic_score_gemma":0.0043712365,"teacher_disagreement_score":0.0042725876,"about_ca_system_score_codex":0.0001595251,"about_ca_system_score_gemma":0.000106555504,"threshold_uncertainty_score":0.00849545},"labels":[],"label_agreement":null},{"id":"W4408362460","doi":"10.3390/beverages11020038","title":"Optimization of Beverage Formulation with Technique for Order of Preference by Similarity to Ideal Solution","year":2025,"lang":"en","type":"article","venue":"Beverages","topic":"Sensory Analysis and Statistical Methods","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":"École de Technologie Supérieure","funders":"","keywords":"Preference; Ideal solution; Ideal (ethics); Similarity (geometry); Mathematics; Order (exchange); Mathematical optimization; Computer science; Statistics; Artificial intelligence; Economics; Thermodynamics; Epistemology; Physics; Philosophy","score_opus":0.035786870496827695,"score_gpt":0.2928888429110722,"score_spread":0.2571019724142445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408362460","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":[0.0046783327,0.00021581537,0.9921049,0.00006260797,0.000035841505,0.00013805414,0.000028984949,0.00011049071,0.002624973],"genre_scores_gemma":[0.13105682,0.000638573,0.8650335,0.00005728262,0.000033545602,0.00045857512,0.00013964598,0.000095939584,0.0024862143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974419,0.00086760166,0.00016352434,0.00028533462,0.0011244176,0.00011733216],"domain_scores_gemma":[0.99907875,0.00044476258,0.00011626356,0.000053261378,0.00028520793,0.000021732443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024109914,0.0015781717,0.0013236683,0.0016175344,0.00059530267,0.0015528514,0.0008701779,0.0007105762,0.0043708943],"category_scores_gemma":[0.0035303596,0.0005665317,0.0021147914,0.0016518224,0.00057930686,0.0011662374,0.0009922411,0.0013429052,0.0007552728],"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":[0.00018762182,0.00025120223,0.0010109447,0.002010355,0.0002957617,0.00024496604,0.0004829276,0.5749192,0.032461315,0.043456484,0.0032999516,0.34137926],"study_design_scores_gemma":[0.000034911875,0.0005991885,0.000468648,0.000067019326,0.0000835013,0.00014854416,0.00013882622,0.96653193,0.007996489,0.01594154,0.007933504,0.000055786837],"about_ca_topic_score_codex":0.002116664,"about_ca_topic_score_gemma":0.0022160974,"teacher_disagreement_score":0.0043708943,"about_ca_system_score_codex":0.0008528652,"about_ca_system_score_gemma":0.0016820519,"threshold_uncertainty_score":0.014622092},"labels":[],"label_agreement":null},{"id":"W4408432797","doi":"10.1016/j.foodqual.2025.105493","title":"Reply to three commentaries on the definition of sensory and consumer science by Jaeger et al. (2024)","year":2025,"lang":"en","type":"article","venue":"Food Quality and Preference","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Innovation Cluster (Canada)","funders":"","keywords":"Sensory system; Psychology; Positive economics; Epistemology; Philosophy; Cognitive psychology; Economics","score_opus":0.26523545633903284,"score_gpt":0.36733175299359017,"score_spread":0.10209629665455733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408432797","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014811709,0.00069426146,0.00018097044,0.92380625,0.07444494,0.000025473251,0.00011680331,0.00005944602,0.00052367157],"genre_scores_gemma":[0.001417037,0.0003353533,0.00015310137,0.97934544,0.017083392,0.00011793055,0.000037655504,0.00007670777,0.0014332765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9577754,0.013055728,0.0066577285,0.0045918263,0.013739111,0.0041802614],"domain_scores_gemma":[0.78007007,0.108305626,0.013187473,0.004372609,0.08291477,0.01114946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049680118,0.0021572937,0.0026013474,0.002579488,0.01092834,0.012080993,0.009383082,0.06780563,0.012209329],"category_scores_gemma":[0.2831887,0.0017446647,0.0039115357,0.0027920378,0.011676551,0.011474799,0.009516938,0.08072425,0.010078743],"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":[0.000021978369,0.000006641309,0.00015240641,0.000121034,0.00000976849,0.0001118834,0.00078237813,0.000017452756,0.00005584557,0.0008597247,0.99661994,0.0012409358],"study_design_scores_gemma":[0.00006348638,0.000040617026,0.0009598778,0.0011498503,0.000051770832,0.00037772406,0.0042827837,0.00019045724,0.00043018017,0.0030858857,0.9891768,0.00019050828],"about_ca_topic_score_codex":0.020168114,"about_ca_topic_score_gemma":0.015225211,"teacher_disagreement_score":0.06780563,"about_ca_system_score_codex":0.019581484,"about_ca_system_score_gemma":0.021732919,"threshold_uncertainty_score":0.26273668},"labels":[],"label_agreement":null},{"id":"W4411070250","doi":"10.1002/sim.70140","title":"High‐Dimensional Multiresponse Partially Functional Linear Regression","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Functional principal component analysis; Estimator; Curse of dimensionality; Scalar (mathematics); Covariate; Mathematics; Principal component analysis; Applied mathematics; Linear regression; Regression analysis; Regression; Partial least squares regression; Consistency (knowledge bases); Computer science; Statistics; Artificial intelligence","score_opus":0.04955572462124787,"score_gpt":0.3533515474465572,"score_spread":0.30379582282530937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411070250","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":[0.008588723,0.00021001254,0.9901247,0.00023466203,0.00002301879,0.000047881163,0.00019154638,0.00016647251,0.00041296915],"genre_scores_gemma":[0.51237,0.0009634916,0.47839332,0.0006560183,0.00028853165,0.0010390652,0.0015187644,0.00018982138,0.004581032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99079144,0.006781223,0.00023456871,0.0012319236,0.0006782598,0.00028255503],"domain_scores_gemma":[0.98733366,0.00822283,0.0014680638,0.0016613709,0.0011304498,0.00018355917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010605181,0.0018010908,0.0018112651,0.0012764983,0.0005504206,0.0013732138,0.0029304996,0.0016318245,0.0021105171],"category_scores_gemma":[0.023256598,0.0008111788,0.0020231658,0.0015238973,0.0016793924,0.0023173885,0.0017964083,0.0017890773,0.00061491865],"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":[0.00025739704,0.00025469638,0.012311638,0.000531106,0.0007539211,0.00036090036,0.00037450014,0.593987,0.0046869675,0.20819528,0.004291291,0.17399527],"study_design_scores_gemma":[0.000014421557,0.00006933963,0.0017985023,0.000023348923,0.000029977326,0.00006591707,0.00002776095,0.95806223,0.0004297919,0.037845913,0.0015950975,0.00003765205],"about_ca_topic_score_codex":0.0028057725,"about_ca_topic_score_gemma":0.0022740674,"teacher_disagreement_score":0.010605181,"about_ca_system_score_codex":0.00077525433,"about_ca_system_score_gemma":0.0012681541,"threshold_uncertainty_score":0.056086242},"labels":[],"label_agreement":null},{"id":"W4411351419","doi":"10.2196/69212","title":"Fermented Food Consumption Across European Regions: Protocol for the Development and Validation of the Web-Based Fermented Foods Frequency Questionnaire (3FQ)","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Protocol (science); Consumption (sociology); Food frequency questionnaire; Environmental health; Computer science; Medicine; World Wide Web; Sociology; Alternative medicine","score_opus":0.3524970499882214,"score_gpt":0.5374947351376188,"score_spread":0.1849976851493974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411351419","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017874615,0.00062892615,0.029342912,0.0006597818,0.0003568232,0.9311976,0.013055791,0.00026200994,0.0066214786],"genre_scores_gemma":[0.0063825645,0.0003364758,0.016798178,0.00032016667,0.000051051822,0.9718417,0.003297067,0.00003291611,0.00093988],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9823961,0.011036452,0.0032338442,0.001220968,0.0010935792,0.0010189682],"domain_scores_gemma":[0.9774146,0.007127825,0.0020429713,0.0034220216,0.009149272,0.0008432511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041568793,0.0021700284,0.0025098897,0.0028729555,0.0022814416,0.001850071,0.001827054,0.002856837,0.03058294],"category_scores_gemma":[0.033186227,0.0016380457,0.0028706752,0.002209939,0.0015282072,0.0016100607,0.0030254996,0.0028891086,0.00972454],"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":[0.030771274,0.016670952,0.045229852,0.042287078,0.0010972872,0.004740057,0.027760878,0.008481357,0.01996005,0.021979308,0.30257395,0.4784479],"study_design_scores_gemma":[0.020581337,0.011596627,0.1760914,0.021129023,0.0005427041,0.0017741093,0.012612221,0.005338265,0.010036713,0.010502649,0.7290496,0.0007453217],"about_ca_topic_score_codex":0.0036171216,"about_ca_topic_score_gemma":0.004683667,"teacher_disagreement_score":0.041568793,"about_ca_system_score_codex":0.0027902075,"about_ca_system_score_gemma":0.011528867,"threshold_uncertainty_score":0.21983945},"labels":[],"label_agreement":null},{"id":"W4411988516","doi":"10.1002/cjce.25773","title":"Sequential optimization of drying and extraction processes for enhanced antioxidant recovery from <i>Parquetina nigrescens</i> leaves: Assessing drying parameters and extraction model reliability","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tertiary Education Trust Fund","keywords":"Extraction (chemistry); Reliability (semiconductor); Chromatography; Antioxidant; Chemistry; Biochemistry; Thermodynamics; Physics","score_opus":0.023711520336436812,"score_gpt":0.2679293983812275,"score_spread":0.24421787804479067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411988516","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":[0.97700524,0.0006027859,0.021745212,0.00003506755,0.000008449619,0.00004659819,0.000114301154,0.00007976532,0.00036239487],"genre_scores_gemma":[0.9872996,0.00037055786,0.011806132,0.0000142636445,0.0000022522931,0.000056283818,0.0001143528,0.000016963895,0.00031954964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998233,0.00004008779,0.000018333796,0.000047338366,0.00005241469,0.000018575383],"domain_scores_gemma":[0.9997639,0.00010801995,0.00006529056,0.000019440839,0.000035474844,0.000007802955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007778543,0.00034937463,0.00045275327,0.00020728694,0.00016023369,0.0004067214,0.000304735,0.00032771425,0.0003861976],"category_scores_gemma":[0.00064478035,0.00019585033,0.0006549029,0.00019438915,0.000120548095,0.00030355222,0.00024866525,0.00033629462,0.00011130495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044828473,0.00014262003,0.0032950393,0.00031128302,0.00007474676,0.000062694286,0.00004723769,0.042846438,0.94101465,0.00014634691,0.00006408037,0.011546656],"study_design_scores_gemma":[0.000033262462,0.0008926741,0.010637332,0.000013466655,0.00011402205,0.000052933214,0.000038622537,0.264535,0.7226908,0.00011648979,0.0008482924,0.000027069182],"about_ca_topic_score_codex":0.0017091915,"about_ca_topic_score_gemma":0.0024654886,"teacher_disagreement_score":0.0017091915,"about_ca_system_score_codex":0.00034395012,"about_ca_system_score_gemma":0.00026254402,"threshold_uncertainty_score":0.004113734},"labels":[],"label_agreement":null},{"id":"W4412336646","doi":"","title":"Saskatoon er egnet til maskinhøst","year":2019,"lang":"da","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.024854164813377013,"score_gpt":0.25992174323933964,"score_spread":0.23506757842596263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412336646","genre_codex":"other","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":[0.22691599,0.007838711,0.13241483,0.008850553,0.011513212,0.0012988452,0.23464222,0.010643488,0.3658822],"genre_scores_gemma":[0.13534893,0.005298576,0.1258739,0.0018494622,0.00022085391,0.0018170507,0.028970703,0.004700467,0.69592005],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99777395,0.00054070697,0.00011025395,0.00071457407,0.0005267859,0.00033372265],"domain_scores_gemma":[0.99455994,0.0020793148,0.0004075189,0.00069582026,0.0018090528,0.00044837192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024604544,0.0015671103,0.0012899578,0.0009050657,0.001331488,0.0043901014,0.0010007985,0.0006005026,0.14509934],"category_scores_gemma":[0.005727919,0.001054045,0.00084460253,0.0023451876,0.001170457,0.0007504016,0.001472446,0.002209833,0.023288626],"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":[0.008075425,0.00047661396,0.020795543,0.0018582568,0.00091956527,0.0014563372,0.0021809253,0.0065356325,0.029602075,0.023907052,0.30253682,0.6016557],"study_design_scores_gemma":[0.000531134,0.000517534,0.08463108,0.00088525965,0.00049105467,0.00029968016,0.0033389665,0.0045462456,0.01515392,0.009878588,0.8794586,0.00026795187],"about_ca_topic_score_codex":0.40691027,"about_ca_topic_score_gemma":0.61165404,"teacher_disagreement_score":0.40691027,"about_ca_system_score_codex":0.004362086,"about_ca_system_score_gemma":0.02075618,"threshold_uncertainty_score":0.8090836},"labels":[],"label_agreement":null},{"id":"W4412418796","doi":"10.1080/10408398.2025.2531220","title":"Sensory evaluation of plant-based cheese: a systematic review with a focus on texture and mouthfeel","year":2025,"lang":"en","type":"review","venue":"Critical Reviews in Food Science and Nutrition","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"European Research Executive Agency; Villum Fonden","keywords":"Mouthfeel; Food science; Texture (cosmology); Focus (optics); Sensory system; Biotechnology; Biology; Computer science; Artificial intelligence","score_opus":0.15419798829185113,"score_gpt":0.41219961849310854,"score_spread":0.25800163020125744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412418796","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":[0.0002941323,0.9992168,0.00007703467,0.0000840326,0.000039688362,0.000028748304,0.00008152063,0.0000032455641,0.00017479376],"genre_scores_gemma":[0.0023953607,0.9969812,0.0002591492,0.00014824021,0.000024510204,0.00004084584,0.00006951492,0.0000022254085,0.00007894821],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9980399,0.00058915827,0.00060971634,0.0002128857,0.0004750867,0.00007327478],"domain_scores_gemma":[0.9909252,0.0069908043,0.0009206444,0.00012315335,0.00096093176,0.00007916347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034858817,0.0012551096,0.0034393328,0.0067443955,0.00036371037,0.0021125858,0.001343328,0.0012313296,0.0044294335],"category_scores_gemma":[0.012911602,0.00052402535,0.0042266133,0.005959244,0.0007007025,0.0016281597,0.00095506065,0.00086953427,0.00043007283],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016175729,0.00003460836,0.0003672642,0.7854253,0.0020644646,0.00011377454,0.00022201914,0.00011621443,0.00039463816,0.00045214564,0.0035758321,0.20707186],"study_design_scores_gemma":[0.000111052796,0.00037149127,0.006692342,0.82004946,0.024290355,0.0010425011,0.0006557064,0.00014293501,0.0006182924,0.0009231997,0.14502591,0.000076854805],"about_ca_topic_score_codex":0.0050484887,"about_ca_topic_score_gemma":0.013238454,"teacher_disagreement_score":0.0067443955,"about_ca_system_score_codex":0.001438004,"about_ca_system_score_gemma":0.0044151475,"threshold_uncertainty_score":0.018435359},"labels":[],"label_agreement":null},{"id":"W4412639567","doi":"10.3390/foods14152594","title":"An Investigation into the Sensory Properties of Luffa (Luffa cylindrica (L.)) Fruit Powder","year":2025,"lang":"en","type":"article","venue":"Foods","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Flavour; Ingredient; Food science; Shelf life; Sensory analysis; Horticulture; Materials science; Chemistry; Biology","score_opus":0.05228495788725377,"score_gpt":0.2903754612860811,"score_spread":0.23809050339882734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412639567","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":[0.99795306,0.0008498202,0.00039996993,0.00004562273,0.000010859771,0.000045485966,0.000079062105,0.0000073271935,0.00060877495],"genre_scores_gemma":[0.9956145,0.00074772694,0.002105581,0.0001963133,0.000013403647,0.000042663578,0.00016571536,0.000007347051,0.0011067225],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982977,0.00003990721,0.000007871883,0.000027452186,0.000075263946,0.000019759853],"domain_scores_gemma":[0.9997991,0.000066047454,0.000039061422,0.000008377035,0.00006580871,0.000021660184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003940208,0.00029851007,0.00036194304,0.00030426617,0.00031220063,0.0003511678,0.00016332993,0.00040860422,0.0016996654],"category_scores_gemma":[0.0004497429,0.00011109362,0.00050706713,0.00025725798,0.00018359594,0.00054225343,0.00027629617,0.0005737648,0.00017131049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017697957,0.00047899343,0.010415161,0.0007796984,0.00006894477,0.0005237277,0.0007793672,0.0000871566,0.9674676,0.00014010393,0.00023801456,0.017251413],"study_design_scores_gemma":[0.00014481683,0.01863023,0.5566122,0.00029691044,0.00042969972,0.0038051791,0.005320507,0.0018946679,0.4005088,0.0003072906,0.011898414,0.00015129246],"about_ca_topic_score_codex":0.00090749346,"about_ca_topic_score_gemma":0.0018769741,"teacher_disagreement_score":0.0016996654,"about_ca_system_score_codex":0.00013890673,"about_ca_system_score_gemma":0.000120871424,"threshold_uncertainty_score":0.0056859255},"labels":[],"label_agreement":null},{"id":"W4412954635","doi":"10.1016/j.jneb.2025.05.137","title":"Sensory Evaluation and Acceptability of Fiber Rich Recipes Formulated from Foods Distributed in Food Pantries, Among Participants of Food Assistance Programs (PFAP) in Reno, Nevada","year":2025,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensory system; Fiber; Food science; Computer science; Business; Psychology; Biology; Chemistry; Cognitive psychology","score_opus":0.0695974967586946,"score_gpt":0.3575263137658394,"score_spread":0.2879288170071448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412954635","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":[0.99966776,0.000013261042,0.000030039088,0.000006784669,0.0000013858601,0.000014112846,0.00004054695,0.000001000962,0.00022512993],"genre_scores_gemma":[0.99874485,0.000036338406,0.0002703491,0.000027846669,9.4532726e-7,0.000019710196,0.00007897571,0.0000018111456,0.00081913726],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996909,0.000085468855,0.000027124865,0.00005930466,0.00010268723,0.00003446651],"domain_scores_gemma":[0.99932003,0.00019380008,0.00011768107,0.000022501632,0.0002537936,0.00009222422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010963071,0.00024604183,0.00018840902,0.00027588446,0.0005621684,0.00050897524,0.00021264717,0.00028697963,0.0014430536],"category_scores_gemma":[0.001528965,0.00018494904,0.00032414554,0.00020543327,0.00022157584,0.00027118693,0.00033014393,0.00048135643,0.000106959444],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062059606,0.003728914,0.902814,0.00027221514,0.0003362688,0.00032694804,0.017406015,0.00029161506,0.04831401,0.00011973898,0.00051776227,0.019666607],"study_design_scores_gemma":[0.000026808695,0.0024145413,0.98753345,0.000017437447,0.000068751906,0.000070352915,0.007034105,0.0003009347,0.0020918732,0.000022597003,0.00040716777,0.000011978823],"about_ca_topic_score_codex":0.020766474,"about_ca_topic_score_gemma":0.050166007,"teacher_disagreement_score":0.020766474,"about_ca_system_score_codex":0.00030904028,"about_ca_system_score_gemma":0.00028795065,"threshold_uncertainty_score":0.041291177},"labels":[],"label_agreement":null},{"id":"W4415610604","doi":"10.1016/j.phytol.2025.103078","title":"The evolution of botanical product quality testing","year":2025,"lang":"en","type":"article","venue":"Phytochemistry Letters","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"","score_opus":0.03906045352326245,"score_gpt":0.31077484195563576,"score_spread":0.27171438843237333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415610604","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":[0.664701,0.021523701,0.1580164,0.01824571,0.000601984,0.00035974706,0.0004494819,0.00038373546,0.13571823],"genre_scores_gemma":[0.8982131,0.0071124732,0.078061506,0.0032157686,0.00035977375,0.00013092361,0.00020363733,0.0002595023,0.0124433115],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939029,0.002641942,0.00025697696,0.001692464,0.0012410942,0.0002646437],"domain_scores_gemma":[0.9813766,0.010335724,0.0010756598,0.0016517761,0.005018852,0.00054130045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013040795,0.0005571664,0.0005149247,0.0020158112,0.00088118797,0.002747281,0.0016570402,0.0017702356,0.0032685848],"category_scores_gemma":[0.019184852,0.0005385964,0.00040628138,0.0011365429,0.0059333104,0.0020245307,0.0018244269,0.00250441,0.00065044296],"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":[0.00058443885,0.000507519,0.055545844,0.0005492961,0.00020896501,0.00044023173,0.0029519324,0.010218651,0.055924144,0.12588078,0.0024334535,0.74475473],"study_design_scores_gemma":[0.00014278667,0.0022474362,0.3213416,0.0013423299,0.000323117,0.0039971033,0.004379211,0.06933769,0.09482717,0.11735706,0.38420004,0.00050438143],"about_ca_topic_score_codex":0.0046311687,"about_ca_topic_score_gemma":0.0038190119,"teacher_disagreement_score":0.013040795,"about_ca_system_score_codex":0.0041667465,"about_ca_system_score_gemma":0.0020390071,"threshold_uncertainty_score":0.06896716},"labels":[],"label_agreement":null},{"id":"W4416169742","doi":"10.1111/joss.70089","title":"The Cross‐Modal Interaction of White Pepper Included in a Bread Formulation: An Investigation Using Hedonic Scales and Rate‐All‐That‐Apply","year":2025,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pepper; Flavor; Perception; Food products; Sensory analysis; White (mutation)","score_opus":0.15361336134754985,"score_gpt":0.40762806538785834,"score_spread":0.25401470404030846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416169742","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":[0.9985648,0.000424806,0.00049653195,0.000021797503,0.000022814498,0.00004523936,0.000044525077,0.000005681107,0.00037372127],"genre_scores_gemma":[0.9958015,0.0004345845,0.00278396,0.00006672563,0.000014235052,0.000048094324,0.00005920737,0.000008111251,0.0007835654],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996018,0.00011074219,0.000026109808,0.00006210018,0.00016619694,0.000033078177],"domain_scores_gemma":[0.9997011,0.000098315504,0.000075570555,0.000014680735,0.000071102135,0.000039143324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005107915,0.00033817848,0.00029907268,0.00016915298,0.00014787441,0.0003456321,0.00015702592,0.00024477646,0.00140754],"category_scores_gemma":[0.00085972925,0.00014904271,0.0002993277,0.00023256596,0.00020698062,0.0003338292,0.00032677548,0.0004996984,0.00010472277],"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":[0.0024199164,0.00065245247,0.0014985569,0.00030456646,0.00006212033,0.000072859875,0.00022260427,0.00008444071,0.9847504,0.000058655154,0.00007834777,0.009795164],"study_design_scores_gemma":[0.00029582175,0.065600984,0.14741361,0.000094999465,0.00047210613,0.00041164205,0.0012529042,0.003024088,0.77700657,0.00013764804,0.004209209,0.00008043823],"about_ca_topic_score_codex":0.00046360985,"about_ca_topic_score_gemma":0.00064337987,"teacher_disagreement_score":0.00140754,"about_ca_system_score_codex":0.00011015919,"about_ca_system_score_gemma":0.00009282947,"threshold_uncertainty_score":0.0047086477},"labels":[],"label_agreement":null},{"id":"W4416562028","doi":"","title":"Sparse DiSTATIS on categorical data","year":2023,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Centre for Addiction and Mental Health","funders":"","keywords":"","score_opus":0.07276981324992898,"score_gpt":0.28499967042992386,"score_spread":0.21222985717999487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416562028","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":[0.0058380803,0.00036121142,0.9906109,0.00096988986,0.00009817513,0.00006413295,0.0009816703,0.0005630838,0.00051290955],"genre_scores_gemma":[0.29188952,0.0021650563,0.67549586,0.0017026678,0.0019499236,0.0011094845,0.011329975,0.0005701309,0.013787394],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919819,0.0032856378,0.0006312137,0.0016505554,0.0020912068,0.0003594687],"domain_scores_gemma":[0.9301259,0.055756953,0.0021677508,0.0076304246,0.0033227827,0.000996322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007440861,0.0011443802,0.002765638,0.0028180534,0.0008443519,0.0022872265,0.0029208353,0.0021203544,0.006642862],"category_scores_gemma":[0.053747725,0.00094197266,0.0022463526,0.004867567,0.0025663872,0.0039706025,0.0057731806,0.0051769027,0.0021826925],"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":[0.0004909372,0.0001905331,0.0038507709,0.00081796927,0.00021925374,0.000303574,0.00036839733,0.27287748,0.003816472,0.11803142,0.021999914,0.5770332],"study_design_scores_gemma":[0.000022193328,0.00009409142,0.00075819134,0.00005892939,0.000020350035,0.00013100791,0.000054304808,0.8040863,0.0008035967,0.19051816,0.0034299889,0.000022795457],"about_ca_topic_score_codex":0.004075569,"about_ca_topic_score_gemma":0.0047202683,"teacher_disagreement_score":0.007440861,"about_ca_system_score_codex":0.0015144179,"about_ca_system_score_gemma":0.0021713255,"threshold_uncertainty_score":0.039351523},"labels":[],"label_agreement":null},{"id":"W4417174801","doi":"10.2196/79306","title":"Exploring the Usability and Acceptability of the FoodMATS-Youth App for Monitoring Food Marketing Exposures: Mixed Methods Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Usability; Social media; Social marketing; Food marketing; Social media marketing; Digital marketing; Marketing research","score_opus":0.315143105674596,"score_gpt":0.4806481513184616,"score_spread":0.1655050456438656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417174801","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":[0.990825,0.00086736976,0.0023990585,0.000111467736,0.000027579468,0.0033148099,0.0003519509,0.000025334519,0.002077365],"genre_scores_gemma":[0.97541374,0.0014062844,0.012500811,0.00050618633,0.000054922664,0.008335941,0.00029955243,0.000030252711,0.0014523304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99083626,0.0046765925,0.00097402115,0.00089771056,0.0020767052,0.00053866167],"domain_scores_gemma":[0.98661995,0.007128789,0.0014772576,0.0006675278,0.003650706,0.00045574128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020823019,0.00066169904,0.0009918909,0.0012464628,0.0011253082,0.0018996999,0.00073117507,0.0007383223,0.0020108407],"category_scores_gemma":[0.018835241,0.0005526617,0.0016747063,0.00054334896,0.0006678371,0.0013858626,0.0011821521,0.0007880845,0.00042055457],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033483235,0.015444459,0.6030991,0.005721157,0.0013600893,0.0013517301,0.117489666,0.00030837173,0.008059881,0.00067065115,0.003119883,0.24002673],"study_design_scores_gemma":[0.0009944978,0.04566342,0.7837521,0.0044505317,0.0021041976,0.0026905965,0.11980017,0.0033083211,0.010322096,0.0009091564,0.025629103,0.00037576776],"about_ca_topic_score_codex":0.002966358,"about_ca_topic_score_gemma":0.0073911836,"teacher_disagreement_score":0.020823019,"about_ca_system_score_codex":0.000947848,"about_ca_system_score_gemma":0.0021336004,"threshold_uncertainty_score":0.11012393},"labels":[],"label_agreement":null},{"id":"W4417280812","doi":"10.1111/joss.70099","title":"An Investigation Into the Sensory Properties of Cookies Made With Luffa Seed Powder","year":2025,"lang":"en","type":"article","venue":"Journal of Sensory Studies","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Acadia University","funders":"","keywords":"Aftertaste; Ingredient; Health benefits; Sensory analysis; Raw material","score_opus":0.07889342454791952,"score_gpt":0.31210354613945307,"score_spread":0.23321012159153354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417280812","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":[0.9990382,0.00015065729,0.0002920894,0.000016464573,0.000006373822,0.000030292,0.000074033545,0.0000050812178,0.0003867888],"genre_scores_gemma":[0.99654657,0.00019768176,0.001894183,0.00008176988,0.000004335435,0.000034475343,0.00014868051,0.000010011964,0.0010823972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998275,0.000047031273,0.00001031976,0.000028013466,0.00006414199,0.000022914634],"domain_scores_gemma":[0.9994935,0.00020819074,0.00006117068,0.000022791724,0.00017263372,0.00004169497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054987817,0.00032879546,0.00028105514,0.00030441937,0.00028333446,0.00034645305,0.0001490906,0.0003570608,0.0030051],"category_scores_gemma":[0.0007644839,0.00016375862,0.0004854729,0.0002337165,0.0002189271,0.000319315,0.00028912476,0.0006631043,0.00023433234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045580734,0.0007823359,0.01219028,0.00070042815,0.000118308955,0.00033451826,0.0011122088,0.00011533975,0.96638364,0.00010225233,0.00017026339,0.013432404],"study_design_scores_gemma":[0.00012860227,0.026956426,0.43975595,0.00020252334,0.00043691654,0.0012806823,0.0040519987,0.0013370462,0.5203178,0.00014559198,0.0052800016,0.00010650619],"about_ca_topic_score_codex":0.0009902597,"about_ca_topic_score_gemma":0.0015976356,"teacher_disagreement_score":0.0030051,"about_ca_system_score_codex":0.00012862007,"about_ca_system_score_gemma":0.000119735276,"threshold_uncertainty_score":0.010053039},"labels":[],"label_agreement":null},{"id":"W4417481418","doi":"10.1016/j.jval.2025.09.3405","title":"MSR120 How Chinese Restaurants Can Help With Robust Preference Insights: A Novel Dirichlet Mixture Model to Account for Complex Heterogeneity in Individual Preference Weights","year":2025,"lang":"en","type":"article","venue":"Value in Health","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"","keywords":"Preference; Mixture model; Sample (material); Dirichlet distribution; Key (lock); Latent Dirichlet allocation","score_opus":0.26859798688179776,"score_gpt":0.3470187803756831,"score_spread":0.07842079349388537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417481418","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":[0.17862299,0.0006944916,0.806592,0.0019977975,0.00022071118,0.00011355128,0.0011638945,0.0009563354,0.009638246],"genre_scores_gemma":[0.88947165,0.0003464362,0.09438327,0.00038648248,0.00012894733,0.00018686247,0.0011137916,0.00025062592,0.013731918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906296,0.0004188027,0.00003792825,0.0002536925,0.00010369505,0.00012300124],"domain_scores_gemma":[0.99847406,0.0009649009,0.000086336084,0.00019799727,0.00018519851,0.000091521884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002436584,0.00064658985,0.00079932227,0.0008750203,0.00061850436,0.0016571134,0.0014426715,0.0012727985,0.006979327],"category_scores_gemma":[0.0070041902,0.00056016113,0.001536245,0.0012988057,0.0006364957,0.0020159704,0.0012205109,0.0014286852,0.0015298991],"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":[0.0016229651,0.0003688411,0.026930245,0.00040770546,0.0006942881,0.00055666076,0.0021037778,0.35770488,0.016139857,0.24304277,0.018694425,0.33173358],"study_design_scores_gemma":[0.000027752378,0.00004043797,0.0030889784,0.000017178747,0.000059958347,0.000072086346,0.000109266075,0.95028496,0.0009577297,0.04248576,0.0028043923,0.00005150325],"about_ca_topic_score_codex":0.016044226,"about_ca_topic_score_gemma":0.016906518,"teacher_disagreement_score":0.016044226,"about_ca_system_score_codex":0.0010538824,"about_ca_system_score_gemma":0.0010225573,"threshold_uncertainty_score":0.031901658},"labels":[],"label_agreement":null},{"id":"W6920829094","doi":"10.6084/m9.figshare.12072393","title":"Additional file 1 of State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Selection (genetic algorithm); State (computer science); Multivariable calculus; Feature selection; Key (lock)","score_opus":0.06583012407213587,"score_gpt":0.29235262202700696,"score_spread":0.2265224979548711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920829094","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001814852,0.000094541625,0.0017403216,0.00027043512,0.00006811779,0.00023254164,0.995479,0.0005560546,0.0013774964],"genre_scores_gemma":[0.011344041,0.0008352466,0.01959318,0.0018962622,0.00048144304,0.0080334805,0.9330598,0.003361899,0.021394655],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978836,0.0007986224,0.0003862062,0.000398763,0.00039646585,0.00013622732],"domain_scores_gemma":[0.8880508,0.09986232,0.002186985,0.003205925,0.005987098,0.00070685765],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006381856,0.0013163766,0.0016757558,0.0030179447,0.0007050319,0.0019987414,0.0027722584,0.0015381331,0.90284854],"category_scores_gemma":[0.10597926,0.00083901844,0.0011816673,0.0054564276,0.00038663487,0.0023666746,0.0015502534,0.0013578455,0.20599319],"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":[0.00022229893,0.000082242135,0.0009407215,0.004732679,0.00006670094,0.00004257216,0.000046340505,0.00029186133,0.000052336192,0.0008810551,0.9764812,0.016160088],"study_design_scores_gemma":[0.0039186026,0.00024461563,0.015313292,0.007990347,0.0003508722,0.0004943299,0.0003371447,0.0023741168,0.0005788437,0.021685963,0.9465483,0.000163654],"about_ca_topic_score_codex":0.0056916303,"about_ca_topic_score_gemma":0.008256642,"teacher_disagreement_score":0.99361813,"about_ca_system_score_codex":0.0008999157,"about_ca_system_score_gemma":0.0026546863,"threshold_uncertainty_score":0.13857478},"labels":[],"label_agreement":null},{"id":"W6920960052","doi":"10.6084/m9.figshare.12072393.v1","title":"Additional file 1 of State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Selection (genetic algorithm); State (computer science); Multivariable calculus; Feature selection; Key (lock)","score_opus":0.0604984209215733,"score_gpt":0.2577330610712083,"score_spread":0.197234640149635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920960052","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016480064,0.00007164881,0.0015419277,0.00017681894,0.000057018144,0.00017858022,0.99585307,0.0006818597,0.0012743428],"genre_scores_gemma":[0.010793719,0.0006122931,0.01904282,0.0015400166,0.00035233467,0.006249229,0.93706316,0.004451656,0.019894775],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981996,0.0006341629,0.00031135164,0.0003693914,0.00034556858,0.00013992438],"domain_scores_gemma":[0.9085187,0.0811924,0.0017157141,0.002869816,0.0050879647,0.000615377],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0055463417,0.0014808042,0.0017066923,0.0030679156,0.0007543442,0.0021864634,0.0028380705,0.0015510714,0.9158236],"category_scores_gemma":[0.083575726,0.00091613433,0.0013709924,0.005266927,0.00039491523,0.0024449818,0.0015549953,0.0013884282,0.25417426],"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":[0.0002283706,0.0000833422,0.0010264359,0.004707955,0.00007708673,0.000042404434,0.0000513738,0.0003866414,0.00006915956,0.00088282867,0.9772942,0.015150165],"study_design_scores_gemma":[0.003421895,0.00024216161,0.015211954,0.0065054297,0.0003626038,0.0004119154,0.00033866326,0.0026114273,0.00064376305,0.019522313,0.95055145,0.00017648387],"about_ca_topic_score_codex":0.0057775206,"about_ca_topic_score_gemma":0.008952645,"teacher_disagreement_score":0.9158236,"about_ca_system_score_codex":0.00085995614,"about_ca_system_score_gemma":0.0024225058,"threshold_uncertainty_score":0.12006736},"labels":[],"label_agreement":null},{"id":"W6924931275","doi":"10.15468/w8macu","title":"Taxonomic review of Hadromerida (Porifera, Demospongiae) from British Columbia, Canada, and adjacent waters, with the description of nine new species","year":2014,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biological classification; Taxonomy (biology); Single specimen","score_opus":0.01812903519772706,"score_gpt":0.17766226551887207,"score_spread":0.15953323032114503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924931275","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018424077,0.0019401592,0.0001231034,0.00013634806,0.00003151475,0.000034265133,0.99219185,0.00014999503,0.003550277],"genre_scores_gemma":[0.008996241,0.0030640538,0.0015113081,0.00010877746,0.000014087854,0.00023468542,0.982226,0.00010029404,0.0037445675],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892163,0.000056885514,0.000097494616,0.00019348314,0.00052791135,0.00020261125],"domain_scores_gemma":[0.9947024,0.00041415897,0.00043987334,0.00028259028,0.0036875764,0.0004733946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090991176,0.0012715347,0.0012871878,0.018232085,0.0029602186,0.00157084,0.0026794537,0.00061772496,0.019294918],"category_scores_gemma":[0.004428338,0.00047884058,0.00049766205,0.03150108,0.0007568579,0.00076151954,0.0017918787,0.0010975924,0.006842384],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008008953,0.000014015478,0.010646746,0.0044760867,0.00009365862,0.000105607636,0.00053623243,0.0003268206,0.00044469177,0.00074307236,0.9541121,0.02842081],"study_design_scores_gemma":[0.000027126807,0.0000051347824,0.059268147,0.0018717588,0.00009544048,0.00007077264,0.0007054115,0.00013443652,0.00026515947,0.0002826285,0.93723494,0.000039058286],"about_ca_topic_score_codex":0.9473903,"about_ca_topic_score_gemma":0.98221225,"teacher_disagreement_score":0.052609682,"about_ca_system_score_codex":0.014650089,"about_ca_system_score_gemma":0.040160265,"threshold_uncertainty_score":0.106294334},"labels":[],"label_agreement":null},{"id":"W6929249466","doi":"10.48550/arxiv.0812.3741","title":"The ultracool eld dwarfs luminosity function from the Canada-France Brown Dwarf Survey","year":2008,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Brown dwarf; Luminosity function; Luminosity; Function (biology); Stellar classification; Massive compact halo object","score_opus":0.09236482045271283,"score_gpt":0.191574342696486,"score_spread":0.09920952224377316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929249466","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":[0.8463423,0.0026007057,0.0027116651,0.0010337111,0.00005192136,0.000111252964,0.12217841,0.0005284702,0.024441468],"genre_scores_gemma":[0.89219695,0.00096495,0.0031120523,0.00018902835,0.000038750066,0.00007358353,0.085552864,0.000120233504,0.01775164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99950457,0.000060212642,0.00001154495,0.00007200725,0.0002218919,0.00012976519],"domain_scores_gemma":[0.9984157,0.00008499616,0.0001655143,0.00009488628,0.0008758174,0.0003629712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059660646,0.00033953105,0.00030487275,0.0030161666,0.000871704,0.00074837054,0.0004013123,0.00018722868,0.0028133593],"category_scores_gemma":[0.0013594228,0.00010970497,0.0002337669,0.0027394616,0.00027324658,0.00020928639,0.0004767951,0.00026848746,0.0007049604],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001737845,0.000032157815,0.8928002,0.00006432916,0.00008915221,0.00015404545,0.00069548807,0.0013338118,0.0019010406,0.002632054,0.064196415,0.03592756],"study_design_scores_gemma":[0.000007276402,0.0000062396743,0.97566706,0.000011393822,0.0000079766405,0.00004906518,0.00014801869,0.00059405825,0.0002628509,0.00012131617,0.023116382,0.000008343852],"about_ca_topic_score_codex":0.92305374,"about_ca_topic_score_gemma":0.9364952,"teacher_disagreement_score":0.07694626,"about_ca_system_score_codex":0.006033085,"about_ca_system_score_gemma":0.005217373,"threshold_uncertainty_score":0.15479875},"labels":[],"label_agreement":null},{"id":"W6929283872","doi":"10.48550/arxiv.1804.01933","title":"Revival of the magnetar PSR J1622-4950: observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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; McGill University","funders":"","keywords":"Magnetar; Pulsar; Neutron star; Flux (metallurgy); Magnetosphere; Millisecond pulsar; Radio telescope","score_opus":0.1424990721731812,"score_gpt":0.18670360589580143,"score_spread":0.04420453372262023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929283872","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":[0.9983772,0.0003119555,0.00014942515,0.000052706993,0.000017016677,0.000008120335,0.00018563958,0.000024500907,0.000873421],"genre_scores_gemma":[0.99874747,0.00014085682,0.00018299709,0.000053289736,0.000025784031,0.0000067305746,0.0005018546,0.0000106863745,0.00033030988],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998405,0.000014254376,0.000011801322,0.000047600082,0.000046232697,0.000039626575],"domain_scores_gemma":[0.99950385,0.000029490406,0.00018271542,0.00006531885,0.00006881953,0.00014983937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034097602,0.0002562521,0.00031278576,0.0006885483,0.0004461193,0.0007678271,0.00037860742,0.00035036425,0.00057857326],"category_scores_gemma":[0.00061053585,0.0002295008,0.00016335765,0.00045176514,0.0005073558,0.00047202894,0.0008592473,0.0005951682,0.0004217101],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047154003,0.00012012114,0.867307,0.000077544726,0.00016478931,0.0019302835,0.000972966,0.0002144297,0.10354526,0.0003272058,0.001636165,0.023232713],"study_design_scores_gemma":[0.0000114819695,0.000121514175,0.9945793,0.000010905001,0.00002195528,0.00063223304,0.00014746186,0.00018166058,0.0020460573,0.000045047418,0.002194155,0.000008239742],"about_ca_topic_score_codex":0.003904981,"about_ca_topic_score_gemma":0.009882575,"teacher_disagreement_score":0.003904981,"about_ca_system_score_codex":0.00042123065,"about_ca_system_score_gemma":0.00019349507,"threshold_uncertainty_score":0.0077644587},"labels":[],"label_agreement":null},{"id":"W6929485043","doi":"10.5063/aa/nceas.926.7","title":"Cross-system synthesis of consumer and nutrient resource control on producer biomass","year":2008,"lang":"en","type":"dataset","venue":"UC Santa Barbara","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Herbivore; Biomass (ecology); Nutrient; Ecosystem; Terrestrial ecosystem; Temperate climate; Habitat; Nutrient cycle; Resource (disambiguation)","score_opus":0.028190638311636044,"score_gpt":0.2776323701384205,"score_spread":0.24944173182678447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929485043","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56796145,0.16887653,0.22085853,0.0019148169,0.00083152816,0.0005949593,0.009036817,0.002793116,0.027132226],"genre_scores_gemma":[0.95845896,0.009577393,0.027725158,0.00041230916,0.00007540929,0.00026720564,0.0018221174,0.00016835085,0.0014930691],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979851,0.0008864858,0.00017191695,0.0006708197,0.00016122483,0.00012441105],"domain_scores_gemma":[0.99466777,0.0026929313,0.0007082526,0.0010276011,0.0006737466,0.00022976894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042129084,0.0012637122,0.0027004082,0.0019173119,0.00051435496,0.0022311555,0.00074917794,0.00052336074,0.0057260483],"category_scores_gemma":[0.0046619046,0.0006067083,0.003697324,0.0015741625,0.00088876265,0.0018551704,0.0017131324,0.000657575,0.0003714991],"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":[0.0031690765,0.00041757233,0.14690666,0.042924877,0.09484531,0.0009191889,0.0013966754,0.06706654,0.23731548,0.09910609,0.005327031,0.30060554],"study_design_scores_gemma":[0.00032654576,0.0031155036,0.590408,0.0020600413,0.08129186,0.0004467812,0.0010903798,0.10448142,0.03376924,0.12681623,0.055613413,0.00058057887],"about_ca_topic_score_codex":0.005678967,"about_ca_topic_score_gemma":0.00574423,"teacher_disagreement_score":0.0057260483,"about_ca_system_score_codex":0.0019747308,"about_ca_system_score_gemma":0.0015344905,"threshold_uncertainty_score":0.022280216},"labels":[],"label_agreement":null},{"id":"W6930212325","doi":"10.5281/zenodo.12156813","title":"testgorilla questions and answers pdf download free","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Sample (material); Download; Test (biology); Situational ethics; Variety (cybernetics); Aptitude; Questions and answers; Personality; Cognition; Need for cognition","score_opus":0.037319905862329784,"score_gpt":0.26126312597602697,"score_spread":0.2239432201136972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930212325","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008154076,0.0004904276,0.0013667548,0.0039641284,0.0035327328,0.00066350744,0.007971263,0.0075547346,0.97364104],"genre_scores_gemma":[0.0029335958,0.00029711565,0.00080407894,0.00215706,0.000461136,0.00045258415,0.003954825,0.0022996862,0.9866399],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99888784,0.0001284802,0.000055585813,0.000100218145,0.00068251626,0.0001452694],"domain_scores_gemma":[0.9960978,0.0006725759,0.00011579236,0.00034262464,0.0019752767,0.00079589646],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007989088,0.0012616016,0.0012544983,0.0013262243,0.0014756739,0.0039736624,0.0018274366,0.0024930073,0.9237308],"category_scores_gemma":[0.009506031,0.00044941803,0.0008348023,0.0013031313,0.00068542437,0.0044731637,0.0031866464,0.0024519733,0.84400815],"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":[0.000037132813,0.00003407882,0.000027163813,0.00010221068,7.555779e-7,0.000023644034,0.000028996195,0.00001613366,0.0001428794,0.0005449069,0.9771689,0.021873238],"study_design_scores_gemma":[0.000023283555,0.00003566876,0.00032729981,0.0000715369,0.0000018132256,0.000031971806,0.00011495739,0.000038579783,0.00018284337,0.0007604751,0.9983997,0.000011928502],"about_ca_topic_score_codex":0.0023040222,"about_ca_topic_score_gemma":0.005242834,"teacher_disagreement_score":0.07626921,"about_ca_system_score_codex":0.001491491,"about_ca_system_score_gemma":0.0013895789,"threshold_uncertainty_score":0.10878879},"labels":[],"label_agreement":null},{"id":"W6930406793","doi":"10.5281/zenodo.11901266","title":"Sae j1128 standard pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Nucleofection; Fusible alloy; Oscillograph; Troubleshooting; Noise (video); Ferroresonance in electricity networks","score_opus":0.05006425337693396,"score_gpt":0.27610480642195745,"score_spread":0.2260405530450235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930406793","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012355455,0.0006290976,0.0062420215,0.0009535562,0.0026194605,0.0003910794,0.009189113,0.0043248273,0.9744152],"genre_scores_gemma":[0.0042898622,0.000707105,0.002395442,0.00040217265,0.00019434803,0.00009374576,0.0082510365,0.001604908,0.98206145],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99730885,0.00011285909,0.00009486673,0.00013739004,0.002118906,0.00022709151],"domain_scores_gemma":[0.996236,0.00012438076,0.00006530066,0.00036853593,0.0029945818,0.00021127393],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001875325,0.0017032438,0.0009616535,0.002813541,0.0016533029,0.0056079966,0.0030797059,0.0037844686,0.6024992],"category_scores_gemma":[0.0037846917,0.0009238833,0.0013307692,0.0021341508,0.0006869671,0.003748491,0.0021276544,0.0021087083,0.63601434],"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":[0.000117625554,0.000060764323,0.0001340127,0.00024112397,0.000008210752,0.00006629885,0.00003310644,0.0005701401,0.0048620803,0.0046651075,0.9241558,0.06508572],"study_design_scores_gemma":[0.000011156785,0.000032914522,0.0002659861,0.00004821209,0.0000036955744,0.0000615959,0.000035467216,0.00020900775,0.002111242,0.00059873646,0.99660945,0.000012405457],"about_ca_topic_score_codex":0.009914395,"about_ca_topic_score_gemma":0.012026911,"teacher_disagreement_score":0.3975008,"about_ca_system_score_codex":0.0022167882,"about_ca_system_score_gemma":0.0025261287,"threshold_uncertainty_score":0.5669867},"labels":[],"label_agreement":null},{"id":"W6930537930","doi":"10.5281/zenodo.12154097","title":"Survival buch pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Adventure; Survival of the fittest; Test (biology); Compensation (psychology); Natural (archaeology)","score_opus":0.06635072047367374,"score_gpt":0.27966779152980276,"score_spread":0.21331707105612902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930537930","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004944428,0.0008634428,0.0018813893,0.0011970795,0.0023893213,0.00014294747,0.009608202,0.0074170884,0.97600603],"genre_scores_gemma":[0.0016751376,0.0007998233,0.0008334465,0.0005750279,0.00038437144,0.00005210944,0.0049236766,0.0022218935,0.9885345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99941564,0.000038048092,0.000032745622,0.00009328343,0.00033232835,0.000087995795],"domain_scores_gemma":[0.9979686,0.00023318303,0.00006871416,0.00026808376,0.0009819957,0.00047933465],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056710566,0.0013870959,0.0009009096,0.002371005,0.0017477493,0.0073324973,0.0017067549,0.0018208823,0.89405304],"category_scores_gemma":[0.0031247651,0.0007262352,0.0010243113,0.00193257,0.0007052755,0.0061371876,0.0033586018,0.0026883408,0.8397116],"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":[0.000049555194,0.000033735552,0.000059993024,0.00022299317,0.0000025544773,0.000068526075,0.00004763968,0.00007038936,0.0005671935,0.0026469936,0.9363128,0.059917666],"study_design_scores_gemma":[0.000007535018,0.000013307767,0.000166612,0.00005228324,0.000001934162,0.00007312267,0.000036895966,0.000033640135,0.0003584939,0.00058638083,0.9986626,0.0000070715982],"about_ca_topic_score_codex":0.0027715915,"about_ca_topic_score_gemma":0.0050294376,"teacher_disagreement_score":0.89405304,"about_ca_system_score_codex":0.0012148607,"about_ca_system_score_gemma":0.0016006784,"threshold_uncertainty_score":0.15112048},"labels":[],"label_agreement":null},{"id":"W6930948137","doi":"10.5281/zenodo.15801434","title":"Parapterogramma simplex Kuwahara, Marshall & Luk, 2025, sp. nov.","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Peck (Imperial); Holotype; Seta; San Joaquin; Cobble","score_opus":0.07037509681330205,"score_gpt":0.2953660464999173,"score_spread":0.22499094968661526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930948137","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":[0.5708137,0.04807792,0.012456598,0.0020928336,0.0028060938,0.0031980742,0.022679033,0.0017495462,0.33612618],"genre_scores_gemma":[0.92605215,0.017914023,0.012604099,0.0014096698,0.00033908643,0.0010052617,0.006724988,0.00012966908,0.03382101],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99979204,0.000017862038,0.000040858704,0.00006774174,0.000047143756,0.000034279565],"domain_scores_gemma":[0.9997507,0.000039087372,0.000098755474,0.000032037,0.000050583007,0.000028885635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015127486,0.001100528,0.0005134088,0.002731076,0.0028064956,0.00086883036,0.0006627884,0.0010794171,0.011249413],"category_scores_gemma":[0.0007102584,0.00042264786,0.00032617446,0.0018963801,0.0008778534,0.0026433594,0.0008395747,0.001008027,0.0051716473],"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":[0.0011292484,0.00030566,0.054773826,0.0023286354,0.00030809993,0.006801316,0.00405408,0.0013493878,0.032226082,0.0041863183,0.052520357,0.8400171],"study_design_scores_gemma":[0.00039204737,0.00042393862,0.49260247,0.002258615,0.00061094516,0.03534418,0.0077050575,0.0016833694,0.0057381494,0.0026351295,0.45043516,0.00017089477],"about_ca_topic_score_codex":0.016638648,"about_ca_topic_score_gemma":0.029089002,"teacher_disagreement_score":0.016638648,"about_ca_system_score_codex":0.0011542651,"about_ca_system_score_gemma":0.00085591327,"threshold_uncertainty_score":0.03763306},"labels":[],"label_agreement":null},{"id":"W6931020704","doi":"10.5281/zenodo.3451488","title":"abagen: A toolbox for the Allen Brain Atlas genetics data","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Toolbox; Python (programming language); Workflow; Atlas (anatomy); Neuroinformatics; Brain atlas","score_opus":0.24701488905633975,"score_gpt":0.3539154210669616,"score_spread":0.10690053201062186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931020704","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001263109,0.00029427206,0.23695661,0.00029284484,0.00030106385,0.00022515462,0.27780023,0.46824583,0.014620873],"genre_scores_gemma":[0.01131799,0.00048191127,0.30881548,0.00094638683,0.0001078652,0.0025055315,0.37824735,0.27580142,0.021775972],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998348,0.00022370643,0.00014489694,0.00048196327,0.00063946896,0.00016209639],"domain_scores_gemma":[0.99789786,0.0007248691,0.0001954769,0.00057928695,0.00042720765,0.00017535409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029916626,0.0024365264,0.0016833552,0.003714929,0.0014679903,0.0033735957,0.0046089417,0.0011850134,0.14305413],"category_scores_gemma":[0.006399408,0.0021215999,0.00216447,0.0028495085,0.000855013,0.002780256,0.0035674688,0.0031314415,0.1342952],"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":[0.00027352798,0.0000614704,0.0013148277,0.0011101379,0.00017099942,0.00021185038,0.00025752836,0.0026379644,0.013552633,0.00824638,0.90779555,0.064367026],"study_design_scores_gemma":[0.00023545216,0.000050190545,0.003739975,0.00027665947,0.0000963601,0.000495056,0.00008896152,0.01831075,0.026654728,0.04477107,0.9049917,0.0002890526],"about_ca_topic_score_codex":0.00436905,"about_ca_topic_score_gemma":0.01003965,"teacher_disagreement_score":0.14305413,"about_ca_system_score_codex":0.0012009506,"about_ca_system_score_gemma":0.002452279,"threshold_uncertainty_score":0.47856373},"labels":[],"label_agreement":null},{"id":"W6931048503","doi":"10.5281/zenodo.4683343","title":"COVID-19 among rural peoples in the Peruvian Amazon: Policy brief","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Indigenous; Peasant; Mortality rate; Rural area; Cash; Social policy; Health policy; Food security","score_opus":0.06120801363324729,"score_gpt":0.2992339224318116,"score_spread":0.23802590879856433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931048503","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040389996,0.027542349,0.0017439452,0.8945838,0.0052168462,0.0028680468,0.0070878565,0.00018387902,0.020383302],"genre_scores_gemma":[0.32460204,0.11682587,0.017788218,0.42373723,0.010088025,0.025997875,0.01024604,0.00012882923,0.070585944],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99383026,0.0027288164,0.00060627674,0.0002852289,0.0008386542,0.001710807],"domain_scores_gemma":[0.97752863,0.012403022,0.0026930096,0.0002769834,0.003051661,0.0040466283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014812424,0.00074392295,0.00073208567,0.0020543272,0.0035349221,0.0055678496,0.0019885048,0.010451707,0.014631996],"category_scores_gemma":[0.024978913,0.0008812694,0.00060415256,0.0022896065,0.00115347,0.0041278517,0.004779228,0.005594003,0.0015425803],"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":[0.000906543,0.001435608,0.05046561,0.024288649,0.0001507281,0.0051515615,0.018321231,0.0022031043,0.0049275244,0.034516513,0.624299,0.23333395],"study_design_scores_gemma":[0.00021792874,0.0012267819,0.08657576,0.010472533,0.00015942061,0.001112767,0.04013206,0.0015375939,0.0013243882,0.0068405448,0.8501923,0.00020773539],"about_ca_topic_score_codex":0.042678703,"about_ca_topic_score_gemma":0.0507312,"teacher_disagreement_score":0.042678703,"about_ca_system_score_codex":0.0069514243,"about_ca_system_score_gemma":0.03422291,"threshold_uncertainty_score":0.08486056},"labels":[],"label_agreement":null},{"id":"W6931119628","doi":"10.5281/zenodo.15485899","title":"Dataset for Mineral heterogeneity dominates the occurrence of shear failure under direct tension in Eastern Himalaya gneiss","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Gneiss; Shear (geology); Tension (geology); Shear zone; Mineral","score_opus":0.06542018187248319,"score_gpt":0.3070418245637968,"score_spread":0.24162164269131362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931119628","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028114854,0.00008566511,0.0001015541,0.000037621023,0.000016677932,0.000008407187,0.9961952,0.00026011563,0.00048328628],"genre_scores_gemma":[0.0035977005,0.000043836757,0.00028655468,0.000017006378,0.000005089245,0.000031220483,0.9955584,0.00002401905,0.00043613938],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996222,0.00004588678,0.00003949894,0.00011486119,0.000097346994,0.00008025213],"domain_scores_gemma":[0.9990816,0.00020092585,0.000118469514,0.00022301631,0.00027851557,0.0000973752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048686526,0.0013889935,0.00076686003,0.001892633,0.0005502638,0.0008650878,0.001531628,0.0013093649,0.01367055],"category_scores_gemma":[0.0017045886,0.00026592636,0.00090867275,0.0026023386,0.00036933552,0.00038293935,0.0010365162,0.0006851397,0.014063648],"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":[0.00037582513,0.00013016142,0.022876918,0.0022240216,0.00029782485,0.00024949547,0.00018530463,0.0038625423,0.0020074404,0.00071380415,0.9523088,0.014767893],"study_design_scores_gemma":[0.0003554904,0.00008550936,0.24303284,0.00056538533,0.00021904823,0.00029822625,0.0006301212,0.0037609502,0.002425011,0.0021650607,0.74633265,0.00012969592],"about_ca_topic_score_codex":0.04136224,"about_ca_topic_score_gemma":0.082878806,"teacher_disagreement_score":0.04136224,"about_ca_system_score_codex":0.0008612325,"about_ca_system_score_gemma":0.0012576244,"threshold_uncertainty_score":0.082242966},"labels":[],"label_agreement":null},{"id":"W6931250039","doi":"10.5281/zenodo.4454915","title":"Tipula (Vestiplex) balioptera Loew","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Period (music); Range (aeronautics); Elevation (ballistics); Altitude (triangle)","score_opus":0.11098448957661287,"score_gpt":0.26689566856470875,"score_spread":0.15591117898809587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931250039","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4413442,0.023423828,0.005114248,0.0005439358,0.0003050546,0.0005284289,0.017111138,0.0014479884,0.5101812],"genre_scores_gemma":[0.94005543,0.0064497255,0.0048908154,0.000732859,0.00008463624,0.00023474335,0.009397307,0.00008551933,0.03806898],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99988973,0.00000854088,0.000009085081,0.00003849319,0.00003203464,0.000022076949],"domain_scores_gemma":[0.9999176,0.00001051927,0.000026706035,0.000007233272,0.000024874202,0.00001299995],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000099774064,0.00073947356,0.00025471265,0.0012321948,0.00077739824,0.00034931203,0.00053549383,0.0003005086,0.019386519],"category_scores_gemma":[0.00020705642,0.00018767516,0.00015334028,0.0008328735,0.00023747538,0.0007396762,0.0009049644,0.00036873776,0.006997922],"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":[0.00038265067,0.000114935865,0.06169299,0.001370617,0.0000844804,0.0018603104,0.0010337014,0.00046686202,0.04655105,0.001672823,0.04669558,0.838074],"study_design_scores_gemma":[0.00008821656,0.00032727735,0.61801964,0.0009889496,0.00012724062,0.0039846855,0.0012740061,0.000383139,0.005091305,0.00095704896,0.36871165,0.000046781854],"about_ca_topic_score_codex":0.0062771137,"about_ca_topic_score_gemma":0.0154465595,"teacher_disagreement_score":0.98061347,"about_ca_system_score_codex":0.00047357703,"about_ca_system_score_gemma":0.00016965259,"threshold_uncertainty_score":0.06485438},"labels":[],"label_agreement":null},{"id":"W6931488110","doi":"10.5282/o-bib/6091","title":"Engaging Users in Creating the Next Library Space","year":2024,"lang":"en","type":"article","venue":"Opin vísindi (Opin vísindi)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Steel Producers Association","funders":"","keywords":"Space (punctuation); Key (lock); The Internet; Perspective (graphical)","score_opus":0.06438337872106878,"score_gpt":0.2938053679690249,"score_spread":0.22942198924795612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931488110","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019463785,0.0022966966,0.046667777,0.031701192,0.010972063,0.00257631,0.0018425037,0.014298119,0.8701817],"genre_scores_gemma":[0.06840395,0.0019856356,0.028454253,0.004116354,0.002631728,0.001651615,0.0010584211,0.0039969506,0.88770115],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99163026,0.0034828768,0.00026165668,0.0006328409,0.0033682976,0.000624123],"domain_scores_gemma":[0.9502692,0.013424253,0.0018231407,0.0068812286,0.018059567,0.009542572],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006977883,0.00075113995,0.0007098963,0.0027313356,0.005449489,0.011551467,0.0019246653,0.002345395,0.43513107],"category_scores_gemma":[0.06457243,0.0005595112,0.0005913997,0.0026071686,0.002148981,0.0074270684,0.0101235425,0.0024428454,0.28165022],"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":[0.000110204404,0.00016411468,0.0009807005,0.00044761112,0.000009399679,0.00031242764,0.006040301,0.00006795138,0.0026357074,0.0029573538,0.6874714,0.29880282],"study_design_scores_gemma":[0.00001160607,0.00005131295,0.0011328727,0.00017156515,0.0000070013357,0.00019707216,0.00520309,0.00023137197,0.0009904767,0.0026266417,0.9893455,0.00003137386],"about_ca_topic_score_codex":0.001535687,"about_ca_topic_score_gemma":0.0054681725,"teacher_disagreement_score":0.43513107,"about_ca_system_score_codex":0.0009573979,"about_ca_system_score_gemma":0.0046790256,"threshold_uncertainty_score":0.805717},"labels":[],"label_agreement":null},{"id":"W6931569959","doi":"10.5281/zenodo.6363372","title":"Bibliometría","year":2022,"lang":"es","type":"book-chapter","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Christian ministry; Context (archaeology); Limiting","score_opus":0.11285720119569842,"score_gpt":0.29179529555810424,"score_spread":0.17893809436240582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931569959","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055578,0.006802481,0.05262384,0.0033480336,0.0022793924,0.0007848024,0.075141944,0.010485515,0.84297615],"genre_scores_gemma":[0.14713566,0.021226523,0.12648053,0.002673655,0.0021039706,0.002380663,0.13530193,0.0071161063,0.55558103],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9891887,0.0018274587,0.0013742398,0.0017929296,0.005346276,0.00047039223],"domain_scores_gemma":[0.98693764,0.0025093257,0.0009946909,0.0026277364,0.0065378663,0.00039264924],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003476811,0.0015269694,0.0016223715,0.018778205,0.002798443,0.013716545,0.0025957897,0.0020004457,0.21791947],"category_scores_gemma":[0.02360124,0.00082992326,0.0014231015,0.034789562,0.0014245985,0.0062351814,0.0043664556,0.0018144664,0.123548076],"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":[0.00020311227,0.00007662193,0.0064119166,0.0029020624,0.00020175932,0.00024031941,0.0012917934,0.0029873757,0.000921165,0.16214435,0.37894702,0.44367254],"study_design_scores_gemma":[0.000014629342,0.000014444268,0.002116835,0.00031511762,0.00003133485,0.000168542,0.00038370973,0.0007650807,0.00047936148,0.012848157,0.98282605,0.000036677848],"about_ca_topic_score_codex":0.0145126255,"about_ca_topic_score_gemma":0.011547842,"teacher_disagreement_score":0.9812218,"about_ca_system_score_codex":0.0042610886,"about_ca_system_score_gemma":0.007655576,"threshold_uncertainty_score":0.7290132},"labels":[],"label_agreement":null},{"id":"W6931738616","doi":"10.5683/sp3/fihlnk","title":"Enquête sur la population active, février 2010 [Canada] [Remanié 2023]","year":2023,"lang":"fr","type":"dataset","venue":"Borealis","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Population; Research methodology; Publics; Social interest","score_opus":0.03818044616870979,"score_gpt":0.28571443436811433,"score_spread":0.24753398819940453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931738616","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01019416,0.004715319,0.001051576,0.004828735,0.00073376426,0.00017905944,0.9472377,0.00032538644,0.030734282],"genre_scores_gemma":[0.091210105,0.012113816,0.0045177313,0.004590337,0.00037257085,0.00077891856,0.8059652,0.00023667289,0.080214635],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983962,0.000086136584,0.000102123144,0.00019796124,0.00090513204,0.0003123639],"domain_scores_gemma":[0.99275243,0.00015811091,0.00020778867,0.00009831215,0.006144911,0.0006383487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019872605,0.0007962191,0.0006500212,0.003279627,0.0018031234,0.002624169,0.0015680133,0.0007173274,0.009709563],"category_scores_gemma":[0.0053856247,0.0004244739,0.00083468336,0.008676514,0.00038304066,0.00064843654,0.0011053187,0.0015807503,0.0032042828],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017906891,0.000028461078,0.06104217,0.00088400225,0.0001746721,0.00007121321,0.0004978249,0.0011327525,0.00017862888,0.0022591704,0.8937849,0.039767087],"study_design_scores_gemma":[0.00008080344,0.00004040886,0.43425983,0.0007348426,0.00013534894,0.00004771565,0.0010616358,0.0011592371,0.00037347205,0.00034833807,0.56167907,0.000079259604],"about_ca_topic_score_codex":0.9959239,"about_ca_topic_score_gemma":0.99592906,"teacher_disagreement_score":0.03654032,"about_ca_system_score_codex":0.03654032,"about_ca_system_score_gemma":0.07217138,"threshold_uncertainty_score":0.2651198},"labels":[],"label_agreement":null},{"id":"W6931745144","doi":"10.5281/zenodo.7259921","title":"btllib JOSS release","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Software release life cycle; Software; Computer software; Materials testing","score_opus":0.0548225478868463,"score_gpt":0.2625519367079769,"score_spread":0.2077293888211306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931745144","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009904003,0.00062571914,0.044105686,0.0007622231,0.00086463045,0.00025524703,0.21675725,0.6508161,0.08482273],"genre_scores_gemma":[0.00491874,0.0004478017,0.027788842,0.0008435779,0.00023296587,0.00046820988,0.2984116,0.57029915,0.09658913],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997835,0.00022732053,0.0001950456,0.00037109168,0.0011287136,0.00024278845],"domain_scores_gemma":[0.99472976,0.0011177493,0.00033270573,0.0010490654,0.0022332612,0.00053740735],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028388237,0.0025518043,0.0017374188,0.0034026692,0.0011521506,0.0047323876,0.003458194,0.0015471802,0.48906636],"category_scores_gemma":[0.010933455,0.0022722518,0.0018828121,0.0030884424,0.000572656,0.0041225725,0.00410524,0.0029077122,0.5574138],"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":[0.00016675123,0.00002869558,0.00023329732,0.00038457144,0.000030950716,0.00004669316,0.000066303626,0.00020132845,0.0011730003,0.0011119563,0.96740437,0.029152025],"study_design_scores_gemma":[0.00014490844,0.000036728816,0.0012706522,0.00017852076,0.00003885571,0.00013760131,0.000041073523,0.0013461744,0.002842117,0.0038137732,0.990051,0.00009856042],"about_ca_topic_score_codex":0.0108000375,"about_ca_topic_score_gemma":0.010251742,"teacher_disagreement_score":0.48906636,"about_ca_system_score_codex":0.0010709633,"about_ca_system_score_gemma":0.001998634,"threshold_uncertainty_score":0.7287848},"labels":[],"label_agreement":null},{"id":"W6931833494","doi":"10.5281/zenodo.7544675","title":"Supplementary Materials for \"High-Performance and Scalable Agent-Based Simulation with BioDynaMo\"","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Scalability; Modular design; Artifact (error); Series (stratigraphy); Data structure; Suite","score_opus":0.045149520581836876,"score_gpt":0.2650692280039437,"score_spread":0.21991970742210684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931833494","genre_codex":"dataset","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":[0.0009934086,0.00042934698,0.12092306,0.002683009,0.003553844,0.00068495667,0.63289934,0.1320644,0.10576859],"genre_scores_gemma":[0.01579545,0.0012672914,0.082085334,0.0034614727,0.001012494,0.002049521,0.6686235,0.101299405,0.1244055],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895847,0.000161073,0.00013732967,0.00015812638,0.00047774537,0.00010721418],"domain_scores_gemma":[0.99415284,0.0020753904,0.00024725197,0.001095463,0.0019082143,0.0005208943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012343369,0.0014350653,0.0014692183,0.0017025985,0.0009455479,0.003437439,0.0034446958,0.0020385105,0.79716814],"category_scores_gemma":[0.011475297,0.0011776446,0.0015068457,0.0031671547,0.00048334707,0.0031459462,0.002901804,0.0024116668,0.45607457],"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":[0.000057433015,0.00003596744,0.00021995211,0.000366926,0.000014937513,0.000043998814,0.000022824412,0.0016407394,0.0003266945,0.0058174315,0.97873026,0.012722826],"study_design_scores_gemma":[0.00022703914,0.000033732584,0.0007505666,0.0001898898,0.000018723693,0.00014618793,0.000041076575,0.011376517,0.0015359872,0.016338388,0.9692854,0.00005651979],"about_ca_topic_score_codex":0.005926758,"about_ca_topic_score_gemma":0.007755614,"teacher_disagreement_score":0.79716814,"about_ca_system_score_codex":0.0017194108,"about_ca_system_score_gemma":0.00213249,"threshold_uncertainty_score":0.289315},"labels":[],"label_agreement":null},{"id":"W6932008598","doi":"10.5281/zenodo.7621646","title":"Eugenia salamensis Donnell Smith, Bot. Gaz.","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Threatened species; Biome; Habitat; Tropical and subtropical dry broadleaf forests; Occupancy; Rainforest","score_opus":0.07589962547037567,"score_gpt":0.2673655260657243,"score_spread":0.19146590059534863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6932008598","genre_codex":"other","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":[0.08619962,0.02641741,0.0027255714,0.005828871,0.0022864542,0.00083103275,0.017750967,0.00050685613,0.8574532],"genre_scores_gemma":[0.5177362,0.022997754,0.006855351,0.0022558211,0.00055527413,0.0005601461,0.018442089,0.00010701215,0.43049043],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99991024,0.000010592024,0.000011501185,0.000028131417,0.00002516089,0.000014438353],"domain_scores_gemma":[0.9998952,0.000016159494,0.00003918185,0.000006417047,0.000030786527,0.000012176185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010060736,0.0005107204,0.0002120155,0.0015714832,0.0008560817,0.00039742084,0.00044063208,0.00049507635,0.033003252],"category_scores_gemma":[0.00027677865,0.0002063418,0.00012530453,0.0012058009,0.00035048235,0.0007447908,0.00071331457,0.0004814225,0.010290394],"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":[0.00025969394,0.00004996419,0.028249592,0.00083476416,0.00003153523,0.0023570103,0.0013704192,0.00036412574,0.0090648625,0.0049862233,0.15494059,0.7974912],"study_design_scores_gemma":[0.000035684017,0.00008288392,0.14983837,0.00045011297,0.000053813317,0.0016647632,0.0012669744,0.00022244458,0.0017365009,0.0010404527,0.8435915,0.000016412949],"about_ca_topic_score_codex":0.045261797,"about_ca_topic_score_gemma":0.09247522,"teacher_disagreement_score":0.045261797,"about_ca_system_score_codex":0.0013669053,"about_ca_system_score_gemma":0.00044814037,"threshold_uncertainty_score":0.110406816},"labels":[],"label_agreement":null},{"id":"W6932061463","doi":"10.5281/zenodo.6984340","title":"O'Connor et al. Species Portfolio Effects Dominate Seasonal Zooplankton Stabilization Within a Large Temperate Lake.","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Zooplankton; Temperate climate; Portfolio; Seasonality; Ecosystem","score_opus":0.032427824015569644,"score_gpt":0.2613252742305689,"score_spread":0.22889745021499924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6932061463","genre_codex":"dataset","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":[0.023427196,0.0013667301,0.053476606,0.0013194667,0.0016276248,0.0006110367,0.85553133,0.032517485,0.030122511],"genre_scores_gemma":[0.10274233,0.0012671724,0.1292511,0.0013967,0.00047938223,0.0021834474,0.6077494,0.05464711,0.1002833],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980399,0.00026325503,0.00012480351,0.00054061983,0.0008513016,0.000180222],"domain_scores_gemma":[0.99037373,0.004262299,0.00081982434,0.00155264,0.002086278,0.0009052173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004980856,0.0020654358,0.001579303,0.0019260909,0.0013084854,0.0016892554,0.0026854493,0.0009918946,0.17631479],"category_scores_gemma":[0.017797986,0.0014000469,0.0019891737,0.0031529344,0.00058769283,0.0023445082,0.0026569054,0.0011158244,0.04532345],"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":[0.0009754049,0.00007376754,0.016697241,0.0017544685,0.0006992444,0.0001078391,0.00018442736,0.0013982948,0.0058935457,0.0022156173,0.90669376,0.06330641],"study_design_scores_gemma":[0.001570822,0.00055365067,0.25719613,0.00062019826,0.0021201507,0.00046980797,0.0003057946,0.016310912,0.022100275,0.015689135,0.6827015,0.0003615553],"about_ca_topic_score_codex":0.039493877,"about_ca_topic_score_gemma":0.061061505,"teacher_disagreement_score":0.17631479,"about_ca_system_score_codex":0.0008695211,"about_ca_system_score_gemma":0.0031344602,"threshold_uncertainty_score":0.5898317},"labels":[],"label_agreement":null},{"id":"W6957641572","doi":"10.60692/49t8k-m7830","title":"Outcomes and Diagnostic Processes in Outpatients with Presumptive Tuberculosis in Zomba District, Malawi","year":2015,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Health Sciences Centre; Sunnybrook Health Science Centre; Public Health Ontario; University of Toronto","funders":"","keywords":"Tuberculosis; Sputum; Human immunodeficiency virus (HIV); Chronic cough; Incidence (geometry); Prospective cohort study; Epidemiology; Population","score_opus":0.06163956524321441,"score_gpt":0.24471547179429068,"score_spread":0.18307590655107628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957641572","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":[0.9994842,0.00014654647,0.000015602416,0.00007843364,0.0000027862538,0.00001163099,0.00016360762,0.0000011256087,0.00009611568],"genre_scores_gemma":[0.9996377,0.000092498274,0.000025546546,0.000026009026,0.0000046157534,0.000010286734,0.00015297407,5.607335e-7,0.000049819908],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921155,0.00019314546,0.00008441981,0.000093165516,0.000073999174,0.0003437337],"domain_scores_gemma":[0.99783605,0.00021515036,0.0012721558,0.000059195812,0.00017653772,0.00044095283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067703874,0.00030657582,0.00030691214,0.00067730335,0.0010011299,0.0011594451,0.0004464292,0.00040889566,0.000988032],"category_scores_gemma":[0.0038793026,0.0003267509,0.0003533505,0.000996991,0.000740465,0.00083969557,0.0014638834,0.0008651245,0.00013570065],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007975225,0.00004677893,0.9978669,0.000019616731,0.000011487714,0.00011910586,0.00078327756,0.000028242572,0.000078155346,0.000017026254,0.00008710233,0.0008625553],"study_design_scores_gemma":[0.0000068003346,0.00009979362,0.99801254,0.00002188465,0.000009888523,0.00013263608,0.0014455715,0.000114310984,0.000032929227,0.000012193543,0.000107253414,0.0000043445502],"about_ca_topic_score_codex":0.05569055,"about_ca_topic_score_gemma":0.062961794,"teacher_disagreement_score":0.05569055,"about_ca_system_score_codex":0.0019146978,"about_ca_system_score_gemma":0.0011893134,"threshold_uncertainty_score":0.110732794},"labels":[],"label_agreement":null},{"id":"W6957768948","doi":"10.60662/vzgb-4g98","title":"Exploration des processus pour l'évaluation des pertes de cadence","year":2023,"lang":"fr","type":"article","venue":"Collection numérique (UQTR)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Work (physics); Context (archaeology); Set (abstract data type); Term (time)","score_opus":0.14500231523000442,"score_gpt":0.34261513323230003,"score_spread":0.1976128180022956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957768948","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":[0.32596725,0.0025914279,0.6650025,0.00018366473,0.000119844124,0.0004095326,0.0006684687,0.0015753161,0.003482025],"genre_scores_gemma":[0.5934472,0.0013241172,0.3958526,0.00007418803,0.000045312754,0.0005840101,0.00049548957,0.00029124203,0.007885787],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9968836,0.0008392611,0.00012751768,0.0004340625,0.0015546646,0.00016098235],"domain_scores_gemma":[0.9954697,0.0028138743,0.00024332052,0.00026759767,0.0011137571,0.00009188255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039572846,0.0010352944,0.0013934996,0.0028687227,0.00078527187,0.0022028962,0.00086762215,0.0013633831,0.0050652656],"category_scores_gemma":[0.0068269228,0.00051169813,0.0011039375,0.002246391,0.00093191693,0.0014607232,0.000735949,0.0011543141,0.0011119483],"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":[0.0014893699,0.0003282046,0.005885401,0.0010846305,0.00010338226,0.000246682,0.0014918192,0.009760962,0.6326543,0.0030478465,0.00068684993,0.34322047],"study_design_scores_gemma":[0.00016579092,0.0024546748,0.072903626,0.00021337204,0.00027671971,0.0008375634,0.0012574258,0.23585293,0.662793,0.004020788,0.018984983,0.00023925745],"about_ca_topic_score_codex":0.006813931,"about_ca_topic_score_gemma":0.005920985,"teacher_disagreement_score":0.006813931,"about_ca_system_score_codex":0.0006690861,"about_ca_system_score_gemma":0.0014183923,"threshold_uncertainty_score":0.020928383},"labels":[],"label_agreement":null},{"id":"W6964826574","doi":"10.26077/q6nr-hp41","title":"Errata: Water Main Break Rates in the USA and Canada: A Comprehensive Study","year":2024,"lang":"en","type":"article","venue":"Digital Commons - USU (Utah State University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Section (typography); Home page; Front page; Title page; Page view","score_opus":0.037743918997717056,"score_gpt":0.24439926872585901,"score_spread":0.20665534972814195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964826574","genre_codex":"dataset","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":[0.014785899,0.012517278,0.0044877366,0.098328516,0.24820267,0.0015928952,0.5686785,0.0035450612,0.047861397],"genre_scores_gemma":[0.19854236,0.022506813,0.020082152,0.11402815,0.020554325,0.0046545696,0.32588726,0.0062658647,0.28747848],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9830618,0.0010211929,0.0039023987,0.0010666632,0.0097989235,0.0011489888],"domain_scores_gemma":[0.7831469,0.024927141,0.009610454,0.004569654,0.17541029,0.0023353936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008494013,0.0010329561,0.0012775535,0.008370323,0.0028662535,0.002767805,0.0031275486,0.0017621735,0.042741742],"category_scores_gemma":[0.1076044,0.00092022074,0.0028228317,0.015579369,0.001345852,0.0018734791,0.0018996784,0.003071758,0.014158708],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035459307,0.000012005645,0.0043424214,0.0005433162,0.000021273587,0.000032152926,0.000098894525,0.000056670622,0.00001912941,0.00021879257,0.985135,0.009484788],"study_design_scores_gemma":[0.00012352981,0.00005967977,0.16110347,0.0037260181,0.0002790077,0.00017914119,0.002370557,0.00037933828,0.00053950615,0.00065108,0.83043075,0.00015797715],"about_ca_topic_score_codex":0.85430455,"about_ca_topic_score_gemma":0.8295189,"teacher_disagreement_score":0.14569545,"about_ca_system_score_codex":0.018249987,"about_ca_system_score_gemma":0.04234166,"threshold_uncertainty_score":0.2931069},"labels":[],"label_agreement":null},{"id":"W6977001726","doi":"10.6084/m9.figshare.22623616","title":"Additional file 1 of Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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; University of Ottawa","funders":"","keywords":"Unhealthy food; Television advertising; Food standards; Healthy food","score_opus":0.03521925544768064,"score_gpt":0.23514751209639553,"score_spread":0.19992825664871489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977001726","genre_codex":"dataset","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":[0.0003243654,0.000008274236,0.00009284879,0.000050864328,0.000009782076,0.00009774546,0.9983297,0.000057517074,0.0010288212],"genre_scores_gemma":[0.014933063,0.00014389631,0.0024457995,0.00036637523,0.00009118292,0.0045472286,0.95982134,0.00033892103,0.017312203],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99935323,0.0001376352,0.000114734066,0.00013968043,0.0001503432,0.00010436757],"domain_scores_gemma":[0.98294246,0.011969939,0.0013808496,0.0008479203,0.0024127767,0.00044610113],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012519688,0.00091466197,0.0010477848,0.0020932567,0.0010879656,0.001087806,0.0017323821,0.00095240853,0.84888107],"category_scores_gemma":[0.024853228,0.0005206582,0.00075054384,0.004262193,0.00019230963,0.0015993912,0.0008912201,0.00089915126,0.124512896],"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":[0.00033817135,0.0001528125,0.004383129,0.0015640709,0.000036927973,0.000046756893,0.0001056463,0.00023200763,0.00004869604,0.00063350674,0.98503727,0.0074209846],"study_design_scores_gemma":[0.009735956,0.0008268959,0.16466895,0.006553858,0.00038876434,0.00070017506,0.0021267089,0.002807976,0.0009604884,0.010346026,0.80064726,0.0002369503],"about_ca_topic_score_codex":0.02145656,"about_ca_topic_score_gemma":0.02474571,"teacher_disagreement_score":0.84888107,"about_ca_system_score_codex":0.0009081222,"about_ca_system_score_gemma":0.0014689525,"threshold_uncertainty_score":0.2155528},"labels":[],"label_agreement":null},{"id":"W6977500025","doi":"10.6084/m9.figshare.24113125","title":"Additional file 6 of Characterizing Canadian funded partnered health research projects between 2011 and 2019: a retrospective analysis","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; University of Manitoba; University of British Columbia; George & Fay Yee Centre for Healthcare Innovation","funders":"","keywords":"General partnership; Field (mathematics); Health services research; MEDLINE; Public health; Health care; Government (linguistics)","score_opus":0.3292246927687673,"score_gpt":0.3868918625877487,"score_spread":0.0576671698189814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977500025","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013545975,0.000012966655,0.000070142545,0.000064358814,0.000008468342,0.00007088332,0.998572,0.000039033577,0.0010267446],"genre_scores_gemma":[0.012645523,0.00029841924,0.0032471153,0.0005980127,0.00007115012,0.0028371436,0.96141726,0.00030475255,0.01858059],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9974088,0.00036087437,0.0005411836,0.00034014418,0.00084498146,0.0005039711],"domain_scores_gemma":[0.95431834,0.02038601,0.0045712814,0.003045477,0.016031373,0.0016475496],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031944655,0.00070046476,0.00093385554,0.005903447,0.0018056106,0.0018622528,0.001719216,0.00090351695,0.66461766],"category_scores_gemma":[0.04706299,0.0005000994,0.0008559802,0.0124389995,0.00042964483,0.0015337975,0.0014285755,0.00091968814,0.053051077],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014131692,0.00002102675,0.004135735,0.0013171476,0.000029662937,0.000026389405,0.00007354583,0.00016098884,0.000022395907,0.00060843595,0.9877874,0.0056758886],"study_design_scores_gemma":[0.0012967467,0.00008474127,0.09523511,0.0060777115,0.00019614313,0.00022707997,0.0014372619,0.0006901394,0.00034440347,0.003459985,0.89080215,0.00014849057],"about_ca_topic_score_codex":0.45794144,"about_ca_topic_score_gemma":0.5111433,"teacher_disagreement_score":0.99680555,"about_ca_system_score_codex":0.006512884,"about_ca_system_score_gemma":0.018715579,"threshold_uncertainty_score":0.91055185},"labels":[],"label_agreement":null},{"id":"W6980977424","doi":"","title":"Des pare-soleil pour réduire l'énergie de refroidissement de votre maison","year":2009,"lang":"fr","type":"article","venue":"NPARC","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Window (computing); Die (integrated circuit); Limiting","score_opus":0.04797619722128266,"score_gpt":0.3152282141755699,"score_spread":0.26725201695428724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980977424","genre_codex":"empirical","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":[0.8912756,0.0024133902,0.08748335,0.00040353998,0.00047302834,0.00018890986,0.00033357312,0.0008925386,0.016536191],"genre_scores_gemma":[0.9324565,0.000841441,0.042265486,0.00022592887,0.000072549716,0.00011351407,0.00025291904,0.00037927512,0.02339226],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994061,0.00012335627,0.000024454012,0.0001453485,0.00022817189,0.00007268081],"domain_scores_gemma":[0.99904805,0.00043943143,0.00009811701,0.00009794162,0.00022002216,0.000096464195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000748146,0.0006041356,0.0005281528,0.00031245998,0.00039871508,0.00079985685,0.0006130937,0.0005492538,0.0132296495],"category_scores_gemma":[0.0018385707,0.00024287158,0.00043568874,0.00025804847,0.00045004545,0.0008263915,0.00056366995,0.0008044921,0.0016280162],"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":[0.0047328304,0.0004472108,0.0020415003,0.0005742328,0.0000612909,0.00012406573,0.00036467728,0.001181123,0.886352,0.0014694547,0.001031047,0.10162055],"study_design_scores_gemma":[0.00030036844,0.005059523,0.029016498,0.00010600426,0.00016071342,0.00066421705,0.00050420617,0.008955398,0.92528266,0.0009363858,0.02890998,0.00010404699],"about_ca_topic_score_codex":0.001853204,"about_ca_topic_score_gemma":0.0034050113,"teacher_disagreement_score":0.0132296495,"about_ca_system_score_codex":0.0004182491,"about_ca_system_score_gemma":0.00045998243,"threshold_uncertainty_score":0.04425752},"labels":[],"label_agreement":null},{"id":"W6985057046","doi":"","title":"19. August 1918","year":2018,"lang":"de","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Square (algebra); Subject (documents); Happening; Quarter (Canadian coin)","score_opus":0.04242820704798908,"score_gpt":0.29065321041680436,"score_spread":0.24822500336881528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6985057046","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061551677,0.0065056556,0.000832696,0.008195647,0.018975299,0.00039595042,0.015453679,0.00094599964,0.94253993],"genre_scores_gemma":[0.0061338223,0.00078629545,0.0001772682,0.0004616304,0.0006717907,0.000061060084,0.002502129,0.00019706602,0.98900896],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917287,0.00006502745,0.000039294722,0.00017574549,0.00035259087,0.00019442996],"domain_scores_gemma":[0.9991848,0.0000509077,0.000049001646,0.00006463043,0.00039692715,0.0002538527],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009796419,0.0009081033,0.0005411268,0.0020285915,0.0027363237,0.003664093,0.00086888537,0.0014679093,0.43162555],"category_scores_gemma":[0.0019854675,0.0003111087,0.0005430751,0.0008281498,0.0006706297,0.0012634114,0.0027408141,0.0018505281,0.24367915],"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":[0.00033164737,0.000060326267,0.0015170128,0.00024637222,0.000013805099,0.00037447357,0.00051954424,0.00007728189,0.0013145772,0.007962403,0.9146889,0.07289364],"study_design_scores_gemma":[0.000004847473,0.000021610802,0.0021560534,0.00006544557,0.0000014193289,0.00003706724,0.00009744017,0.000006902598,0.00010766191,0.00025667268,0.997241,0.000003943957],"about_ca_topic_score_codex":0.013926679,"about_ca_topic_score_gemma":0.05573977,"teacher_disagreement_score":0.56837445,"about_ca_system_score_codex":0.0028636204,"about_ca_system_score_gemma":0.0014554719,"threshold_uncertainty_score":0.81071717},"labels":[],"label_agreement":null},{"id":"W6987417859","doi":"","title":"Sucralosio un dolcificante artificiale","year":2012,"lang":"it","type":"article","venue":"CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Second line; Context (archaeology); Identity (music)","score_opus":0.1367380673650668,"score_gpt":0.31215544608888673,"score_spread":0.17541737872381993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987417859","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":[0.67927736,0.1524682,0.04183681,0.0053089852,0.0019499845,0.0007716001,0.0019740881,0.0022012307,0.11421176],"genre_scores_gemma":[0.80388284,0.062650144,0.031896587,0.0036343972,0.00053052057,0.00031758356,0.0020383827,0.00034738414,0.094702154],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994074,0.000084077095,0.00003951848,0.00012052704,0.0002844826,0.00006391883],"domain_scores_gemma":[0.99964404,0.00006130242,0.00009260884,0.00003279987,0.00010694116,0.00006237894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040586366,0.00048090305,0.0004889985,0.0006509812,0.00034774738,0.0013189757,0.00045596284,0.0008340274,0.00775134],"category_scores_gemma":[0.0007398179,0.00020492991,0.0007443864,0.0005577405,0.0005137804,0.0008515498,0.0007924625,0.0007565795,0.0025979325],"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":[0.0016075252,0.00044761112,0.005878586,0.0035882818,0.00018820877,0.0014880471,0.00045602594,0.00046267547,0.58359665,0.0038951228,0.009596217,0.38879502],"study_design_scores_gemma":[0.0002451614,0.0049124444,0.022734825,0.00089343154,0.00025811093,0.00696224,0.0006899237,0.002138903,0.33629504,0.0019666809,0.6227764,0.00012691387],"about_ca_topic_score_codex":0.0020885684,"about_ca_topic_score_gemma":0.0021016211,"teacher_disagreement_score":0.00775134,"about_ca_system_score_codex":0.0004761524,"about_ca_system_score_gemma":0.0005653557,"threshold_uncertainty_score":0.025930822},"labels":[],"label_agreement":null},{"id":"W6998547432","doi":"","title":"Apprentissage basé sur le Qini pour la prédiction de l’effet causal conditionnel","year":2021,"lang":"fr","type":"other","venue":"Open MIND","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Statistical analysis; Domain (mathematical analysis); Cultural environment","score_opus":0.0864111925891943,"score_gpt":0.3215508874465942,"score_spread":0.2351396948573999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998547432","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":[0.04194056,0.0012422827,0.95061696,0.00087155326,0.00013072566,0.00014550603,0.0005673984,0.0010703254,0.0034147287],"genre_scores_gemma":[0.63183486,0.00271924,0.346826,0.00052222074,0.0003190187,0.00076887687,0.0020800002,0.0007766684,0.014153203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973061,0.0012984386,0.00011178141,0.00051627937,0.0005332675,0.00023418113],"domain_scores_gemma":[0.9868988,0.01036618,0.000687086,0.0006945222,0.0011551947,0.00019822472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008560735,0.0021995374,0.0018811335,0.0015289839,0.00062037585,0.0023615977,0.0015564924,0.0012285752,0.007711043],"category_scores_gemma":[0.025562517,0.0010271814,0.0020662476,0.0012694418,0.0010358676,0.0018031389,0.0013853626,0.00326477,0.0016145986],"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":[0.0010889483,0.00029224704,0.029666895,0.001077225,0.0011175929,0.00044168005,0.0007881616,0.5934114,0.008488661,0.039389584,0.0081887515,0.31604892],"study_design_scores_gemma":[0.00006812914,0.0002517037,0.009090542,0.00017970757,0.00018048698,0.00012151462,0.00013927337,0.96118295,0.0032765062,0.019869596,0.0055541364,0.000085462794],"about_ca_topic_score_codex":0.020812951,"about_ca_topic_score_gemma":0.014814713,"teacher_disagreement_score":0.020812951,"about_ca_system_score_codex":0.0012354384,"about_ca_system_score_gemma":0.0029220227,"threshold_uncertainty_score":0.04527402},"labels":[],"label_agreement":null},{"id":"W7009543911","doi":"","title":"The effect of information on consumer perception of high pressure processed apple puree","year":2023,"lang":"en","type":"article","venue":"University of Southern Denmark Research Portal (University of Southern Denmark)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Nucleofection; Gestational period; Dysgeusia; TSG101; Liquation; Diafiltration; Triacetin; Emperipolesis; Fusible alloy","score_opus":0.02462146014754055,"score_gpt":0.25495951952895807,"score_spread":0.23033805938141752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009543911","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":[0.9981014,0.0001569271,0.00007646667,0.00005786966,0.000013776193,0.00001555562,0.00006126272,0.000004388112,0.0015122587],"genre_scores_gemma":[0.9973947,0.0002504732,0.0004264485,0.00011493392,0.000017451139,0.000014460769,0.00017148213,0.000017246477,0.0015928025],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99954045,0.00015707873,0.000031001422,0.00007188064,0.00014576243,0.000053888507],"domain_scores_gemma":[0.98887056,0.009257668,0.0006647404,0.0001503485,0.0006755376,0.0003810134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007646373,0.00034436252,0.00028310897,0.00028430455,0.00034765538,0.00156915,0.00018698694,0.0007819373,0.00786038],"category_scores_gemma":[0.0066892263,0.00029122815,0.00033239028,0.000269334,0.00031392937,0.0011136057,0.0005980916,0.0008679533,0.0003077736],"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":[0.120264225,0.005017039,0.064968795,0.0022463172,0.00048191805,0.0012458884,0.006149599,0.0005501675,0.752958,0.00073105365,0.0010046995,0.044382352],"study_design_scores_gemma":[0.0005547151,0.014322216,0.91954184,0.000206676,0.0006547862,0.00039552632,0.0032722934,0.0017087007,0.056697074,0.00047383516,0.0020641491,0.00010819339],"about_ca_topic_score_codex":0.0040957723,"about_ca_topic_score_gemma":0.002968913,"teacher_disagreement_score":0.00786038,"about_ca_system_score_codex":0.00037312222,"about_ca_system_score_gemma":0.00028793255,"threshold_uncertainty_score":0.026295602},"labels":[],"label_agreement":null},{"id":"W7015283520","doi":"","title":"Sensory testing of seafood - fresh versus frozen - and development of frozen seafood recipes&#13;\\nSensory testing – sub-component","year":2019,"lang":"en","type":"book","venue":"Queensland Department of Agriculture and Fisheries archive of scientific and research publications (Queensland Department of Agriculture and Fisheries)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Fish <Actinopterygii>; Product (mathematics); Significant difference; Quality (philosophy); Fisheries Research; Sensory analysis; Table (database); Congelation","score_opus":0.08611258801779893,"score_gpt":0.28121925487083166,"score_spread":0.19510666685303274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015283520","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":[0.54215974,0.0039652954,0.021118756,0.0009988397,0.00047959012,0.0028138089,0.008290222,0.0009315744,0.4192422],"genre_scores_gemma":[0.44958147,0.0049584378,0.041394975,0.00063090154,0.00006959512,0.0011021519,0.008850209,0.00043659846,0.49297565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966896,0.000051734583,0.000018962159,0.00002721161,0.00021440219,0.000018775949],"domain_scores_gemma":[0.9993272,0.00023356483,0.000028525095,0.000031683052,0.00034350998,0.000035563626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000523605,0.00029764813,0.00016496741,0.0007043351,0.00019169835,0.00055670564,0.00032245956,0.00018613841,0.022951767],"category_scores_gemma":[0.0008373054,0.00009982155,0.0002084927,0.00064728135,0.00017851828,0.00032077162,0.00042983697,0.00030792854,0.003704624],"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":[0.0012003564,0.00096995063,0.018036071,0.0014622991,0.000038312366,0.00034935458,0.0012387492,0.00078408147,0.13403416,0.0030983859,0.05821175,0.78057647],"study_design_scores_gemma":[0.00006555692,0.0047999183,0.44962996,0.0009778409,0.00010371438,0.0018325315,0.0037437044,0.0051468066,0.18445335,0.0039788643,0.34517065,0.00009708699],"about_ca_topic_score_codex":0.0033700143,"about_ca_topic_score_gemma":0.008044557,"teacher_disagreement_score":0.022951767,"about_ca_system_score_codex":0.00037525716,"about_ca_system_score_gemma":0.00036220453,"threshold_uncertainty_score":0.07678127},"labels":[],"label_agreement":null},{"id":"W7018403730","doi":"","title":"Deleuze and His Critics: Review of Gilles Deleuze and Film Criticism: Philosophy, Theory, and the Individual Film","year":2024,"lang":"en","type":"other","venue":"Human Biology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Film studies","score_opus":0.06012375931634507,"score_gpt":0.33047800649418285,"score_spread":0.2703542471778378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018403730","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":[0.00011930337,0.937136,0.00010078672,0.052009284,0.0040654466,0.000003926731,0.000012276567,0.000004876328,0.006548024],"genre_scores_gemma":[0.0112983845,0.93048406,0.00031120214,0.030251965,0.015520412,0.00003899649,0.00004472876,0.000049516835,0.012000762],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964232,0.001704865,0.00020672173,0.00031523436,0.0011552136,0.00019479233],"domain_scores_gemma":[0.9860211,0.009992749,0.0005963717,0.00028054556,0.0027020015,0.00040724347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069831586,0.0010314338,0.0015774169,0.0070867925,0.0037402764,0.0064167287,0.0021520434,0.00671881,0.010375263],"category_scores_gemma":[0.02389668,0.0005626839,0.00058125536,0.007571982,0.012517189,0.008786398,0.003265819,0.0074160253,0.002985804],"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":[0.000028179998,0.000023156814,0.00020007869,0.0019355756,0.000027300386,0.00015555641,0.0048254803,0.000094167706,0.000048848437,0.07652803,0.81214356,0.10398999],"study_design_scores_gemma":[0.000009069879,0.000010050084,0.0005886588,0.005881795,0.000011767556,0.00034322363,0.0016283722,0.000035293055,0.000039561917,0.013940058,0.97749317,0.000019005598],"about_ca_topic_score_codex":0.017081883,"about_ca_topic_score_gemma":0.024788313,"teacher_disagreement_score":0.017081883,"about_ca_system_score_codex":0.00624729,"about_ca_system_score_gemma":0.0051436354,"threshold_uncertainty_score":0.045327544},"labels":[],"label_agreement":null},{"id":"W7019313897","doi":"","title":"Exploratory analysis of relationships between blocks of variables of natures using optimal scaling : application in alimentation, epidemiology and health","year":2023,"lang":"fr","type":"article","venue":"theses.fr (ABES)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval; Mitacs","keywords":"Multidimensional scaling; Exploratory analysis; Point (geometry); Exploratory data analysis; Principal component analysis","score_opus":0.29769304051668777,"score_gpt":0.42378846925308183,"score_spread":0.12609542873639407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019313897","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":[0.039420813,0.0012317883,0.9556867,0.00040843597,0.00006869334,0.0002301956,0.0005982741,0.00061102514,0.0017440799],"genre_scores_gemma":[0.2733325,0.0011669792,0.72176117,0.000115227136,0.000094586954,0.00071691227,0.001037813,0.0002826601,0.0014920824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546814,0.0025400887,0.00026729325,0.00075809646,0.000764969,0.0002014436],"domain_scores_gemma":[0.9858994,0.01069627,0.0010028981,0.0010410763,0.0011529034,0.00020745868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054631284,0.001455011,0.0016104548,0.004183265,0.000957714,0.0024007484,0.00087336934,0.0006879148,0.00471847],"category_scores_gemma":[0.019483643,0.0006321679,0.0028166133,0.004814973,0.0014983809,0.0018175098,0.0020266608,0.0012654298,0.00065538706],"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":[0.00082880666,0.00036122225,0.047946263,0.0025450052,0.001361308,0.00063799816,0.00391573,0.09201896,0.01637906,0.07442385,0.006232368,0.75334936],"study_design_scores_gemma":[0.0001074666,0.0005565299,0.065497264,0.00064477656,0.0004890234,0.00072519726,0.0031740814,0.6239582,0.008360454,0.2672537,0.028944714,0.0002885439],"about_ca_topic_score_codex":0.0039088316,"about_ca_topic_score_gemma":0.0032470606,"teacher_disagreement_score":0.0054631284,"about_ca_system_score_codex":0.0006802059,"about_ca_system_score_gemma":0.0021044712,"threshold_uncertainty_score":0.0288921},"labels":[],"label_agreement":null},{"id":"W7027350039","doi":"","title":"Change-Point Detection in Business Cycles using Machine Learning Algorithms","year":2022,"lang":"en","type":"dissertation","venue":"Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Feature selection; Recession; Classifier (UML); Robustness (evolution); Binary classification; Time series; Business cycle; Model selection; Ensemble learning","score_opus":0.023876179936535503,"score_gpt":0.2901220663441224,"score_spread":0.2662458864075869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027350039","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":[0.15196063,0.0025976177,0.8395003,0.00058054534,0.000107041415,0.0001288826,0.00044634886,0.0015328055,0.0031457713],"genre_scores_gemma":[0.71969795,0.00090029987,0.27610856,0.0001077129,0.000116403455,0.00015786588,0.0009907787,0.000086906555,0.001833494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992349,0.00021262208,0.00007440129,0.00022404913,0.00017547,0.000078477446],"domain_scores_gemma":[0.9979486,0.0013370209,0.00022361244,0.00010618881,0.00032545318,0.000059073052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016863196,0.0007962646,0.0009956193,0.0026376005,0.000427446,0.0014509679,0.00068620313,0.0007573561,0.000833328],"category_scores_gemma":[0.0051598535,0.0003408577,0.0009295873,0.0022262458,0.0003479019,0.0010346703,0.000600131,0.0013154311,0.00045045317],"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":[0.00012560819,0.00020583294,0.016496593,0.00011231647,0.00020427788,0.0000815996,0.00012483649,0.42693937,0.0028603412,0.003145408,0.0023456754,0.54735816],"study_design_scores_gemma":[0.0000024485416,0.00001334892,0.002060163,0.0000099071885,0.000006881482,0.000010873751,0.000021211583,0.99497,0.00050390384,0.0020744796,0.00032083303,0.0000059942354],"about_ca_topic_score_codex":0.004899111,"about_ca_topic_score_gemma":0.0037168337,"teacher_disagreement_score":0.004899111,"about_ca_system_score_codex":0.00073747314,"about_ca_system_score_gemma":0.00060051895,"threshold_uncertainty_score":0.009741187},"labels":[],"label_agreement":null},{"id":"W7027510474","doi":"","title":"Correspondence analysis and two-way clustering","year":2005,"lang":"en","type":"article","venue":"RIUR (Universidad de La Rioja)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"McGill University; Statistics Canada","funders":"","keywords":"Cluster analysis; Correspondence analysis; Pattern recognition (psychology); Representation (politics); Multiple correspondence analysis; Row; Data Matrix; Matrix (chemical analysis); Principal component analysis","score_opus":0.017114936270001154,"score_gpt":0.2842128004897196,"score_spread":0.26709786421971843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027510474","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":[0.0020503728,0.00016913179,0.9962291,0.00010832046,0.00005541011,0.00009316859,0.0000970982,0.00034562396,0.00085164857],"genre_scores_gemma":[0.06882216,0.00027403343,0.92717105,0.0001046628,0.00012002052,0.00081060734,0.0005569448,0.00024557428,0.0018949086],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97622335,0.011309407,0.0012361093,0.005426887,0.0051537976,0.000650463],"domain_scores_gemma":[0.9777735,0.013229108,0.0017395621,0.003267858,0.003599999,0.00039011915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0114870295,0.0019992553,0.00300228,0.007855543,0.0031353661,0.006096415,0.003278131,0.002743983,0.0058503044],"category_scores_gemma":[0.03658048,0.0011399693,0.0033291597,0.009653435,0.0044056354,0.004098765,0.0041316543,0.0030876643,0.0024557458],"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":[0.00048058009,0.00028217677,0.0044420036,0.0011781403,0.0010571104,0.00035411568,0.0018800423,0.1936408,0.006003154,0.3518194,0.008248758,0.4306137],"study_design_scores_gemma":[0.00006027281,0.0001467222,0.0029025727,0.00009739564,0.00010134443,0.00037162213,0.00046997896,0.6204179,0.0047753192,0.35279238,0.017681636,0.00018289291],"about_ca_topic_score_codex":0.0033088229,"about_ca_topic_score_gemma":0.0028411746,"teacher_disagreement_score":0.0114870295,"about_ca_system_score_codex":0.002442083,"about_ca_system_score_gemma":0.0032563442,"threshold_uncertainty_score":0.06074989},"labels":[],"label_agreement":null},{"id":"W7036635204","doi":"","title":"Correspondence analysis approach to examine the Nobel Prize","year":2019,"lang":"en","type":"article","venue":"NOVA (University of Newcastle Australia)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Correspondence analysis; Contingency table; Sample (material); Period (music); Contingency; Multiple correspondence analysis","score_opus":0.19921204439529544,"score_gpt":0.30146594162655554,"score_spread":0.1022538972312601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036635204","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":[0.41115338,0.0011299878,0.4797292,0.0011320959,0.0011157738,0.0042851456,0.015575533,0.0013280542,0.08455091],"genre_scores_gemma":[0.82009184,0.00041008223,0.14867696,0.00023206134,0.00032440174,0.007809051,0.005444748,0.00039815428,0.016612735],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.96881795,0.017735686,0.001563126,0.003998848,0.006605582,0.0012788584],"domain_scores_gemma":[0.92212653,0.059922516,0.005508805,0.004766055,0.0069376254,0.0007384493],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.015617803,0.0010973467,0.0016815253,0.012290623,0.0024281032,0.0036349797,0.002077919,0.0011131337,0.027107226],"category_scores_gemma":[0.08093371,0.00039564457,0.0017372866,0.018154055,0.001391203,0.0024083012,0.0030766784,0.0022331637,0.0046498966],"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":[0.0026859231,0.0017438218,0.29560176,0.0017375111,0.0031739944,0.001341764,0.012146621,0.012072949,0.0031430025,0.19651903,0.03517882,0.4346549],"study_design_scores_gemma":[0.0002451458,0.0038654935,0.41144022,0.0007690216,0.0012174292,0.0019681319,0.034007236,0.16810915,0.0068631065,0.19648103,0.17455682,0.00047721816],"about_ca_topic_score_codex":0.0036110173,"about_ca_topic_score_gemma":0.002957488,"teacher_disagreement_score":0.9877094,"about_ca_system_score_codex":0.0017335998,"about_ca_system_score_gemma":0.0027659475,"threshold_uncertainty_score":0.090682745},"labels":[],"label_agreement":null},{"id":"W7036695899","doi":"","title":"Characteristics of soy bread users and their beliefs about soy products","year":2002,"lang":"en","type":"article","venue":"Figshare","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Health benefits; Soy milk; Telephone survey; Health claims on food labels; Soy protein; Quarter (Canadian coin); Soy flour","score_opus":0.08270243021117996,"score_gpt":0.24986513239418068,"score_spread":0.16716270218300072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036695899","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":[0.9996276,0.00003623972,0.000010494659,0.000028473622,0.0000011409321,0.0000022767615,0.000068203386,6.9996855e-7,0.00022491402],"genre_scores_gemma":[0.99960274,0.000036970236,0.000019734483,0.000022297148,0.000002175171,0.0000033366382,0.00006452891,5.2358865e-7,0.0002477017],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998311,0.000050638962,0.000017576845,0.000020601903,0.000044805784,0.00003536751],"domain_scores_gemma":[0.9989857,0.00023652222,0.00043475677,0.00003552697,0.00010427925,0.000203118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003166554,0.000096153926,0.00015199346,0.00047570534,0.0003506157,0.00046518177,0.00010505293,0.00031200633,0.0025217],"category_scores_gemma":[0.0016768276,0.00018552199,0.0001391395,0.00048168757,0.00021850296,0.00027553883,0.00017594645,0.00034980406,0.00033117348],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084480984,0.00008617861,0.99715495,0.000006520547,0.000012684124,0.00006850312,0.0006373831,0.000013483778,0.00026027733,0.000011562317,0.00009104194,0.0015728776],"study_design_scores_gemma":[0.0000029672265,0.000115737304,0.9984711,0.0000024907852,0.000004753622,0.000119956334,0.0010534188,0.000056270303,0.000032549397,0.0000071172663,0.0001316434,0.0000020625578],"about_ca_topic_score_codex":0.007612786,"about_ca_topic_score_gemma":0.008746761,"teacher_disagreement_score":0.007612786,"about_ca_system_score_codex":0.00015324965,"about_ca_system_score_gemma":0.00012723943,"threshold_uncertainty_score":0.015136957},"labels":[],"label_agreement":null},{"id":"W7038233431","doi":"","title":"The Gaussian Process Density Sampler","year":2009,"lang":"en","type":"article","venue":"Edinburgh Research Explorer (University of Edinburgh)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; Gates Cambridge Trust; Cambridge Trust; Government of Canada","keywords":"Density estimation; Gaussian process; Markov chain Monte Carlo; Gaussian; Probability density function; Importance sampling; Posterior probability; Slice sampling; Gaussian random field; Bayesian inference","score_opus":0.13947980740091676,"score_gpt":0.35202637447989005,"score_spread":0.2125465670789733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038233431","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":[0.001821657,0.00019612056,0.9958805,0.00018329412,0.000044885957,0.00009014686,0.0001403376,0.00024221976,0.0014008454],"genre_scores_gemma":[0.14129879,0.0011909823,0.84480816,0.00056169135,0.000376353,0.0013045872,0.00155698,0.00044509995,0.00845733],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974179,0.001338637,0.00008771,0.0004157136,0.0005453587,0.00019454434],"domain_scores_gemma":[0.9883676,0.008495881,0.0003550098,0.00094343105,0.0016108311,0.00022722427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006392678,0.0011358183,0.00223698,0.0017887522,0.0012464717,0.0027398812,0.0038225732,0.00273446,0.00964414],"category_scores_gemma":[0.02954395,0.0011080803,0.0018491248,0.002161755,0.0025132045,0.0034751925,0.002438502,0.0041955146,0.0032925585],"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":[0.00015696038,0.00011655076,0.0030527518,0.00020836788,0.00011364085,0.00021096847,0.00026056994,0.21090314,0.0010454135,0.6871822,0.011668905,0.08508061],"study_design_scores_gemma":[0.00005309144,0.0000222115,0.00031401,0.00006150732,0.000030417188,0.000089137946,0.00003799539,0.7683506,0.0004725959,0.22469038,0.005849373,0.000028753766],"about_ca_topic_score_codex":0.0094730025,"about_ca_topic_score_gemma":0.008688622,"teacher_disagreement_score":0.00964414,"about_ca_system_score_codex":0.0018303632,"about_ca_system_score_gemma":0.003454942,"threshold_uncertainty_score":0.033808112},"labels":[],"label_agreement":null},{"id":"W70622757","doi":"10.1007/978-4-431-68544-9_3","title":"Quantification of Ordinal Variables: A Critical Inquiry into Polychoric and Canonical Correlation","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Polychoric correlation; Mathematics; Canonical correlation; Categorical variable; Ordinal data; Statistics; Covariance matrix; Multivariate normal distribution; Univariate; Multidimensional scaling; Correlation; Multivariate statistics; Ordinal Scale; Bivariate analysis; Canonical analysis","score_opus":0.09778110251565887,"score_gpt":0.3181975821308521,"score_spread":0.22041647961519323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W70622757","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":[0.017793749,0.018893886,0.94022,0.005072751,0.0005581937,0.00017236352,0.00024267082,0.00018885385,0.016857559],"genre_scores_gemma":[0.44680855,0.01030264,0.5363456,0.0012058208,0.001629019,0.0008715221,0.00031301842,0.0003187763,0.002205137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96915764,0.020880079,0.0016373417,0.00275225,0.0052158963,0.00035678004],"domain_scores_gemma":[0.86911786,0.10802987,0.0038447075,0.0093120625,0.008924979,0.0007704984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04153054,0.0014000682,0.0021174084,0.008108946,0.0031012548,0.008902481,0.0038022867,0.0012194873,0.002794479],"category_scores_gemma":[0.16477406,0.0010704906,0.0013795806,0.011568439,0.025666952,0.013238971,0.0051734094,0.005866651,0.00035031303],"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":[0.000024642603,0.000025390505,0.0033188642,0.00028941518,0.00010127226,0.000045031786,0.003099061,0.0027960874,0.0002930041,0.9145733,0.0025911187,0.07284282],"study_design_scores_gemma":[0.0000051024945,0.000022293516,0.0040867864,0.0001524433,0.000021965394,0.00012225624,0.001445713,0.0085371565,0.0001465886,0.97948384,0.0059252353,0.00005058812],"about_ca_topic_score_codex":0.0038918362,"about_ca_topic_score_gemma":0.0049267565,"teacher_disagreement_score":0.04153054,"about_ca_system_score_codex":0.0028810455,"about_ca_system_score_gemma":0.0031764219,"threshold_uncertainty_score":0.2196371},"labels":[],"label_agreement":null},{"id":"W7067730637","doi":"","title":"MUS PRC-A Applied Percussion Kuo Spring 2024","year":2024,"lang":"en","type":"article","venue":"Scholarly and Creative Works from DePauw University (DePauw University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Piano; Quarter (Canadian coin); Course (navigation); The arts; Music education; Audit; Permission; Percussion; Dance","score_opus":0.01778010216840722,"score_gpt":0.20534733671423264,"score_spread":0.1875672345458254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067730637","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014315603,0.0001896521,0.0046807053,0.0013369289,0.00080817164,0.0022903807,0.01584793,0.003698506,0.95683205],"genre_scores_gemma":[0.020813556,0.00016015733,0.004448367,0.00036038354,0.00012266064,0.00083786406,0.0092701195,0.0006575542,0.9633293],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994006,0.000057779882,0.000037688627,0.00009083223,0.00031290637,0.00010023938],"domain_scores_gemma":[0.99782217,0.00013322035,0.00007742661,0.00022050248,0.0009003165,0.0008463525],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007917944,0.00088487566,0.00074748317,0.0013329071,0.0016124255,0.0012265969,0.0016589392,0.00086977053,0.81804675],"category_scores_gemma":[0.0022866009,0.00043938344,0.0003592921,0.0006601287,0.00027644687,0.000764387,0.002511847,0.00085389183,0.5189516],"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":[0.000649124,0.00070585386,0.0014395025,0.00037316637,0.0000062796557,0.0002815548,0.00012110109,0.00017852952,0.0057929163,0.0018489546,0.7989424,0.18966064],"study_design_scores_gemma":[0.00011206018,0.00073994295,0.014538298,0.00016962767,0.0000042957217,0.00051886274,0.00024970583,0.0005262277,0.0022709148,0.0010540797,0.9797947,0.000021306767],"about_ca_topic_score_codex":0.0021830923,"about_ca_topic_score_gemma":0.011284185,"teacher_disagreement_score":0.81804675,"about_ca_system_score_codex":0.00062553893,"about_ca_system_score_gemma":0.0021454727,"threshold_uncertainty_score":0.25953424},"labels":[],"label_agreement":null},{"id":"W7084081332","doi":"10.64357/neya-gjnps-inc-vr-vl-pr-02","title":"Understanding Inclusivity in Virtual Volunteer Programs: Principles, Challenges, and Strategies","year":2025,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cornerstone; Empowerment; Bridging (networking); Equity (law); Cultural competence; Competence (human resources)","score_opus":0.25155697982191016,"score_gpt":0.3178597783164676,"score_spread":0.06630279849455745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084081332","genre_codex":"commentary","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":[0.19179143,0.023585929,0.15399471,0.39024663,0.0015229404,0.0018177266,0.00009406032,0.0002555769,0.23669098],"genre_scores_gemma":[0.93858445,0.0064795334,0.0364534,0.010669343,0.00027633342,0.0016338063,0.000040406445,0.0000642439,0.00579836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97306925,0.022038095,0.00049839454,0.0012881351,0.001396696,0.0017093593],"domain_scores_gemma":[0.97062093,0.01907044,0.0023430765,0.0014029375,0.0016961951,0.004866445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035038624,0.0006334634,0.0006362952,0.002304019,0.0110087935,0.018617924,0.0033749777,0.005002771,0.0036149703],"category_scores_gemma":[0.02470473,0.0005812155,0.00060247554,0.0019666606,0.039573517,0.024858736,0.023968019,0.006962955,0.0004538028],"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":[0.00005106721,0.00047018833,0.009257914,0.0012106656,0.000024043391,0.00032997137,0.18900098,0.00079102855,0.00032505023,0.6723179,0.008461482,0.1177597],"study_design_scores_gemma":[0.000023234186,0.00012752226,0.0034421491,0.0043971105,0.000020374511,0.0002777716,0.31548375,0.0013409765,0.00028832484,0.5358579,0.13868453,0.000056409266],"about_ca_topic_score_codex":0.0039648456,"about_ca_topic_score_gemma":0.0076465067,"teacher_disagreement_score":0.035038624,"about_ca_system_score_codex":0.007367213,"about_ca_system_score_gemma":0.020653926,"threshold_uncertainty_score":0.18530416},"labels":[],"label_agreement":null},{"id":"W7090772074","doi":"10.4236/fns.2025.1610086","title":"Technology Development for Panna Cotta Enriched with Grape Skin Powder with Focus on Nutritional Value and Sustainability","year":2025,"lang":"en","type":"article","venue":"Food and Nutrition Sciences","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Agence Universitaire de la Francophonie","keywords":"Antioxidant capacity; Polyphenol; Sustainability; Dietary fiber; Winemaking; Functional food; Sensory analysis; Food products","score_opus":0.023002893897966108,"score_gpt":0.28829882919579597,"score_spread":0.26529593529782985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7090772074","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":[0.86402625,0.015675174,0.10374359,0.00071348535,0.0004940901,0.0006503353,0.0007157883,0.000647736,0.013333578],"genre_scores_gemma":[0.8925698,0.008710531,0.08970079,0.00027793954,0.00006519444,0.00034855845,0.00066954084,0.000134022,0.0075237127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978656,0.000021497257,0.000013632025,0.000069809386,0.00008162361,0.00002694481],"domain_scores_gemma":[0.9998889,0.000012796365,0.000025220845,0.000010210201,0.000041361967,0.000021402591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035225015,0.0005431688,0.00031808656,0.0006246977,0.00023850056,0.0006270655,0.0003309535,0.0005660863,0.0016993111],"category_scores_gemma":[0.00029016336,0.00028421782,0.0006052274,0.00034342977,0.00017099161,0.0007458626,0.00047893415,0.0007690448,0.00071034336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018526673,0.000018512934,0.0001261581,0.00016368649,0.0000071676777,0.000087207925,0.000021854607,0.00007452769,0.99391925,0.00015265847,0.000054403197,0.0053561796],"study_design_scores_gemma":[0.000013606474,0.00062817277,0.0037866835,0.000058364567,0.00007785576,0.00050400855,0.00010689039,0.0017568206,0.9775179,0.00013079622,0.0153983105,0.000020548003],"about_ca_topic_score_codex":0.00047127475,"about_ca_topic_score_gemma":0.0012429121,"teacher_disagreement_score":0.0016993111,"about_ca_system_score_codex":0.00030043957,"about_ca_system_score_gemma":0.00030689398,"threshold_uncertainty_score":0.0056847334},"labels":[],"label_agreement":null},{"id":"W7095316707","doi":"","title":"on the SubjectsAn Example&amp;apos;","year":2016,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Multivariate statistics; Multivariate analysis; Repeated measures design; Multivariate analysis of variance; Covariate; Design matrix; Variance (accounting)","score_opus":0.34060077670687755,"score_gpt":0.35589295417248373,"score_spread":0.015292177465606183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095316707","genre_codex":"methods","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":[0.01468963,0.0007463581,0.8219879,0.0027873213,0.0065912665,0.003450931,0.008249312,0.032023236,0.10947397],"genre_scores_gemma":[0.040416736,0.0006305951,0.6859197,0.0034868885,0.0008944452,0.007974956,0.002885082,0.006969725,0.25082186],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973743,0.0010439232,0.00021152744,0.00052691967,0.0007112206,0.00013212631],"domain_scores_gemma":[0.9924845,0.004221616,0.00024234761,0.00208464,0.0008489715,0.00011788122],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039175153,0.0019429548,0.0011254514,0.001320777,0.00075812225,0.00060451345,0.0015251435,0.0012093517,0.2794132],"category_scores_gemma":[0.016753789,0.00072953774,0.00085615716,0.0012371099,0.0011284949,0.0012810219,0.001515189,0.0019280904,0.11529097],"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":[0.001093566,0.00042307354,0.0011804893,0.0009891179,0.000042402782,0.00055344705,0.0005633682,0.0005326018,0.019278083,0.020755077,0.4102789,0.54431],"study_design_scores_gemma":[0.00070555886,0.0011116839,0.01239936,0.00046564356,0.00007613437,0.001271176,0.00025796707,0.008878179,0.029526332,0.029685471,0.91550875,0.00011363369],"about_ca_topic_score_codex":0.0009112939,"about_ca_topic_score_gemma":0.0026576598,"teacher_disagreement_score":0.7205868,"about_ca_system_score_codex":0.0004476507,"about_ca_system_score_gemma":0.0006926912,"threshold_uncertainty_score":0.93473023},"labels":[],"label_agreement":null},{"id":"W7095612498","doi":"","title":"Email: Address: Email:","year":2003,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Ranking (information retrieval); Index (typography); Measure (data warehouse); Openness to experience; Multivariate statistics","score_opus":0.05227255773995223,"score_gpt":0.28009505654815403,"score_spread":0.2278224988082018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095612498","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010594694,0.0012741429,0.0016607008,0.008946646,0.0040447754,0.0004592959,0.008598275,0.0030353393,0.9613862],"genre_scores_gemma":[0.016702494,0.0010587221,0.0014499004,0.0016099529,0.0006285938,0.00015036266,0.0026791203,0.00056004367,0.9751608],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999564,0.000041198677,0.000025614065,0.00015299431,0.0001226862,0.00009341729],"domain_scores_gemma":[0.99822897,0.000660432,0.000074224095,0.00013956359,0.00040436324,0.0004924079],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042806193,0.0016492554,0.0014102827,0.0010833322,0.0013455092,0.002390033,0.0007218644,0.0019955623,0.95057976],"category_scores_gemma":[0.0027623272,0.00034860426,0.00045734734,0.00073153584,0.0004699624,0.0020538257,0.0014270392,0.001802262,0.8834509],"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":[0.0005601366,0.00044331126,0.003237514,0.0006099223,0.000028826036,0.0007113692,0.00041465243,0.00018476226,0.002631483,0.0025105786,0.75483227,0.23383527],"study_design_scores_gemma":[0.0000907732,0.00023086694,0.0047773533,0.0002950066,0.000024929945,0.00037937626,0.00037996826,0.00023387565,0.0010431902,0.0016715312,0.9908409,0.000032231357],"about_ca_topic_score_codex":0.00097305555,"about_ca_topic_score_gemma":0.0011807143,"teacher_disagreement_score":0.049420238,"about_ca_system_score_codex":0.00044580194,"about_ca_system_score_gemma":0.0004812851,"threshold_uncertainty_score":0.07049191},"labels":[],"label_agreement":null},{"id":"W7099273441","doi":"","title":"and","year":2016,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Ozone; Air pollution; Pollutant; Air pollutants; Atmosphere (unit)","score_opus":0.02841338670522759,"score_gpt":0.2371234779164102,"score_spread":0.2087100912111826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099273441","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025015756,0.0009247177,0.008525608,0.0032368388,0.0020828971,0.00053729763,0.026194464,0.0019539755,0.9315285],"genre_scores_gemma":[0.085998364,0.00061167835,0.005843378,0.0015350792,0.00021814679,0.00018900866,0.02092534,0.0003240761,0.884355],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993389,0.000043179134,0.00004074169,0.0001536467,0.0002890552,0.00013451869],"domain_scores_gemma":[0.999469,0.000029858964,0.000030720006,0.00006519677,0.00034375078,0.000061500716],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045311594,0.00062791986,0.00026855196,0.0009978053,0.00156825,0.002452608,0.00073460856,0.00077314617,0.40302247],"category_scores_gemma":[0.0010682951,0.0001687467,0.00034554923,0.00081907585,0.00038454513,0.0010497641,0.0013998555,0.00059891504,0.1875864],"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":[0.0006177148,0.00026435163,0.02419039,0.00061722577,0.00004814866,0.0009597159,0.0015830256,0.00057580177,0.010024697,0.05452305,0.46584278,0.44075316],"study_design_scores_gemma":[0.000019988564,0.00006826854,0.008845195,0.000090219546,0.000012697717,0.0003066153,0.00053431455,0.00025005982,0.0020013945,0.00309311,0.9847627,0.00001531821],"about_ca_topic_score_codex":0.024563678,"about_ca_topic_score_gemma":0.03410981,"teacher_disagreement_score":0.59697753,"about_ca_system_score_codex":0.0014259748,"about_ca_system_score_gemma":0.0015493651,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7099398204","doi":"","title":"4 Factor Analysis 5 Latent Semantic Indexing 6 Independent Component Analysis","year":2009,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Dimensionality reduction; Position (finance); Dimension (graph theory); Curse of dimensionality; Scalar (mathematics); Basis (linear algebra); Set (abstract data type); Principal component analysis; Scalar multiplication; Pattern recognition (psychology)","score_opus":0.04867839364707628,"score_gpt":0.29240118022897926,"score_spread":0.243722786581903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099398204","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":[0.0034229073,0.012861801,0.9554631,0.004879506,0.0007903526,0.00047749732,0.004528969,0.005441858,0.0121339],"genre_scores_gemma":[0.070070915,0.014869845,0.8821464,0.001334107,0.0015531046,0.0010277189,0.013494785,0.0013110896,0.014192004],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99323857,0.002751204,0.000510569,0.0014849035,0.0017023042,0.00031244944],"domain_scores_gemma":[0.99425006,0.0022936391,0.00048680863,0.0013030955,0.0015134361,0.00015305495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051041096,0.003014174,0.002210976,0.007343018,0.0016181853,0.005155305,0.0021742356,0.0024912013,0.01875887],"category_scores_gemma":[0.018662423,0.000981449,0.0038803497,0.011797111,0.0016818357,0.0057737255,0.0032461332,0.0037425554,0.014136602],"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":[0.00019795922,0.00014554772,0.0027314257,0.001284557,0.000440578,0.00017873486,0.00034207417,0.023581007,0.002441375,0.11605368,0.06870385,0.78389925],"study_design_scores_gemma":[0.000056197536,0.00012324534,0.004854835,0.0007716201,0.0002661523,0.00032572701,0.0004831047,0.19273448,0.0042630527,0.6204753,0.17541394,0.00023238869],"about_ca_topic_score_codex":0.008523892,"about_ca_topic_score_gemma":0.005752646,"teacher_disagreement_score":0.01875887,"about_ca_system_score_codex":0.0025738215,"about_ca_system_score_gemma":0.003654719,"threshold_uncertainty_score":0.06275463},"labels":[],"label_agreement":null},{"id":"W7100140206","doi":"","title":"AOPs Adopted by COSMOS","year":2016,"lang":"en","type":"article","venue":"","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Set (abstract data type); Agency (philosophy); Product (mathematics); Focus (optics); Class (philosophy)","score_opus":0.01909203740329367,"score_gpt":0.22879430216941093,"score_spread":0.20970226476611725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100140206","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019788495,0.0118288165,0.038104996,0.004367981,0.005806475,0.00090309617,0.0680205,0.025015263,0.843974],"genre_scores_gemma":[0.013221391,0.015797133,0.06865197,0.003816013,0.0012516013,0.0019980944,0.05662922,0.020752715,0.8178819],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979056,0.00032285976,0.000205351,0.0002541986,0.0011809259,0.0001311353],"domain_scores_gemma":[0.9982704,0.00031933616,0.00014580732,0.0002988863,0.000805826,0.00015977783],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024871496,0.0015200011,0.00060202845,0.0048394627,0.0010930279,0.003914123,0.0019057193,0.0016454927,0.37961516],"category_scores_gemma":[0.005190501,0.0009987368,0.0010995975,0.0045601055,0.0009162098,0.0028451856,0.002333471,0.0024950183,0.30009624],"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":[0.00012309785,0.00005504673,0.00034649138,0.0008268341,0.000012148268,0.000096340904,0.00023176115,0.00020289137,0.0013340635,0.013387747,0.72379595,0.25958768],"study_design_scores_gemma":[0.000008046709,0.000012310953,0.0002970156,0.00011479761,0.0000041931635,0.00007664846,0.000036812267,0.000031835854,0.00054636877,0.001369121,0.99749327,0.000009498518],"about_ca_topic_score_codex":0.007918424,"about_ca_topic_score_gemma":0.009742308,"teacher_disagreement_score":0.37961516,"about_ca_system_score_codex":0.0008990715,"about_ca_system_score_gemma":0.0022919541,"threshold_uncertainty_score":0.88490367},"labels":[],"label_agreement":null},{"id":"W7117491237","doi":"10.2196/86393","title":"Correction: Fermented Food Consumption Across European Regions: Protocol for the Development and Validation of the Web-Based Fermented Foods Frequency Questionnaire (3FQ)","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Protocol (science); Fermentation in food processing; Consumption (sociology); Food frequency questionnaire; Food safety; Fermentation","score_opus":0.31182774460681256,"score_gpt":0.5246286363826619,"score_spread":0.21280089177584932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117491237","genre_codex":"editorial","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013365825,0.0013107074,0.0067707486,0.05586623,0.9071577,0.00040274937,0.019849474,0.002316403,0.004989367],"genre_scores_gemma":[0.0895851,0.010560124,0.058570553,0.15485175,0.25695568,0.006033975,0.04910448,0.012152154,0.3621862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9866348,0.004024333,0.0031982071,0.0012614261,0.004085914,0.00079537084],"domain_scores_gemma":[0.9160522,0.028214278,0.005192481,0.006528605,0.042311687,0.0017007818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011840399,0.0020620076,0.0017491898,0.0037339565,0.0022093956,0.002698262,0.003222732,0.004826003,0.10801958],"category_scores_gemma":[0.16539344,0.0010899288,0.0020283002,0.0021916493,0.0020717129,0.001789333,0.0025405432,0.0062252586,0.030567756],"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":[0.00011631723,0.000009528843,0.00020921773,0.00052960386,0.00003515286,0.00020585649,0.00012349944,0.00004419614,0.00012075278,0.0005552591,0.9896263,0.008424294],"study_design_scores_gemma":[0.00021848797,0.00006404939,0.003583828,0.002255446,0.00012868877,0.00081979105,0.00038410875,0.00042527466,0.00071326375,0.002119248,0.9891824,0.000105507475],"about_ca_topic_score_codex":0.014994852,"about_ca_topic_score_gemma":0.01660465,"teacher_disagreement_score":0.10801958,"about_ca_system_score_codex":0.0025108664,"about_ca_system_score_gemma":0.0064758104,"threshold_uncertainty_score":0.3613615},"labels":[],"label_agreement":null},{"id":"W7120814514","doi":"","title":"Use of artificial saliva during the instrumental analysis of texture of expanded snacks for correlating with sensory analysis","year":2021,"lang":"pt","type":"dissertation","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological 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":"Saliva; Texture (cosmology); Sensory analysis; Snack food; Chewing gum","score_opus":0.044330970531007,"score_gpt":0.2811295525543169,"score_spread":0.2367985820233099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7120814514","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":[0.92019206,0.0057655354,0.06767825,0.00015657424,0.0003070461,0.00057433674,0.00092404324,0.00027113684,0.0041310047],"genre_scores_gemma":[0.8658951,0.0031795176,0.1237861,0.0003825519,0.00009486498,0.0009370421,0.00071094336,0.000161552,0.004852272],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998254,0.0004992921,0.00011412882,0.0003684758,0.0006133894,0.00015074319],"domain_scores_gemma":[0.9986528,0.00046949435,0.00020333187,0.00014758002,0.00046065068,0.0000661699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001488038,0.0008220549,0.0006172595,0.0008306064,0.0004440302,0.0012496826,0.00045106286,0.0006888401,0.0018446661],"category_scores_gemma":[0.0026100643,0.0003663293,0.0007255452,0.000814976,0.0005475513,0.0005199837,0.0005864702,0.000853368,0.0005020746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011746482,0.00013493774,0.010499426,0.00056225236,0.00010302261,0.00015290579,0.0006082219,0.00021319152,0.95828944,0.00023183227,0.00021145561,0.027818587],"study_design_scores_gemma":[0.000035869554,0.0024648127,0.08994359,0.00013932935,0.00030628528,0.0009089791,0.00077098096,0.0050987094,0.89248306,0.00038513567,0.0073668687,0.000096393895],"about_ca_topic_score_codex":0.0010776068,"about_ca_topic_score_gemma":0.0024414966,"teacher_disagreement_score":0.0018446661,"about_ca_system_score_codex":0.00025059594,"about_ca_system_score_gemma":0.0005526946,"threshold_uncertainty_score":0.007869601},"labels":[],"label_agreement":null},{"id":"W7125342958","doi":"10.18280/mmep.121203","title":"Selection Criteria of Appropriate Methods Between Covariance-Based, Partial Least Squares, and Generalized Structured Component Analysis in Structural Modeling","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Direcció General de Recerca, Generalitat de Catalunya","keywords":"Selection (genetic algorithm); Component (thermodynamics); Model selection; Component analysis; Principal component analysis","score_opus":0.05623058002334302,"score_gpt":0.3154629820050291,"score_spread":0.25923240198168607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125342958","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":[0.0032256155,0.000377518,0.9931641,0.0003082601,0.00013387813,0.0013013706,0.00017401403,0.000299959,0.0010151324],"genre_scores_gemma":[0.04334481,0.00033740923,0.9473013,0.0001806583,0.000110904824,0.007472774,0.00037899075,0.00046131836,0.00041180625],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.78835356,0.18437546,0.009780724,0.0071093948,0.009326582,0.0010543294],"domain_scores_gemma":[0.6707153,0.28816882,0.007887475,0.012135693,0.019708786,0.0013839195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14665471,0.0040836516,0.0044714273,0.007142011,0.0031862645,0.0050703073,0.0033323083,0.0029885548,0.006983165],"category_scores_gemma":[0.41270277,0.0017332805,0.004789359,0.009437988,0.0032838099,0.0042000874,0.005185773,0.0058338093,0.0015742696],"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":[0.0011893832,0.0007971837,0.024364945,0.004518768,0.0036205018,0.0014700012,0.00334755,0.06346995,0.0039821514,0.35738587,0.016659588,0.519194],"study_design_scores_gemma":[0.0010492582,0.0015119112,0.012032599,0.00264699,0.0014135339,0.00067257916,0.0019871623,0.540096,0.003953352,0.39226827,0.04197218,0.00039626565],"about_ca_topic_score_codex":0.003852936,"about_ca_topic_score_gemma":0.007205993,"teacher_disagreement_score":0.14665471,"about_ca_system_score_codex":0.001878538,"about_ca_system_score_gemma":0.009770243,"threshold_uncertainty_score":0.7755935},"labels":[],"label_agreement":null},{"id":"W7133256288","doi":"10.2310/derm.1.2005.2052","title":"Use of Consumer Product Ingredients for Patch Testing","year":2005,"lang":"en","type":"article","venue":"Dermatitis","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Product (mathematics); Patch testing; Product testing; Consumer safety; New product development","score_opus":0.14832348009883742,"score_gpt":0.3046312416351591,"score_spread":0.15630776153632167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133256288","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":[0.6942253,0.0356061,0.026093474,0.004895044,0.0023037086,0.005870953,0.012763446,0.0032344286,0.21500744],"genre_scores_gemma":[0.92212003,0.009268588,0.042997796,0.0029781954,0.00078294607,0.001421719,0.00713229,0.0005633767,0.0127349235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994072,0.0015494765,0.00092052866,0.0006839082,0.0026055092,0.00016853446],"domain_scores_gemma":[0.98751175,0.003772665,0.0032079108,0.0020858455,0.0031387887,0.00028311452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034322597,0.0006973895,0.0004989692,0.0030711696,0.0005043023,0.00079059415,0.00082752266,0.000836842,0.01711036],"category_scores_gemma":[0.011008046,0.00025344762,0.0004703612,0.0018446152,0.00041617858,0.00097073,0.00051588856,0.00065949454,0.005513597],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015139249,0.001177471,0.4778592,0.0033025744,0.00020289778,0.0032786806,0.0011301426,0.00016647742,0.023561338,0.0005436334,0.030540764,0.4567229],"study_design_scores_gemma":[0.00022915943,0.0033848,0.56198394,0.0041225967,0.00087438826,0.02816704,0.0013027017,0.0017646393,0.06501423,0.00078864064,0.33222413,0.00014374798],"about_ca_topic_score_codex":0.0015061063,"about_ca_topic_score_gemma":0.001149501,"teacher_disagreement_score":0.01711036,"about_ca_system_score_codex":0.00050808553,"about_ca_system_score_gemma":0.0006344987,"threshold_uncertainty_score":0.05723983},"labels":[],"label_agreement":null},{"id":"W7133407938","doi":"","title":"Développement d'un outil d'évaluation des processus de contrôle sémantique","year":2024,"lang":"fr","type":"article","venue":"ORBi UMONS","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Process (computing); Work (physics); Context (archaeology); Set (abstract data type); Term (time)","score_opus":0.07575845523091092,"score_gpt":0.3415381655647094,"score_spread":0.2657797103337985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133407938","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":[0.22482719,0.00087407714,0.7559161,0.00038491006,0.00030704346,0.0020218445,0.0006541733,0.0061634495,0.00885123],"genre_scores_gemma":[0.4002,0.00071277923,0.5762816,0.00013579454,0.000098153105,0.001884137,0.0015793721,0.0009179819,0.018190244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98933417,0.0038647668,0.0005288384,0.0013317172,0.0045052767,0.00043526047],"domain_scores_gemma":[0.97414994,0.011896974,0.0009206986,0.0024781618,0.009822935,0.00073135196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009490055,0.0015757589,0.0011869243,0.002472902,0.000974833,0.0046948683,0.001468254,0.0013841077,0.0065799085],"category_scores_gemma":[0.020207895,0.0006852448,0.001145053,0.0010417452,0.0011725695,0.0037354114,0.0015565901,0.0010004176,0.0014433503],"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":[0.002415904,0.0014879873,0.011668242,0.0015201326,0.00026259528,0.00033806718,0.0029794148,0.019497724,0.20179193,0.008850846,0.0038535486,0.7453335],"study_design_scores_gemma":[0.00053516607,0.0042990427,0.040772576,0.0004249251,0.0004292502,0.0005224789,0.0023903302,0.31847313,0.57255316,0.0055841957,0.053693697,0.0003220631],"about_ca_topic_score_codex":0.014174045,"about_ca_topic_score_gemma":0.0071432116,"teacher_disagreement_score":0.014174045,"about_ca_system_score_codex":0.001850809,"about_ca_system_score_gemma":0.0050964565,"threshold_uncertainty_score":0.05018884},"labels":[],"label_agreement":null},{"id":"W7133455692","doi":"","title":"Analyse Typologique des Marques de Luxe sur Internet: Application aux Secteurs de la Mode","year":2013,"lang":"fr","type":"article","venue":"ORBi UMONS","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Measure (data warehouse); Mode (computer interface); Value (mathematics); Key (lock)","score_opus":0.0354441231454512,"score_gpt":0.32307803192439977,"score_spread":0.28763390877894857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133455692","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":[0.9668009,0.0006030927,0.015342126,0.00008987336,0.00002432379,0.00014625484,0.0023610322,0.00022540099,0.0144070415],"genre_scores_gemma":[0.9806029,0.00037837835,0.010910325,0.00001775455,0.00001461514,0.00016210403,0.0011259598,0.00006923645,0.006718708],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99907756,0.00020812257,0.00006659664,0.00018522122,0.00034572877,0.00011677813],"domain_scores_gemma":[0.9967043,0.0015604893,0.00038376468,0.00013457576,0.0010891426,0.00012774547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082080136,0.00053004705,0.0003402812,0.0073899627,0.0011382728,0.002104211,0.00034636536,0.00066164666,0.0077957544],"category_scores_gemma":[0.0024872713,0.00018210753,0.0008013777,0.0061443406,0.0007313455,0.000993911,0.00053835934,0.00046828206,0.000934727],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011799713,0.00020831564,0.63632566,0.0010139857,0.00027201578,0.002160037,0.016981445,0.0060232827,0.10026712,0.008651815,0.002977771,0.22393864],"study_design_scores_gemma":[0.000017843062,0.00024342533,0.9298353,0.00018551224,0.00010305479,0.0018918493,0.021814404,0.009891976,0.008180337,0.0018940013,0.025858473,0.00008383841],"about_ca_topic_score_codex":0.027723677,"about_ca_topic_score_gemma":0.03572023,"teacher_disagreement_score":0.027723677,"about_ca_system_score_codex":0.0014796053,"about_ca_system_score_gemma":0.00073059875,"threshold_uncertainty_score":0.05512458},"labels":[],"label_agreement":null},{"id":"W73449135","doi":"10.1007/978-3-319-01264-3_7","title":"Structural Representation of Categorical Data and Cluster Analysis Through Filters","year":2013,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorical variable; Dimensionality reduction; Dimension (graph theory); Representation (politics); Data reduction; Multidimensional scaling; Mathematics; Simple (philosophy); Reduction (mathematics); Space (punctuation); Cluster (spacecraft); Algorithm; Scaling; Data analysis; Computer science; Data mining; Statistics; Artificial intelligence; Pure mathematics","score_opus":0.2617969241649431,"score_gpt":0.3981748959171858,"score_spread":0.1363779717522427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W73449135","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":[0.0011321852,0.00024609533,0.99681795,0.00017524452,0.000026219192,0.000025724477,0.00011594671,0.00033895954,0.0011215281],"genre_scores_gemma":[0.023298707,0.00052465353,0.97375464,0.00008446042,0.000054817323,0.00019428831,0.00047706513,0.00020079147,0.0014105226],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973757,0.0010006888,0.00019898424,0.00062599854,0.0006886559,0.000109856526],"domain_scores_gemma":[0.9942703,0.004007581,0.00021342236,0.00074327085,0.00068888976,0.000076537246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004917006,0.00091590954,0.0012808308,0.003917936,0.00093226106,0.0037470558,0.0021215493,0.00082893134,0.0054116766],"category_scores_gemma":[0.012413539,0.00066983287,0.0023576163,0.005184457,0.0027581851,0.0047128145,0.001798013,0.0022590056,0.0016221892],"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":[0.000110108274,0.000045683526,0.0009549915,0.0005334956,0.00015120163,0.000046026373,0.0014413851,0.012169873,0.005673924,0.4576353,0.008735475,0.51250255],"study_design_scores_gemma":[0.000015169936,0.0000457867,0.0014191542,0.000119866076,0.00006990633,0.00013476172,0.00037938968,0.120884575,0.0037024666,0.8516039,0.021568315,0.00005669453],"about_ca_topic_score_codex":0.0043027657,"about_ca_topic_score_gemma":0.0040276414,"teacher_disagreement_score":0.0054116766,"about_ca_system_score_codex":0.0016950729,"about_ca_system_score_gemma":0.0017642984,"threshold_uncertainty_score":0.026003957},"labels":[],"label_agreement":null}]}