{"meta":{"query_hash":"e7642a1bc9c1","filters":{"venue":"Lecture notes in statistics"},"cohort_total":26,"direct_labels_cover":0,"predictions_cover":26,"exported":26,"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/e7642a1bc9c1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Lecture+notes+in+statistics"},"results":[{"id":"W118748914","doi":"10.1007/978-1-4613-0111-0_9","title":"Defective renewal equations","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision 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 Waterloo","funders":"","keywords":"Geometric distribution; Connection (principal bundle); Mathematics; Queueing theory; Geometric probability; Distribution (mathematics); Renewal theory; Expression (computer science); Geometric progression; Master equation; Applied mathematics; Geometric series; Probability distribution; Mathematical analysis; Computer science; Combinatorics; Geometry; Statistics; Physics","score_opus":0.13177874700760323,"score_gpt":0.3817506749713828,"score_spread":0.2499719279637796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118748914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000068334243,0.00047208363,0.791861,0.00022994557,0.0004274551,0.0002878774,0.0011853061,0.00003152507,0.205498],"genre_scores_gemma":[0.32273126,0.0014566015,0.36919975,0.0027891072,0.0019600242,0.00009054322,0.0010707563,0.0003599547,0.30034202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99644345,0.00012958559,0.0009331537,0.0008287971,0.0013262797,0.000338725],"domain_scores_gemma":[0.98579454,0.012270905,0.00040238982,0.00090149924,0.00051703857,0.00011359826],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011278181,0.00038249555,0.00064002426,0.00043514904,0.00015808968,0.000157399,0.0006537009,0.0006338057,0.0019163967],"category_scores_gemma":[0.012814903,0.00029906398,0.00014713191,0.00021971566,0.00032139785,0.000080352955,0.00016484811,0.0008607364,0.0005882325],"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":[0.000036826474,0.000034338278,0.00014986125,0.00001052994,0.00003035725,0.00008872252,0.00069205684,0.023595724,0.0000026939763,0.750702,0.0015102784,0.22314657],"study_design_scores_gemma":[0.00016987165,0.000070555085,0.000036535843,0.000051094725,0.00003644095,0.000008821921,0.0000029792936,0.0062960656,0.000008858779,0.9490888,0.043904196,0.00032576962],"about_ca_topic_score_codex":0.00010975069,"about_ca_topic_score_gemma":0.0063693887,"teacher_disagreement_score":0.42266122,"about_ca_system_score_codex":0.00023217681,"about_ca_system_score_gemma":0.00030802115,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W131830864","doi":"10.1007/978-1-4613-0175-2_5","title":"The Various Tables","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Forecasting Techniques and Applications","field":"Decision 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":"Statistics Canada","funders":"","keywords":"Computer science","score_opus":0.08763052945530098,"score_gpt":0.37066671086800385,"score_spread":0.28303618141270287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W131830864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013048707,0.00056437217,0.7827913,0.00068257947,0.00019030542,0.0002508563,0.00064779475,0.000053625095,0.21481784],"genre_scores_gemma":[0.013035789,0.002546079,0.50869566,0.0014912732,0.0009127853,0.00011126097,0.00034298157,0.00022301266,0.47264114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976518,0.000033419845,0.0007042945,0.0004688997,0.00085349573,0.00028808278],"domain_scores_gemma":[0.99005306,0.0082223825,0.0003950349,0.0009806238,0.0002896325,0.000059249007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000900924,0.00027087168,0.00033751293,0.000146106,0.00030861908,0.0003226121,0.0010039981,0.00031524966,0.00062693824],"category_scores_gemma":[0.0038278687,0.00016560327,0.00007210011,0.0001774896,0.00030121265,0.000023251432,0.0001782399,0.0006442881,0.0002362225],"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":[0.0000051007473,0.0000053480508,0.00003166107,0.000002175351,0.000006213518,0.000021940148,0.000042302283,0.0002837353,9.732457e-7,0.55773616,0.03356276,0.40830165],"study_design_scores_gemma":[0.000025252999,0.000018845798,0.000013334567,0.00001828057,0.000007536575,0.000011282927,7.483383e-7,0.0011856864,0.000003782216,0.5218421,0.47676763,0.00010551325],"about_ca_topic_score_codex":0.00008431637,"about_ca_topic_score_gemma":0.0016260166,"teacher_disagreement_score":0.44320485,"about_ca_system_score_codex":0.00007779716,"about_ca_system_score_gemma":0.00010899104,"threshold_uncertainty_score":0.6864534},"labels":[],"label_agreement":null},{"id":"W135223351","doi":"10.1007/978-1-4613-0049-6_7","title":"Additional Selected Topics","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision 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 Waterloo","funders":"","keywords":"Maximization; Computer science; TRACE (psycholinguistics); Variety (cybernetics); Block (permutation group theory); Mathematical optimization; Optimal design; Mathematics; Artificial intelligence; Combinatorics; Machine learning","score_opus":0.11270832870251576,"score_gpt":0.3942728854141296,"score_spread":0.28156455671161384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135223351","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0139076e-7,0.0003819849,0.63044447,0.00014548826,0.0005612327,0.00028629194,0.045785014,0.000053194373,0.32234192],"genre_scores_gemma":[0.000060930703,0.000043483135,0.73878556,0.00056762603,0.00050470827,0.000021643573,0.0027042674,0.00007434717,0.25723746],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99591976,0.00017260098,0.00096340606,0.00081059895,0.0017699674,0.0003636711],"domain_scores_gemma":[0.9859672,0.012264739,0.0004264504,0.0006517057,0.00055745686,0.00013244704],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004645802,0.00046930963,0.00069352967,0.00044614897,0.00009288735,0.00019004216,0.0006934394,0.0006199037,0.35389036],"category_scores_gemma":[0.01428279,0.00040951063,0.00010798938,0.00030290138,0.00024198828,0.00007377109,0.0001464536,0.0009385272,0.0018200491],"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":[0.000030198475,0.000038188853,0.000014536093,0.000011174744,0.00003543269,0.00023980433,0.00016975505,0.00038003622,0.000030681647,0.081546865,0.58352315,0.33398017],"study_design_scores_gemma":[0.00012446541,0.000085109416,0.000066831584,0.00005031097,0.000012100941,0.000021178079,9.690871e-7,0.002168739,0.00006170221,0.45161453,0.545468,0.00032609407],"about_ca_topic_score_codex":0.0000055170894,"about_ca_topic_score_gemma":0.000049395963,"teacher_disagreement_score":0.37006766,"about_ca_system_score_codex":0.00023109313,"about_ca_system_score_gemma":0.00015871678,"threshold_uncertainty_score":0.99983567},"labels":[],"label_agreement":null},{"id":"W1566366787","doi":"10.1007/978-1-4613-0141-7_12","title":"Ancillary Information for Statistical Inference","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scalar (mathematics); Mathematics; Inference; Nuisance parameter; Dimension (graph theory); Affine transformation; Applied mathematics; Variable (mathematics); Order (exchange); Third order; Tangent; Computer science; Statistics; Estimator; Mathematical analysis; Pure mathematics; Artificial intelligence","score_opus":0.07038244963229102,"score_gpt":0.3723573659195813,"score_spread":0.3019749162872903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566366787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.273705e-7,0.00003910988,0.9221521,0.00022162028,0.00015001472,0.0009881423,0.031520892,0.000108435095,0.044818755],"genre_scores_gemma":[0.006459856,0.00017164962,0.9696377,0.00094142224,0.00023375337,0.00030868268,0.01767702,0.000113139424,0.004456806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978117,0.000024123696,0.0010050867,0.00033278926,0.00044279345,0.0003835246],"domain_scores_gemma":[0.99073476,0.007778002,0.00041293516,0.00041315984,0.00050811004,0.00015301367],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021734659,0.0004438136,0.00057313655,0.00018884908,0.0001319237,0.00009336486,0.000218731,0.0005018076,0.0017179911],"category_scores_gemma":[0.008655442,0.00045191342,0.000069787806,0.00009281074,0.00019986684,0.00010892068,0.000052449886,0.0005351243,0.00020983988],"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":[0.000028623295,0.000030784093,0.000007359147,0.0002828787,0.00002299175,0.0000071429145,0.000060964332,0.00010365164,8.744902e-7,0.9122262,0.013687987,0.073540516],"study_design_scores_gemma":[0.00042961605,0.000065441636,0.00007569918,0.000108165485,0.000092053124,0.0000085561005,0.000002135494,0.011282708,0.000006191226,0.83593374,0.15158953,0.00040614634],"about_ca_topic_score_codex":0.0000102788235,"about_ca_topic_score_gemma":0.00010620514,"teacher_disagreement_score":0.13790154,"about_ca_system_score_codex":0.0002672431,"about_ca_system_score_gemma":0.00023244256,"threshold_uncertainty_score":0.99979323},"labels":[],"label_agreement":null},{"id":"W195199240","doi":"10.1007/978-3-642-14104-1_8","title":"Robust regression with infinite moving average errors","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"","keywords":"Statistics; Regression; Mathematics; Computer science; Econometrics","score_opus":0.08750569966658531,"score_gpt":0.3620935658755106,"score_spread":0.2745878662089253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W195199240","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010745375,0.00011918639,0.97502303,0.00005487357,0.00031044782,0.00047133697,0.0012205706,0.00009176529,0.02269806],"genre_scores_gemma":[0.0009892313,0.00010732628,0.98588973,0.00020381135,0.00021411135,0.000018098304,0.00015925398,0.00024723532,0.012171196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99747896,0.000067604975,0.00069426873,0.000689176,0.00056849385,0.0005014703],"domain_scores_gemma":[0.992178,0.006178277,0.00053827884,0.0007222335,0.00020674376,0.00017643135],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00031078703,0.0007404003,0.0009794884,0.00021891692,0.00014466105,0.00005068179,0.00026963258,0.0008620397,0.0005985789],"category_scores_gemma":[0.0034033505,0.00055229635,0.00007205544,0.00006720548,0.0002954923,0.000059687656,0.0001294279,0.002510534,0.000011716612],"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":[0.00016345287,0.000050416318,0.000019371433,0.00062777475,0.000071380324,0.0007933409,0.00057492394,0.005384796,0.000103753235,0.8822418,0.00024172718,0.1097273],"study_design_scores_gemma":[0.0004546666,0.00015390752,0.0000047871404,0.0010227723,0.00012248501,0.00003712963,0.0000022755453,0.0067613996,0.0001257691,0.9827996,0.007791797,0.00072341115],"about_ca_topic_score_codex":0.000016716325,"about_ca_topic_score_gemma":0.0007896582,"teacher_disagreement_score":0.10900389,"about_ca_system_score_codex":0.00011443521,"about_ca_system_score_gemma":0.00013378913,"threshold_uncertainty_score":0.9997907},"labels":[],"label_agreement":null},{"id":"W2222558555","doi":"10.1007/978-3-642-35407-6_5","title":"Assessing and Modeling Asymmetry in Bivariate Continuous Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bivariate analysis; Asymmetry; Econometrics; Computer science; Statistics; Mathematics; Physics; Particle physics","score_opus":0.2183110533761694,"score_gpt":0.4111082095713619,"score_spread":0.1927971561951925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222558555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000064387765,0.00056271657,0.97490525,0.00007010031,0.0002258904,0.0003541302,0.00118288,0.000035284917,0.022599384],"genre_scores_gemma":[0.013131062,0.00022139875,0.9852377,0.00017754768,0.00013581662,0.000007699907,0.00020835521,0.00011187021,0.0007685061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99756587,0.00011428749,0.00086805294,0.0007236039,0.00032679393,0.0004013869],"domain_scores_gemma":[0.9907703,0.007883643,0.00028949627,0.00082759693,0.00012810966,0.00010086713],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00071069086,0.00047781618,0.00095721905,0.00023723107,0.00005182116,0.00025247264,0.00040235015,0.0005517955,0.0004577695],"category_scores_gemma":[0.010112122,0.0004372492,0.000023149554,0.00006475347,0.00013209805,0.00010504785,0.00041689948,0.0012095261,0.000021969858],"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":[0.000008622784,0.000024553818,0.000057336514,0.00036246827,0.00003085587,0.000110803085,0.00012129806,0.00010012973,0.000007344806,0.69388723,0.00028849283,0.30500087],"study_design_scores_gemma":[0.0002504959,0.000029292469,0.000038664206,0.00061850506,0.00006895083,0.0000103352195,0.0000055540577,0.22887419,0.0000022771953,0.7692063,0.00051171536,0.00038372373],"about_ca_topic_score_codex":0.00022900365,"about_ca_topic_score_gemma":0.00020753691,"teacher_disagreement_score":0.30461714,"about_ca_system_score_codex":0.000082980034,"about_ca_system_score_gemma":0.000095832416,"threshold_uncertainty_score":0.99980795},"labels":[],"label_agreement":null},{"id":"W2478895115","doi":"10.1007/978-3-319-31260-6_3","title":"Zero-Inflated Spatial Models: Application and Interpretation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Western University; Simon Fraser University","funders":"","keywords":"Overdispersion; Statistics; Count data; Mathematics; Multivariate statistics; Econometrics; Generalized linear model; Computer science; Poisson distribution","score_opus":0.03482270418686196,"score_gpt":0.3273210291152349,"score_spread":0.29249832492837297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478895115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002308578,0.00013054095,0.9703506,0.000074498574,0.00014459195,0.00048746375,0.001139369,0.0000644454,0.027606143],"genre_scores_gemma":[0.025717018,0.00017485427,0.9722171,0.00012263196,0.00013945575,0.000036332476,0.000109579196,0.00010490033,0.0013781152],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982488,0.000066530876,0.00063483685,0.00050356047,0.0002914479,0.0002548001],"domain_scores_gemma":[0.9944722,0.00453572,0.00035909083,0.00036448176,0.00017599399,0.0000924839],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023312117,0.00041494702,0.000591746,0.00015943969,0.000053285392,0.000049975268,0.00015071663,0.00051858573,0.00018049352],"category_scores_gemma":[0.0015932304,0.0003382724,0.00004075343,0.00003529334,0.00019215429,0.00005406751,0.000076410164,0.00048636395,0.00002426748],"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":[0.00003417839,0.0000066889725,0.0000049005394,0.0001222233,0.000019018518,0.000009343263,0.0001229831,0.0000070290157,0.000021923088,0.55942035,0.000056635214,0.44017473],"study_design_scores_gemma":[0.00028019,0.00007644842,0.000014805737,0.00042464057,0.00009721291,0.0000086259,5.095416e-7,0.0625298,0.000037294667,0.9356317,0.000520286,0.0003784661],"about_ca_topic_score_codex":0.00003086222,"about_ca_topic_score_gemma":0.00020249009,"teacher_disagreement_score":0.43979627,"about_ca_system_score_codex":0.000116551804,"about_ca_system_score_gemma":0.00006507836,"threshold_uncertainty_score":0.99990696},"labels":[],"label_agreement":null},{"id":"W2498801788","doi":"10.1007/978-3-319-31260-6_8","title":"Penalized Generalized Quasi-Likelihood Based Variable Selection for Longitudinal Data","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Feature selection; Computer science; Likelihood function; Covariate; Penalty method; Inference; Model selection; Biometrics; Variable (mathematics); Selection (genetic algorithm); Function (biology); Variance (accounting); Algorithm; Mathematics; Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Estimation theory","score_opus":0.15339423298841404,"score_gpt":0.3865815205119637,"score_spread":0.23318728752354967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498801788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010226277,0.00015887892,0.97072345,0.00013831216,0.000539923,0.0010015414,0.017912207,0.00009480212,0.009429843],"genre_scores_gemma":[0.00014549392,0.00004679541,0.9912458,0.00028206842,0.0005681577,0.000081206104,0.0012749399,0.00019519805,0.0061603417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99658245,0.00015873775,0.001009004,0.001071101,0.0005221049,0.0006566162],"domain_scores_gemma":[0.98260736,0.015044388,0.00055839017,0.0011400155,0.00048874196,0.00016111425],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001085349,0.0006916606,0.0011934622,0.00021635287,0.00014526039,0.00009895415,0.0006607263,0.00069076836,0.0030667782],"category_scores_gemma":[0.013966967,0.0005579784,0.00009507693,0.000094825715,0.00015529414,0.00006332376,0.00016919423,0.00061278767,0.000028580158],"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":[0.00026015835,0.00008244099,0.000036544876,0.0006144479,0.00011788042,0.000013066713,0.000017740269,0.000015965217,0.00007750436,0.9341831,0.0059034964,0.058677632],"study_design_scores_gemma":[0.00170981,0.00029549524,0.0000071602117,0.00052530685,0.00041161862,0.000009955107,2.923489e-7,0.039846357,0.00008972231,0.9274438,0.028975958,0.0006845441],"about_ca_topic_score_codex":0.000057296686,"about_ca_topic_score_gemma":0.00041920177,"teacher_disagreement_score":0.05799309,"about_ca_system_score_codex":0.0002300841,"about_ca_system_score_gemma":0.0005379793,"threshold_uncertainty_score":0.9996872},"labels":[],"label_agreement":null},{"id":"W2505248766","doi":"10.1007/978-3-319-31260-6_6","title":"Dynamic Models for Longitudinal Ordinal Non-stationary Categorical Data","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Categorical variable; Ordinal data; Ordinal regression; Longitudinal data; Econometrics; Computer science; Mathematics; Statistics; Data mining","score_opus":0.12132006274639773,"score_gpt":0.3954614555441705,"score_spread":0.2741413927977728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505248766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.5406515e-7,0.00016468359,0.9607505,0.00022521007,0.00044509806,0.0007835824,0.025681004,0.000051473548,0.011898064],"genre_scores_gemma":[0.0014815792,0.00013245364,0.9906735,0.00010101501,0.00026983858,0.00005567576,0.0016668767,0.00017591297,0.005443144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99683505,0.00005676369,0.0009361553,0.001069208,0.0005315799,0.0005712282],"domain_scores_gemma":[0.98385537,0.014018586,0.0004209217,0.0012084653,0.00033807344,0.00015860453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004966039,0.00065859535,0.0010088662,0.00021313771,0.000122579,0.00006868091,0.0008085007,0.00061002804,0.00050661265],"category_scores_gemma":[0.0035812042,0.00052808126,0.000089820955,0.000062983665,0.0002744861,0.000120888675,0.00031089492,0.00070312835,0.00003415666],"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":[0.00009898394,0.000039836264,0.0000078493695,0.0004422991,0.00007745491,0.00012596803,0.000046199784,0.000028599952,0.0000035545947,0.83260226,0.0028223302,0.16370466],"study_design_scores_gemma":[0.00051165384,0.0001607248,0.000021702106,0.00029858755,0.00023162932,0.0000362764,0.0000010596381,0.10606242,0.000002323996,0.8902059,0.0018510348,0.0006166646],"about_ca_topic_score_codex":0.00001430421,"about_ca_topic_score_gemma":0.00022199152,"teacher_disagreement_score":0.163088,"about_ca_system_score_codex":0.00026478287,"about_ca_system_score_gemma":0.00035017336,"threshold_uncertainty_score":0.99971706},"labels":[],"label_agreement":null},{"id":"W25970070","doi":"10.1007/978-1-4613-0175-2_6","title":"Modelling of the Easter Effect","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Solar and Space Plasma Dynamics","field":"Physics and Astronomy","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":"Statistics Canada","funders":"","keywords":"Autoregressive integrated moving average; Component (thermodynamics); Econometrics; Computer science; Statistics; Mathematics; Time series; Physics; Thermodynamics","score_opus":0.013122911661035571,"score_gpt":0.230072225994421,"score_spread":0.21694931433338543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W25970070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040803276,0.00007815695,0.9117557,0.000020676316,0.0002728874,0.00021503455,0.0007913375,0.000006413512,0.086451754],"genre_scores_gemma":[0.9416581,0.000031561296,0.02776691,0.000081340964,0.00055187766,0.0000119635715,0.00039686426,0.0001747242,0.029326687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991455,0.000025133397,0.00025091326,0.00020003434,0.00019626706,0.00018215296],"domain_scores_gemma":[0.99881566,0.0005914884,0.00018589123,0.00032888437,0.000049581853,0.00002851665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068882255,0.00028555593,0.0003816095,0.000051811825,0.000041116233,0.00001925568,0.0001890674,0.0001653939,0.0002800513],"category_scores_gemma":[0.000016288028,0.00019761751,0.00012714312,0.000036476333,0.0000771569,0.000013838745,0.00006017027,0.0006139534,0.000014621596],"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":[0.000046821187,0.000029698062,0.012412128,0.00013956403,0.00015697787,0.000011162323,0.0003924944,0.6482483,0.00001119811,0.2884659,0.0001981211,0.049887598],"study_design_scores_gemma":[0.000492278,0.00007196653,0.00003239718,0.00036676225,0.00021902309,0.0000021475637,0.0000026720606,0.41430637,0.00015366168,0.57604104,0.007838606,0.0004730403],"about_ca_topic_score_codex":0.00011185861,"about_ca_topic_score_gemma":0.00006595853,"teacher_disagreement_score":0.94125,"about_ca_system_score_codex":0.000028139504,"about_ca_system_score_gemma":0.00004675622,"threshold_uncertainty_score":0.8058609},"labels":[],"label_agreement":null},{"id":"W341900282","doi":"10.1007/978-1-4613-0111-0_3","title":"Mixed Poisson distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision 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 Waterloo","funders":"","keywords":"Poisson distribution; Compound Poisson distribution; Mathematics; Zero-inflated model; Poisson binomial distribution; Distribution (mathematics); Statistics; Applied mathematics; Statistical physics; Compound Poisson process; Poisson regression; Mathematical analysis; Poisson process; Population; Physics","score_opus":0.1295048111341935,"score_gpt":0.3655369418093195,"score_spread":0.23603213067512602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W341900282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028092118,0.0006533068,0.925795,0.00074725243,0.0007197955,0.0002834915,0.0061404873,0.000043820673,0.06558873],"genre_scores_gemma":[0.23424342,0.0034772938,0.4098346,0.0020887947,0.002103543,0.00007472392,0.0050827446,0.00037679623,0.34271806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963323,0.00010698884,0.0010371208,0.00081778556,0.0012958735,0.00040993563],"domain_scores_gemma":[0.9926621,0.0053215986,0.00038978303,0.0010457112,0.00043343133,0.00014741496],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010498693,0.00041373714,0.0007082804,0.0002955901,0.00017395106,0.00018614024,0.0008003735,0.0007025053,0.002094119],"category_scores_gemma":[0.0059489454,0.00031837457,0.00015566594,0.00021849481,0.0003571057,0.000076913704,0.00013902395,0.0009829756,0.000634267],"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":[0.000032510798,0.00004421216,0.00019770964,0.000015190173,0.000023305938,0.00013644605,0.00022360706,0.0031822508,0.0000034184327,0.730474,0.010680585,0.25498676],"study_design_scores_gemma":[0.00013569248,0.00004173249,0.00015320037,0.000046158566,0.000027012724,0.000013766577,0.0000014141502,0.001959139,0.000015089751,0.75811213,0.23921354,0.00028113736],"about_ca_topic_score_codex":0.00006232981,"about_ca_topic_score_gemma":0.002882916,"teacher_disagreement_score":0.51596045,"about_ca_system_score_codex":0.0002274053,"about_ca_system_score_gemma":0.00019369756,"threshold_uncertainty_score":0.9999268},"labels":[],"label_agreement":null},{"id":"W34298671","doi":"10.1007/978-1-4614-3520-4_35","title":"Statistical Analyses of Data Cubes","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Dendrogram; Object (grammar); Position (finance); Data cube; Computer science; Domain (mathematical analysis); Statistical physics; Pattern recognition (psychology); Data mining; Artificial intelligence; Mathematics; Physics; Mathematical analysis","score_opus":0.12745194972331872,"score_gpt":0.4091473613771846,"score_spread":0.2816954116538659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34298671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022454551,0.0009201076,0.8909465,0.000016533562,0.00016356853,0.00015072247,0.04353495,0.00000697203,0.06423817],"genre_scores_gemma":[0.574877,0.00024182291,0.35240233,0.00006932125,0.0016356879,0.000015565385,0.04442648,0.00017575981,0.026156016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983411,0.000032147276,0.00041604126,0.00039208445,0.0004793997,0.00033927054],"domain_scores_gemma":[0.9975566,0.0011927999,0.00017834839,0.0008495709,0.00010448031,0.000118166645],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020574237,0.0002579497,0.0004668663,0.00014101165,0.000044429282,0.00006367851,0.00051537185,0.00010011966,0.0070101046],"category_scores_gemma":[0.00018752167,0.00021681018,0.000042658292,0.000057962774,0.00039723783,0.00012152585,0.00031348178,0.0003902635,0.000069387934],"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":[0.000049164082,0.00008820356,0.003404922,0.00015388944,0.00023812374,0.000017417351,0.00014792454,0.00017406096,0.000107712345,0.7300553,0.011756756,0.25380656],"study_design_scores_gemma":[0.00034302004,0.00006764443,0.00056890823,0.00013457352,0.0002316307,0.0000010306549,0.000017256736,0.0016725308,0.0009394622,0.95008284,0.045430917,0.0005101993],"about_ca_topic_score_codex":0.00037679408,"about_ca_topic_score_gemma":0.00012016273,"teacher_disagreement_score":0.57485455,"about_ca_system_score_codex":0.000019861865,"about_ca_system_score_gemma":0.00021336881,"threshold_uncertainty_score":0.9938976},"labels":[],"label_agreement":null},{"id":"W4236908534","doi":"10.1007/978-1-4613-0049-6_3","title":"Optimal Regression Designs in Asymmetric Domains","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision 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 Waterloo","funders":"","keywords":"Mathematics; Quadratic equation; Inverse; Regression; Linear regression; Polynomial regression; Optimality criterion; Minification; Reduction (mathematics); Regression analysis; Applied mathematics; Mathematical optimization; Statistics; Geometry","score_opus":0.16339756209150985,"score_gpt":0.42620436567210607,"score_spread":0.2628068035805962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236908534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000039863186,0.0023485224,0.86453754,0.00007560802,0.00069883635,0.00057868526,0.00042232295,0.000041272666,0.13125739],"genre_scores_gemma":[0.010301974,0.00037161607,0.9541565,0.00031943273,0.00017102911,0.000021273061,0.00006102858,0.00014560593,0.03445151],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99322397,0.00054629846,0.0017414677,0.0013626706,0.0024461066,0.00067946664],"domain_scores_gemma":[0.9838301,0.01385302,0.0007356996,0.0011258798,0.0002433022,0.00021202459],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025833843,0.0007928645,0.0013166063,0.0023865725,0.00010492396,0.00026761869,0.0012142095,0.0010239861,0.0032144752],"category_scores_gemma":[0.011634121,0.00062760577,0.00017815571,0.000998304,0.0003395558,0.00014951536,0.0003330065,0.0015718971,0.00065249857],"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":[0.00034190636,0.00023516724,0.0009540931,0.000054303022,0.000051834315,0.0028041308,0.0014874442,0.017811278,0.00051914423,0.09557174,0.009554106,0.8706148],"study_design_scores_gemma":[0.001813872,0.0008236857,0.00083784247,0.0006620535,0.00006619123,0.000098798046,0.000028180819,0.033450276,0.0018942782,0.907635,0.0506987,0.0019911672],"about_ca_topic_score_codex":0.00004564078,"about_ca_topic_score_gemma":0.00009918849,"teacher_disagreement_score":0.8686237,"about_ca_system_score_codex":0.0005756356,"about_ca_system_score_gemma":0.00014105374,"threshold_uncertainty_score":0.9996175},"labels":[],"label_agreement":null},{"id":"W4237324981","doi":"10.1007/978-1-4613-0175-2_1","title":"Introduction","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Census and Population Estimation","field":"Mathematics","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":"Statistics Canada","funders":"","keywords":"Computer science","score_opus":0.042694429710670063,"score_gpt":0.31990001944217744,"score_spread":0.2772055897315074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237324981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013777468,0.00012480975,0.9220718,0.0006211693,0.0006849606,0.00027645376,0.00028200005,0.00007356401,0.07585147],"genre_scores_gemma":[0.009044648,0.00049945107,0.80804604,0.00044276795,0.0071466784,0.00002126582,0.0034888696,0.00035995423,0.17095034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998811,0.000018077213,0.00045637734,0.000280956,0.00026376502,0.00016982814],"domain_scores_gemma":[0.9986324,0.0006015965,0.00027720613,0.00032649358,0.00012395403,0.00003835739],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013543504,0.00026930924,0.00034005792,0.0001667406,0.000048357888,0.000023981012,0.00007868132,0.00036178637,0.0027404018],"category_scores_gemma":[0.0009038082,0.00026866395,0.000044400418,0.000046781734,0.000045057845,0.000030390824,0.000022474656,0.00046057516,0.000102910126],"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":[0.000014508898,0.000015887734,0.00006809471,0.0001247525,0.000018757524,0.00002452497,0.00012679445,0.0007424554,0.0000027176975,0.88859403,0.019529479,0.090737976],"study_design_scores_gemma":[0.00012122335,0.000023223227,0.00006142461,0.00005739845,0.00005031872,0.000020387366,2.6737018e-7,0.0015274584,0.00000712356,0.77560467,0.2223111,0.00021541034],"about_ca_topic_score_codex":0.000015016488,"about_ca_topic_score_gemma":0.00039792364,"teacher_disagreement_score":0.20278162,"about_ca_system_score_codex":0.00016317495,"about_ca_system_score_gemma":0.000036017464,"threshold_uncertainty_score":0.9999766},"labels":[],"label_agreement":null},{"id":"W4238614988","doi":"10.1007/978-1-4613-0111-0_4","title":"Compound distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Probability and Risk Models","field":"Decision 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":"University of Waterloo","funders":"","keywords":"Portfolio; Aggregate (composite); Random variable; Econometrics; Probabilistic logic; Heavy-tailed distribution; Distribution (mathematics); Ruin theory; Mathematics; Actuarial science; Joint probability distribution; Risk model; Statistical physics; Economics; Statistics; Financial economics; Physics","score_opus":0.11492176943364184,"score_gpt":0.3760107584306925,"score_spread":0.26108898899705063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238614988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001408242,0.0005841188,0.8756438,0.00048868876,0.00049874606,0.00024309929,0.005563044,0.000034144177,0.11693029],"genre_scores_gemma":[0.2421177,0.0035324334,0.3818946,0.0028931727,0.0022473691,0.0000615431,0.005296205,0.0003574704,0.36159953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99667317,0.00008323311,0.00097070186,0.00072034285,0.0011997349,0.00035283872],"domain_scores_gemma":[0.9922623,0.005908975,0.00034304147,0.00096114376,0.00039819826,0.00012635384],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008783706,0.00036886003,0.0006621444,0.00025386087,0.00017938027,0.00020704577,0.00076682057,0.0005613723,0.0023093163],"category_scores_gemma":[0.0034238768,0.00028443817,0.0001311532,0.00017971738,0.00045438908,0.000070084265,0.00013395389,0.00093596184,0.00064480805],"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":[0.000028593204,0.00004802351,0.00026944262,0.00001577509,0.000025560588,0.0002111439,0.00023696056,0.0048990143,0.0000020195866,0.83199024,0.01045707,0.15181613],"study_design_scores_gemma":[0.00011159297,0.00003005001,0.00007756845,0.000035826415,0.000020137459,0.000019441439,8.882293e-7,0.001972876,0.0000039399188,0.70107555,0.2964204,0.00023172397],"about_ca_topic_score_codex":0.000048858336,"about_ca_topic_score_gemma":0.0021879706,"teacher_disagreement_score":0.4937492,"about_ca_system_score_codex":0.00021031103,"about_ca_system_score_gemma":0.00018701285,"threshold_uncertainty_score":0.9999608},"labels":[],"label_agreement":null},{"id":"W4239009738","doi":"10.1007/978-1-4613-0111-0_2","title":"Reliability background","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Class (philosophy); Failure rate; Residual; Reliability (semiconductor); Distribution (mathematics); Portfolio; Stochastic ordering; Statistics; Reliability theory; Regular polygon; Applied mathematics; Computer science; Mathematical analysis; Economics; Physics; Algorithm; Thermodynamics; Artificial intelligence; Financial economics","score_opus":0.08216684177435717,"score_gpt":0.3677752897054315,"score_spread":0.2856084479310743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239009738","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005538009,0.00005285389,0.822046,0.00020827679,0.00012153841,0.0004049877,0.004558504,0.00009882025,0.17250352],"genre_scores_gemma":[0.022983167,0.00027385092,0.8982013,0.0008984892,0.00045561203,0.00012649376,0.0044849156,0.0002644387,0.072311774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979789,0.00003562527,0.00076575764,0.0004992757,0.00041181588,0.0003085738],"domain_scores_gemma":[0.99455416,0.0040962296,0.0002899444,0.00065526436,0.00027364964,0.00013074181],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020968758,0.00041370146,0.00054204214,0.000105850595,0.00009914093,0.000052928735,0.0002262714,0.0004854599,0.006163371],"category_scores_gemma":[0.002758115,0.0004072292,0.00008198505,0.00009713662,0.00025413278,0.000032110594,0.000060580707,0.0007479849,0.00043670725],"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":[0.0000105284125,0.000056169054,0.0000075845446,0.00013180437,0.000016059157,0.000026958149,0.000029249217,0.00006598282,0.0000020846358,0.97279495,0.009434547,0.017424086],"study_design_scores_gemma":[0.00022386244,0.000026806016,0.00015787296,0.00009460001,0.00007623366,0.000012290844,0.000001381027,0.0018894364,0.000005851594,0.90094525,0.09619955,0.00036689264],"about_ca_topic_score_codex":0.000016637125,"about_ca_topic_score_gemma":0.00015353308,"teacher_disagreement_score":0.100191735,"about_ca_system_score_codex":0.0003206504,"about_ca_system_score_gemma":0.000115367744,"threshold_uncertainty_score":0.99983793},"labels":[],"label_agreement":null},{"id":"W4241079235","doi":"10.1007/978-1-4613-0175-2_4","title":"Moving Averages","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","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":"Statistics Canada","funders":"","keywords":"Seasonality; Series (stratigraphy); Moving average; A priori and a posteriori; Seasonal adjustment; Econometrics; Mathematics; Construct (python library); Simple (philosophy); Statistics; Computer science; Philosophy; Epistemology; Mathematical analysis; Geology","score_opus":0.01030989042104478,"score_gpt":0.23807617021262042,"score_spread":0.22776627979157565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241079235","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000099111116,0.00022872341,0.4502388,0.0001313033,0.00012900941,0.00009688485,0.00012237838,0.000029643119,0.54892415],"genre_scores_gemma":[0.3429872,0.0022838283,0.14275452,0.0056913514,0.0008177458,0.000022643331,0.0009656447,0.00029529442,0.50418174],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988149,0.000023004393,0.0002638957,0.00038590058,0.00024683817,0.00026550516],"domain_scores_gemma":[0.9991279,0.00039097288,0.00012094496,0.000294776,0.0000061601277,0.000059247675],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000109303124,0.0002728441,0.00033144795,0.00007330514,0.00007830818,0.000017077651,0.00020212254,0.0004314314,0.025353676],"category_scores_gemma":[0.00015055158,0.00025932267,0.00006123638,0.00006111076,0.00022340607,0.000033391672,0.00012607084,0.0005819589,0.0012528349],"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":[0.0000948522,0.00011098005,0.04193715,0.00009384287,0.00033946979,0.004130988,0.0015945615,0.2200754,0.00013462311,0.057424366,0.02109231,0.65297145],"study_design_scores_gemma":[0.00020800324,0.000058761816,0.0009898717,0.000042984844,0.000158228,0.0000301349,7.09052e-7,0.0071016764,0.00003532014,0.70733935,0.2834112,0.00062379736],"about_ca_topic_score_codex":0.00018046964,"about_ca_topic_score_gemma":0.003247542,"teacher_disagreement_score":0.6523477,"about_ca_system_score_codex":0.00015836784,"about_ca_system_score_gemma":0.000011768724,"threshold_uncertainty_score":0.9999859},"labels":[],"label_agreement":null},{"id":"W50856701","doi":"10.1007/978-1-4613-0111-0_5","title":"Bounds based on reliability classifications","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Section (typography); Simple (philosophy); Mathematics; Reliability (semiconductor); Function (biology); Aggregate (composite); Residual; Order (exchange); Distribution (mathematics); Combinatorics; Applied mathematics; Discrete mathematics; Computer science; Algorithm; Mathematical analysis; Physics; Economics; Thermodynamics","score_opus":0.07748339662381937,"score_gpt":0.3620999574960659,"score_spread":0.2846165608722465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W50856701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013747491,0.000013670969,0.79599607,0.0010609148,0.00010901554,0.00049090455,0.007197594,0.000121020305,0.19500941],"genre_scores_gemma":[0.07721147,0.000093178176,0.86499023,0.0026125805,0.00040141155,0.00039155464,0.010328673,0.0003292239,0.043641675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977219,0.000053573345,0.0008000833,0.00058716023,0.00052665215,0.0003105958],"domain_scores_gemma":[0.99199826,0.006236048,0.00034101817,0.00096165005,0.0003144979,0.00014854938],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023295073,0.00044619682,0.0005009509,0.00019159804,0.00017575399,0.00006750404,0.00026057663,0.00050828414,0.0037275034],"category_scores_gemma":[0.00527839,0.00044181867,0.000100340614,0.00015292144,0.00031278946,0.00002265345,0.000028901017,0.00086482294,0.00038365036],"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":[0.000018871062,0.00015012953,0.000017642416,0.000099289544,0.0000105212575,0.000012218832,0.00002023669,0.00091298175,0.000001566066,0.9761202,0.010941372,0.011695012],"study_design_scores_gemma":[0.00028886457,0.000054558204,0.00026141922,0.00012931372,0.00007865356,0.0000027125222,8.3653487e-7,0.04087234,0.000007902257,0.8668988,0.09102871,0.0003759183],"about_ca_topic_score_codex":0.000009121578,"about_ca_topic_score_gemma":0.000094836265,"teacher_disagreement_score":0.15136774,"about_ca_system_score_codex":0.0004527857,"about_ca_system_score_gemma":0.0002284909,"threshold_uncertainty_score":0.99980336},"labels":[],"label_agreement":null},{"id":"W51237428","doi":"10.1007/978-1-4613-0141-7_6","title":"Bayes and Empirical Bayes Estimates of Survival and Hazard Functions of a Class of Distributions","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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 Regina","funders":"","keywords":"Bayes' theorem; Estimator; Statistics; Mathematics; Class (philosophy); Hazard ratio; Point estimation; Monte Carlo method; Hazard; Econometrics; Applied mathematics; Computer science; Bayesian probability; Artificial intelligence; Confidence interval; Chemistry","score_opus":0.06551921107562778,"score_gpt":0.36050122841628063,"score_spread":0.2949820173406529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W51237428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039744884,0.0002476157,0.9624952,0.000206553,0.00005138257,0.0003133718,0.030540325,0.000025002168,0.005723073],"genre_scores_gemma":[0.53777885,0.0007360852,0.45602942,0.000038608803,0.00007598246,0.00005704008,0.0039119096,0.00010801284,0.0012641081],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822104,0.00003588691,0.000944656,0.00030832263,0.0003080394,0.00018202602],"domain_scores_gemma":[0.9926199,0.0060298303,0.0005384707,0.00030269442,0.00041043884,0.00009870048],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015989775,0.00030341098,0.00082866446,0.00015393463,0.00006875478,0.0000141573055,0.00010407937,0.000313997,0.00038647346],"category_scores_gemma":[0.0030648299,0.0002893734,0.00006529333,0.00013641019,0.000840139,0.00002715443,0.000064260435,0.0003169986,0.0000022684821],"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":[0.000028119575,0.000116111565,0.0011145958,0.00062806375,0.00008092921,0.0000031974353,0.00011187579,0.000032372413,0.00005795277,0.98900974,0.0009033075,0.007913736],"study_design_scores_gemma":[0.00042757596,0.00012472017,0.004249007,0.0003095269,0.00036146518,0.000014932533,0.000017455272,0.005664639,0.00020129049,0.98607004,0.002295267,0.00026408673],"about_ca_topic_score_codex":0.000021208574,"about_ca_topic_score_gemma":0.00021086285,"teacher_disagreement_score":0.5373814,"about_ca_system_score_codex":0.000045199133,"about_ca_system_score_gemma":0.00010224427,"threshold_uncertainty_score":0.99995583},"labels":[],"label_agreement":null},{"id":"W56489066","doi":"10.1007/978-1-4613-0141-7_9","title":"Bayesian and Likelihood Inference for the Generalized Fieller—Creasy Problem","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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; University of Regina","funders":"","keywords":"Frequentist inference; Prior probability; Markov chain Monte Carlo; Mathematics; Bayesian inference; Bayesian probability; Inference; Bayes factor; Matching (statistics); Bayesian statistics; Applied mathematics; Algorithm; Computer science; Statistics; Artificial intelligence","score_opus":0.04676580047843084,"score_gpt":0.3477503579115554,"score_spread":0.3009845574331246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W56489066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.4393014e-7,0.00071702455,0.97444266,0.00040090847,0.00019809871,0.0012274642,0.0015366686,0.000050012415,0.021426309],"genre_scores_gemma":[0.00047334758,0.0013900801,0.9935434,0.00045238817,0.00031475705,0.00012409582,0.00008582101,0.00011899855,0.003497141],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99774206,0.00008567156,0.0007419419,0.0005763393,0.00033278982,0.00052121934],"domain_scores_gemma":[0.97683394,0.021935362,0.00034377066,0.00053669844,0.00021176695,0.0001384633],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047283244,0.00059184874,0.000825617,0.00011498746,0.00018319019,0.00013213576,0.00033633682,0.0005546624,0.00079489633],"category_scores_gemma":[0.004565144,0.00041813328,0.000091908914,0.000069235415,0.00028392134,0.000028779548,0.00011979772,0.0007875578,0.000006023913],"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":[0.00004294956,0.000017940296,0.000031496096,0.00029995225,0.000060598948,0.000024829327,0.00018013129,0.000006352393,0.0000031858215,0.7106372,0.0011631133,0.2875322],"study_design_scores_gemma":[0.0005602552,0.00016371408,0.000028653714,0.0003389846,0.00025718487,0.000021740027,0.000003263262,0.007530179,0.000012794437,0.9692753,0.021292748,0.0005151898],"about_ca_topic_score_codex":0.00008621963,"about_ca_topic_score_gemma":0.0009803473,"teacher_disagreement_score":0.28701705,"about_ca_system_score_codex":0.00006820903,"about_ca_system_score_gemma":0.00015905823,"threshold_uncertainty_score":0.999827},"labels":[],"label_agreement":null},{"id":"W566021556","doi":"10.1007/978-1-4613-0147-9_11","title":"Some Statistical Aspects of Magnetoencephalography","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Polytechnique Montréal","funders":"","keywords":"Magnetoencephalography; Inverse problem; Computer science; Statistical physics; Nonlinear system; Simplicity; Noise (video); Algorithm; Dipole; Mathematics; Artificial intelligence; Physics; Mathematical analysis; Psychology; Electroencephalography; Image (mathematics)","score_opus":0.03151330268191886,"score_gpt":0.2728391427682101,"score_spread":0.2413258400862912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566021556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009010242,0.0025892677,0.5433222,0.0013445482,0.0024340607,0.0011576861,0.013323682,0.0001953073,0.43554315],"genre_scores_gemma":[0.74288017,0.011743061,0.17564534,0.023954505,0.0047141956,0.00018040497,0.0010529975,0.0011466302,0.038682695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973776,0.00007663336,0.00056069257,0.0008390002,0.0007287137,0.0004173689],"domain_scores_gemma":[0.9776924,0.021379469,0.00030338648,0.0004264818,0.00011287021,0.000085345884],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012928755,0.0004979581,0.000737321,0.00043524458,0.00009247715,0.000027182305,0.0002984959,0.0003336285,0.0007320339],"category_scores_gemma":[0.012514613,0.00049418386,0.00009669302,0.00016866416,0.00088527665,0.00006472442,0.00014407025,0.0008368776,0.000068526395],"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":[0.00006897961,0.000057925,0.000110391615,0.00014793413,0.000027680979,0.0005615336,0.00007077578,0.0003335609,0.0006119926,0.9819695,0.002629127,0.013410649],"study_design_scores_gemma":[0.0002664538,0.00037117559,0.00035359096,0.00011177133,0.000057330395,0.00004456179,9.944137e-7,0.00019697995,0.001210995,0.96766335,0.029271692,0.00045113487],"about_ca_topic_score_codex":0.00003138931,"about_ca_topic_score_gemma":0.00027464677,"teacher_disagreement_score":0.74279004,"about_ca_system_score_codex":0.000112295194,"about_ca_system_score_gemma":0.00014073552,"threshold_uncertainty_score":0.999751},"labels":[],"label_agreement":null},{"id":"W57323835","doi":"10.1007/978-1-4613-0049-6_6","title":"Designs in the Presence of Trends","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision 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 Waterloo","funders":"","keywords":"Mathematics; TRACE (psycholinguistics); Block (permutation group theory); Class (philosophy); Block size; Optimal design; Block design; Degree (music); Matrix (chemical analysis); Binary number; Term (time); Combinatorics; Mathematical optimization; Computer science; Arithmetic; Statistics; Artificial intelligence","score_opus":0.2325307607797628,"score_gpt":0.4434055247947282,"score_spread":0.2108747640149654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W57323835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012641872,0.0010960356,0.7907212,0.00014581525,0.0003011992,0.00028154798,0.00048132127,0.000009955238,0.2069503],"genre_scores_gemma":[0.05998426,0.00016068957,0.8888351,0.0005026163,0.00014868441,0.000023824135,0.000045493474,0.00008281025,0.050216522],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956441,0.00053787575,0.0011259797,0.0005992475,0.0017879006,0.0003049326],"domain_scores_gemma":[0.982117,0.01623111,0.00049733004,0.00095538434,0.00014876568,0.000050395858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023986027,0.00036507068,0.00070608326,0.00068583223,0.00004041114,0.00009940439,0.0014424348,0.00037661902,0.0035295743],"category_scores_gemma":[0.005345373,0.00023703807,0.00010662086,0.0004740882,0.0004137479,0.00006789788,0.00013307593,0.0007707997,0.00007080145],"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":[0.00010654542,0.00012320805,0.0003937868,0.000026996515,0.000025976879,0.0003771031,0.005724764,0.005303473,0.00042094014,0.20492776,0.010993266,0.77157617],"study_design_scores_gemma":[0.00037458795,0.00035872214,0.00067996565,0.00011839549,0.000029559409,0.000025236317,0.000034159486,0.008118229,0.0007186662,0.9627958,0.026274148,0.000472556],"about_ca_topic_score_codex":0.00004818571,"about_ca_topic_score_gemma":0.00015825551,"teacher_disagreement_score":0.7711036,"about_ca_system_score_codex":0.00008212275,"about_ca_system_score_gemma":0.00005803183,"threshold_uncertainty_score":0.9973813},"labels":[],"label_agreement":null},{"id":"W634979280","doi":"10.1007/0-387-35439-5","title":"Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series","year":2006,"lang":"en","type":"book","venue":"Lecture notes in statistics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Series (stratigraphy); Distribution (mathematics); Computer science; Geography; Mathematics; Economics; Geology; Management; Paleontology","score_opus":0.013476075620100949,"score_gpt":0.28415602883806973,"score_spread":0.2706799532179688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W634979280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015388896,0.00048990897,0.995911,0.0001297477,0.00021181676,0.00025490051,0.0011608006,0.000047382764,0.0017929049],"genre_scores_gemma":[0.000053568256,0.000026053309,0.9733472,0.00006300061,0.0002795756,0.000019173069,0.0044432674,0.000024081783,0.021744085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984672,0.00008576876,0.00050130126,0.00050699624,0.0001560552,0.00028269392],"domain_scores_gemma":[0.9979632,0.0010709213,0.00039145479,0.0003092358,0.00021688904,0.000048307567],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055216526,0.00029467934,0.0004899327,0.000096260934,0.00015653117,0.00022011886,0.00026674167,0.00029079817,0.000027016149],"category_scores_gemma":[0.00056883023,0.0002848843,0.00006992771,0.00016957494,0.00009238388,0.00015846717,0.00012640379,0.00025858515,0.000002682534],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017224336,0.00001588571,0.00024926665,0.00021286689,0.00006296913,0.000009496614,0.00026212525,0.0012052963,0.0000075743496,0.053371884,0.041955635,0.9026298],"study_design_scores_gemma":[0.00018627378,0.0001722347,0.0001980121,0.00008932868,0.00006992966,0.000012666937,5.775138e-7,0.28708905,0.00003414498,0.3156336,0.39607987,0.00043431256],"about_ca_topic_score_codex":0.00005504872,"about_ca_topic_score_gemma":0.00028870217,"teacher_disagreement_score":0.90219545,"about_ca_system_score_codex":0.00019659738,"about_ca_system_score_gemma":0.00019141832,"threshold_uncertainty_score":0.9999603},"labels":[],"label_agreement":null},{"id":"W77330898","doi":"10.1007/978-1-4614-6871-4_6","title":"Consistent Estimation in Incomplete Longitudinal Binary Models","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"Memorial University of Newfoundland; Carleton University","funders":"","keywords":"Estimation; Binary number; Econometrics; Computer science; Statistics; Mathematics; Economics; Arithmetic","score_opus":0.1594795387666528,"score_gpt":0.361704570262022,"score_spread":0.20222503149536922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W77330898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005907292,0.00022876973,0.96191484,0.00009246421,0.0002444513,0.00064531405,0.000979609,0.000047418882,0.035788048],"genre_scores_gemma":[0.021309137,0.00009602771,0.9759916,0.0001526522,0.000068025365,0.000049079837,0.00017674646,0.00010553989,0.002051173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973719,0.00010953011,0.0010475283,0.00057260715,0.00047037168,0.00042802317],"domain_scores_gemma":[0.9916898,0.0070929267,0.00039141794,0.00050631334,0.00020631787,0.000113239126],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036261053,0.0005649448,0.000977551,0.00032606637,0.000054255237,0.00006760689,0.00024785116,0.00052830175,0.0013865867],"category_scores_gemma":[0.0031760428,0.0005256537,0.00007329348,0.00008617868,0.00024168647,0.0000653499,0.00014066859,0.0010359535,0.00010168846],"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":[0.000025040541,0.000047750145,0.000043919106,0.00039740343,0.000031352236,0.0001642234,0.00016571254,0.0034272822,0.0000052110045,0.92773396,0.0007184951,0.06723964],"study_design_scores_gemma":[0.00028743595,0.000111803056,0.00019385935,0.0005719125,0.000061026898,0.0000146271905,0.0000014335419,0.22882584,0.0000053123445,0.7692838,0.00021793415,0.00042506095],"about_ca_topic_score_codex":0.0001366484,"about_ca_topic_score_gemma":0.0003424355,"teacher_disagreement_score":0.22539856,"about_ca_system_score_codex":0.00028170887,"about_ca_system_score_gemma":0.00013804063,"threshold_uncertainty_score":0.9997195},"labels":[],"label_agreement":null},{"id":"W952142941","doi":"10.1007/978-1-4613-0141-7_13","title":"The Relevance Weighted Likelihood With Applications","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Regina","funders":"","keywords":"Nonparametric statistics; Weighting; Relevance (law); Extension (predicate logic); Variance (accounting); Econometrics; Parametric statistics; Computer science; Nonparametric regression; Statistics; Mathematics; Economics","score_opus":0.04150135593609128,"score_gpt":0.35500000452598135,"score_spread":0.31349864858989007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W952142941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.2729431e-7,0.000910055,0.93019676,0.00019931796,0.000087566215,0.00087708567,0.0009486694,0.00007536251,0.06670493],"genre_scores_gemma":[0.00009195067,0.0017108179,0.9795873,0.000180238,0.0002332764,0.00017269241,0.00009828539,0.00017139778,0.01775404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978677,0.00005474348,0.0006022818,0.0005379203,0.00044637505,0.00049097877],"domain_scores_gemma":[0.9852308,0.013195028,0.00038585582,0.0008076259,0.00026157132,0.00011912956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025153946,0.0005029708,0.0005814308,0.00007232317,0.00025659017,0.000055368353,0.00033718516,0.00034333635,0.00011077624],"category_scores_gemma":[0.001173384,0.0003259777,0.000053458127,0.000098018136,0.00034472888,0.000028183997,0.00006632838,0.001068635,0.000028751392],"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":[0.000037592723,0.000017811348,0.0000014267251,0.00007276273,0.000035968733,0.000042670363,0.000041044594,0.000040673796,0.000001545322,0.70147115,0.0003821536,0.29785523],"study_design_scores_gemma":[0.00021321198,0.00007954994,0.0000011700932,0.00014920744,0.00009878942,0.00001933094,0.000001604943,0.0008578164,0.000010792223,0.75575453,0.24248019,0.0003338189],"about_ca_topic_score_codex":0.0000058655423,"about_ca_topic_score_gemma":0.00069749565,"teacher_disagreement_score":0.2975214,"about_ca_system_score_codex":0.00014369159,"about_ca_system_score_gemma":0.00012826842,"threshold_uncertainty_score":0.99991924},"labels":[],"label_agreement":null},{"id":"W968624666","doi":"10.1007/978-1-4614-6871-4_8","title":"Response-Dependent Sampling with Clustered and Longitudinal Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Sampling (signal processing); Context (archaeology); Inference; Sampling design; Statistics; Parametric statistics; Computer science; Clinical study design; Sample size determination; Longitudinal data; Econometrics; Data mining; Medicine; Artificial intelligence; Mathematics; Clinical trial; Environmental health; Population; Geography; Pathology","score_opus":0.15140007317682053,"score_gpt":0.38632894439278825,"score_spread":0.23492887121596773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W968624666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023432873,0.00023944229,0.9908692,0.00015639505,0.00013031867,0.0005065915,0.0038740665,0.000050141978,0.004150362],"genre_scores_gemma":[0.0013974383,0.00009410195,0.99530435,0.0001310702,0.00011939017,0.000012654591,0.0001931546,0.00013084016,0.0026170306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975181,0.00014172717,0.00061214075,0.00084488816,0.00048782106,0.00039529958],"domain_scores_gemma":[0.98426235,0.013953111,0.00033435875,0.0011410065,0.00016339666,0.00014580715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006881548,0.000548966,0.00081567536,0.00016115878,0.00009233491,0.0001588678,0.00046108625,0.0004194547,0.00091123534],"category_scores_gemma":[0.005225211,0.0004311152,0.000021001406,0.000045074856,0.00029863408,0.00006716958,0.00040858155,0.0009247616,0.000032288655],"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":[0.00059071527,0.000034503635,0.0001658009,0.00052848115,0.0001295454,0.00034348364,0.0002050987,0.000007570533,0.000015615962,0.8011026,0.000979815,0.19589677],"study_design_scores_gemma":[0.0004773741,0.00024247868,0.00038983766,0.00067632867,0.00020168246,0.000108134635,0.0000036196825,0.0018220263,0.000009489489,0.993014,0.0024713427,0.0005837343],"about_ca_topic_score_codex":0.000047866513,"about_ca_topic_score_gemma":0.0005146246,"teacher_disagreement_score":0.19531304,"about_ca_system_score_codex":0.00009185049,"about_ca_system_score_gemma":0.00015469849,"threshold_uncertainty_score":0.9998141},"labels":[],"label_agreement":null}]}