{"meta":{"query_hash":"135cd23e8e8d","filters":{"venue":"Data Science in Science"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/135cd23e8e8d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Data+Science+in+Science"},"results":[{"id":"W4399754935","doi":"10.1080/26941899.2024.2360892","title":"Model Selection for Exposure-Mediator Interaction","year":2024,"lang":"en","type":"article","venue":"Data Science in Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Eisai Incorporated; Canadian Institutes of Health Research; University of Southern California; AbbVie Canada; National Institutes of Health; Genentech; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; Biogen; BioClinica; Roche; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Novartis Pharmaceuticals Corporation; Merck; Alzheimer's Association","keywords":"Mediator; Selection (genetic algorithm); Psychology; Computer science; Medicine; Artificial intelligence; Internal medicine","score_opus":0.405217553711427,"score_gpt":0.5323269213190341,"score_spread":0.12710936760760716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399754935","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":[0.0114129195,0.00031886832,0.98615813,0.0005898361,0.000094384035,0.0003680806,0.00025752827,0.0004590263,0.00034127932],"genre_scores_gemma":[0.32584494,0.00051849446,0.66337025,0.0007522357,0.00039051654,0.003830854,0.0015658082,0.0002892388,0.003437724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9746687,0.021787778,0.0005663877,0.001698955,0.0007958347,0.0004823075],"domain_scores_gemma":[0.96362036,0.03202059,0.0008557576,0.0017643725,0.0013520523,0.00038681665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03666107,0.0024937738,0.0032589626,0.0023307786,0.0016459957,0.0017062129,0.004066697,0.0023033745,0.009436379],"category_scores_gemma":[0.050928928,0.0010291914,0.0047713644,0.002528665,0.0013704534,0.0017074243,0.0027311721,0.004592246,0.0012714197],"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":[0.0028889833,0.0009090828,0.048427608,0.001553665,0.0071280934,0.0031160533,0.0019114262,0.41321456,0.005024012,0.1696377,0.017087294,0.32910153],"study_design_scores_gemma":[0.0003403265,0.00030709655,0.0022261366,0.000076149874,0.00047171538,0.00018380959,0.00013332524,0.92735547,0.00086880545,0.06477947,0.0032058905,0.00005191881],"about_ca_topic_score_codex":0.006050504,"about_ca_topic_score_gemma":0.0064616585,"teacher_disagreement_score":0.03666107,"about_ca_system_score_codex":0.0010829287,"about_ca_system_score_gemma":0.004684645,"threshold_uncertainty_score":0.19388461},"labels":[],"label_agreement":null},{"id":"W4416757099","doi":"10.1080/26941899.2025.2583507","title":"Are Neural Representation Learning Methods a Viable Alternative to TMLE for Causal Estimation?","year":2025,"lang":"en","type":"article","venue":"Data Science in Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Feature learning; Representation (politics); Estimator; Variance (accounting); Causal inference; Artificial neural network; Deep learning","score_opus":0.42050086401940895,"score_gpt":0.6220922742342814,"score_spread":0.20159141021487242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416757099","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":[0.011920799,0.0008643266,0.98344594,0.0018848461,0.00005434735,0.00005781953,0.00023396083,0.0004396155,0.0010982447],"genre_scores_gemma":[0.4294671,0.0013434005,0.56343853,0.001828397,0.00018263189,0.0005698846,0.0010081857,0.00024712968,0.0019148157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99240327,0.0061769155,0.00024964692,0.00054126425,0.00051762495,0.000111333546],"domain_scores_gemma":[0.93628436,0.05416998,0.0027977205,0.004666753,0.0016598091,0.00042132186],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02535079,0.000960428,0.0014801438,0.0015090842,0.00039538817,0.0018438841,0.002539439,0.0019336545,0.004384817],"category_scores_gemma":[0.102719225,0.00054981734,0.0013069946,0.0017523167,0.0016719264,0.004578697,0.0025378729,0.0032102023,0.0005526869],"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":[0.0005080155,0.00016106982,0.00957976,0.0008246999,0.0010131191,0.00016975543,0.00026259822,0.53495556,0.00088319567,0.19231735,0.006369065,0.2529558],"study_design_scores_gemma":[0.00007482604,0.00010514673,0.00068224134,0.000119364195,0.000055057888,0.00006368142,0.000027135651,0.8217729,0.0005832331,0.1743041,0.0021865058,0.00002588376],"about_ca_topic_score_codex":0.0027643077,"about_ca_topic_score_gemma":0.0033387805,"teacher_disagreement_score":0.9746492,"about_ca_system_score_codex":0.0011944694,"about_ca_system_score_gemma":0.0016466217,"threshold_uncertainty_score":0.13406944},"labels":[],"label_agreement":null}]}