{"id":"W4398810728","doi":"10.7910/dvn/u1jec0/p0u9z8","title":"CanRelIncs.tab","year":2020,"lang":"hu","type":"dataset","venue":"Harvard Dataverse","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Identity (music); Genealogy; Political science; Internet privacy; Biology; Computer science; History; Art; Virology; Aesthetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.002599397,0.001397993,0.001660519,0.0003485172,0.0008749394,0.002328722,0.006935121,0.0007450648,0.3152153],"category_scores_gemma":[0.004530448,0.001392382,0.0003038864,0.0008350836,0.001070896,0.001273951,0.00513925,0.001459271,0.9325083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003434596,"about_ca_system_score_gemma":0.001137401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004563105,"about_ca_topic_score_gemma":0.0003284368,"domain_scores_codex":[0.9900046,0.00130417,0.001680811,0.003054584,0.002168488,0.001787333],"domain_scores_gemma":[0.9924373,0.0004394854,0.001427038,0.004303298,0.0002541712,0.001138697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000214052,0.0001519189,0.00001434899,0.0007096371,0.0000404862,0.0007110611,0.0001855479,0.000617235,0.03586353,0.000114444,0.9612851,0.00009269454],"study_design_scores_gemma":[0.0007824478,0.0002781374,0.00006622347,0.0003574242,0.0002965299,0.0001008851,0.0001190254,0.001367431,0.001984923,0.00008009061,0.9930737,0.001493185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005768283,0.000003707801,0.0007184469,0.0001282679,0.01114202,0.000880405,0.9856959,0.000357531,0.0004968677],"genre_scores_gemma":[0.000249718,0.0006002294,0.00828544,0.002752842,0.003704019,0.00006670286,0.983049,0.0001413259,0.001150708],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6172931,"threshold_uncertainty_score":0.999877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684449904306025,"score_gpt":0.2578574978988302,"score_spread":0.24101299885577,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}