{"id":"W6887912783","doi":"10.17632/p68644t3rc","title":"Keewaywin Formation Data","year":2020,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mineral; Work (physics); Data analysis; Feature (linguistics); Data collection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.001558431,0.0007276739,0.0007717381,0.0002925486,0.0001832693,0.0004125111,0.02221495,0.0004362119,0.001693631],"category_scores_gemma":[0.001585489,0.0007385249,0.00004357845,0.000670394,0.00008748872,0.004441814,0.02584733,0.001180028,0.1694707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949194,"about_ca_system_score_gemma":0.0004061059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004865588,"about_ca_topic_score_gemma":0.001358055,"domain_scores_codex":[0.9943672,0.0003245106,0.0009624164,0.002250932,0.001383755,0.0007111967],"domain_scores_gemma":[0.9724522,0.0001247096,0.0007436952,0.02626981,0.00007533375,0.0003342482],"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":[0.00005267189,0.00009638748,2.968538e-7,0.0003498461,0.0001559332,0.00008793832,0.00001452358,0.000001327499,0.00003402345,0.000003972189,0.9977763,0.001426782],"study_design_scores_gemma":[0.0005620339,0.00003860626,0.000002015519,0.0001375566,0.0005052689,0.00004869905,0.00004321281,0.005203389,0.00001581652,0.00002841004,0.9926592,0.0007558372],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[2.329087e-7,0.0003722506,0.0002468025,0.0004181545,0.0006248782,0.000716492,0.9971101,0.0003295979,0.0001814674],"genre_scores_gemma":[0.000001990035,0.0007444045,0.00121826,0.0007003252,0.001158067,0.00002445231,0.9959407,0.0001637209,0.00004808201],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.167777,"threshold_uncertainty_score":0.9995066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3027163280349188,"score_gpt":0.3727687870665841,"score_spread":0.07005245903166529,"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."}}