{"id":"W6963232747","doi":"10.21227/x2px-b132","title":"PlantSC for active learning","year":2022,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Active learning (machine learning); Set (abstract data type); Experiential learning; Active perception","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008758651,0.0006006808,0.0007527215,0.0004431324,0.0005055994,0.00008874993,0.001642539,0.0003169796,0.04319917],"category_scores_gemma":[0.0004505889,0.0006724765,0.0002102081,0.0003294279,0.00008700065,0.0003304892,0.0004768602,0.001797956,0.005858846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003932927,"about_ca_system_score_gemma":0.0004876817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006805479,"about_ca_topic_score_gemma":0.0002559496,"domain_scores_codex":[0.9964857,0.0001559667,0.0005270526,0.001175719,0.0008940761,0.0007615015],"domain_scores_gemma":[0.9968429,0.00033734,0.0008310987,0.001725513,0.00008249495,0.0001806933],"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":[0.0004019358,0.00013434,0.000005480687,0.0001455266,0.0003158302,0.0002554219,0.00002214988,0.00009436268,0.00009462191,0.000001455304,0.998226,0.0003029009],"study_design_scores_gemma":[0.0006762529,0.0001743692,0.00001448968,0.00003415239,0.0003803802,0.0001026262,0.0001128925,0.0000121453,0.0001336947,0.0000100651,0.997604,0.0007449101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003403693,0.00003413777,0.00001708264,0.00000859243,0.002164772,0.00106653,0.9962884,0.0002165616,0.0001698655],"genre_scores_gemma":[0.000003181369,0.0000552816,0.00005787731,0.0001302639,0.0007306879,0.0008889443,0.9966824,0.0002304492,0.001220929],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03734032,"threshold_uncertainty_score":0.9995726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04667618116840967,"score_gpt":0.3294183844703594,"score_spread":0.2827422033019497,"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."}}