{"id":"W4393834656","doi":"10.5281/zenodo.6780842","title":"High resolution global mass coral bleaching dataset Version 2.0","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coral; Coral bleaching; Environmental science; Resolution (logic); Oceanography; High resolution; Remote sensing; Geology; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007055266,0.001156794,0.0008422714,0.002083722,0.0004197857,0.001151746,0.001414473,0.0009397764,0.0222382],"category_scores_gemma":[0.001989198,0.0004154944,0.0007692208,0.003713763,0.0002056702,0.0007897301,0.001179031,0.001063505,0.03433782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007516003,"about_ca_system_score_gemma":0.001158642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01692902,"about_ca_topic_score_gemma":0.02548278,"domain_scores_codex":[0.9995422,0.00006763537,0.00006868684,0.0001338879,0.0001159964,0.0000716558],"domain_scores_gemma":[0.9993572,0.00009103206,0.00008919408,0.0001768323,0.000226483,0.00005931212],"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":[0.00009179287,0.00003717436,0.003807566,0.0005887478,0.00006634179,0.00005333555,0.0000390071,0.001091491,0.0006285103,0.0008527883,0.9868228,0.005920301],"study_design_scores_gemma":[0.0001810998,0.00002060072,0.02245129,0.000227686,0.00004267946,0.00009720097,0.0001107754,0.001167692,0.0008606549,0.001477636,0.9733196,0.00004301231],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004932072,0.00005108713,0.0001644174,0.0000367045,0.00001507705,0.00001218913,0.9982679,0.0003060046,0.0006533007],"genre_scores_gemma":[0.0006469999,0.00003256962,0.0004033532,0.00002559951,0.000004014803,0.00005140188,0.9983817,0.00005988949,0.0003943681],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0222382,"threshold_uncertainty_score":0.07439417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02737295418511743,"score_gpt":0.2398055335136322,"score_spread":0.2124325793285148,"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."}}