{"id":"W4366428877","doi":"10.21203/rs.3.rs-2294878/v2","title":"A collaborative and near-comprehensive North Pacific humpback whale photo-ID dataset","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"Office of Naval Research; Fisheries and Oceans Canada; National Oceanic and Atmospheric Administration; Parks Canada; Strong; Consejo Nacional de Ciencia y Tecnología; U.S. Department of Defense","keywords":"Humpback whale; Geography; Whale; Marine mammal; Fishery; Resource (disambiguation); Scale (ratio); Structural basin; Oceanography; Cartography; Computer science; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008763653,0.0009163499,0.0006092449,0.003960186,0.0008858833,0.001071828,0.001269001,0.001091282,0.03768038],"category_scores_gemma":[0.003089889,0.000495711,0.0006666934,0.005789582,0.0002964355,0.0009896891,0.002184677,0.0009674688,0.03910568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009379538,"about_ca_system_score_gemma":0.002760114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09082773,"about_ca_topic_score_gemma":0.1741947,"domain_scores_codex":[0.9992704,0.00006713647,0.00004666445,0.0002361654,0.0002441554,0.0001353806],"domain_scores_gemma":[0.9976329,0.0003953226,0.000149339,0.0007869974,0.0007129783,0.0003225208],"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":[0.00007921875,0.00004992435,0.005418984,0.0003308527,0.00005784581,0.00005745172,0.0001045727,0.0006483745,0.0008961115,0.0005891079,0.9773727,0.01439473],"study_design_scores_gemma":[0.00006904828,0.00001880992,0.05228595,0.0001419214,0.0000549444,0.0001011631,0.0003461764,0.001295433,0.0012906,0.001285637,0.9430665,0.00004377083],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001553198,0.00004887503,0.0003318735,0.00006035715,0.00002092051,0.00002469377,0.9951764,0.0004217357,0.00236195],"genre_scores_gemma":[0.001383434,0.00003360106,0.000828471,0.00002291916,0.000004599582,0.00006732545,0.9961509,0.00008557907,0.001423169],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09082773,"threshold_uncertainty_score":0.1805981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09164995179186602,"score_gpt":0.3758641224920348,"score_spread":0.2842141707001688,"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."}}