{"id":"W4378901918","doi":"10.1371/journal.pone.0285702","title":"Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Northeast Fisheries Science Center; Fisheries and Oceans Canada; Japan Student Services Organization; National Marine Fisheries Service; Washington and Lee University; National Fish and Wildlife Foundation","keywords":"Otolith; Pelagic zone; Fishery; Spatial ecology; Fish migration; Ecology; Geography; Oceanography; Environmental science; Biology; Fish <Actinopterygii>; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000695034,0.0003862541,0.000284499,0.002507169,0.0003215018,0.0005676746,0.000483986,0.0004322203,0.00042701],"category_scores_gemma":[0.002557243,0.0001811849,0.0005550391,0.001563775,0.0002734817,0.0007434449,0.000798092,0.0004062254,0.0002344591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006061176,"about_ca_system_score_gemma":0.0004835231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03771292,"about_ca_topic_score_gemma":0.07264246,"domain_scores_codex":[0.9997488,0.00004168045,0.00001753244,0.0001185032,0.00003929023,0.00003419906],"domain_scores_gemma":[0.9992594,0.0001762028,0.0002278692,0.00008437025,0.0001846498,0.00006751532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008471849,0.00003689564,0.8773891,0.00003231112,0.0001725531,0.0001400747,0.0001992168,0.04209553,0.004093604,0.0003960565,0.0008286707,0.07453129],"study_design_scores_gemma":[0.000007069304,0.00004976047,0.5743653,0.00002677361,0.00007915212,0.0001146453,0.000306783,0.4198309,0.001662194,0.001726308,0.001794082,0.0000369369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720529,0.0004244292,0.02435043,0.0001480169,0.00002193288,0.00001054593,0.00136378,0.000245253,0.001382621],"genre_scores_gemma":[0.9911475,0.00008192072,0.00740652,0.00001639795,0.00001224027,0.000008623708,0.001120804,0.00001951167,0.0001865831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03771292,"threshold_uncertainty_score":0.07498682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03900159447577039,"score_gpt":0.2346541537304281,"score_spread":0.1956525592546577,"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."}}