{"id":"W4220861638","doi":"10.1007/s40009-022-01110-0","title":"Application of Machine Learning to Investigate the Impact of Climatic Variables on Marine Fish Landings","year":2022,"lang":"en","type":"article","venue":"National Academy Science Letters","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Universiti Tenaga Nasional; Kementerian Pendidikan","keywords":"Fish <Actinopterygii>; Marine fish; Decision tree; Random forest; Fishery; Environmental science; Linear regression; Regression analysis; Statistics; Regression; Value (mathematics); Mathematics; Computer science; Machine learning; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.002051746,0.00007338867,0.00009242385,0.0001053079,0.0003943032,0.0000131942,0.0006051713,0.00001878866,0.0003806496],"category_scores_gemma":[0.0005797544,0.00005156616,0.00003908563,0.001211374,0.0005296547,0.0001184932,0.0004744369,0.0003139952,0.00002157927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003712475,"about_ca_system_score_gemma":0.00001584888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003881324,"about_ca_topic_score_gemma":8.830127e-7,"domain_scores_codex":[0.9981527,0.0000770246,0.0002038769,0.0002418705,0.001130741,0.0001937797],"domain_scores_gemma":[0.9994676,0.000215406,0.0001845074,0.00006072668,0.000009445872,0.00006227805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001015757,0.00002105593,0.05791064,0.000001515852,0.000002197914,1.084993e-7,0.0001452225,0.7537822,0.1866625,0.0003676523,0.0004465951,0.0006502184],"study_design_scores_gemma":[0.0001587641,0.0002190781,0.519915,0.000006770524,0.000005098572,0.00001127175,0.000009175956,0.4713328,0.003781955,0.003499593,0.0009375369,0.0001229444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867055,4.806265e-7,0.0001669283,0.01169734,0.00001242065,0.0001622983,0.00001578668,0.00001559054,0.001223603],"genre_scores_gemma":[0.9930701,3.018474e-7,0.0009343073,0.005923488,0.00001323571,0.00002536794,0.00000542732,0.000004286214,0.00002345419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4620044,"threshold_uncertainty_score":0.4167846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004901072623623,"score_gpt":0.2776676361497191,"score_spread":0.2576186254234829,"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."}}