{"id":"W3194453017","doi":"10.3390/su13169124","title":"Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Institut Alam Sekitar dan Pembangunan, Universiti Kebangsaan Malaysia; Universiti Tenaga Nasional; Universiti Kebangsaan Malaysia; Ministry of Higher Education, Malaysia; Tenaga Nasional Berhad","keywords":"Aquaculture; Production (economics); Gradient boosting; Brackish water; Random forest; Fishery; Fishing; Predictive power; Machine learning; Linear regression; Artificial neural network; Environmental science; Fish <Actinopterygii>; Artificial intelligence; Computer science; Ecology; Biology; Economics","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":[0.0004899599,0.00009033843,0.0001042886,0.00001418571,0.0002932324,0.00004985381,0.00006290199,0.0000645587,0.000304228],"category_scores_gemma":[0.001165654,0.00008744201,0.00002577467,0.0001784169,0.000091526,0.0003457414,0.0003244034,0.0001740985,6.043807e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005246745,"about_ca_system_score_gemma":0.00009832664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003571909,"about_ca_topic_score_gemma":0.001507692,"domain_scores_codex":[0.9989099,0.00009046509,0.0001415455,0.0004412235,0.0001680422,0.0002488162],"domain_scores_gemma":[0.9995165,0.00002169393,0.00002902449,0.0001956449,0.0001612105,0.00007588184],"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":[0.0001640252,0.0001182863,0.8027998,0.0002935668,0.00001064975,0.000005797828,0.001380482,0.006555225,0.0007443151,0.0006363041,0.0003547182,0.1869368],"study_design_scores_gemma":[0.0006011946,0.0002019365,0.2384997,0.000003543174,0.0000190835,0.00002467885,0.001490952,0.6653897,0.001297897,0.04288447,0.04927099,0.0003158798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795412,0.000003881088,0.01086971,0.005599458,0.000041382,0.000630528,0.000008361364,0.00007567121,0.00322977],"genre_scores_gemma":[0.9793665,0.00003851713,0.006133339,0.000124752,0.00005676065,0.0001097209,0.0001937803,0.00001449151,0.0139621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6588345,"threshold_uncertainty_score":0.3565782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836331857409129,"score_gpt":0.2735843774477801,"score_spread":0.2552210588736888,"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."}}