{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001238449,0.0006473014,0.0006958231,0.0006785244,0.0002730703,0.0006296766,0.000815124,0.0007173004,0.0007014959],"category_scores_gemma":[0.0015022,0.0003301993,0.0009072168,0.0006022267,0.0001084638,0.0006652723,0.0004629214,0.0006956639,0.000267478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004588425,"about_ca_system_score_gemma":0.0007791519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02151638,"about_ca_topic_score_gemma":0.01843685,"domain_scores_codex":[0.9997717,0.00006158843,0.00001849744,0.00007192628,0.000035776,0.00004066281],"domain_scores_gemma":[0.9994658,0.0002503715,0.00005905283,0.00003144034,0.0001666519,0.00002660694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007841844,0.0001012368,0.01543906,0.00003060905,0.0001379126,0.00007434867,0.00003214704,0.9188442,0.001376799,0.0003365735,0.0008591531,0.06268951],"study_design_scores_gemma":[0.000001410186,0.00001141391,0.0007196438,0.000001935349,0.000008748284,0.000002948017,0.000003604478,0.9989756,0.0001258724,0.00008663314,0.00006014297,0.00000195812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6505587,0.001190464,0.342833,0.0006265021,0.0001762431,0.00006343439,0.0006911878,0.0008859411,0.002974435],"genre_scores_gemma":[0.9648838,0.0002384596,0.03254342,0.00005023052,0.00003392475,0.0000472708,0.000660462,0.00001901919,0.001523506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02151638,"threshold_uncertainty_score":0.04278231,"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."}}