{"id":"W4407399706","doi":"10.32614/cran.package.sspm","title":"sspm: Spatial Surplus Production Model Framework for Northern Shrimp Populations","year":2022,"lang":"en","type":"dataset","venue":"","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shrimp; Production (economics); Fishery; Geography; Production model; Biology; Environmental science; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001591442,0.001174735,0.0007260415,0.001456341,0.0004002286,0.001337187,0.002941565,0.001515864,0.06984482],"category_scores_gemma":[0.005419778,0.0007742359,0.001557044,0.002730425,0.0002259045,0.001114949,0.001546414,0.001486743,0.03635392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001680498,"about_ca_system_score_gemma":0.002322715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08333881,"about_ca_topic_score_gemma":0.1375297,"domain_scores_codex":[0.9995337,0.0001598074,0.00004346554,0.0001263741,0.00007234139,0.00006432434],"domain_scores_gemma":[0.9990501,0.0003799133,0.00009036602,0.0001766307,0.0002357166,0.00006726097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009803437,0.00005082484,0.005991979,0.0006073979,0.0001412032,0.00005848643,0.00005824286,0.01837414,0.000120943,0.004325989,0.9604165,0.009756174],"study_design_scores_gemma":[0.0009211426,0.00005228516,0.01371238,0.0008153765,0.0001062675,0.0001354437,0.0002517793,0.0445929,0.0005569112,0.02409954,0.9146375,0.000118437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007240425,0.00006519927,0.002475848,0.0001712992,0.00002541873,0.00003151232,0.9943886,0.001112696,0.001005247],"genre_scores_gemma":[0.005711527,0.0001428447,0.009865452,0.0001445798,0.00001635786,0.0005518344,0.9809384,0.0004940237,0.002134842],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08333881,"threshold_uncertainty_score":0.2336542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0518480851134254,"score_gpt":0.311693685948027,"score_spread":0.2598456008346016,"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."}}