{"id":"W4394272930","doi":"10.6084/m9.figshare.3516605","title":"Appendix B. Model selection analyses and parameter estimates for non-catch-related attributed for predicting trip choices by Thunder Bay area anglers.","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thunder; Bay; Selection (genetic algorithm); Geography; Statistics; Operations research; Computer science; Mathematics; Archaeology; Artificial intelligence; Meteorology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004362088,0.002092644,0.001302378,0.002869614,0.0008249679,0.001938762,0.003353288,0.002020087,0.3446723],"category_scores_gemma":[0.02792451,0.001320975,0.00197426,0.004116305,0.0004223794,0.001180758,0.001594056,0.002052946,0.1296048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001655247,"about_ca_system_score_gemma":0.002780176,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03797458,"about_ca_topic_score_gemma":0.07716534,"domain_scores_codex":[0.9985127,0.0004854317,0.0002608277,0.0003602839,0.0002610832,0.0001197114],"domain_scores_gemma":[0.9769785,0.01555782,0.001437924,0.002251752,0.003201408,0.000572629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008770207,0.00004305304,0.003007528,0.000753087,0.0001294197,0.00002074461,0.00001950929,0.00176682,0.00004434216,0.0003417924,0.9911324,0.002653565],"study_design_scores_gemma":[0.004805724,0.0001429815,0.02778746,0.002267839,0.0004255206,0.0001767119,0.000217462,0.009531919,0.0004930701,0.008109942,0.9459096,0.0001317698],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001287781,0.00001805001,0.0002305817,0.00003479129,0.000009547606,0.00002936324,0.9990823,0.0001897822,0.0002767297],"genre_scores_gemma":[0.001651811,0.00003037307,0.001841619,0.0000900899,0.00001091171,0.0007130842,0.9944181,0.0002087864,0.001035189],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9620254,"threshold_uncertainty_score":0.9347454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05336717443127736,"score_gpt":0.2965003054105219,"score_spread":0.2431331309792445,"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."}}