{"id":"W2739530168","doi":"","title":"Detection Probability of Spotted Gar in Canada, Based on Gear Type","year":2014,"lang":"en","type":"article","venue":"144th Annual Meeting of the American Fisheries Society","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science","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":[0.0006165216,0.0003829488,0.0003774972,0.001405922,0.0008728944,0.0009433437,0.0007524384,0.0004171408,0.00211156],"category_scores_gemma":[0.003297642,0.0002610312,0.0005446131,0.0009266824,0.0007690408,0.0003530437,0.0005305636,0.0003993918,0.0002750296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005147241,"about_ca_system_score_gemma":0.006748214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9557775,"about_ca_topic_score_gemma":0.9725065,"domain_scores_codex":[0.999453,0.00003888321,0.00002066835,0.00009352849,0.0001485647,0.0002453879],"domain_scores_gemma":[0.9975405,0.0005487487,0.0002707125,0.00007082828,0.001094457,0.0004746932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000497837,0.00002981733,0.9856907,0.0000271368,0.0000971633,0.0001676134,0.00028118,0.002778974,0.00201125,0.0001520451,0.001329085,0.006937217],"study_design_scores_gemma":[0.000009339745,0.00002937678,0.9905488,0.00001639695,0.00004688651,0.00008836568,0.00057413,0.007869532,0.0004209769,0.00007040693,0.000302453,0.00002339518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967862,0.0001420778,0.0002892094,0.00008160046,0.00001250052,0.000009768135,0.001223552,0.00003251382,0.001422552],"genre_scores_gemma":[0.9983186,0.00008718564,0.0001701262,0.00002201986,0.000002996008,0.0000025827,0.0006938339,0.000004776069,0.0006980294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04422247,"threshold_uncertainty_score":0.08896577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004706320623220326,"score_gpt":0.1728874923378569,"score_spread":0.1681811717146365,"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."}}