{"id":"W4377079771","doi":"10.2139/ssrn.4452552","title":"Finding the Needles in the Haystack: A Collaborative Approach to Ghost Gear Localisation Using Local Fishers’ Knowledge and a Towed Camera System","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Human Motion and Animation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Haystack; Computer science; Human–computer interaction; Engineering; Artificial intelligence","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002416098,0.0002564599,0.000267831,0.000277278,0.0003033536,0.0003787386,0.0003164979,0.0001718834,0.000002754186],"category_scores_gemma":[0.00004283902,0.0001796456,0.00006933788,0.0005204658,0.00004721119,0.0001175445,0.00008955417,0.002449432,0.00001615021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002290399,"about_ca_system_score_gemma":0.0007233395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001035459,"about_ca_topic_score_gemma":0.0008310477,"domain_scores_codex":[0.997876,0.0003359855,0.0004019981,0.0002307928,0.0002536844,0.0009015338],"domain_scores_gemma":[0.999463,0.00008597479,0.0001175657,0.0001836965,0.00009420278,0.00005553745],"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":[0.00005191101,0.00007615266,0.0002866572,0.0006989621,0.0004536833,0.00001006168,0.12256,0.8237425,0.0003518159,0.03942886,0.001201957,0.01113744],"study_design_scores_gemma":[0.0005615012,0.0000816351,0.001192878,0.0007023092,0.00009076283,0.0002385071,0.2209661,0.7717311,0.00002712569,0.003517542,0.0004663588,0.0004242217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4326647,0.00324636,0.5595661,0.0004481179,0.0005877695,0.001294036,0.0000197172,0.0001950799,0.001978103],"genre_scores_gemma":[0.9989348,0.0003738333,0.0001430082,0.00002865439,0.0003130643,0.00004829489,0.00001774472,0.0000501807,0.00009040494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5662701,"threshold_uncertainty_score":0.9998519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03267459596258843,"score_gpt":0.2609887607878816,"score_spread":0.2283141648252932,"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."}}