{"id":"W7070482327","doi":"","title":"Quebec lobster fisheries: Management, fishing boats, equipment and working processes","year":2015,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Fishing; Work (physics); Production (economics); Netting; Government (linguistics)","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":[0.0005920967,0.0003749523,0.0002769703,0.00156181,0.001166924,0.001630199,0.0007088083,0.0005169203,0.0125934],"category_scores_gemma":[0.001676665,0.0002088546,0.0003962923,0.003747645,0.0004899778,0.0005455956,0.000377623,0.0005763211,0.001042567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02344117,"about_ca_system_score_gemma":0.01524449,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929461,"about_ca_topic_score_gemma":0.9966335,"domain_scores_codex":[0.9996238,0.00005937798,0.0000198392,0.000065749,0.0001243978,0.0001069436],"domain_scores_gemma":[0.9988208,0.0001832776,0.0001807115,0.00004027941,0.0005781626,0.0001966692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000220899,0.00009629346,0.8575268,0.0002373463,0.000294193,0.0003846081,0.0007856475,0.003963064,0.000912703,0.003285228,0.04040049,0.09189278],"study_design_scores_gemma":[0.00001368095,0.00003091738,0.967019,0.0001116021,0.00006948827,0.000084486,0.00127783,0.003903711,0.0002373784,0.0003122968,0.0269076,0.00003192468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8519688,0.01052648,0.004938021,0.007053906,0.0002125833,0.0001827595,0.0756205,0.0002562064,0.0492408],"genre_scores_gemma":[0.9352728,0.003342308,0.00270314,0.0006240436,0.00003696086,0.0000906544,0.01111933,0.00004554409,0.04676515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02344117,"threshold_uncertainty_score":0.1700783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089359172146459,"score_gpt":0.2493929434187368,"score_spread":0.2284993516972722,"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."}}