{"id":"W7133277904","doi":"","title":"Réponse des Sciences : Mise à jour sur l’état du stock de homard de la ZPH 33","year":2024,"lang":"fr","type":"other","venue":"Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock (firearms); Information scientist; Western europe; Context (archaeology)","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":[0.003180982,0.0003618359,0.0004573397,0.003012051,0.003696697,0.003153239,0.001156767,0.0008949055,0.02545357],"category_scores_gemma":[0.005847448,0.0001969973,0.0004893722,0.002896146,0.001061376,0.001055376,0.001965818,0.001049171,0.003826485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01019454,"about_ca_system_score_gemma":0.02580482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7216108,"about_ca_topic_score_gemma":0.8633842,"domain_scores_codex":[0.9979001,0.0001903493,0.00004992615,0.0002104077,0.001337249,0.0003119486],"domain_scores_gemma":[0.9913452,0.0006095068,0.0006134878,0.0003471209,0.00570308,0.001381686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008084683,0.0001827355,0.3346441,0.00110185,0.0003113872,0.001490736,0.01314551,0.0008250218,0.01462198,0.009388653,0.1461626,0.477317],"study_design_scores_gemma":[0.00001813048,0.0002753024,0.5715323,0.0005399294,0.000108562,0.0002876442,0.01498151,0.0003573794,0.00527125,0.001751944,0.4048043,0.00007170692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5670205,0.02498164,0.006467627,0.04050868,0.005275543,0.0005607408,0.03012677,0.000607072,0.3244514],"genre_scores_gemma":[0.5649717,0.01806749,0.0125702,0.004581375,0.0007483148,0.0002879307,0.01422801,0.0002604666,0.3842845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2783892,"threshold_uncertainty_score":0.5600573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395971942013846,"score_gpt":0.2564274830109708,"score_spread":0.2424677635908324,"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."}}