{"id":"W7097095895","doi":"","title":"Krill population dynamics in the Scotia Sea: variability","year":2001,"lang":"en","type":"article","venue":"","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Population; Krill; Nova scotia; Fishing; Selection (genetic algorithm); Ectotherm","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":[0.0003491559,0.0001123432,0.0001810362,0.0006055938,0.0002677081,0.0004747244,0.0002039782,0.0001834573,0.0006376277],"category_scores_gemma":[0.001183459,0.0001616862,0.0001936272,0.0006516302,0.0004393449,0.0002306527,0.0002997589,0.0001794453,0.0001075982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139291,"about_ca_system_score_gemma":0.0005646115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3644932,"about_ca_topic_score_gemma":0.5706757,"domain_scores_codex":[0.9998639,0.00001941221,0.00001127969,0.00004501258,0.00002419075,0.00003620907],"domain_scores_gemma":[0.9991884,0.0001642654,0.0001924069,0.00006844612,0.0002665933,0.0001200291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001262381,0.000008848489,0.9935726,0.00001170953,0.0001109407,0.0000917435,0.0003513565,0.0007435228,0.001902625,0.00005778602,0.0002438587,0.002778827],"study_design_scores_gemma":[0.000001502781,0.000005706333,0.9994859,0.000001294403,0.000007022304,0.00001851656,0.00008636927,0.000295321,0.00002178366,0.000009164551,0.00006547296,0.000001966872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994906,0.00004508594,0.00003031872,0.00001580825,0.000001607365,9.296589e-7,0.0001765905,0.000001601584,0.0002374142],"genre_scores_gemma":[0.9996464,0.00002589928,0.00002355142,0.000005682229,0.000001061267,8.17902e-7,0.0001616069,0.000001134918,0.0001338387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3644932,"threshold_uncertainty_score":0.7247431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134617980991652,"score_gpt":0.2499281529867407,"score_spread":0.2385819731768242,"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."}}