{"id":"W7133277100","doi":"","title":"California sea lion population assessment","year":2023,"lang":"en","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":"Fisheries and Oceans Canada","keywords":"Sea lion; Overwintering; Abundance (ecology); Aerial survey; Population; Sea ice","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006481583,0.0004766206,0.000294952,0.002701638,0.0007427626,0.0006918431,0.0009059907,0.0002238829,0.008305938],"category_scores_gemma":[0.001536664,0.0002296976,0.0002816979,0.001932038,0.0001447308,0.0003212239,0.0006246027,0.0004577619,0.001154678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002421233,"about_ca_system_score_gemma":0.002296907,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5525633,"about_ca_topic_score_gemma":0.8050767,"domain_scores_codex":[0.9996333,0.00002709639,0.00003334973,0.00007349726,0.000174473,0.00005824695],"domain_scores_gemma":[0.9987682,0.0000385846,0.0001314188,0.00003911634,0.0008969092,0.0001258181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001653299,0.0001534796,0.8351263,0.0002750611,0.0001502114,0.0001926498,0.0009425368,0.002019819,0.0005617184,0.000697602,0.079882,0.07983329],"study_design_scores_gemma":[0.00002440395,0.0001084852,0.9235482,0.0002097895,0.00008336164,0.00009167805,0.001014936,0.002461306,0.0001904371,0.0001751072,0.07206062,0.000031581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7766516,0.001581714,0.003866372,0.0005096612,0.0001214843,0.002386236,0.08523695,0.0006470802,0.1289988],"genre_scores_gemma":[0.7623625,0.002954906,0.01217968,0.0005033807,0.00006581344,0.003058109,0.1294364,0.000117685,0.08932154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5525633,"threshold_uncertainty_score":0.9001434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030217279726133,"score_gpt":0.2601583193642568,"score_spread":0.2498561465669954,"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."}}