{"id":"W3211198996","doi":"","title":"Quantifying Lake Ontario coregonine habitat use dynamic’s across space and time to inform assessment and restoration","year":2021,"lang":"en","type":"article","venue":"","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Habitat; Geography; Environmental science; Environmental resource management; Ecology; Biology","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":[0.000404942,0.0001697835,0.000176081,0.001143433,0.0008866945,0.001236453,0.0003636036,0.0002700298,0.002050878],"category_scores_gemma":[0.001593215,0.0001699098,0.0002039358,0.001801513,0.0004085855,0.0007240826,0.0006132977,0.0001966426,0.000195577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00643534,"about_ca_system_score_gemma":0.005155334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.848416,"about_ca_topic_score_gemma":0.968116,"domain_scores_codex":[0.9997618,0.00003527307,0.00001187708,0.00006449716,0.00006503761,0.00006145745],"domain_scores_gemma":[0.9993116,0.0000798055,0.0001913206,0.00005128413,0.0003006909,0.00006534914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009539569,0.000032376,0.9413087,0.0000895781,0.0001609943,0.00006151413,0.001876986,0.005724634,0.004152327,0.0007017772,0.003111413,0.04268424],"study_design_scores_gemma":[0.000003118233,0.00001618101,0.9866241,0.00002172308,0.00002514105,0.00001948909,0.001580364,0.004918961,0.0004092724,0.0002038766,0.006168417,0.000009242995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877115,0.0002935078,0.002435759,0.0002360375,0.000007583109,0.00003797536,0.002361917,0.00003489617,0.006880904],"genre_scores_gemma":[0.9941465,0.0001644839,0.002039734,0.00002992796,0.000002538518,0.00002584174,0.0009552268,0.00001336277,0.002622572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.151584,"threshold_uncertainty_score":0.3049535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0333307007555546,"score_gpt":0.2823052246193371,"score_spread":0.2489745238637825,"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."}}