{"id":"W3018624093","doi":"10.1016/j.jglr.2020.03.014","title":"Seasonal habitat-use differences among Lake Erie’s walleye stocks","year":2020,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor; Great Lakes Fishery Commission; Adam Taliaferro Foundation","keywords":"Habitat; Fishery; Stock (firearms); Structural basin; Stock assessment; Temporal scales; Geography; Ecology; Spatial ecology; Population; Environmental science; Biology; Fishing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008280295,0.0001290315,0.0002683861,0.00007906883,0.0003798263,0.00009252586,0.0005232917,0.00007313093,0.008438902],"category_scores_gemma":[0.0006649707,0.00009461285,0.0001017046,0.0002890608,0.0007837983,0.0005737505,0.0005490947,0.0006421523,0.0002640058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007691143,"about_ca_system_score_gemma":0.00002395187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001545728,"about_ca_topic_score_gemma":0.01500783,"domain_scores_codex":[0.9977633,0.0002649022,0.0002964684,0.0002226015,0.001004317,0.0004484613],"domain_scores_gemma":[0.9990468,0.0003582445,0.0001210409,0.0001245283,0.00007922028,0.0002701566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000118627,0.00005141381,0.7829579,0.00001176653,0.00006845486,0.0001900128,0.0006297803,0.00004795736,0.00005091224,0.00003442972,0.2151386,0.00070014],"study_design_scores_gemma":[0.0003638107,0.0005948872,0.9297954,0.0000191163,0.00001675686,0.00001145051,0.0004043496,0.0002633488,0.00002114944,0.0002547237,0.06815793,0.00009706197],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755193,0.00004399784,0.000076833,0.009550132,0.0001037024,0.000170981,0.00001364222,0.0000136809,0.01450775],"genre_scores_gemma":[0.9943421,0.0002261382,0.0002457425,0.0005178871,0.0001454566,0.000006228352,0.000001712149,0.00001096401,0.004503805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1469806,"threshold_uncertainty_score":0.9924675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06371787227332004,"score_gpt":0.3015651086238341,"score_spread":0.2378472363505141,"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."}}