{"id":"W4404195792","doi":"10.1016/j.jglr.2024.102460","title":"Rehabilitation progress can’t be assessed without a measuring stick: Development of a recruitment index survey for lake sturgeon in Lake Superior","year":2024,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Michigan Department of Natural Resources; Michigan Technological University; U.S. Fish and Wildlife Service; Wisconsin Department of Natural Resources; Parks Canada; Ontario Ministry of Natural Resources and Forestry; Minnesota Department of Natural Resources","keywords":"Index (typography); Rehabilitation; Sturgeon; Fishery; Lake sturgeon; Geography; Acipenser; Environmental science; Biology; Fish <Actinopterygii>; Medicine; Physical therapy; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799542,0.0003837837,0.0002102446,0.0007038262,0.0007800928,0.0003655902,0.0004785408,0.0005134338,0.001079014],"category_scores_gemma":[0.00203146,0.0002747414,0.0003136099,0.0006535047,0.0004100357,0.0008285827,0.0006942436,0.0003685283,0.0003302679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387314,"about_ca_system_score_gemma":0.002425065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09490263,"about_ca_topic_score_gemma":0.3840812,"domain_scores_codex":[0.9995382,0.000108423,0.00004646448,0.0000725973,0.0001501293,0.00008419972],"domain_scores_gemma":[0.9990833,0.0000694218,0.0001913096,0.00004721045,0.0003761098,0.0002327268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007788352,0.0001651435,0.9704046,0.00004701577,0.00003806251,0.0001477875,0.00186642,0.0003176464,0.00556995,0.00007300587,0.00128256,0.02001],"study_design_scores_gemma":[0.000008398893,0.0004394477,0.9954715,0.00002081144,0.00001756729,0.00005429364,0.001502418,0.0009290943,0.0004009022,0.00003786098,0.001107165,0.00001055256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981194,0.00002452983,0.0006066749,0.000206777,0.000009804202,0.00006541981,0.0001774243,0.00001587241,0.0007741349],"genre_scores_gemma":[0.9909692,0.00007133171,0.00596113,0.0001338238,0.000009440732,0.0002215701,0.0005859221,0.000007942021,0.002039712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09490263,"threshold_uncertainty_score":0.1887005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516775910759351,"score_gpt":0.3883246443467634,"score_spread":0.2366470532708284,"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."}}