{"id":"W4409441228","doi":"10.22541/au.174470062.24167792/v1","title":"Advancing environmental DNA as a tool for fisheries management by predicting salmon abundance across a range of spawning habitats","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada","keywords":"Fishery; Abundance (ecology); Habitat; Range (aeronautics); Environmental DNA; Fisheries management; Fish <Actinopterygii>; Ecology; Geography; Environmental science; Fishing; Biology; Biodiversity; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.001536947,0.0008083693,0.0005822573,0.002275,0.0003633318,0.001674175,0.0007653302,0.0007632406,0.001938441],"category_scores_gemma":[0.00351878,0.0003323301,0.0004234926,0.002325269,0.0004243531,0.001591585,0.001352936,0.0008381639,0.0008911747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006794854,"about_ca_system_score_gemma":0.0009592779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005909901,"about_ca_topic_score_gemma":0.01723162,"domain_scores_codex":[0.9992377,0.0001533742,0.00006922545,0.0003174183,0.0001860214,0.00003632577],"domain_scores_gemma":[0.9977708,0.0007107407,0.0006686553,0.0001517575,0.0005629322,0.0001350352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001156787,0.0001167778,0.6558142,0.0004634064,0.0002005184,0.0001048694,0.0004968154,0.008647596,0.05368396,0.001715718,0.001835548,0.2768049],"study_design_scores_gemma":[0.00003710765,0.0007564407,0.7746656,0.0009361803,0.0006112632,0.0004895672,0.002247482,0.09929794,0.04910207,0.01439373,0.05717639,0.0002862324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6421229,0.009141741,0.3069995,0.002755183,0.0003048642,0.0002301374,0.02409773,0.002161176,0.01218677],"genre_scores_gemma":[0.6679087,0.004175602,0.3141076,0.001362901,0.0001302374,0.0001967026,0.008504948,0.0001648903,0.003448367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005909901,"threshold_uncertainty_score":0.011751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008360703220470047,"score_gpt":0.2312006805456785,"score_spread":0.2228399773252085,"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."}}