{"id":"W2097131685","doi":"10.3354/esr00178","title":"Application of forensic techniques to enhance fish conservation and management: injury detection using presumptive tests for blood","year":2008,"lang":"en","type":"article","venue":"Endangered Species Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Carleton University","funders":"Natural Resources Canada; Fisheries and Oceans Canada; Ontario Ministry of Research and Innovation; Queen's University; Ministry of Natural Resources","keywords":"Fish <Actinopterygii>; Fishery; Endangered species; Environmental science; Computer science; Ecology; Biology; Habitat","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":[0.003245576,0.0007616923,0.0005383901,0.00438585,0.0005846903,0.0008935087,0.0008762358,0.001474723,0.001401323],"category_scores_gemma":[0.005575265,0.0003322845,0.0003581424,0.001113514,0.001174967,0.001400889,0.0008415458,0.001001794,0.0005539704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003264825,"about_ca_system_score_gemma":0.000734181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000675507,"about_ca_topic_score_gemma":0.001442325,"domain_scores_codex":[0.9984627,0.00060487,0.0001111304,0.0001594144,0.0005993235,0.00006261658],"domain_scores_gemma":[0.9973194,0.001209522,0.0006193597,0.0001503188,0.0006387581,0.00006254583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004552256,0.0003643336,0.05051988,0.004821219,0.0001479154,0.002612478,0.0008064008,0.001293361,0.3451515,0.003505104,0.002496723,0.5878259],"study_design_scores_gemma":[0.00008897699,0.002988017,0.08702708,0.00155459,0.0004887783,0.03787821,0.001990764,0.009964747,0.8117404,0.008620703,0.03740503,0.0002527388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3803712,0.08322697,0.5089203,0.004675903,0.0006681092,0.0008455625,0.0005586309,0.000859464,0.01987396],"genre_scores_gemma":[0.6281852,0.0373127,0.3285617,0.001216256,0.0001761574,0.0002408084,0.0001994678,0.00004498522,0.004062793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00438585,"threshold_uncertainty_score":0.01716447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0527578467884644,"score_gpt":0.3440494409850199,"score_spread":0.2912915941965555,"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."}}