{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003986966,0.00007670089,0.0001080979,0.0001329977,0.000400438,0.00000886462,0.0001174951,0.00004548084,0.00003668856],"category_scores_gemma":[0.0001087167,0.00007959321,0.00001875344,0.00035466,0.000412858,0.0001392422,0.0003279175,0.00007591594,0.00000780126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009773756,"about_ca_system_score_gemma":0.000003289745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001514673,"about_ca_topic_score_gemma":0.001626256,"domain_scores_codex":[0.9990633,0.00005031514,0.0001467617,0.0002840116,0.0002351288,0.0002205505],"domain_scores_gemma":[0.9995714,0.00012462,0.00005462502,0.000166887,0.00004966938,0.00003276975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008001593,0.0005375797,0.2304649,0.0005610476,0.0002710836,0.00001208463,0.003389507,0.00006259818,0.5536847,0.002008857,0.1062692,0.1019383],"study_design_scores_gemma":[0.0001469362,0.0003156981,0.59364,0.00001957083,0.00001883727,0.000002056727,0.0002241755,0.0002991132,0.3787911,0.0008224199,0.02560544,0.0001146287],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811046,0.0000117777,0.01030215,0.0006038168,0.00002423399,0.001788222,0.00002041005,0.00003832332,0.006106534],"genre_scores_gemma":[0.9865956,0.0002954427,0.0107122,0.0001673321,0.00003270099,0.0005487691,0.000005754014,0.00001016731,0.001632025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3631751,"threshold_uncertainty_score":0.3245717,"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."}}