{"id":"W2090722999","doi":"10.1577/m05-099.1","title":"Visible Implant Elastomer Color Determination, Tag Visibility, and Tag Loss: Potential Sources of Error for Mark–Recapture Studies","year":2006,"lang":"en","type":"article","venue":"North American Journal of Fisheries Management","topic":"Aquatic life and conservation","field":"Agricultural and Biological Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"McGill University","keywords":"Orange (colour); Mark and recapture; Visibility; Skin color; Confusion; Computer vision; Artificial intelligence; Computer science; Biology; Horticulture; Geography; Psychology; Medicine","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":[0.0002685516,0.0001327172,0.0003347377,0.00003518647,0.0001288189,0.000045174,0.0001629928,0.00002112698,0.00002539555],"category_scores_gemma":[0.00003097646,0.00005863111,0.000101133,0.0002322241,0.0002664399,0.000195713,0.00006762853,0.00005474914,5.107785e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002552707,"about_ca_system_score_gemma":0.000008846621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001263027,"about_ca_topic_score_gemma":0.000982224,"domain_scores_codex":[0.9988382,0.0000549647,0.0005045464,0.0001597653,0.0002658809,0.0001765671],"domain_scores_gemma":[0.9987984,0.0001207135,0.0007044303,0.00004731823,0.0002812928,0.00004789026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006673923,0.0002185474,0.484933,0.000160657,0.0002876271,0.0000382785,0.0004497672,0.00008843973,0.0006718083,0.0001097092,0.007297185,0.5050776],"study_design_scores_gemma":[0.0004049383,0.001870064,0.9723487,0.00004018347,0.0001639447,0.00003824765,0.005602481,0.0001370863,0.0001580178,0.0006724274,0.01838975,0.0001741308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966249,0.0001425191,0.0002819879,0.002468632,0.0001070274,0.0002498225,0.00003043853,0.000008263723,0.00008645143],"genre_scores_gemma":[0.9973927,0.0001293005,0.001723687,0.0002732569,0.0001787118,0.00001398112,0.00001399122,0.000001586516,0.0002728011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5049035,"threshold_uncertainty_score":0.2390907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671383542908612,"score_gpt":0.2406193651229409,"score_spread":0.2239055296938547,"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."}}