{"id":"W4399577333","doi":"10.32614/cran.package.detect","title":"detect: Analyzing Wildlife Data with Detection Error","year":2011,"lang":"en","type":"dataset","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wildlife; Computer science; Geography; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002517746,0.0002514588,0.0001771383,0.000128923,0.0001424241,0.00007351475,0.0009540653,0.0003534539,0.0003317961],"category_scores_gemma":[0.0001033833,0.0002147584,0.00005115921,0.0001656836,0.00009572312,0.000007456767,0.0002753919,0.0001893699,0.0003281805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009954804,"about_ca_system_score_gemma":0.0001086898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001743234,"about_ca_topic_score_gemma":0.002080499,"domain_scores_codex":[0.9984034,0.00006675926,0.0003112319,0.000843657,0.0001733993,0.000201539],"domain_scores_gemma":[0.9966329,0.000005406496,0.0002757658,0.002841596,0.0001560446,0.00008833249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004904634,0.0000387975,0.000006302923,0.00003599194,0.0001127256,8.35095e-7,0.000001803398,0.000001053363,0.007295765,0.000003309404,0.9918297,0.0006246608],"study_design_scores_gemma":[0.0001818968,0.0001148853,0.00007657464,0.00001459977,0.0001179799,0.00002166532,0.0000166116,0.00001372004,0.02724061,0.000002094574,0.9718821,0.0003172529],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002524533,0.0003086845,0.00683756,0.00005034769,0.0004186164,0.0002629834,0.9916114,0.00003280454,0.0002250994],"genre_scores_gemma":[0.002438721,0.0004658198,0.0005059888,0.0002693423,0.0002694873,0.00003543434,0.9945538,0.00002894648,0.001432457],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0199476,"threshold_uncertainty_score":0.8757594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05339149696102556,"score_gpt":0.2991228085906537,"score_spread":0.2457313116296281,"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."}}