{"id":"W4200340691","doi":"10.1101/2021.12.05.471316","title":"Utilizing occupancy-detection models with museum specimen data: promise and pitfalls","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Simon Fraser University; Western Canada Research Grid; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Liber Ero Foundation; Georgetown University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Occupancy; Computer science; Inference; Data collection; Focus (optics); Workflow; Data science; Suite; Data mining; Geography; Ecology; Artificial intelligence; Archaeology; Statistics; Database; Mathematics","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":[0.05617693,0.001467631,0.002087421,0.001844864,0.001050086,0.005097818,0.004751473,0.002639406,0.001717541],"category_scores_gemma":[0.1389234,0.001788661,0.002594638,0.003406301,0.002844908,0.006473501,0.002659926,0.00357105,0.0009325422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001562157,"about_ca_system_score_gemma":0.002413288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03418867,"about_ca_topic_score_gemma":0.02934223,"domain_scores_codex":[0.9811106,0.01455084,0.0006490529,0.00193539,0.00142712,0.0003269676],"domain_scores_gemma":[0.8133887,0.1517634,0.00673689,0.02043439,0.006223425,0.001453172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004293945,0.0004192484,0.2072791,0.0007570923,0.002081198,0.0002382784,0.001051411,0.6069437,0.001764889,0.02594825,0.009076811,0.1440106],"study_design_scores_gemma":[0.00003278207,0.00005169358,0.01195886,0.0001194334,0.00005764153,0.00008497485,0.0002749926,0.9351275,0.0005492315,0.04848113,0.0031716,0.00009023521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1541609,0.001812358,0.8303268,0.0070246,0.0003084764,0.000178144,0.001867673,0.001886684,0.002434387],"genre_scores_gemma":[0.5853527,0.001072394,0.4073821,0.0009179283,0.000459877,0.0003843728,0.002519773,0.0005581221,0.001352755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05617693,"threshold_uncertainty_score":0.2970955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04066714443187562,"score_gpt":0.2282251227368577,"score_spread":0.1875579783049821,"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."}}