{"id":"W4281296297","doi":"10.1111/2041-210x.13896","title":"Occupancy–detection models with museum specimen data: Promise and pitfalls","year":2022,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Compute Canada; Georgetown University; Simon Fraser University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Occupancy; Computer science; Inference; Suite; Workflow; Data science; Data collection; Focus (optics); Statistical inference; Data mining; Geography; Ecology; Artificial intelligence; Statistics; Database; Archaeology; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009745823,0.00007985457,0.0001138212,0.0000417072,0.0002716556,0.000009953713,0.0001078648,0.0000509447,0.002561893],"category_scores_gemma":[0.00003232511,0.00007529821,0.000007250177,0.0001886581,0.0001657743,0.0002480985,0.0004691647,0.0001618197,0.000009449711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004790578,"about_ca_system_score_gemma":0.000007666981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002122379,"about_ca_topic_score_gemma":0.001793402,"domain_scores_codex":[0.9989295,0.0003377198,0.0001187678,0.0003388161,0.00008761648,0.0001875792],"domain_scores_gemma":[0.9996544,0.00006005988,0.0000528454,0.0001880818,0.000003322518,0.00004131043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009115575,0.0006050587,0.932998,0.00004013815,0.0000419354,0.00003726774,0.001624142,0.0025739,0.02029675,0.005949248,0.002745031,0.03217699],"study_design_scores_gemma":[0.0006504445,0.0002198875,0.9713337,0.00000147609,0.00001575274,0.00007873516,0.00141721,0.01694694,0.0001302303,0.00337983,0.005691732,0.0001340788],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772261,0.0001427613,0.01789525,0.0003083276,0.000177635,0.0002677066,0.00004830991,0.00002947469,0.003904467],"genre_scores_gemma":[0.9906433,0.00007793961,0.008892688,0.00009624806,0.00001250216,0.00007990735,0.00003543616,0.000005672454,0.0001562646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03833571,"threshold_uncertainty_score":0.9983499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05728199411530923,"score_gpt":0.3230391037336881,"score_spread":0.2657571096183789,"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."}}