{"id":"W7084095155","doi":"10.6084/m9.figshare.30117679","title":"Field data collected for \"Estimating the ecological drivers of insect abundance when detection is imperfect\"","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Agriculture, Water, and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Abundance (ecology); Aerial survey; Vegetation (pathology); Timer; Wildflower; Field survey","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002482831,0.0005302218,0.000504924,0.001590717,0.0005133504,0.0003937931,0.0006195404,0.000439574,0.02820608],"category_scores_gemma":[0.005630836,0.0003765453,0.0003708171,0.001752148,0.0002362007,0.0005477035,0.000348246,0.0005938479,0.01211343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006217812,"about_ca_system_score_gemma":0.0008461117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0133508,"about_ca_topic_score_gemma":0.04262962,"domain_scores_codex":[0.9989569,0.0002593302,0.0001057269,0.0002470957,0.0003419716,0.00008884761],"domain_scores_gemma":[0.9913305,0.002763918,0.001253711,0.001284591,0.002934489,0.0004326894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001001257,0.0008151729,0.1685351,0.001205886,0.0001424243,0.0002552929,0.0007534401,0.004909871,0.006777612,0.001336668,0.6901555,0.1241118],"study_design_scores_gemma":[0.0003075063,0.0006254697,0.6384374,0.0003590876,0.0001132771,0.0002155667,0.00107618,0.006073822,0.005279057,0.00108554,0.3463211,0.0001059032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08514395,0.00008417776,0.008831956,0.0002938538,0.000125562,0.001525402,0.8851849,0.002491575,0.01631852],"genre_scores_gemma":[0.1954805,0.0001966103,0.03823006,0.0005180145,0.00009434482,0.006921014,0.7300953,0.0008754472,0.02758867],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02820608,"threshold_uncertainty_score":0.09435874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04753640542004412,"score_gpt":0.2823625731551975,"score_spread":0.2348261677351534,"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."}}