{"id":"W6924669874","doi":"10.15468/dl.w4svkm","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Legal Cases and Commentary","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Download; Range (aeronautics); State (computer science); Identification (biology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001064854,0.001600816,0.001240886,0.004764891,0.0009707531,0.002715576,0.002636316,0.002244293,0.185577],"category_scores_gemma":[0.007736032,0.0007876761,0.0009943083,0.008826911,0.0005319613,0.002006038,0.002483755,0.002104887,0.2184274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001887457,"about_ca_system_score_gemma":0.002913566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0259339,"about_ca_topic_score_gemma":0.04479339,"domain_scores_codex":[0.9989905,0.0001583349,0.0001390262,0.0003128955,0.0002346746,0.0001645559],"domain_scores_gemma":[0.9968879,0.001064367,0.000317908,0.0006721786,0.0007195537,0.0003380889],"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":[0.00002231505,0.000007891212,0.000312585,0.0003768704,0.000008520928,0.00001364369,0.0000173733,0.0001035777,0.00004751867,0.0004492663,0.9974691,0.001171367],"study_design_scores_gemma":[0.00006746883,0.000005755633,0.001284742,0.0002112417,0.000009560677,0.00003212721,0.00006100815,0.0001110914,0.0001119079,0.0009509478,0.9971402,0.00001411436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003332298,0.00002751267,0.00003514275,0.00005465404,0.00001261869,0.000005144561,0.9987077,0.0002388882,0.0008850919],"genre_scores_gemma":[0.0001959703,0.00004873325,0.0002062759,0.00008277165,0.000005495399,0.0000491113,0.998475,0.0001341738,0.0008023725],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.814423,"threshold_uncertainty_score":0.6208168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658931671354574,"score_gpt":0.2358516697501314,"score_spread":0.2192623530365857,"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."}}