{"id":"W6887207198","doi":"10.15468/dl.veymh6","title":"Occurrence Download","year":2023,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Tamarix; Range (aeronautics); Variety (cybernetics)","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":[0.001001538,0.001994455,0.001590662,0.004723733,0.0009852035,0.00247081,0.002891835,0.002055254,0.1416707],"category_scores_gemma":[0.005641764,0.0009256799,0.001198248,0.009703499,0.0004358578,0.002230042,0.00246571,0.002010783,0.2036503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642165,"about_ca_system_score_gemma":0.002334963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475499,"about_ca_topic_score_gemma":0.03929732,"domain_scores_codex":[0.9989823,0.0001362047,0.0001292843,0.0003492035,0.0002344119,0.0001686717],"domain_scores_gemma":[0.9976533,0.0006494311,0.0002222863,0.0006143043,0.0005904026,0.0002703435],"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":[0.00002924307,0.00001190163,0.0003850895,0.0004861817,0.00001486452,0.00001517907,0.00002404807,0.0001284365,0.0001177447,0.0003925631,0.9970434,0.001351349],"study_design_scores_gemma":[0.00007362504,0.000008011676,0.001967094,0.0001784375,0.00001418838,0.00003701563,0.00007159563,0.0001671154,0.0002028325,0.0007699241,0.996492,0.00001810611],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004394509,0.00002459634,0.00004542901,0.00003530549,0.00001117866,0.000004964176,0.9988157,0.0003985978,0.0006203576],"genre_scores_gemma":[0.0001571542,0.0000291386,0.000185959,0.00004067564,0.000002965455,0.00003754334,0.9989834,0.0001484833,0.0004146216],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8583293,"threshold_uncertainty_score":0.4739358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365312271414112,"score_gpt":0.2392044605254057,"score_spread":0.2155513378112646,"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."}}