{"id":"W6924705780","doi":"10.15468/dl.rnakmw","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":"Matching (statistics); Download; Range (aeronautics); Nest (protein structural motif)","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":[0.0008954816,0.002032234,0.001611864,0.004862519,0.0009427543,0.002797742,0.002720546,0.001972716,0.1814676],"category_scores_gemma":[0.005875072,0.0009389606,0.001428775,0.009844268,0.0004009088,0.002547082,0.002666746,0.001946769,0.2613243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001512548,"about_ca_system_score_gemma":0.002175192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01917878,"about_ca_topic_score_gemma":0.02984386,"domain_scores_codex":[0.9989702,0.0001339184,0.0001376865,0.0003669417,0.000222326,0.0001690041],"domain_scores_gemma":[0.9977525,0.0006102724,0.0001956857,0.0006253449,0.0005629765,0.000253309],"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":[0.00003205708,0.00001003269,0.0003824154,0.0006009378,0.00001642093,0.0000146504,0.00002257343,0.0001464257,0.0001098848,0.0003577933,0.9963877,0.00191912],"study_design_scores_gemma":[0.0000706604,0.000008573837,0.001574937,0.0001889726,0.00001374891,0.00003476864,0.00006555624,0.0001829555,0.0001588397,0.0007658018,0.9969193,0.00001594485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003803431,0.00003136501,0.0000516843,0.00003785926,0.0000139414,0.000005714792,0.9986439,0.0005163025,0.0006611859],"genre_scores_gemma":[0.0001565798,0.00004223373,0.0002360445,0.00005092709,0.000004229816,0.00004268693,0.9987582,0.0001883331,0.0005206923],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8185323,"threshold_uncertainty_score":0.6070697,"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."}}