{"id":"W6887179669","doi":"10.15468/dl.r4k54d","title":"Occurrence Download","year":2022,"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; Barcode; Matching (statistics); Range (aeronautics); DNA barcoding; Entomology","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":[0.001074802,0.002121981,0.001708777,0.005985128,0.0009907566,0.002845325,0.002896421,0.002172312,0.1933969],"category_scores_gemma":[0.007375807,0.001036197,0.001447851,0.01089955,0.0003952497,0.002683002,0.002886723,0.001941237,0.2462264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001682749,"about_ca_system_score_gemma":0.002361375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02171667,"about_ca_topic_score_gemma":0.03260868,"domain_scores_codex":[0.9987265,0.0001603368,0.0001864323,0.0004406175,0.0002694009,0.000216564],"domain_scores_gemma":[0.9970594,0.0008239093,0.0002810459,0.0007456851,0.0007699605,0.0003199346],"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":[0.00003891489,0.00001199521,0.0004613961,0.0006350858,0.00001685073,0.0000162078,0.00002286035,0.0001421521,0.0001067507,0.000344353,0.9963924,0.001811082],"study_design_scores_gemma":[0.00009145727,0.00001083791,0.002168038,0.0002340226,0.00001581115,0.00003928598,0.00008263453,0.0002184113,0.0001750705,0.0007477931,0.9961975,0.00001922502],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003485057,0.00002108368,0.00003864109,0.00002929964,0.00001037852,0.000005489281,0.9990007,0.00037454,0.0004850891],"genre_scores_gemma":[0.0001464104,0.00003242562,0.0002169253,0.00004197852,0.000003677408,0.00004869139,0.9989225,0.000155031,0.000432201],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8066031,"threshold_uncertainty_score":0.6469772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850871640220096,"score_gpt":0.2277224731265546,"score_spread":0.2092137567243537,"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."}}