{"id":"W6905971324","doi":"10.15468/dl.q2p9fx","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arctic; Download; Marine mammal; Mammal; Ursus maritimus; Range (aeronautics)","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":[0.0008575648,0.002195499,0.001633612,0.004638582,0.0009961274,0.002703394,0.00294534,0.002070436,0.1431664],"category_scores_gemma":[0.005545781,0.0008531201,0.001431299,0.008643287,0.0004002708,0.002572688,0.002558898,0.00201851,0.2365941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556752,"about_ca_system_score_gemma":0.00197882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01544151,"about_ca_topic_score_gemma":0.02694643,"domain_scores_codex":[0.998907,0.0001361207,0.0001513331,0.0003999207,0.0002346505,0.000170977],"domain_scores_gemma":[0.9980738,0.0004528155,0.0001518164,0.0005658033,0.0005317217,0.0002239859],"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":[0.00003393209,0.0000137606,0.0003444123,0.0004466359,0.00001428121,0.00001462449,0.00001672525,0.0001407082,0.000103569,0.0003045225,0.9966543,0.001912379],"study_design_scores_gemma":[0.00008269603,0.00001214005,0.00159075,0.0001707844,0.00001396747,0.00004361621,0.00007039181,0.0002990818,0.0002322457,0.0008606858,0.9966062,0.00001738073],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005618828,0.00003579497,0.00005901435,0.00004366323,0.0000169351,0.000008030763,0.9985211,0.0005813201,0.0006779039],"genre_scores_gemma":[0.0001538063,0.0000362038,0.0002416589,0.00004785008,0.000004162156,0.00004573325,0.998831,0.0001369956,0.000502567],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8568336,"threshold_uncertainty_score":0.4789392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339926633362599,"score_gpt":0.2143482362051887,"score_spread":0.2009489698715627,"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."}}