{"id":"W6943302965","doi":"10.15468/dl.ss8yda","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Impulse Buying and Technology Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Alien; Range (aeronautics); State (computer science)","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":[0.001070997,0.001879322,0.001431051,0.004495933,0.0008939216,0.002642756,0.002673675,0.00229398,0.1371963],"category_scores_gemma":[0.006529217,0.0007914859,0.001212671,0.009288154,0.0004395724,0.001993801,0.002264806,0.002005257,0.210458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608407,"about_ca_system_score_gemma":0.002235218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02125805,"about_ca_topic_score_gemma":0.0333831,"domain_scores_codex":[0.9989919,0.0001540187,0.0001262264,0.0003380849,0.0002286294,0.0001610455],"domain_scores_gemma":[0.997363,0.0007748688,0.0002550608,0.0006829916,0.0006420225,0.0002820733],"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":[0.00002589645,0.0000134544,0.0004445228,0.0004208747,0.00001357097,0.00001393797,0.00001743056,0.0001575672,0.00006941857,0.0003891659,0.9971027,0.001331392],"study_design_scores_gemma":[0.0001014014,0.00001096675,0.002297062,0.0002096701,0.00001499526,0.00003593196,0.00007270558,0.0002519619,0.0001665769,0.0009847282,0.9958359,0.00001803964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004687861,0.00002699506,0.00004179235,0.00004343831,0.00001200109,0.000005698214,0.9989504,0.000320724,0.0005520114],"genre_scores_gemma":[0.000182273,0.00003552822,0.0001934849,0.0000497668,0.000004538279,0.00004815318,0.9988456,0.0001120762,0.0005285693],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8628037,"threshold_uncertainty_score":0.4589673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562301790233924,"score_gpt":0.2050330630326239,"score_spread":0.1794100451302846,"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."}}