{"id":"W7106011310","doi":"10.15468/7xhf88","title":"DATASAPROX : standardised database of saproxylic beetles from flight interception traps in continental France and Corsica","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Neurotrauma Foundation","funders":"","keywords":"Interception; Metadata; Raw data; Biodiversity; Dead wood; Selection (genetic algorithm)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00147228,0.0016147,0.001208433,0.007036727,0.0005812584,0.001881488,0.002255058,0.001428597,0.03050106],"category_scores_gemma":[0.006049933,0.0006709152,0.0008528287,0.009359002,0.0004009353,0.001435191,0.001919724,0.001221386,0.02602617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001996667,"about_ca_system_score_gemma":0.00308152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05091317,"about_ca_topic_score_gemma":0.05123866,"domain_scores_codex":[0.998572,0.0002191147,0.0002830349,0.0004222217,0.0003363877,0.0001672357],"domain_scores_gemma":[0.9970251,0.0006881303,0.0005541526,0.0005298271,0.000934059,0.0002688191],"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":[0.0001779499,0.00005445836,0.008802433,0.002825584,0.0001259933,0.0001450198,0.0003148829,0.00087685,0.001128032,0.001986992,0.9725276,0.01103419],"study_design_scores_gemma":[0.0001263745,0.00002844505,0.02863011,0.00067683,0.00005700711,0.0001570244,0.0003053957,0.0004253324,0.0006870189,0.001178542,0.9676545,0.00007343008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005418913,0.00008304523,0.0001373738,0.00003280087,0.000009569651,0.00001356885,0.9985579,0.0001670777,0.000456825],"genre_scores_gemma":[0.0008168117,0.00007504992,0.00048884,0.00002582881,0.00000306985,0.0001100522,0.9981167,0.00005791262,0.0003058927],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9694989,"threshold_uncertainty_score":0.1020362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008751389088746672,"score_gpt":0.2281201548169462,"score_spread":0.2193687657281995,"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."}}