{"id":"W6893338574","doi":"10.5281/zenodo.15103223","title":"Tanytarsus miriforceps","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pupa; Glacial period; Global biodiversity; Taxonomy (biology); Fauna; Latitude","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":[],"consensus_categories":[],"category_scores_codex":[0.000197538,0.0006257443,0.0002482083,0.002125331,0.0008243733,0.0002276414,0.0003344695,0.0002139281,0.005303872],"category_scores_gemma":[0.0003209267,0.000173707,0.0001559236,0.0005898555,0.0003537749,0.0005560486,0.0006328376,0.0002236329,0.001899182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003465805,"about_ca_system_score_gemma":0.0002111108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003696045,"about_ca_topic_score_gemma":0.01088221,"domain_scores_codex":[0.9998878,0.00001267341,0.00001398784,0.00003629627,0.00003116703,0.00001817017],"domain_scores_gemma":[0.9998662,0.00002349995,0.00006443644,0.000008678479,0.00002107295,0.00001598671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001391968,0.0004053877,0.2163994,0.001813128,0.0001706338,0.006316635,0.003271859,0.0009912378,0.09978272,0.004390639,0.01116932,0.6538971],"study_design_scores_gemma":[0.0001117471,0.001000275,0.9005691,0.0003782282,0.0002333115,0.009076218,0.002293812,0.0009194557,0.005023832,0.0009428043,0.07939397,0.00005734308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9311773,0.006285557,0.00161218,0.0001547989,0.0002345419,0.0001969231,0.001384342,0.0002551025,0.05869932],"genre_scores_gemma":[0.9925528,0.001099534,0.001236901,0.00008300897,0.00005386085,0.00005549385,0.0006624124,0.00000628325,0.004249806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005303872,"threshold_uncertainty_score":0.01774317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03576045647495694,"score_gpt":0.2691221197486465,"score_spread":0.2333616632736895,"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."}}