{"id":"W6931597081","doi":"10.5281/zenodo.6343522","title":"Micropygomyia (Coquillettimyia) vexator","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subspecies; Species group; Nearctic ecozone; Lutzomyia","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":[0.0001495049,0.0004690152,0.0001751804,0.0006937482,0.0006858978,0.000234407,0.000446744,0.0003393361,0.007323829],"category_scores_gemma":[0.0002744798,0.0001653334,0.0001047141,0.0003115507,0.0003143909,0.0005673524,0.0007289581,0.0003421116,0.001217582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005022907,"about_ca_system_score_gemma":0.0001801477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195594,"about_ca_topic_score_gemma":0.02273512,"domain_scores_codex":[0.9999257,0.000009734363,0.000007145106,0.00002753931,0.00001743273,0.00001238268],"domain_scores_gemma":[0.9999108,0.00002063324,0.00003890885,0.000007692562,0.00001250774,0.000009436169],"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":[0.000756555,0.0001534332,0.137015,0.001118676,0.00009319194,0.002838626,0.002958145,0.0008068734,0.08138248,0.003603937,0.02152647,0.7477466],"study_design_scores_gemma":[0.00009333626,0.0004608323,0.8973657,0.0004874032,0.00007734616,0.004172196,0.0009175222,0.0006244517,0.003432949,0.0006869024,0.09165797,0.00002332459],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8779054,0.004287289,0.002064038,0.0003933727,0.000154899,0.0002166003,0.001914549,0.0002970234,0.1127668],"genre_scores_gemma":[0.978292,0.001613383,0.002559561,0.0002702751,0.00006443172,0.000170465,0.001496875,0.00001269989,0.01552043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01195594,"threshold_uncertainty_score":0.02450067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510912715706308,"score_gpt":0.2342202801975745,"score_spread":0.1991111530405115,"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."}}