{"id":"W6968070815","doi":"10.5281/zenodo.16805105","title":"Ditaeniella parallela 1853","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Scientific Innovation and Industrial Efficiency","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Introduced species; Population; Taxonomy (biology); Freshwater mollusc","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":[0.0001238122,0.0006994638,0.0003747088,0.002174145,0.002283279,0.0005011845,0.0005300483,0.0003921801,0.03740388],"category_scores_gemma":[0.0003525507,0.0002687431,0.0001547036,0.001432405,0.0007531655,0.001277022,0.00107328,0.0007985287,0.01065156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067334,"about_ca_system_score_gemma":0.0005396414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01467368,"about_ca_topic_score_gemma":0.03370041,"domain_scores_codex":[0.9998634,0.00001542977,0.00001158509,0.00004332474,0.0000484369,0.00001793213],"domain_scores_gemma":[0.9999143,0.00001108982,0.00003127259,0.000007827108,0.00002564152,0.000009921483],"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":[0.0003428971,0.0001549091,0.03287121,0.001154369,0.00006907091,0.001569633,0.002064285,0.0007845275,0.01212672,0.01419372,0.1242421,0.8104264],"study_design_scores_gemma":[0.00004490574,0.00008761508,0.1131724,0.0002605965,0.00003655089,0.00194876,0.000578594,0.0002839075,0.0006927101,0.001678892,0.881198,0.00001697629],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.107469,0.009952914,0.004205053,0.0009440018,0.001151623,0.0008241563,0.01060215,0.0006683973,0.8641827],"genre_scores_gemma":[0.7975587,0.006451614,0.00499772,0.000976788,0.0006767546,0.0004425604,0.009153059,0.0001241886,0.1796185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03740388,"threshold_uncertainty_score":0.1251284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2595321845033127,"score_gpt":0.3711383093846134,"score_spread":0.1116061248813007,"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."}}