{"id":"W6911897852","doi":"10.5281/zenodo.14597208","title":"Megamymar waorani Huber 2022","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Collembola Taxonomy and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Holotype; Scale (ratio); Term (time); Simple (philosophy); Class (philosophy)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001826579,0.0002508517,0.0001588911,0.0006347916,0.0006274406,0.0002380705,0.0004922089,0.0002377964,0.01393398],"category_scores_gemma":[0.0003684528,0.0001538065,0.00006808974,0.0003987801,0.0001893842,0.0006751399,0.0007886841,0.000350237,0.004346486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002163325,"about_ca_system_score_gemma":0.0002129206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693155,"about_ca_topic_score_gemma":0.01028604,"domain_scores_codex":[0.9999181,0.000008738718,0.000005416553,0.00002356153,0.00001933833,0.00002490313],"domain_scores_gemma":[0.9998777,0.000007741585,0.0000557484,0.00001729126,0.00002419722,0.0000172901],"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":[0.0002851171,0.00006332997,0.03523792,0.0003775262,0.00004255461,0.001832262,0.002878631,0.0001515545,0.01139665,0.008836473,0.117473,0.821425],"study_design_scores_gemma":[0.00001460532,0.0001267543,0.1527739,0.0001923008,0.00003380677,0.004107282,0.001346557,0.0001557103,0.002400439,0.001030564,0.8378006,0.00001736119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5447029,0.02493604,0.00579585,0.00463792,0.001374068,0.0005219029,0.005251729,0.0009968292,0.4117827],"genre_scores_gemma":[0.8374066,0.007364561,0.006999843,0.001063586,0.0003763482,0.0001859785,0.002487004,0.000100857,0.1440153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01393398,"threshold_uncertainty_score":0.04661381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03520058798848559,"score_gpt":0.218101284552649,"score_spread":0.1829006965641634,"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."}}