{"id":"W6967777143","doi":"10.5281/zenodo.15801424","title":"Pseudopterogramma magnum Kuwahara & Marshall & Luk 2025","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Peck (Imperial); State (computer science); Holotype; National laboratory","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":[0.00006739562,0.0008217303,0.0002745737,0.001689315,0.001410598,0.0003990687,0.0006031322,0.0007907705,0.02931389],"category_scores_gemma":[0.0002214356,0.0002795522,0.0002944126,0.0007687595,0.0005294999,0.001564638,0.0008443262,0.0005731516,0.01463701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003273583,"about_ca_system_score_gemma":0.0002400523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004750382,"about_ca_topic_score_gemma":0.01066648,"domain_scores_codex":[0.9998955,0.00001207427,0.00001408519,0.00004143184,0.00002047187,0.00001642287],"domain_scores_gemma":[0.9998894,0.00001548805,0.00004420889,0.00002240936,0.00001625196,0.00001225482],"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":[0.0003697279,0.0001627479,0.01824285,0.0009518731,0.00005661872,0.00551196,0.002146418,0.0004199402,0.02200102,0.002708349,0.03896489,0.9084637],"study_design_scores_gemma":[0.00007634851,0.0002446407,0.351734,0.0007439816,0.0001467848,0.01876015,0.002800416,0.0008290734,0.003262337,0.00120906,0.6201364,0.0000570063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3259006,0.01357967,0.01140379,0.001625826,0.002055953,0.00111949,0.008785786,0.002012503,0.6335164],"genre_scores_gemma":[0.841639,0.007533992,0.01097528,0.001117596,0.0004742807,0.0005961077,0.005048632,0.0001674814,0.1324476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02931389,"threshold_uncertainty_score":0.09806472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198984049822196,"score_gpt":0.2518171239122095,"score_spread":0.2198272834139875,"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."}}