{"id":"W7114899711","doi":"10.5281/zenodo.17901272","title":"fishglob/FishGlob_data: FishGlob_data 2.0.2","year":2025,"lang":"","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Documentation; Workflow; Flagging; Troubleshooting; Missing data; Trimming; Function (biology)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005481314,0.001383878,0.001377628,0.004194238,0.001150193,0.004527879,0.003412448,0.001265476,0.3468473],"category_scores_gemma":[0.02103975,0.002386927,0.001589888,0.00444012,0.0006720866,0.005356013,0.005447466,0.002176201,0.4112555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001579049,"about_ca_system_score_gemma":0.002996555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0140137,"about_ca_topic_score_gemma":0.01321807,"domain_scores_codex":[0.9976147,0.0002667289,0.0002690938,0.0006873852,0.0009389269,0.0002231608],"domain_scores_gemma":[0.9867388,0.002668264,0.0008538318,0.004869789,0.004176126,0.0006931241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007230988,0.0000106386,0.001263352,0.0002501341,0.00002419058,0.00001747136,0.0001063749,0.0001046329,0.0003733517,0.0004409874,0.9879897,0.009346905],"study_design_scores_gemma":[0.00007014628,0.00001437079,0.003755527,0.0001449999,0.00002291709,0.00003378413,0.00007490005,0.0002436181,0.001077118,0.001152029,0.9933516,0.00005892871],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008340325,0.00008056548,0.007480941,0.0006677072,0.0003224089,0.0001844303,0.8653957,0.1077126,0.01732152],"genre_scores_gemma":[0.002925778,0.0001267559,0.01365855,0.0008668402,0.0001107222,0.0008014801,0.8457334,0.1081761,0.02760027],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6531527,"threshold_uncertainty_score":0.9316431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06007453244719578,"score_gpt":0.3542184186202488,"score_spread":0.294143886173053,"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."}}