{"id":"W3034603929","doi":"10.1111/faf.12477","title":"Mesohabitat modelling in fish ecology: A global synthesis","year":2020,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mesoscale meteorology; River ecosystem; Habitat; Ecology; Fish <Actinopterygii>; Process (computing); Environmental resource management; Linkage (software); Relevance (law); Computer science; Environmental science; Geography; Fishery; Biology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008762873,0.0001223659,0.0001913349,0.000009679803,0.0001372575,0.00002751217,0.0001233582,0.00007540369,0.001007276],"category_scores_gemma":[0.0001093653,0.0001209832,0.0000264815,0.0001623436,0.0002503134,0.000233091,0.0003000962,0.00008491893,0.00005891735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004126814,"about_ca_system_score_gemma":0.000003466203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008348799,"about_ca_topic_score_gemma":0.009430527,"domain_scores_codex":[0.9991754,0.00003523895,0.0001520523,0.0002997967,0.00007874275,0.0002587707],"domain_scores_gemma":[0.9997433,0.0000723952,0.00003480431,0.00007537877,0.000002684859,0.0000713926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003182669,0.00002206654,0.8306499,0.0000163138,0.00001416594,0.00002132094,0.0004432185,0.0003793737,0.000001060998,0.00007289571,0.1676434,0.000704422],"study_design_scores_gemma":[0.0002309111,0.00007916155,0.9471627,0.000006496045,0.00001950491,0.000001920816,0.0005661191,0.003398806,0.00001144016,0.001204449,0.04713659,0.0001818839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8757917,0.00001185275,0.0002161043,0.05745522,0.00008568446,0.0001947676,0.00003232617,0.00007499878,0.06613734],"genre_scores_gemma":[0.9782029,0.0001855452,0.0007388594,0.02058477,0.00002696309,0.00006001661,0.00000371337,0.000006702774,0.000190506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1205068,"threshold_uncertainty_score":0.9999059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582132540406039,"score_gpt":0.1898180157603752,"score_spread":0.1739966903563148,"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."}}