{"id":"W2998189238","doi":"10.1093/icesjms/fsz099","title":"Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment","year":2019,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Nova Scotia Community College; Memorial University of Newfoundland","funders":"Department of the Environment, Australian Government; ArcticNet; Government of Nunavut","keywords":"Abundance (ecology); Environmental science; Transect; Spatial analysis; Arctic; Spatial ecology; Scale (ratio); Habitat; Fishery; Physical geography; Geography; Oceanography; Ecology; Cartography; Remote sensing; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.006822963,0.0007549784,0.0005835281,0.002183502,0.0004901135,0.001422179,0.0009820051,0.0008982174,0.0004157166],"category_scores_gemma":[0.009107796,0.000526061,0.001273807,0.001148404,0.0003549248,0.001123426,0.001279088,0.0006292163,0.0001684416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040113,"about_ca_system_score_gemma":0.0008126906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04591291,"about_ca_topic_score_gemma":0.05704568,"domain_scores_codex":[0.9983393,0.0007236864,0.0001442016,0.0004542754,0.0002324841,0.0001060199],"domain_scores_gemma":[0.9958271,0.001898417,0.0005576308,0.0007394068,0.0008879519,0.00008949422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003449603,0.0003596642,0.2579323,0.00006737407,0.0007714209,0.0001072009,0.0001391286,0.6803649,0.00337602,0.0004219404,0.0005752062,0.05553987],"study_design_scores_gemma":[0.000008185431,0.00004106736,0.03174349,0.00001539691,0.00003711394,0.00002038842,0.00003690668,0.9672546,0.0005750118,0.0001570258,0.00009741251,0.00001326497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547151,0.0002238571,0.04306461,0.0001198966,0.00001712882,0.00003674805,0.0006763582,0.0004743283,0.0006719411],"genre_scores_gemma":[0.9794657,0.00003278784,0.01941141,0.00001998467,0.000007714068,0.00002469757,0.0008543221,0.00001352772,0.0001699612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04591291,"threshold_uncertainty_score":0.09129137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03955479474258473,"score_gpt":0.3112814123444798,"score_spread":0.2717266176018951,"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."}}