{"id":"W2186024379","doi":"10.5623/cig2015-107","title":"Cosine: A Tool for Constraining Spatial Neighbourhoods in Marine Environments","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Universidad Santiago de Cali","keywords":"Neighbourhood (mathematics); Computer science; Geographic information system; Spatial ecology; Multivariate interpolation; Spatial analysis; Geography; Trigonometric functions; Cartography; Data mining; Remote sensing; Ecology; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110803,0.00007987516,0.0001640584,0.00009113234,0.0001819294,0.0000486967,0.0001169889,0.00005862272,0.0001109504],"category_scores_gemma":[0.0005882531,0.00007842851,0.00003953171,0.0001457435,0.0001771936,0.0001719611,0.00006047651,0.00004701433,0.00008070373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007430991,"about_ca_system_score_gemma":0.00008480093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001332164,"about_ca_topic_score_gemma":0.001029313,"domain_scores_codex":[0.9989083,0.00005429045,0.0003231885,0.00009525861,0.0003185736,0.0003004269],"domain_scores_gemma":[0.9995248,0.0001493098,0.000107299,0.0001022681,0.00004055071,0.00007582921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008585481,0.0002033271,0.5506074,0.000138966,0.0001325947,0.000009186963,0.244703,0.000130377,0.00003003615,0.1405155,0.005858011,0.05758575],"study_design_scores_gemma":[0.009819714,0.0004012944,0.303511,0.0003133341,0.00007106177,0.00001199664,0.2176818,0.003032419,0.00006756494,0.05073781,0.4131047,0.001247395],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6212716,0.00004139427,0.01451379,0.003954528,0.0008614899,0.002473885,0.00003554202,0.0001398806,0.3567079],"genre_scores_gemma":[0.9958426,0.000004257307,0.003282881,0.0001530125,0.0001084545,0.0001273947,0.000009458866,0.000005158287,0.0004667402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4072467,"threshold_uncertainty_score":0.3198222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03622901244665704,"score_gpt":0.2961306889901654,"score_spread":0.2599016765435084,"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."}}