{"id":"W1963612984","doi":"10.2112/jcoastres-d-14-00059.1","title":"Mapping Coastal Information across Canada's Northern Regions Based on Low-Altitude Helicopter Videography in Support of Environmental Emergency Preparedness Efforts","year":2014,"lang":"en","type":"article","venue":"Journal of Coastal Research","topic":"Arctic and Russian Policy Studies","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environmental Studies Research Funds","keywords":"Shore; Intertidal zone; Videography; Geospatial analysis; Baseline (sea); Arctic; Geography; Preparedness; Geographic information system; Coastal management; Coastal geography; Environmental resource management; Physical geography; Oceanography; Environmental science; Remote sensing; Geology","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":[0.0004830997,0.0003308967,0.0002095024,0.00513493,0.001788483,0.001626428,0.0006385408,0.0002098571,0.001676103],"category_scores_gemma":[0.002573842,0.0001586276,0.000218569,0.007938591,0.0002673561,0.0003890668,0.001082376,0.0002906257,0.0003140395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008248922,"about_ca_system_score_gemma":0.02659861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9898051,"about_ca_topic_score_gemma":0.9957553,"domain_scores_codex":[0.9994863,0.00003426157,0.00002926684,0.00007453137,0.0002518497,0.0001236395],"domain_scores_gemma":[0.9971856,0.0001492287,0.00028309,0.00005838578,0.002013791,0.0003097974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002122951,0.0001293042,0.7739596,0.0005900641,0.0001336553,0.0007042006,0.006502579,0.003926452,0.006300292,0.001006807,0.02734579,0.1791889],"study_design_scores_gemma":[0.000009311263,0.00002344898,0.9694569,0.0002098367,0.00004446096,0.00004845377,0.009240838,0.003743878,0.0008386881,0.00007465605,0.01628294,0.00002659158],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9270254,0.001668085,0.004371046,0.0007646445,0.00005024479,0.0004093263,0.04097866,0.0002619668,0.02447067],"genre_scores_gemma":[0.951048,0.002411617,0.01601424,0.0001444881,0.00001338492,0.0002829007,0.02333642,0.00004116983,0.00670774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0101949,"threshold_uncertainty_score":0.05985039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568701990906572,"score_gpt":0.3447801887005791,"score_spread":0.3190931687915134,"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."}}