{"id":"W2154202889","doi":"10.5194/isprsarchives-xxxviii-4-c26-21-2012","title":"3D geospatial modelling and visualization for marine environment: Study of the marine pelagic ecosystem of the south-eastern Beaufort Sea, Canadian Arctic","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Takuvik Joint International Laboratory","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Pelagic zone; Arctic; Oceanography; Beaufort sea; Visualization; Marine ecosystem; Ecosystem; Geography; Environmental science; Physical geography; Environmental resource management; Geology; Remote sensing; Computer science; Ecology; Data mining","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":[0.0002843054,0.0003620148,0.0002180432,0.001329849,0.001285127,0.001393771,0.0005516617,0.0002363394,0.0009930119],"category_scores_gemma":[0.0004680105,0.0002241146,0.0004967258,0.002328511,0.0006128951,0.0003040759,0.0005204017,0.0002085168,0.0001061806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004976217,"about_ca_system_score_gemma":0.006386123,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9525489,"about_ca_topic_score_gemma":0.9710877,"domain_scores_codex":[0.9998609,0.00002600404,0.000004757234,0.00001898111,0.00006054429,0.00002873739],"domain_scores_gemma":[0.9998515,0.0000294117,0.00001529519,0.00001054589,0.00006646422,0.00002678277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005101694,0.0003481107,0.391906,0.0006222475,0.0003447606,0.00248529,0.006340923,0.3501385,0.04047816,0.008037692,0.008221838,0.1905664],"study_design_scores_gemma":[0.00003006897,0.00006449076,0.5784837,0.00008155958,0.0000947275,0.0003879067,0.005515338,0.3971647,0.004153605,0.001067905,0.01284681,0.0001092423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872771,0.0004301389,0.006316581,0.0002525682,0.00001004996,0.00006479785,0.001269572,0.0001736301,0.004205618],"genre_scores_gemma":[0.9797316,0.0005004382,0.01722991,0.00002244843,0.000004502275,0.00003374418,0.0006777033,0.00003308687,0.001766478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04745108,"threshold_uncertainty_score":0.09546101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597097936850238,"score_gpt":0.229886173357039,"score_spread":0.2139151939885366,"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."}}