{"id":"W2128820428","doi":"10.1109/amtrsi.2005.1469876","title":"The application of the getis statistic to high resolution imagery to detect change in the spatial structure of submerged tropical corals between image dates","year":2005,"lang":"en","type":"article","venue":"","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bathymetry; Coral reef; Remote sensing; Statistic; Reef; Image resolution; Identification (biology); Change detection; Environmental resource management; Computer science; Geology; Environmental science; Oceanography; Ecology; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002923928,0.0003479361,0.0004521266,0.002083135,0.0003808048,0.0007348785,0.0002963956,0.0003078555,0.001237241],"category_scores_gemma":[0.01117485,0.0001127309,0.0004182403,0.001389152,0.0005086169,0.0006265303,0.000385743,0.0004825612,0.0002740549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627604,"about_ca_system_score_gemma":0.0006268409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00378447,"about_ca_topic_score_gemma":0.00614207,"domain_scores_codex":[0.9993361,0.0002472142,0.00004872474,0.00009574795,0.0002217528,0.00005056868],"domain_scores_gemma":[0.994779,0.003789636,0.0004351306,0.0003213103,0.0005125131,0.0001624092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001552723,0.0002773481,0.3062399,0.0002662478,0.0004477886,0.0003151341,0.0005135905,0.06101841,0.03362842,0.009794217,0.009784358,0.5761618],"study_design_scores_gemma":[0.0001074653,0.001365608,0.2381331,0.00002416053,0.0001311422,0.0007217691,0.001040417,0.7170357,0.0282692,0.007888415,0.005188537,0.00009443565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7021125,0.0002337243,0.2890127,0.0006532516,0.0001031235,0.0001756623,0.001298185,0.001852992,0.004557757],"genre_scores_gemma":[0.8675293,0.0001075422,0.1299267,0.0000669078,0.00005913739,0.0000690463,0.001205049,0.0001190874,0.0009171339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00378447,"threshold_uncertainty_score":0.01546335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497573464316945,"score_gpt":0.2530354418554466,"score_spread":0.2380597072122771,"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."}}