{"id":"W3169473499","doi":"10.3390/ijgi10060375","title":"Evaluating and Visualizing Drivers of Coastline Change: A Lake Ontario Case Study","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Brock University; Marine Environmental Observation Prediction and Response Network","keywords":"Revetment; Coastal erosion; Climate change; Vulnerability (computing); Geography; Storm; Environmental resource management; Environmental change; Land use, land-use change and forestry; Physical geography; Shore; Environmental science; Archaeology; Oceanography; Geology; Agriculture; Meteorology","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":[0.0007550548,0.0003433846,0.0002089028,0.002184776,0.003750723,0.001815239,0.0008017852,0.0005057337,0.00237188],"category_scores_gemma":[0.002709977,0.0002443656,0.0003538296,0.00502959,0.001439318,0.0007996046,0.001218932,0.0004006111,0.000160077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02693516,"about_ca_system_score_gemma":0.01158443,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9606394,"about_ca_topic_score_gemma":0.9911228,"domain_scores_codex":[0.9992183,0.0002023005,0.00003464959,0.00007888441,0.0002497592,0.0002159895],"domain_scores_gemma":[0.9986461,0.0003956177,0.0001776341,0.00008068098,0.000504312,0.0001957605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004064872,0.0004189104,0.6741673,0.0007984534,0.0001799129,0.01266963,0.1775767,0.0093934,0.008340588,0.006660575,0.01391724,0.09547079],"study_design_scores_gemma":[0.00004455969,0.0001669648,0.6674955,0.0002756193,0.0001211039,0.0007288183,0.264305,0.01189991,0.001657361,0.0009827639,0.05222258,0.0000998513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844214,0.0003479929,0.001052853,0.0007412774,0.000009410246,0.0002328215,0.001124564,0.00003534054,0.01203438],"genre_scores_gemma":[0.9893138,0.0006253823,0.004463509,0.00006982364,0.000007280753,0.0001093722,0.0006237967,0.00001932114,0.004767759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03936058,"threshold_uncertainty_score":0.1954292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03873406411566252,"score_gpt":0.318878909594813,"score_spread":0.2801448454791505,"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."}}