{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048107,0.00006721868,0.0001305232,0.0001711909,0.00005512897,0.00008825293,0.0001016968,0.00002430518,0.000311828],"category_scores_gemma":[0.0001292655,0.0000605605,0.00005719142,0.00008169441,0.00002010198,0.001693729,0.00004810365,0.0001271972,0.000005309584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000178857,"about_ca_system_score_gemma":0.0001589777,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01359166,"about_ca_topic_score_gemma":0.2889978,"domain_scores_codex":[0.9988539,0.0000374524,0.0004936836,0.00004531927,0.0004907563,0.00007887368],"domain_scores_gemma":[0.9986178,0.00006763792,0.0004566799,0.00004625711,0.0007561077,0.00005551758],"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":[0.0001282311,0.00005428084,0.4553368,0.00002868179,0.0001287124,0.0005567477,0.0138444,0.00286965,0.0000121236,0.00003516741,0.00002930574,0.5269759],"study_design_scores_gemma":[0.003927034,0.001362483,0.8692519,0.0002436689,0.0001664612,0.01370075,0.03339057,0.07061123,0.00006022352,0.0002656004,0.006750314,0.000269754],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977775,0.00002767557,0.0008170264,0.0001701438,0.0006611347,0.00009024867,0.00005831783,0.000003793196,0.0003941552],"genre_scores_gemma":[0.998305,0.00003184272,0.001298956,0.0001379492,0.00008369861,3.263065e-7,0.0001002273,9.387148e-7,0.00004107695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5267062,"threshold_uncertainty_score":0.9929769,"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."}}