{"id":"W2530021072","doi":"","title":"Supporting Local Climate Change Adaptation with the Participatory Geoweb: Findings from Coastal Nova Scotia","year":2015,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nova scotia; Climate change adaptation; Nova (rocket); Adaptation (eye); Citizen journalism; Climate change; Geography; Environmental resource management; Environmental planning; Oceanography; Environmental science; Computer science; Engineering; Geology; World Wide Web; Archaeology; Psychology; Aeronautics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008948013,0.0001951303,0.0003339364,0.0001767672,0.0008090141,0.00005172153,0.000332769,0.000219384,0.0001600643],"category_scores_gemma":[0.00002371246,0.0001712002,0.00009979637,0.0003256873,0.0004579853,0.0006072294,0.00006514408,0.0002069109,0.0001332228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008680583,"about_ca_system_score_gemma":0.0001754957,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6083824,"about_ca_topic_score_gemma":0.9531422,"domain_scores_codex":[0.9982272,0.000128662,0.0001870113,0.0002261081,0.0007773406,0.000453692],"domain_scores_gemma":[0.9984469,0.00006652102,0.0005910556,0.0001799806,0.0006093023,0.0001062648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000120136,0.00001515229,0.01027242,0.00007545284,0.0001002925,0.000008570407,0.9863565,0.00002593079,0.00001089057,0.0003303779,0.001488088,0.001196202],"study_design_scores_gemma":[0.0003723463,0.00006026439,0.02776466,0.0001923802,0.0001614263,2.941302e-7,0.9695769,0.000120423,0.00001734033,0.00004774471,0.001478689,0.0002075096],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955904,0.00006721454,0.0000281568,0.001057874,0.000401969,0.0005146678,0.0001359676,0.00006668478,0.002137017],"genre_scores_gemma":[0.9826672,0.00004039533,0.0001174851,0.00002662369,0.00008257398,0.000001765385,0.0005145925,0.00001472155,0.01653463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3447598,"threshold_uncertainty_score":0.698134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516447816844205,"score_gpt":0.2733868875537862,"score_spread":0.2282224093853441,"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."}}