{"id":"W2913839858","doi":"","title":"Soft path approach as a water management strategy: a case study in Thunder Bay, Ontario","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Thunder; Bay; Path (computing); Geography; Environmental resource management; Environmental planning; Engineering; Water resource management; Civil engineering; Environmental science; Computer science; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061655,0.0002883743,0.0001928874,0.0005623539,0.01000674,0.00230134,0.001076935,0.001360843,0.003303124],"category_scores_gemma":[0.002895076,0.000188369,0.0002455855,0.001504015,0.002337914,0.0008663796,0.001401604,0.0009830638,0.0002227589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02793842,"about_ca_system_score_gemma":0.02585012,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9230392,"about_ca_topic_score_gemma":0.9854498,"domain_scores_codex":[0.9989635,0.0003462244,0.00002373346,0.00008009575,0.0002509981,0.0003354337],"domain_scores_gemma":[0.9984863,0.0005272879,0.0001238235,0.00006344766,0.0004534204,0.0003457826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008185991,0.003095833,0.3551829,0.000627111,0.0001791245,0.03010059,0.2678623,0.03527839,0.01148494,0.04866924,0.03080351,0.2158975],"study_design_scores_gemma":[0.000163457,0.001210681,0.2199975,0.0002414195,0.0001429957,0.001358233,0.6259794,0.03308346,0.003267822,0.007112299,0.1073297,0.0001130393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733253,0.0000649627,0.001462533,0.001494815,0.00001023467,0.0001727877,0.0000917814,0.00002168932,0.02335594],"genre_scores_gemma":[0.9826931,0.0002147013,0.003052057,0.0001349652,0.000003867425,0.00004898612,0.00005901206,0.00001822329,0.01377504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0769608,"threshold_uncertainty_score":0.2027084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185612981291546,"score_gpt":0.2800325679845879,"score_spread":0.2614712698554333,"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."}}