{"id":"W7126593503","doi":"","title":"Local perspectives on regional adaptation: Vertical, horizontal, and temporal coordination on New York?s Lake Ontario Shoreline","year":2022,"lang":"en","type":"report","venue":"eCommons (Cornell University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shore; Watershed; Water resources; State (computer science); Hydrology (agriculture)","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":[0.0008184424,0.0001487587,0.0001242963,0.0008187133,0.002490425,0.001630256,0.000385153,0.0002984936,0.00911766],"category_scores_gemma":[0.001607341,0.0001088032,0.0001183853,0.00232178,0.001411109,0.0009842827,0.001333272,0.0004480016,0.0002918738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01442134,"about_ca_system_score_gemma":0.01204881,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9410103,"about_ca_topic_score_gemma":0.9876854,"domain_scores_codex":[0.9994886,0.0001028238,0.00001790306,0.00004739086,0.0001084473,0.0002348376],"domain_scores_gemma":[0.9988919,0.0001209626,0.0001584271,0.00004002235,0.0005787762,0.0002099679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003813906,0.00006368331,0.5318987,0.000590381,0.00009208202,0.00157204,0.1115802,0.003809433,0.006047981,0.0608429,0.111843,0.1712781],"study_design_scores_gemma":[0.000006758396,0.00002670893,0.8191106,0.000159431,0.00002927033,0.00005506636,0.07705887,0.0003926521,0.0002795487,0.001010244,0.1018376,0.00003325451],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6973382,0.003944851,0.0007884596,0.02695863,0.00009903764,0.0001006923,0.003126321,0.00005147485,0.2675923],"genre_scores_gemma":[0.9779285,0.001677384,0.0003103141,0.0003098992,0.00002633369,0.00003730873,0.0004162893,0.00001393318,0.01928001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9410103,"threshold_uncertainty_score":0.1186742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09414297580399522,"score_gpt":0.2374743736421923,"score_spread":0.1433313978381971,"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."}}