{"id":"W7061291193","doi":"","title":"Places to Stand, Places to Grow: Measuring Membership Characteristics and Land Use/Land Cover Change in Ontario’s Conservation Authorities","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Land cover; Cover (algebra); Land use; Government (linguistics)","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":[0.0005719442,0.0001253724,0.0001765792,0.001411592,0.002995296,0.001503589,0.001006467,0.0004100177,0.004000065],"category_scores_gemma":[0.003558214,0.0002719183,0.0003018959,0.003606636,0.0009066456,0.0008139741,0.0013933,0.0005073548,0.0006063542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01716261,"about_ca_system_score_gemma":0.01972761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9829694,"about_ca_topic_score_gemma":0.9965797,"domain_scores_codex":[0.9993106,0.0000648103,0.0000420171,0.00008101419,0.0002638324,0.0002375902],"domain_scores_gemma":[0.995663,0.0003487036,0.0009698747,0.0001361604,0.001379456,0.001502856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003871953,0.00003614496,0.9845762,0.0000232668,0.00001822429,0.00004378151,0.006947096,0.0001302647,0.0001622706,0.0001728321,0.002537749,0.005313467],"study_design_scores_gemma":[0.000001402694,0.000006747971,0.9867453,0.000015116,0.000004375489,0.000007315541,0.01128571,0.0001134356,0.00002359914,0.00001708879,0.001775797,0.000004028437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901837,0.0000756818,0.000098178,0.0004415119,0.000007852454,0.00004831912,0.002587032,0.000006550442,0.006551339],"genre_scores_gemma":[0.9889493,0.0001657689,0.0003558894,0.00008073353,0.000006279387,0.00006209366,0.001832456,0.000009010269,0.008538537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01716261,"threshold_uncertainty_score":0.1245241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04816998860313182,"score_gpt":0.2626084257475568,"score_spread":0.214438437144425,"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."}}