{"id":"W7071048835","doi":"","title":"The Red + Green: creating a regenerative narrative through the industrial wastelands of Sudbury, Ontario","year":2023,"lang":"en","type":"dissertation","venue":"Lu Zone Ul (Laurentian University)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thriving; Stewardship (theology); Environmental stewardship; Tailings; Narrative; Storytelling; Subject (documents); Industrial heritage; Sustainable development; Situated; Land use","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":[0.001905154,0.0006170748,0.0002651919,0.0007777631,0.03100187,0.007907602,0.001592158,0.001351156,0.01254099],"category_scores_gemma":[0.00190109,0.0003383284,0.0002625638,0.001035074,0.01788462,0.002997169,0.006072944,0.001736676,0.0009191734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04589241,"about_ca_system_score_gemma":0.03819839,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8074074,"about_ca_topic_score_gemma":0.9709294,"domain_scores_codex":[0.998207,0.0007729476,0.00002948854,0.0001475713,0.0003799148,0.0004630347],"domain_scores_gemma":[0.9987125,0.0004829835,0.0000795795,0.00007891309,0.0001988079,0.0004472598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004791812,0.00002874499,0.002060306,0.0001217579,0.000004154027,0.001081306,0.9231899,0.0001948014,0.001332095,0.03650576,0.01759423,0.01783902],"study_design_scores_gemma":[0.000005673293,0.00002215576,0.002110594,0.0001733165,0.000007088776,0.0001564854,0.5646482,0.0001358546,0.0004881561,0.002158782,0.4300693,0.00002443307],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5100606,0.003313148,0.007458168,0.01719746,0.0004190657,0.0002144658,0.000424289,0.0001979625,0.4607149],"genre_scores_gemma":[0.8445213,0.002246397,0.004102764,0.001123731,0.00003242434,0.0001233539,0.000163291,0.0001757309,0.147511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1925926,"threshold_uncertainty_score":0.3874535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323463238138832,"score_gpt":0.2313445655222359,"score_spread":0.2181099331408476,"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."}}