{"id":"W6901626666","doi":"10.60692/1139g-bw647","title":"Large‐Scale Transdisciplinary Collaboration for Adaptation Research: Challenges and Insights","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McGill University","funders":"","keywords":"General partnership; Adaptation (eye); Interpersonal communication; Climate change; Climate change adaptation; Transdisciplinarity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054687,0.0004817032,0.0009129207,0.002444778,0.01760751,0.0171343,0.002825026,0.002835752,0.003835204],"category_scores_gemma":[0.03931782,0.0006310007,0.0007576452,0.004300027,0.01662753,0.01739055,0.02099352,0.005031881,0.0004866616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006294398,"about_ca_system_score_gemma":0.01815578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00404885,"about_ca_topic_score_gemma":0.01085814,"domain_scores_codex":[0.9261365,0.06329195,0.001168807,0.002401824,0.003346304,0.003654568],"domain_scores_gemma":[0.9249852,0.05505492,0.003402908,0.006917644,0.00437589,0.005263414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000793107,0.0004940854,0.03539634,0.001194111,0.0001169557,0.001987088,0.7184491,0.001446065,0.00122894,0.1219522,0.0052882,0.1123677],"study_design_scores_gemma":[0.00002306867,0.0001324499,0.0129785,0.0008665572,0.00002953041,0.0006563794,0.8738101,0.001288078,0.0003893547,0.06134136,0.04844317,0.00004146298],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7353381,0.01143641,0.06302743,0.09659041,0.0005607528,0.0009397428,0.0001660504,0.0001430943,0.09179803],"genre_scores_gemma":[0.9797314,0.002728991,0.01417297,0.001542496,0.00007038451,0.0004682393,0.00004451512,0.00004328739,0.001197725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.054687,"threshold_uncertainty_score":0.289216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153383319142896,"score_gpt":0.2865678690301102,"score_spread":0.1712295371158206,"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."}}