{"id":"W4409410189","doi":"10.1136/bmj.r175","title":"Reparative justice and COP29","year":2025,"lang":"en","type":"editorial","venue":"BMJ","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Data science; Economic Justice; World Wide Web; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008634219,0.0001170827,0.0001858463,0.00007541508,0.0004230122,0.0001292926,0.0001254384,0.0004712208,0.00008573107],"category_scores_gemma":[0.00644165,0.0001253466,0.00003364872,0.0001951017,0.0001470296,0.0001290938,0.00003750634,0.0002046355,0.00003599229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334897,"about_ca_system_score_gemma":0.0009590795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003362452,"about_ca_topic_score_gemma":0.04001996,"domain_scores_codex":[0.9985862,0.000213624,0.0002124132,0.0002719858,0.0005496703,0.0001660816],"domain_scores_gemma":[0.9976883,0.001517954,0.000192078,0.000140544,0.0004086957,0.00005243806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001372456,0.000006890873,0.000004349842,0.00007366176,0.00001205637,0.000001934218,0.01785422,6.164242e-7,0.000001799997,0.002295964,0.9792359,0.000498858],"study_design_scores_gemma":[0.0001213548,0.00001760021,0.00002658171,0.00009378058,0.0001413606,5.366119e-8,0.009302828,0.0000170822,0.00000210024,0.0005219763,0.9896257,0.000129566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00006264616,0.0004175566,0.00005925725,0.002156648,0.8307906,0.0005328889,0.0001577753,0.00007710586,0.1657455],"genre_scores_gemma":[0.00005619465,0.001540071,0.0002933243,0.00007628966,0.9177526,0.00007373901,0.0003336079,0.000009780595,0.07986441],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.08696193,"threshold_uncertainty_score":0.9774972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09339116349568924,"score_gpt":0.4167640980476923,"score_spread":0.323372934552003,"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."}}