{"id":"W3193565221","doi":"10.20944/preprints202108.0326.v1","title":"Before the Next COP: How to Stop Missing our Environmental Policy Targets","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University","funders":"Belgian Federal Science Policy Office; Agence Nationale de la Recherche; Austrian Science Fund; Biodiversa+","keywords":"Agency (philosophy); Deliberation; Stakeholder; Stakeholder engagement; Deliberative democracy; Attribution; Politics; Democracy; Political science; Management science; Business; Public relations; Sociology; Psychology; Economics; Social psychology; Social science","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009636498,0.0004327915,0.000388383,0.000236307,0.0004801461,0.0006783723,0.003264433,0.0001915101,0.00008867757],"category_scores_gemma":[0.0004921944,0.0003682683,0.000285003,0.0004717076,0.00010403,0.0006343538,0.01325753,0.0008842036,0.0006834037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000353616,"about_ca_system_score_gemma":0.0005006018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002188212,"about_ca_topic_score_gemma":0.00004315812,"domain_scores_codex":[0.996209,0.0002387956,0.0003356214,0.00171844,0.0007577527,0.0007404208],"domain_scores_gemma":[0.9970673,0.00004243005,0.0002451245,0.002211252,0.00008984832,0.000344085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002301227,0.0006128669,0.1754055,0.0002705992,0.0003918626,0.0004117069,0.1210041,0.001436475,0.1525837,0.003764569,0.001073624,0.543022],"study_design_scores_gemma":[0.000516128,0.00007308495,0.872355,0.0007220607,0.00006339094,0.0001276392,0.01171581,0.007153512,0.0601956,0.007345715,0.03809201,0.001640012],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8185039,0.0002922332,0.08581283,0.08933642,0.001778722,0.001144117,0.00002158554,0.0002732018,0.002837067],"genre_scores_gemma":[0.9909775,0.00004110895,0.003517826,0.002792368,0.0005367778,0.0001067908,0.00001725749,0.00002424911,0.001986132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6969495,"threshold_uncertainty_score":0.9998769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217078306011372,"score_gpt":0.3381348099989051,"score_spread":0.2164269793977679,"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."}}