{"id":"W2059576665","doi":"10.1139/x09-107","title":"Aggregation of preferences in participatory forest planning with multiple criteria: an application to the urban forest in Lycksele, Sweden","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kempe Foundation","keywords":"Pairwise comparison; Stakeholder; Group decision-making; Preference; Metric (unit); Forest management; Citizen journalism; Participatory management; Compromise; Participatory planning; Social preferences; Computer science; Management science; Business; Mathematics; Sociology; Economics; Statistics; Forestry; Geography; Marketing; Environmental planning; Psychology; Social psychology; Political science; Microeconomics; Artificial intelligence; Public relations","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001615577,0.0001019415,0.0001552315,0.0005419535,0.0001306292,0.00007558292,0.0006155958,0.00005520224,0.00008814818],"category_scores_gemma":[0.0001937327,0.00007262125,0.00002406885,0.0009377997,0.0002197276,0.0004216551,0.00003015604,0.000311247,0.00003361118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858763,"about_ca_system_score_gemma":0.0002051782,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04707027,"about_ca_topic_score_gemma":0.8672303,"domain_scores_codex":[0.998298,0.0001767377,0.0003596866,0.0001713066,0.0004706833,0.0005235999],"domain_scores_gemma":[0.9990879,0.00009498739,0.0001201934,0.0002527665,0.0000565905,0.0003875109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000884994,0.00003407625,0.9482484,0.000006633299,0.000003136656,0.00002887289,0.002627626,0.04363722,0.0000436099,0.0004097525,0.002292326,0.002579871],"study_design_scores_gemma":[0.0003612155,0.0006590331,0.9892538,0.000110317,0.000003476115,0.000005253714,0.0003101017,0.004696087,0.00004216901,0.001278573,0.003196361,0.00008367443],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963768,0.00006512452,0.00008025495,0.0009038294,0.0000237414,0.000457293,0.000003765442,0.000002299188,0.002086911],"genre_scores_gemma":[0.9995205,0.000005046952,0.0001679133,0.0000726758,0.00008334273,0.00002752944,0.00000602881,0.000008884713,0.0001080955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.82016,"threshold_uncertainty_score":0.9592754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0739719875029566,"score_gpt":0.3507813978990734,"score_spread":0.2768094103961168,"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."}}