{"id":"W2755331679","doi":"10.1186/s13750-017-0105-z","title":"Lessons for introducing stakeholders to environmental evidence synthesis","year":2017,"lang":"en","type":"article","venue":"Environmental Evidence","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Timeline; Scope (computer science); Stakeholder; Business; Stakeholder engagement; Process (computing); Stakeholder analysis; Systematic review; Empirical evidence; Public relations; Environmental resource management; Knowledge management; Political science; Process management; Computer science; Geography; Economics; MEDLINE","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.6858682,0.002669577,0.004379023,0.009219704,0.01264903,0.03650372,0.009799022,0.03093579,0.008332042],"category_scores_gemma":[0.767334,0.003667381,0.00446304,0.008852403,0.03574178,0.06642336,0.03808101,0.05619308,0.00302346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02047541,"about_ca_system_score_gemma":0.1174518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201736,"about_ca_topic_score_gemma":0.02223619,"domain_scores_codex":[0.2198832,0.685366,0.04838762,0.0091919,0.03165707,0.00551433],"domain_scores_gemma":[0.1208021,0.7168096,0.01735058,0.02545344,0.1024856,0.01709883],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005055658,0.0004174361,0.003680439,0.02208419,0.0005744182,0.002071714,0.1894245,0.002031863,0.001760581,0.2450545,0.2168467,0.3155479],"study_design_scores_gemma":[0.0006317737,0.0003810551,0.0008884508,0.04035595,0.0003440795,0.000878054,0.05907557,0.003030411,0.001098834,0.4296743,0.4631735,0.0004680148],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001231304,0.01007921,0.06621663,0.9100845,0.008316196,0.0009057149,0.00007171914,0.0003460022,0.002748701],"genre_scores_gemma":[0.08145525,0.01718083,0.455568,0.421142,0.008038626,0.01220055,0.0002293029,0.0008144536,0.003370996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3141318,"threshold_uncertainty_score":0.3873805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136383731203803,"score_gpt":0.3459345220605842,"score_spread":0.232296148940204,"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."}}