{"id":"W3044708105","doi":"10.1111/rec.13251","title":"The power of data synthesis to shape the future of the restoration community and capacity","year":2020,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Deutsche Forschungsgemeinschaft","keywords":"Context (archaeology); Scope (computer science); Restoration ecology; Scale (ratio); Computer science; Set (abstract data type); Data science; Environmental resource management; Ecology; Environmental science; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.6269564,0.00409982,0.00927064,0.0328164,0.007747314,0.04489161,0.009378355,0.01017395,0.01008348],"category_scores_gemma":[0.772813,0.003093923,0.00705586,0.02031033,0.02584455,0.05464806,0.03353739,0.02315774,0.003275227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02280071,"about_ca_system_score_gemma":0.07999988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009430532,"about_ca_topic_score_gemma":0.008646053,"domain_scores_codex":[0.3469586,0.539812,0.04026707,0.02399685,0.04559937,0.00336602],"domain_scores_gemma":[0.1047312,0.7383795,0.01751974,0.08487672,0.05053335,0.003959536],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006558272,0.0003076482,0.009524711,0.03973988,0.00569988,0.0003254748,0.03186889,0.007937467,0.001058506,0.4438352,0.1080802,0.3509662],"study_design_scores_gemma":[0.0002543499,0.0002108316,0.002145443,0.05738412,0.001564179,0.0001056941,0.01136293,0.004882731,0.001083886,0.7017592,0.2188451,0.0004015073],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007883112,0.09334984,0.383453,0.4492533,0.02055477,0.005922998,0.008090299,0.001560772,0.02993202],"genre_scores_gemma":[0.1683193,0.05241464,0.6435071,0.09625958,0.01055079,0.01870823,0.005115971,0.001829868,0.003294611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6269564,"threshold_uncertainty_score":0.4600292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07593577567206933,"score_gpt":0.2715424752332596,"score_spread":0.1956066995611903,"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."}}