{"id":"W6969560110","doi":"10.5683/sp3/imryzi","title":"Data from: Assessing the recovery gap in forest restoration within the Brazilian Atlantic Forest","year":2025,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto; University of British Columbia","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Species richness; Biodiversity; Restoration ecology; Forest restoration; Vegetation (pathology); Abundance (ecology); Atlantic forest; Invertebrate","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":[],"consensus_categories":[],"category_scores_codex":[0.009543394,0.0005364937,0.001630709,0.004705922,0.000713573,0.0009277968,0.001424339,0.0007413978,0.003867206],"category_scores_gemma":[0.0388318,0.000356408,0.004268571,0.007283817,0.0004792383,0.0007995254,0.001435991,0.0008598989,0.0004324655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001565023,"about_ca_system_score_gemma":0.002902211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1134495,"about_ca_topic_score_gemma":0.1403488,"domain_scores_codex":[0.9929045,0.002603194,0.001605991,0.001125844,0.00148988,0.0002707037],"domain_scores_gemma":[0.9684627,0.0156514,0.005946274,0.003333315,0.006084198,0.0005221243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001896181,0.0002120424,0.7538201,0.06311727,0.02723826,0.0004513765,0.002849504,0.001966005,0.001307578,0.001903618,0.03252336,0.1127146],"study_design_scores_gemma":[0.0002482303,0.0002505076,0.9075166,0.01013352,0.01343823,0.000190928,0.001693449,0.001140036,0.0008658438,0.0007504231,0.06367652,0.00009579472],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.490391,0.07537752,0.01012654,0.003094343,0.0006028176,0.001582118,0.4051419,0.0003977236,0.01328601],"genre_scores_gemma":[0.8649167,0.01017279,0.00953092,0.0008335621,0.0001166173,0.002382321,0.1106605,0.000104577,0.001281934],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1134495,"threshold_uncertainty_score":0.2255783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06396747245939534,"score_gpt":0.3414378790799688,"score_spread":0.2774704066205734,"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."}}