{"id":"W4411891327","doi":"10.1007/s10980-025-02121-0","title":"Using old fields for new purposes: ecosystem service outcomes of restoring marginal agricultural land to forests","year":2025,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Jewish Community Foundation of Montreal; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Ecosystem services; Landscape ecology; Agriculture; Restoration ecology; Environmental resource management; Context (archaeology); Agricultural land; Agricultural productivity; Land use; Land degradation; Environmental science; Geography; Ecosystem; Ecology; Agroforestry; Habitat; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000139985,0.0001291649,0.0003130381,0.00005099104,0.00009498933,0.00002047221,0.0002659356,0.0001243361,0.0002601333],"category_scores_gemma":[0.00002100588,0.00008923432,0.00006214278,0.0002280511,0.000002187614,0.0001098415,0.0001724583,0.00005152312,0.00005311126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006530931,"about_ca_system_score_gemma":0.00001982457,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001239513,"about_ca_topic_score_gemma":0.1891739,"domain_scores_codex":[0.9990802,0.00002676497,0.0002643325,0.0002463468,0.00009038507,0.0002920188],"domain_scores_gemma":[0.9994835,0.0001326223,0.00009053105,0.0001903829,0.00001763541,0.00008536864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000408638,0.00002212388,0.9890027,0.00013096,0.00003896257,0.000001579855,0.0001889587,0.008193361,0.0002551865,0.00006389843,0.001966279,0.00009510552],"study_design_scores_gemma":[0.0008168757,0.0001052586,0.9885577,0.00005882106,0.00005498706,0.000009093696,0.0001271576,0.004343383,0.000324254,0.0002073048,0.005244847,0.0001503276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959425,0.00002260306,0.0002780253,0.001119714,0.000656514,0.0004238563,0.00001533462,0.00002493921,0.001516492],"genre_scores_gemma":[0.9977782,0.000002542228,0.00103554,0.0004384237,0.00009489125,0.00003260234,0.000009463229,0.000006749541,0.0006015549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1879344,"threshold_uncertainty_score":0.8256216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714376389305737,"score_gpt":0.2590409524303504,"score_spread":0.241897188537293,"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."}}