{"id":"W4386887060","doi":"10.33548/scientia934","title":"Enhancing Forest Productivity with Compensatory Growth","year":2023,"lang":"en","type":"article","venue":"","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Compensatory growth (organ); Agroforestry; Natural resource economics; Environmental science; Economics; Biology; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160809,0.0004892731,0.0001836353,0.0005522654,0.0003168484,0.0007223713,0.000684516,0.000347759,0.004446344],"category_scores_gemma":[0.004431,0.0001009153,0.0004041055,0.0005273102,0.0006138041,0.0008951473,0.001131396,0.0006405657,0.0008815865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000586833,"about_ca_system_score_gemma":0.0009707425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816216,"about_ca_topic_score_gemma":0.004088276,"domain_scores_codex":[0.9995012,0.0001410933,0.00002005179,0.00006929524,0.0001679862,0.0001003974],"domain_scores_gemma":[0.9987198,0.0004250073,0.0001599237,0.0002330834,0.0002119809,0.0002501603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007513358,0.0009773838,0.02091174,0.0006368655,0.0002117293,0.0005733859,0.000596649,0.03531458,0.04353822,0.06088645,0.02004514,0.8155565],"study_design_scores_gemma":[0.0009680844,0.004019759,0.163934,0.0009746959,0.0005123298,0.003953614,0.001088497,0.1548404,0.05274789,0.3609412,0.2557573,0.0002622516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5055987,0.006933079,0.3393637,0.01019531,0.001236065,0.0003757041,0.0007217774,0.002743824,0.1328318],"genre_scores_gemma":[0.9618984,0.001170124,0.03013713,0.0005070068,0.0001938564,0.00009784259,0.0001597911,0.0001298448,0.005705913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004446344,"threshold_uncertainty_score":0.01487452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009840326023632658,"score_gpt":0.2135779956125621,"score_spread":0.2037376695889295,"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."}}