{"id":"W4405125256","doi":"10.1016/j.foreco.2024.122442","title":"Identifying forest harvesting practices: Clear-cutting and thinning in diverse tree species using dense Landsat time series","year":2024,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sport Centre Pacific; Canadian Forest Service","funders":"Gobierno de Navarra; Ministerio de Economía y Competitividad; European Regional Development Fund; Ministerio de Ciencia, Innovación y Universidades","keywords":"Thinning; Logging; Series (stratigraphy); Agroforestry; Tree (set theory); Clearcutting; Remote sensing; Environmental science; Forestry; Ecology; Geography; Biology; Mathematics; Paleontology","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":[0.0007933283,0.0003826799,0.0002200851,0.002191722,0.0002087561,0.0004800583,0.0002313185,0.0002642804,0.000262834],"category_scores_gemma":[0.0008172519,0.000112748,0.0003180772,0.001380693,0.000132328,0.0004232517,0.0002367662,0.0001297418,0.0001358704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002450118,"about_ca_system_score_gemma":0.0001964344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007030152,"about_ca_topic_score_gemma":0.02207825,"domain_scores_codex":[0.9996666,0.00004171373,0.0000403239,0.00009985088,0.0001144906,0.00003705241],"domain_scores_gemma":[0.9994789,0.0001015562,0.0001803299,0.00004950213,0.000154312,0.0000353933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001910165,0.0002703951,0.8301523,0.0001104612,0.0001508223,0.0001945502,0.0004019537,0.008469316,0.02458626,0.0001319151,0.0004880301,0.134853],"study_design_scores_gemma":[0.000003940109,0.00008441536,0.9673952,0.00001426078,0.00004090392,0.0001236482,0.0004302669,0.02769915,0.003656648,0.00008691676,0.000450431,0.00001422164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925922,0.0001060735,0.006124407,0.00001169991,0.000005517727,0.00003722454,0.0004711845,0.00005165224,0.0006000414],"genre_scores_gemma":[0.9858773,0.0001140194,0.0124493,0.00001045714,0.000007473714,0.00002683159,0.001190247,0.000005771378,0.0003184511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007030152,"threshold_uncertainty_score":0.01397848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962493042310065,"score_gpt":0.2690435244239108,"score_spread":0.2394185940008102,"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."}}