{"id":"W4394623567","doi":"10.55905/rdelosv17.n54-009","title":"Determinação da eficiência energética em máquinas de colheita florestal em sistema Full-tree","year":2024,"lang":"pt","type":"article","venue":"DELOS Desarrollo Local Sostenible","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Environmental science","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":[0.0002936373,0.0003426705,0.0005978093,0.0002363378,0.0003199714,0.0006320212,0.0003459312,0.0002438714,0.001431915],"category_scores_gemma":[0.0002442945,0.0001569581,0.0004725395,0.0003885814,0.0002321798,0.0004064633,0.0002391076,0.0003911373,0.0001630292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007849067,"about_ca_system_score_gemma":0.0003071205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00756652,"about_ca_topic_score_gemma":0.02017122,"domain_scores_codex":[0.999817,0.00001838563,0.00000858193,0.00008176306,0.00005099756,0.00002328485],"domain_scores_gemma":[0.9996792,0.00008487629,0.00007032225,0.00002406898,0.00009556067,0.00004602364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001102934,0.0001972489,0.0645465,0.0002538921,0.0001182826,0.0001295985,0.0002829569,0.001848389,0.9175743,0.00009471778,0.0001232052,0.01372813],"study_design_scores_gemma":[0.00002612558,0.003943561,0.8024687,0.00002250354,0.000178706,0.0001269079,0.0008187888,0.005421425,0.1839883,0.0002119472,0.002753558,0.00003952577],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986695,0.0002347046,0.0005674863,0.0000157462,0.00000449581,0.0000121727,0.0001723056,0.00001532643,0.0003082623],"genre_scores_gemma":[0.996358,0.0002092067,0.001412926,0.00002999871,0.000002469676,0.00003143076,0.0003698795,0.00001482268,0.001571311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00756652,"threshold_uncertainty_score":0.01504493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629658705466168,"score_gpt":0.242687509205375,"score_spread":0.2263909221507133,"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."}}