{"id":"W6906541685","doi":"10.17605/osf.io/zqpt5","title":"Fire-smart Bioenergy nexus","year":2025,"lang":"en","type":"dataset","venue":"OSF Preprints (OSF Preprints)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nexus (standard); Bioenergy; Indigenous; Production (economics); Economic impact analysis; Sustainability","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"category_scores_codex":[0.008665072,0.002144643,0.002357078,0.001275175,0.0006149983,0.0006933939,0.008615253,0.002342958,0.8069634],"category_scores_gemma":[0.01297091,0.002556249,0.001215672,0.00170315,0.0007779849,0.0005619443,0.01284818,0.003322903,0.9826674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850474,"about_ca_system_score_gemma":0.001843214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007843615,"about_ca_topic_score_gemma":0.001595707,"domain_scores_codex":[0.9816893,0.002595192,0.002400137,0.009266546,0.001941959,0.002106831],"domain_scores_gemma":[0.9695758,0.001527739,0.001545047,0.02580229,0.0006148358,0.000934321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003828764,0.001228554,0.0002650036,0.0007703568,0.001241107,0.0003229989,0.000122409,0.001016926,0.0003293771,0.0002350982,0.9896846,0.004400693],"study_design_scores_gemma":[0.001199068,0.000001926426,0.000402505,0.0006817009,0.0007861269,0.00009879019,0.00004634579,0.0002635778,0.001201578,0.001370422,0.9919823,0.001965596],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001380858,0.000008369651,0.00004491243,0.0001692062,0.001986664,0.001564671,0.6902297,0.0006198729,0.3052386],"genre_scores_gemma":[0.00006354702,0.0003825139,0.0002589297,0.0004323725,0.0003338003,0.0009174655,0.6458054,0.0001697456,0.3516363],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1757041,"threshold_uncertainty_score":0.9991294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144253665755753,"score_gpt":0.2688585712206926,"score_spread":0.2544332046451173,"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."}}