{"id":"W6943851634","doi":"10.17605/osf.io/96wmg","title":"Bioenergy supply chain statistical analysis: Canada","year":2022,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bioenergy; Supply chain; Statistical analysis; Biomass (ecology); Survey data collection; Production (economics); Statistical survey","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001037044,0.0002121885,0.0002789062,0.0002592076,0.0001939621,0.00005500177,0.0006613983,0.00004484316,0.5684564],"category_scores_gemma":[0.0001710968,0.00026324,0.0001015081,0.0007464418,0.00004648513,0.00006537867,0.0008809484,0.0002317164,0.04221378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000642013,"about_ca_system_score_gemma":0.0001339611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05324183,"about_ca_topic_score_gemma":0.1608802,"domain_scores_codex":[0.9975922,0.0002286174,0.0003989352,0.0008717191,0.0005192922,0.0003892236],"domain_scores_gemma":[0.9980317,0.0001064468,0.00005980432,0.001568075,0.00003675206,0.0001972737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000245736,0.0001206165,0.03533772,0.00007205814,0.001139686,0.0001058739,0.0002712207,0.6858402,0.0004342482,0.02599162,0.2439863,0.006675847],"study_design_scores_gemma":[0.0003757639,8.088934e-7,0.04607058,0.000004567444,0.0002449588,0.000008546841,0.0003461568,0.1217582,0.0009816729,0.0004641608,0.8293102,0.0004343186],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08432301,0.00000574474,0.06595152,0.0005219399,0.00113956,0.000775398,0.0003717956,0.0006657146,0.8462453],"genre_scores_gemma":[0.9319053,0.00002777514,0.0005845611,0.0002306586,0.00002266444,0.0002670064,0.0002373255,0.00003571884,0.06668895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8475823,"threshold_uncertainty_score":0.999982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006133466284048559,"score_gpt":0.2030787333721083,"score_spread":0.1969452670880598,"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."}}