{"id":"W4394324714","doi":"10.6084/m9.figshare.22587139","title":"Canuel et al. - Post-harvest woody debris and regeneration interactions are driven by ecological factors rather than wood procurement intensity in temperate and boreal forests of eastern Canada","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coarse woody debris; Boreal; Temperate climate; Regeneration (biology); Debris; Taiga; Ecology; Temperate rainforest; Snag; Geography; Forestry; Environmental science; Temperate forest; Procurement; Agroforestry; Habitat; Ecosystem; Biology; Business","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":[0.0008705731,0.000465797,0.0006237708,0.002136409,0.001486288,0.001569767,0.001465628,0.0004846652,0.008900432],"category_scores_gemma":[0.004069088,0.0003344169,0.0005748182,0.004886758,0.0004018574,0.0003839017,0.0009712291,0.0006680119,0.002915463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005929459,"about_ca_system_score_gemma":0.009933599,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9756394,"about_ca_topic_score_gemma":0.990045,"domain_scores_codex":[0.9995537,0.0000570999,0.00003639264,0.0001068994,0.0001204413,0.000125445],"domain_scores_gemma":[0.9974344,0.0004470501,0.0003112249,0.0002602699,0.001205258,0.0003418598],"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":[0.0002602066,0.00006546777,0.4251677,0.0008462135,0.0004158939,0.000147511,0.0007571871,0.002031405,0.0003059272,0.001596937,0.5487087,0.01969697],"study_design_scores_gemma":[0.0002013874,0.00001975435,0.7220938,0.0007697413,0.0002422704,0.0001097181,0.001564654,0.00272214,0.0003796349,0.0006966798,0.2711412,0.00005901897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04206777,0.00115519,0.0002448151,0.000593785,0.00005456243,0.00003516515,0.9522359,0.0001532096,0.003459535],"genre_scores_gemma":[0.124603,0.0007545769,0.001175252,0.0002051873,0.00002080037,0.0001100999,0.8675447,0.0001011793,0.005485305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0243606,"threshold_uncertainty_score":0.04900807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550658284512487,"score_gpt":0.2293555291905993,"score_spread":0.1938489463454744,"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."}}