{"id":"W2192626670","doi":"10.1890/07-0513.1","title":"LAC CROCHE UNDERSTORY VEGETATION DATA SET (1998–2006)","year":2007,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Understory; Basal area; Transect; Vegetation (pathology); Sampling (signal processing); Forestry; Canopy; Ecology; Tree canopy; Environmental science; Temperate rainforest; Physical geography; Geography; Temperate forest; Elevation (ballistics); Temperate climate; Ecosystem; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006222949,0.0007319825,0.0006762245,0.003049499,0.0007358445,0.0007510558,0.001474302,0.0004232664,0.008516708],"category_scores_gemma":[0.001858929,0.0002706961,0.0004247184,0.002796122,0.0002084498,0.0004404348,0.0006407883,0.0005218551,0.00381738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003360738,"about_ca_system_score_gemma":0.002847883,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8180569,"about_ca_topic_score_gemma":0.920404,"domain_scores_codex":[0.9995098,0.00004051129,0.00004265741,0.0001270222,0.000183358,0.00009657001],"domain_scores_gemma":[0.9972534,0.0001172949,0.0002878919,0.0003566032,0.001811679,0.0001731619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002038248,0.0004127653,0.2671755,0.0009457644,0.0007311421,0.0005352806,0.0004006306,0.003601995,0.005460445,0.001515137,0.6514496,0.06573359],"study_design_scores_gemma":[0.0003675403,0.0000908542,0.7586616,0.0002578037,0.0001301564,0.0002378702,0.0002502386,0.003346869,0.001644585,0.0001776247,0.2347609,0.00007393801],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06514004,0.0003814845,0.0003679682,0.000101387,0.00002797909,0.0001699182,0.930428,0.0003119509,0.003071137],"genre_scores_gemma":[0.05614029,0.0001054075,0.001061195,0.0000609046,0.00001609741,0.0002300996,0.9389749,0.0000435697,0.003367543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8180569,"threshold_uncertainty_score":0.3660291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03141976769544023,"score_gpt":0.281910346533156,"score_spread":0.2504905788377157,"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."}}