{"id":"W2170131456","doi":"10.1016/j.foreco.2009.07.009","title":"Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery","year":2009,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snag; Coarse woody debris; Environmental science; Dead wood; Canopy; Temperate forest; Forest ecology; Temperate rainforest; Forest management; Remote sensing; Vegetation (pathology); Ecosystem; Biodiversity; Ecology; Forestry; Geography; Habitat; Agroforestry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001300964,0.0001385147,0.0001529746,0.0006083655,0.0002855642,0.0002602237,0.0003019748,0.0001746451,0.0005485237],"category_scores_gemma":[0.0002962832,0.0001600077,0.0001025427,0.0004643473,0.0001790595,0.0002655585,0.0002539689,0.0001108042,0.00009699704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001698002,"about_ca_system_score_gemma":0.0001704884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02061221,"about_ca_topic_score_gemma":0.04400115,"domain_scores_codex":[0.9999355,0.000006575154,0.000003986307,0.00002222226,0.00001705363,0.00001465393],"domain_scores_gemma":[0.9998524,0.00004397632,0.00002207939,0.00001484799,0.00003641616,0.00003025387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001454729,0.0006292935,0.7378919,0.0001683785,0.0001451017,0.001179751,0.002393467,0.01313615,0.1370419,0.0004266026,0.0009287751,0.1046039],"study_design_scores_gemma":[0.00002340895,0.00006963061,0.9862099,0.000005189078,0.00002611462,0.0002262655,0.0003569028,0.01136725,0.00131283,0.00007556631,0.0003173418,0.000009548736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991931,0.00002349555,0.0003114946,0.000005462751,0.000001011334,0.000004442524,0.000118103,0.000009155097,0.0003337346],"genre_scores_gemma":[0.9979394,0.00002379486,0.001580218,0.00000370445,0.000002349902,0.000005709982,0.0001798603,0.000002247701,0.0002626999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02061221,"threshold_uncertainty_score":0.04098445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579248526721931,"score_gpt":0.1997346270946415,"score_spread":0.1839421418274222,"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."}}