{"id":"W4408213318","doi":"10.36227/techrxiv.174137781.13803217/v1","title":"Global, Multi-Scale Standing Deadwood Segmentation In Centimeter-Scale Aerial Images","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of British Columbia","funders":"","keywords":"Scale (ratio); Segmentation; Centimeter; Remote sensing; Geography; Geology; Computer science; Artificial intelligence; Cartography; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002736635,0.0007500605,0.0004641013,0.001958637,0.0002314491,0.0005663373,0.0005781592,0.0004808788,0.0007675037],"category_scores_gemma":[0.0005030097,0.0002414088,0.0006036573,0.0008580176,0.0002677668,0.0005419445,0.0006309857,0.000436884,0.0008121239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982782,"about_ca_system_score_gemma":0.0002636178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006604813,"about_ca_topic_score_gemma":0.02313778,"domain_scores_codex":[0.9997615,0.00001408623,0.00001260626,0.0001036089,0.0000561375,0.00005207013],"domain_scores_gemma":[0.9997981,0.0000368828,0.00002462425,0.00004104951,0.00007546569,0.0000238556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008638193,0.0003413618,0.03106979,0.0007108087,0.0003341217,0.001258153,0.0007816168,0.07870972,0.2024439,0.001102776,0.017853,0.6645309],"study_design_scores_gemma":[0.00005884033,0.0002555269,0.1652392,0.000175411,0.0001924948,0.001443147,0.00132767,0.7371228,0.07888131,0.002969275,0.01224855,0.0000857667],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8207431,0.001811583,0.1565641,0.0002613774,0.000163777,0.000278475,0.007470142,0.006688388,0.006019006],"genre_scores_gemma":[0.8548213,0.0006301875,0.1247868,0.0001634809,0.00008010918,0.00008973839,0.01655455,0.0002483567,0.002625592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006604813,"threshold_uncertainty_score":0.01313275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0282602743373758,"score_gpt":0.2597470226570578,"score_spread":0.2314867483196821,"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."}}