{"id":"W2099867025","doi":"10.1080/01431160701311291","title":"Improved topographic correction of forest image data using a 3‐D canopy reflectance model in multiple forward mode","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; University of Lethbridge","funders":"","keywords":"Terrain; Remote sensing; Canopy; Scale (ratio); Pixel; Bidirectional reflectance distribution function; Vegetation (pathology); Tree canopy; Geology; Reflectivity; Environmental science; Geography; Cartography; Physics; Optics","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":[0.0002884502,0.0003889042,0.0002958206,0.0003615711,0.000223133,0.0004906658,0.0005318145,0.0002794717,0.000659372],"category_scores_gemma":[0.0006888873,0.0002129444,0.000423432,0.0005081773,0.0001517128,0.0003742533,0.000289618,0.0004080937,0.0003289949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005725226,"about_ca_system_score_gemma":0.001245275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05851288,"about_ca_topic_score_gemma":0.1045758,"domain_scores_codex":[0.9998704,0.00001347855,0.000005178714,0.00003261796,0.00006494451,0.00001338099],"domain_scores_gemma":[0.9998465,0.00003012367,0.00002055577,0.00002856435,0.00006667549,0.000007671349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001290598,0.00007937575,0.01221036,0.00008119534,0.00006318286,0.0001209755,0.0001587841,0.6621091,0.05886098,0.001199279,0.002005636,0.2629821],"study_design_scores_gemma":[0.000009875286,0.00001258648,0.006074936,0.000003027972,0.000008430605,0.00003107191,0.00001315103,0.9896281,0.003214036,0.0001691955,0.0008193291,0.00001634814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3248253,0.0002032026,0.66699,0.0001870919,0.00007082271,0.00007190419,0.0005831691,0.004534727,0.002533859],"genre_scores_gemma":[0.7612916,0.0001152912,0.2352295,0.00004895095,0.00001483929,0.00004191752,0.0007474933,0.0002186655,0.002291579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05851288,"threshold_uncertainty_score":0.1163446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02956448803068986,"score_gpt":0.3160503694935734,"score_spread":0.2864858814628836,"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."}}