{"id":"W4377862149","doi":"10.1080/15481603.2023.2214994","title":"Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario","year":2023,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Biome; Taiga; Boreal; Categorization; Geography; Physical geography; Remote sensing; Forestry; Cartography; Environmental science; Ecology; Archaeology; Ecosystem; Computer science; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007064689,0.0002929877,0.00025797,0.0007596843,0.0009579185,0.001406164,0.0003706633,0.0003219739,0.0004270576],"category_scores_gemma":[0.004965809,0.0001529822,0.0002839819,0.00071841,0.0003921905,0.0005677801,0.0004820426,0.0002232588,0.00007104297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004057262,"about_ca_system_score_gemma":0.002623872,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6728374,"about_ca_topic_score_gemma":0.806637,"domain_scores_codex":[0.9994298,0.00006607093,0.00003924624,0.0001467706,0.0002161122,0.0001019945],"domain_scores_gemma":[0.9982315,0.0006816895,0.0002836337,0.00010084,0.0005929014,0.0001094496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000227236,0.0000279912,0.9272892,0.00006732032,0.0001151428,0.0002994451,0.0006713424,0.01952146,0.005509187,0.0002320274,0.0004179633,0.04562173],"study_design_scores_gemma":[0.000005151411,0.00002437317,0.9425908,0.00002011609,0.00005389272,0.0001294016,0.0009540304,0.05449201,0.0008231793,0.0002351855,0.000646573,0.00002538618],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979237,0.0001893975,0.0007906567,0.00006085205,0.000003627421,0.00001024558,0.0002633907,0.00001615983,0.000741915],"genre_scores_gemma":[0.9991779,0.00003507052,0.0003954712,0.000004905893,0.000002197653,0.000001648308,0.0002166859,0.000002272554,0.0001639429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3271626,"threshold_uncertainty_score":0.6581786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148880743035671,"score_gpt":0.2298789832249725,"score_spread":0.2149909089214054,"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."}}