{"id":"W4393440786","doi":"10.5281/zenodo.10385407","title":"Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Natural Resources Canada","funders":"","keywords":"Canopy; Cover (algebra); Tree canopy; Tree (set theory); Geography; Environmental science; Forestry; Series (stratigraphy); Remote sensing; Geology; Mathematics; Archaeology; Engineering","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":[0.0002046758,0.0002963863,0.0001677746,0.001067374,0.001238555,0.0006654439,0.0005505355,0.0002459023,0.002554912],"category_scores_gemma":[0.001136816,0.0001483516,0.0002336615,0.002944865,0.0003696929,0.0002582732,0.0003447707,0.0002263377,0.0005800953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01534811,"about_ca_system_score_gemma":0.01010877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940414,"about_ca_topic_score_gemma":0.9978262,"domain_scores_codex":[0.9997024,0.00001067715,0.00001641371,0.0000593153,0.0001427952,0.00006832059],"domain_scores_gemma":[0.9987921,0.00006726567,0.0001437424,0.00006619358,0.0008073674,0.0001234179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002523982,0.00009407716,0.9234278,0.0002233405,0.0001307643,0.0003172589,0.001923576,0.005156577,0.003738816,0.0004044746,0.02376238,0.04056862],"study_design_scores_gemma":[0.000009372786,0.00001083716,0.9874278,0.00001781899,0.00001957128,0.00002254306,0.0007824847,0.002557484,0.0004844209,0.00003724753,0.008616157,0.00001423051],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9063505,0.0002485327,0.0006690894,0.0002460471,0.00001392907,0.0001107748,0.08287364,0.0001329791,0.009354509],"genre_scores_gemma":[0.9450745,0.0002466099,0.001573343,0.00006244957,0.000008483901,0.00008086194,0.04462321,0.00003567562,0.008294792],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01534811,"threshold_uncertainty_score":0.1113588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06624338120268847,"score_gpt":0.258489743300689,"score_spread":0.1922463620980006,"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."}}