{"id":"W2295128736","doi":"10.1111/cag.12251","title":"Pre‐settlement vegetation maps generated using Ontario early survey: An online database providing enhanced map access for researchers","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Vegetation (pathology); Geospatial analysis; Geography; Settlement (finance); Baseline (sea); Key (lock); Database; Survey data collection; Remote sensing; Cartography; Environmental resource management; Computer science; World Wide Web; Environmental science; Geology; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001486605,0.0005829823,0.0006159932,0.007601534,0.002154693,0.002307967,0.001357767,0.000443254,0.0759359],"category_scores_gemma":[0.007028212,0.0005159169,0.0003122036,0.01758825,0.0005115048,0.0017366,0.002005275,0.0005121671,0.01749538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01274728,"about_ca_system_score_gemma":0.03296003,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9445007,"about_ca_topic_score_gemma":0.9802172,"domain_scores_codex":[0.9989536,0.00005909658,0.00008948516,0.0001320594,0.0006177766,0.0001480354],"domain_scores_gemma":[0.9894956,0.0009327315,0.0005528239,0.00122302,0.006642499,0.001153266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002238998,0.00007313638,0.02758157,0.0007254945,0.00003308083,0.000224451,0.002706297,0.0006834614,0.00185685,0.001760667,0.8682306,0.09590039],"study_design_scores_gemma":[0.0001251883,0.00001749927,0.1308994,0.0003141846,0.00004066382,0.0000861562,0.003292752,0.00190132,0.001469113,0.000822518,0.8608878,0.0001433235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01729987,0.0002272734,0.004086886,0.0006314511,0.00008501095,0.0005299102,0.9254963,0.004574767,0.04706844],"genre_scores_gemma":[0.09783322,0.001874312,0.05471835,0.0002484881,0.00009656164,0.002040631,0.7653677,0.00278434,0.07503637],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0759359,"threshold_uncertainty_score":0.2540309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08605246153403433,"score_gpt":0.2894608565580434,"score_spread":0.203408395024009,"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."}}