{"id":"W4237713316","doi":"10.5194/isprsarchives-xli-b8-965-2016","title":"EXAMINING URBAN EXPANSION USING MULTI-TEMPORAL LANDSAT IMAGERY: A CASE STUDY OF THE MONTREAL CENSUS METROPOLITAN AREA FROM 1975 TO 2015, CANADA","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metropolitan area; Urbanization; Census; Geography; Population; Land use; Urban planning; Built-up area; Satellite imagery; Cartography; Urban climate; Vegetation (pathology); Urban area; Physical geography; Remote sensing; Economic growth; Ecology; Demography","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":[0.000429268,0.0004743544,0.0002097955,0.002604269,0.001774039,0.001229823,0.001016837,0.0003238194,0.0009380186],"category_scores_gemma":[0.001207489,0.0002288394,0.0004072923,0.007005938,0.0008156535,0.0004177791,0.0006612905,0.0004721858,0.0001317371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02797813,"about_ca_system_score_gemma":0.01502652,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952264,"about_ca_topic_score_gemma":0.9984035,"domain_scores_codex":[0.9995168,0.00004592349,0.00001514049,0.00006274602,0.0001714592,0.0001878798],"domain_scores_gemma":[0.9992359,0.00007246828,0.00009242406,0.00003137193,0.0004599637,0.0001079711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001561798,0.0002106125,0.9122964,0.0002496574,0.0001803444,0.005883956,0.007054504,0.01045543,0.002183822,0.001792738,0.009412448,0.050124],"study_design_scores_gemma":[0.00000624219,0.000024635,0.9754816,0.00005912555,0.00004245013,0.0002265788,0.01039143,0.007624199,0.0003875776,0.00007159099,0.005652549,0.00003194583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879547,0.0007183582,0.0004696791,0.0003621341,0.00001255777,0.0001202035,0.005277072,0.00002194078,0.005063342],"genre_scores_gemma":[0.9932847,0.0007171742,0.001090013,0.00005575095,0.000008045401,0.00003012636,0.002735207,0.00001086242,0.002068114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02797813,"threshold_uncertainty_score":0.2029965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02589602819573133,"score_gpt":0.2527346132141228,"score_spread":0.2268385850183915,"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."}}