{"id":"W2098761820","doi":"10.1109/jstars.2010.2045881","title":"A Conceptual Model for Multi-Temporal Landscape Monitoring in an Object-Based Environment","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Geological Survey","keywords":"Computer science; Spurious relationship; Object (grammar); Change detection; Artificial intelligence; Feature (linguistics); Computer vision; Thematic map; Land cover; Data mining; Land use; Machine learning; Cartography; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001761611,0.0008169728,0.0006009598,0.001894717,0.0009284141,0.004836407,0.00371846,0.001675796,0.005014408],"category_scores_gemma":[0.002553178,0.0005649711,0.00166001,0.002372277,0.002452072,0.008229837,0.001814438,0.001583027,0.001252135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804399,"about_ca_system_score_gemma":0.001550172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007451123,"about_ca_topic_score_gemma":0.006445741,"domain_scores_codex":[0.9992459,0.0001940032,0.00006070994,0.0002316223,0.0002055297,0.00006232223],"domain_scores_gemma":[0.9989402,0.0003551212,0.0001439492,0.0001968415,0.00023214,0.0001318018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002175451,0.00004480471,0.0009712124,0.0001263582,0.00004515939,0.0002643926,0.0007090075,0.05363319,0.001427264,0.9114501,0.001801901,0.02950484],"study_design_scores_gemma":[0.00002345418,0.00008987125,0.0008880919,0.00009349517,0.00007343729,0.0003664592,0.0003396156,0.4684855,0.0006442215,0.4751953,0.05374688,0.00005369765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001948071,0.0002390801,0.9917411,0.0005359547,0.0000481564,0.00005000187,0.0001212473,0.0002561506,0.005060228],"genre_scores_gemma":[0.1486238,0.001014945,0.841256,0.0003015093,0.000175443,0.0007005429,0.0004405957,0.0001634318,0.007323899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007451123,"threshold_uncertainty_score":0.01677489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0377839794729874,"score_gpt":0.2505192230341892,"score_spread":0.2127352435612018,"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."}}