{"id":"W2289377756","doi":"10.1007/978-94-017-9813-6_4","title":"Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo in Ontario, Canada","year":2015,"lang":"en","type":"book-chapter","venue":"Springer remote sensing/photogrammetry","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Remote sensing; Land cover; Variety (cybernetics); Geography; Land use; Cover (algebra); Term (time); Cartography; Computer science; Environmental resource management; Environmental science; Engineering; Civil engineering; Artificial intelligence","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":[0.00023892,0.0003815159,0.0003129393,0.002875104,0.002008897,0.0016101,0.000826736,0.0002832143,0.01097551],"category_scores_gemma":[0.0008231598,0.0003205087,0.0004043433,0.009594139,0.0003368664,0.0007365078,0.0005682051,0.0004444742,0.001636914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03261559,"about_ca_system_score_gemma":0.04822034,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9989655,"about_ca_topic_score_gemma":0.9997589,"domain_scores_codex":[0.9996655,0.000008296694,0.00001852427,0.00003889655,0.0001932695,0.00007557796],"domain_scores_gemma":[0.999191,0.00002579669,0.00006016385,0.00001867148,0.0006190385,0.00008534444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001298732,0.00006989548,0.4353764,0.001003137,0.0002377179,0.0006542043,0.007323824,0.003632942,0.002756474,0.004418527,0.3350829,0.2093142],"study_design_scores_gemma":[0.00001187074,0.000008492556,0.8790669,0.0001842607,0.00005565214,0.0000873322,0.002836209,0.00173354,0.000307114,0.0001835074,0.1154851,0.00003983391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4270904,0.01558866,0.003518248,0.004494748,0.0002577244,0.0004084367,0.4292761,0.0008577174,0.1185079],"genre_scores_gemma":[0.6645958,0.01534292,0.008870404,0.0003850677,0.00007879311,0.0002348551,0.1330212,0.0004038644,0.1770669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03261559,"threshold_uncertainty_score":0.2366437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04355760226893098,"score_gpt":0.2268499647666307,"score_spread":0.1832923624976997,"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."}}