{"id":"W2624259107","doi":"10.14358/pers.83.6.429","title":"Impervious Comparison of NLCD versus a Detailed Dataset Over Time","year":2017,"lang":"en","type":"article","venue":"Photogrammetric Engineering & Remote Sensing","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Lethbridge","keywords":"Impervious surface; Geography; Cartography; Environmental science; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002710808,0.0007524933,0.0008521192,0.003435764,0.0006170263,0.002204984,0.0009879129,0.001832395,0.01104867],"category_scores_gemma":[0.008984376,0.0003266089,0.0008801339,0.002236295,0.0008839285,0.002705654,0.002064335,0.0008041223,0.004372808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006814548,"about_ca_system_score_gemma":0.0007391396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01839632,"about_ca_topic_score_gemma":0.02866319,"domain_scores_codex":[0.9978636,0.0002512061,0.0001651279,0.0008328977,0.0005577351,0.0003293833],"domain_scores_gemma":[0.9930606,0.002575065,0.0004145924,0.002037356,0.001701201,0.0002112539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01214398,0.001297014,0.1206672,0.002343919,0.001434393,0.001468941,0.0009740622,0.2659907,0.06156941,0.005290627,0.08540677,0.441413],"study_design_scores_gemma":[0.000322585,0.0008399017,0.3924759,0.000476596,0.0006023173,0.001678811,0.002041131,0.4569301,0.04316798,0.006508366,0.0946903,0.0002660695],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.805456,0.003664511,0.05294693,0.00107142,0.00110178,0.0002277158,0.09693022,0.01507046,0.02353096],"genre_scores_gemma":[0.8736463,0.0003587827,0.01638272,0.0003574779,0.0001438027,0.00005545945,0.1003392,0.001137344,0.007579016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01839632,"threshold_uncertainty_score":0.0369615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02795608461620161,"score_gpt":0.2996996837790945,"score_spread":0.2717435991628928,"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."}}