{"id":"W1608664608","doi":"10.1109/icassp.1988.196770","title":"Long correlation random field image models","year":2003,"lang":"en","type":"article","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Random field; Markov random field; Conditional random field; Generalization; Computer science; Field (mathematics); Artificial intelligence; Markov chain; Image (mathematics); Algorithm; Machine learning; Mathematics; Statistics; Image segmentation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001013593,0.00004095642,0.0000419992,0.000006938757,0.00005017365,0.00001647222,0.00003495821,0.00002122501,0.004743399],"category_scores_gemma":[0.00005962319,0.00003549932,0.00001562509,0.00004958885,0.00001734346,0.0001230693,0.0000182143,0.00003567329,0.0004434448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001583653,"about_ca_system_score_gemma":0.000002171989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002223844,"about_ca_topic_score_gemma":0.00006009409,"domain_scores_codex":[0.9996325,0.00001489664,0.00007102232,0.00009679756,0.00008780271,0.00009699756],"domain_scores_gemma":[0.9998008,0.00006794825,0.0000163385,0.00007925107,0.00000257364,0.0000331158],"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":[0.0001277568,0.0002738575,0.1677872,0.00002650861,0.00004295722,0.00008173528,0.002059999,0.205282,0.007390583,0.2540986,0.2304869,0.1323418],"study_design_scores_gemma":[0.003069682,0.00008824231,0.02382161,0.00001390167,0.00002768608,0.00002229137,0.0002179063,0.8058423,0.0064792,0.1458071,0.01407584,0.0005342876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01206174,0.000005070378,0.5906069,0.00004922073,0.00006289266,0.00004823468,3.003865e-7,0.00001403091,0.3971516],"genre_scores_gemma":[0.9803035,0.000007073427,0.01340866,0.0003504659,0.000005522181,0.000003837628,0.000001834345,0.000003596468,0.005915475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682418,"threshold_uncertainty_score":0.9961664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009448337099311742,"score_gpt":0.2130452320412291,"score_spread":0.2035968949419173,"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."}}