{"id":"W1861076065","doi":"10.4141/cjss10054","title":"Estimating soil water content from surface digital image gray level measurements under visible spectrum","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Soil water; Remote sensing; Environmental science; Soil science; Water content; Terrain; Neutron probe; Digital elevation model; Digital soil mapping; Digital image; Hydrology (agriculture); Soil classification; Geology; Image processing; Neutron; Geography; Computer science; Neutron temperature; Physics; Cartography; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007847379,0.0001839021,0.0002073188,0.0001130062,0.0005033717,0.0003214853,0.0006757022,0.00005298942,0.0003331469],"category_scores_gemma":[0.0001090614,0.0001269333,0.00009198963,0.0003159248,0.001135845,0.001376443,0.00008267971,0.0002369911,0.0002762295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648736,"about_ca_system_score_gemma":0.0004236818,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1731315,"about_ca_topic_score_gemma":0.213137,"domain_scores_codex":[0.9977644,0.00002719642,0.0003852675,0.0003124968,0.0007541576,0.0007564954],"domain_scores_gemma":[0.9985003,0.00001853201,0.0001807153,0.0002438194,0.00008042101,0.0009761645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003227576,0.00009038368,0.687654,0.000007559005,0.00007005936,0.0005812817,0.009354494,0.01025353,0.2679783,0.00002403991,0.001326006,0.0226281],"study_design_scores_gemma":[0.0004929966,0.0001156586,0.8278289,0.0001087065,0.00003154707,0.0002115933,0.0009123816,0.001972189,0.1643404,0.003460051,0.0001293077,0.0003962406],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667801,0.00003727641,0.002053256,0.0004072331,0.0009823086,0.0000601291,0.000005746449,0.00000834938,0.02966556],"genre_scores_gemma":[0.992608,0.000001012926,0.006791791,0.0002751893,0.0000943711,7.03355e-8,0.000001003585,0.00001523532,0.0002133476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401749,"threshold_uncertainty_score":0.8323748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07545787098876666,"score_gpt":0.2225582687560606,"score_spread":0.1471003977672939,"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."}}