{"id":"W3177036350","doi":"10.5194/isprs-annals-v-3-2021-257-2021","title":"A COMPARISON OF MACHINE LEARNING MODELS FOR SOIL SALINITY ESTIMATION USING MULTI-SPECTRAL EARTH OBSERVATION DATA","year":2021,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Environmental science; Soil salinity; Salinity; Soil science; Dryland salinity; Arid; Mean squared error; Earth observation; Remote sensing; Arable land; Hydrology (agriculture); Soil water; Satellite; Soil fertility; Geology; Mathematics; Statistics; Geography; Engineering; Soil biodiversity","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001045515,0.00009786554,0.0001976784,0.0000579095,0.0004017692,0.0001038977,0.0001960888,0.00004445953,0.000004930871],"category_scores_gemma":[0.0008555634,0.00007900978,0.00004914105,0.0004501406,0.0002696983,0.0004970991,0.0002410548,0.00009227967,4.964236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009248998,"about_ca_system_score_gemma":0.00004582306,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.139292,"about_ca_topic_score_gemma":0.02417173,"domain_scores_codex":[0.9987137,0.0000789692,0.0004815414,0.0001674632,0.0003764347,0.0001818467],"domain_scores_gemma":[0.998935,0.0001490716,0.0005262844,0.0002236831,0.0001212728,0.00004464366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001335422,0.00001689753,0.002382939,0.00003389849,0.000007674696,5.444233e-8,0.0008853107,0.3317276,0.002936156,0.00001162381,0.00002101584,0.6619635],"study_design_scores_gemma":[0.0001765714,0.00004871079,0.004508548,0.00005852536,0.00001549532,0.000003318624,0.0004810089,0.9701943,0.02335424,0.0008025735,0.0002670611,0.00008962035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.257899,0.00003454962,0.7414977,0.0001783352,0.0001047295,0.0001344406,0.00004483804,0.000009066543,0.00009741363],"genre_scores_gemma":[0.9085773,0.00002899688,0.09119918,0.0001128587,0.000008888242,8.126079e-8,0.00006084387,0.000003249962,0.000008615306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6618739,"threshold_uncertainty_score":0.9936346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2530764229251078,"score_gpt":0.3818490249472078,"score_spread":0.1287726020221,"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."}}