{"id":"W4400889525","doi":"10.1080/01431161.2024.2377228","title":"Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Water content; Moisture; Remote sensing; Surface (topology); Soil science; Geology; Meteorology; Geography; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002846552,0.000607543,0.0004496955,0.0004617117,0.0001954498,0.0003071445,0.0006044895,0.0004181556,0.000661178],"category_scores_gemma":[0.0005302977,0.000258368,0.0009299142,0.0004952951,0.0001246209,0.0004708933,0.0003364065,0.0005269827,0.0002280961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002612571,"about_ca_system_score_gemma":0.0003115961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01274485,"about_ca_topic_score_gemma":0.01774105,"domain_scores_codex":[0.9998978,0.00001370042,0.000005082536,0.00004930357,0.00001431683,0.00001970185],"domain_scores_gemma":[0.9998708,0.00003813453,0.00001158967,0.0000243356,0.00004065048,0.00001452307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000137379,0.0002115835,0.03742884,0.00005941863,0.0002332968,0.000224749,0.00008541933,0.8426042,0.01340631,0.0003831704,0.002940132,0.1022854],"study_design_scores_gemma":[0.000004872043,0.00001273407,0.005757011,0.000002154912,0.000015617,0.000009327132,0.00001117283,0.9927816,0.001016898,0.0001719218,0.0002097875,0.000006953098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209964,0.0003118609,0.07257689,0.0001396356,0.0001028883,0.00002828233,0.002557357,0.001915767,0.001370936],"genre_scores_gemma":[0.9728702,0.0000937202,0.02262906,0.00004328908,0.00002893727,0.00002338592,0.003819556,0.00004248971,0.0004493651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01274485,"threshold_uncertainty_score":0.02534133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009725774006570844,"score_gpt":0.2456714917741203,"score_spread":0.2359457177675494,"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."}}