{"id":"W2772509791","doi":"10.1016/j.rse.2017.12.007","title":"Validation of the SMAP freeze/thaw product using categorical triple collocation","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Center for Northern Studies; University of British Columbia; Université de Sherbrooke; University of Guelph; Environment and Climate Change Canada","funders":"National Key Research and Development Program of China; Center for the Environment, Harvard University; National Natural Science Foundation of China; Canadian Space Agency; Tsinghua National Laboratory for Information Science and Technology; National Science Foundation","keywords":"Representativeness heuristic; Environmental science; Satellite; Categorical variable; Remote sensing; Scale (ratio); Collocation (remote sensing); Latitude; Meteorology; Product (mathematics); Temporal resolution; Climatology; Atmospheric sciences; Statistics; Mathematics; Geography; Cartography; Geodesy; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003684433,0.0001651201,0.0002372786,0.0000272471,0.0004289763,0.0000251147,0.0002712524,0.00008267458,0.0000280583],"category_scores_gemma":[0.0001318706,0.0001251416,0.0001215102,0.00007995025,0.0005626839,0.0001201992,0.0002903459,0.0001342826,0.0000197209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310035,"about_ca_system_score_gemma":0.00001988964,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007566371,"about_ca_topic_score_gemma":0.0001663597,"domain_scores_codex":[0.9984162,0.0001106796,0.0003691484,0.0003430127,0.0005427255,0.0002182303],"domain_scores_gemma":[0.9981738,0.00002911802,0.0006107627,0.001122001,0.0000104879,0.00005381159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002948535,0.00007678225,0.02153011,0.00002790921,0.00003421947,0.000004736009,0.000527876,0.01687199,0.799685,0.00000798795,0.0002116711,0.1609922],"study_design_scores_gemma":[0.0002892472,0.00003071306,0.3476277,0.00004529848,0.00007859374,0.00002961108,0.00005747741,0.0109629,0.6394906,0.0004583259,0.0007679555,0.0001615364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877316,0.00004087326,0.004494606,0.0004772093,0.0004883936,0.0003550134,0.000001338085,0.00001078246,0.006400158],"genre_scores_gemma":[0.985384,0.00002327263,0.01416028,0.00002317435,0.00009017756,1.983077e-8,0.000002834712,0.00002039849,0.000295782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3260976,"threshold_uncertainty_score":0.9990423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293466984329157,"score_gpt":0.2417241550974193,"score_spread":0.2187894852541277,"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."}}