{"id":"W2315396283","doi":"10.1061/41036(342)442","title":"Automated Selection of Anchor Pixels for Landsat Based Evapotranspiration Estimation","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Evapotranspiration; Sensible heat; Pixel; Metric (unit); Water cycle; Latent heat; Computer science; Remote sensing; Residual; Environmental science; Flux (metallurgy); Artificial intelligence; Algorithm; Meteorology; Geography; Engineering","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":[0.0006243364,0.0005491773,0.0007134057,0.001469276,0.0004409474,0.000633104,0.0005635633,0.0002923643,0.006495885],"category_scores_gemma":[0.002484503,0.0002760079,0.000219099,0.001399699,0.0001582655,0.000511065,0.0004675252,0.0003608072,0.003530184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001564003,"about_ca_system_score_gemma":0.0004178339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522127,"about_ca_topic_score_gemma":0.003537024,"domain_scores_codex":[0.9997364,0.0000628403,0.00001699935,0.00005174676,0.0001035303,0.00002837339],"domain_scores_gemma":[0.9993787,0.0001284138,0.00005575593,0.0001024744,0.0003073641,0.00002723652],"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":[0.0006252598,0.0001811953,0.008102158,0.00028428,0.00003218829,0.0003259016,0.000330478,0.01092665,0.1502954,0.003075954,0.02778838,0.7980321],"study_design_scores_gemma":[0.0001545095,0.0003017206,0.08480071,0.0001023579,0.0001168431,0.0005810732,0.0005515071,0.3438935,0.439768,0.00767193,0.1218915,0.000166225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1170184,0.0003892553,0.8595028,0.00007884562,0.0001546628,0.0005368271,0.00252361,0.01381116,0.005984429],"genre_scores_gemma":[0.13513,0.0001473498,0.8575485,0.00002576276,0.00002557481,0.0003644681,0.003110811,0.000960802,0.002686757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006495885,"threshold_uncertainty_score":0.0217309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005032280394983054,"score_gpt":0.1946596065525295,"score_spread":0.1896273261575464,"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."}}