{"id":"W2148893532","doi":"10.1109/igarss.2002.1025808","title":"Seasonal and spatial variability of surface hydraulic properties","year":2003,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Environmental science; Snow; Remote sensing; Backscatter (email); Water content; Radar; Land cover; Satellite; Atmospheric sciences; Meteorology; Geology; Land use; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001313623,0.00007778747,0.0001105655,0.000231126,0.0001933654,0.0002868371,0.0001449472,0.0001085984,0.0006065313],"category_scores_gemma":[0.000410865,0.00007851212,0.00008583048,0.000338852,0.0002338117,0.000143888,0.0001278011,0.00007319681,0.00008112274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232778,"about_ca_system_score_gemma":0.0006138134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3749833,"about_ca_topic_score_gemma":0.4853402,"domain_scores_codex":[0.9999238,0.000005425507,0.000002613896,0.00002268506,0.00002010115,0.00002535006],"domain_scores_gemma":[0.9997932,0.00004412701,0.00003613359,0.00001956502,0.00008278732,0.00002416529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001394566,0.00002310673,0.9557695,0.00002286362,0.00004566663,0.0001616557,0.0005160866,0.007708578,0.02465751,0.0001842914,0.0004622195,0.01030907],"study_design_scores_gemma":[0.000002138929,0.000005684164,0.9958858,8.981473e-7,0.000005482999,0.00002033014,0.00009257646,0.003265192,0.0003648385,0.00001741895,0.0003367041,0.000002957628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989532,0.00002097037,0.00016149,0.00001439153,4.961179e-7,0.000001945783,0.0002612084,0.000008494567,0.0005776567],"genre_scores_gemma":[0.9996002,0.00001075984,0.00003839396,9.967888e-7,3.123297e-7,0.000001246019,0.0001748248,0.00000138719,0.0001717838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3749833,"threshold_uncertainty_score":0.7456013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009601401577976655,"score_gpt":0.1892262189438802,"score_spread":0.1796248173659036,"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."}}