{"id":"W2610894313","doi":"","title":"Monitoring water levels by integrating optical and synthetic aperture radar water masks with lidar DEMs","year":2014,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Remote sensing; Lidar; Synthetic aperture radar; Environmental science; Interferometric synthetic aperture radar; Geology","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":[0.000316442,0.0003051513,0.0003201795,0.0008527771,0.0001518652,0.0003874597,0.0003749618,0.0003457849,0.0007842145],"category_scores_gemma":[0.0007228626,0.0002203894,0.000167733,0.0008519551,0.0001197672,0.0007548756,0.0004880355,0.0002142253,0.0005015791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002434839,"about_ca_system_score_gemma":0.0003743378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003822591,"about_ca_topic_score_gemma":0.009730759,"domain_scores_codex":[0.9997889,0.00002726226,0.000009278692,0.00004498268,0.00009847955,0.00003113008],"domain_scores_gemma":[0.9996935,0.00004534755,0.00005409176,0.00003870098,0.0001449183,0.00002342961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006969625,0.0004515983,0.1334205,0.0002619145,0.0001416043,0.000149398,0.0003497941,0.07413054,0.3311364,0.001521831,0.004487898,0.4532516],"study_design_scores_gemma":[0.00009998253,0.0002406356,0.2023864,0.00003809144,0.0001240465,0.0001280419,0.0002061027,0.7201294,0.06763449,0.001540079,0.007401877,0.00007078864],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9142939,0.0002587579,0.07327402,0.0002201943,0.00009262717,0.00007138932,0.001917605,0.002057409,0.007814125],"genre_scores_gemma":[0.9300645,0.0001204225,0.06742761,0.00006071352,0.0000318417,0.00003247152,0.001033116,0.00005685195,0.001172513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003822591,"threshold_uncertainty_score":0.007600665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108348947979846,"score_gpt":0.1927662889085052,"score_spread":0.1819313941105206,"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."}}