{"id":"W7095387828","doi":"","title":"Extract relevant features from DEM for groundwater potential mapping","year":2016,"lang":"en","type":"article","venue":"","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital elevation model; Groundwater; Thematic map; Drainage density; Lineament; Hydrology (agriculture); Topographic Wetness Index; Extraction (chemistry); Terrain","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001360086,0.0001449091,0.0001446088,0.00003090104,0.0001310501,0.0000723049,0.0002205647,0.00006892796,0.005659636],"category_scores_gemma":[0.000009906212,0.00007467734,0.0001599896,0.00005950725,0.00007597437,0.000342631,0.0001118335,0.00004003532,0.000918083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008935446,"about_ca_system_score_gemma":0.000002201214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321453,"about_ca_topic_score_gemma":0.0002515403,"domain_scores_codex":[0.9988548,0.00002672309,0.0001832284,0.0003865578,0.0002024361,0.0003462175],"domain_scores_gemma":[0.9995742,0.00005300392,0.00003830354,0.0002351792,0.000005897386,0.00009335898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009589517,0.0001135746,0.04428757,0.000005172319,0.0001342102,0.00002010892,0.000354702,0.00006135058,0.8037974,0.00007733117,0.02391993,0.1271328],"study_design_scores_gemma":[0.003514627,0.0002996532,0.5581864,0.00007062112,0.000317333,0.00003860822,0.0005168793,0.002255257,0.1791666,0.05289179,0.2011188,0.0016235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6779138,0.00001979745,0.3163958,0.002309379,0.0001890026,0.0001646806,0.00001438351,0.00008055974,0.002912581],"genre_scores_gemma":[0.9713815,0.000009650706,0.01014104,0.0003923718,0.0001290718,0.00002025364,0.00001660488,0.00001599008,0.01789348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6246308,"threshold_uncertainty_score":0.9998598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009971140052973053,"score_gpt":0.2048109113710097,"score_spread":0.1948397713180366,"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."}}