{"id":"W4409616243","doi":"10.1038/s43247-025-02271-8","title":"DNA-sequencing method maps subsurface fluid flow paths for enhanced monitoring","year":2025,"lang":"en","type":"article","venue":"Communications Earth & Environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China-Yunnan Joint Fund; National Natural Science Foundation of China","keywords":"DNA sequencing; Flow (mathematics); DNA; Computational biology; Computer science; Environmental science; Geology; Biology; Genetics; Mechanics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005998547,0.0001833942,0.0002051777,0.00004888892,0.0008144966,0.00002288216,0.0007822728,0.000074981,0.0001731071],"category_scores_gemma":[0.00004000866,0.0001867525,0.00008729025,0.0001329562,0.0002961018,0.0001421694,0.001245477,0.0001543962,0.0003575137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002199187,"about_ca_system_score_gemma":0.000007497851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004413491,"about_ca_topic_score_gemma":0.00002685716,"domain_scores_codex":[0.9986758,0.0001772793,0.0002921511,0.0003748232,0.0001422333,0.0003376665],"domain_scores_gemma":[0.9980683,0.0002455393,0.00007686101,0.001553292,0.000004207131,0.00005179319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001120052,0.0006666325,0.03373509,0.00009845035,0.0005890278,0.000003476603,0.005335436,0.274621,0.5347356,0.004063815,0.005409261,0.1406303],"study_design_scores_gemma":[0.001628755,0.0001690806,0.03969891,0.000102533,0.0003096303,0.000001319433,0.001317945,0.03274335,0.2818103,0.007936808,0.6334488,0.0008325259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2278742,0.001830248,0.7076973,0.009957514,0.000507575,0.002420472,0.0000434796,0.0002405164,0.04942865],"genre_scores_gemma":[0.7706909,0.001656032,0.2221169,0.0002894167,0.0000157379,0.0005025098,0.00003281705,0.00001535266,0.004680354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6280395,"threshold_uncertainty_score":0.7615548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02760307299865579,"score_gpt":0.2794868191159686,"score_spread":0.2518837461173128,"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."}}