Model estimates of runoff in the closed, semiarid Estancia basin, central New Mexico, USA
Bibliographic record
Abstract
The 5000 km2 topographically closed Estancia basin in central New Mexico has been the focus of several palaeoclimatic studies based on changes in the level of late Pleistocene Lake Estancia. A large, unknown volume of surface runoff and groundwater from adjacent mountains contributed to the hydrological balance during highstands and lowstands. The US Department of Agriculture hydrological model SWAT (Soil and Water Assessment Tool) and the US Geological Survey groundwater flow model MODFLOW, with the LAK2 package, were used in this study to estimate runoff and water balance under present climate. A Geographic Information Systems (GIS) interface was used for SWAT, digitized data were applied for soils and vegetation, and limited streamflow data were used to obtain an approximate calibration for the model. Simulated streamflow is generally within 30% of observed values, and simulated runoff for the entire basin is about 8% of the annual inflow volume needed to support lowstands of the former Lake Estancia. Results from the combined models suggest application to other palaeoclimate investigations in semiarid lake basins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".