Groundwater forensics approach for differentiating local and regional springs in arid Eastern California, USA
Bibliographic record
Abstract
While water sources that sustain many of the springs in the Mojave Desert have been poorly understood, desert wildlife and ecosystems can be highly dependent on such resources. With ever expanding use of desert groundwater, the effect of groundwater extraction on groundwater-dependent ecosystems in the Sonoran and Mojave Deserts is an ongoing concern. Springs that are more susceptible to impacts from groundwater withdrawals are typically those in hydraulic connection with surrounding basin-fill aquifer systems. Since spatial and/or temporal data gaps prevent a detailed model of the groundwater system, this evaluation of groundwater forensic approaches identifies a range of characteristics and parameters that demonstrate key indicators of spring-aquifer connectivity using data collected during a California Mojave Desert-wide spring survey conducted during 2015–2016, and subsequent monitoring and sampling events in both the California Mojave and Sonoran Deserts. In total, monitoring and sampling took place at nearly 400 springs primarily in lands managed by the U.S. Bureau of Land Management (BLM), and scattered private lands where accessible. Springs in National Park Service units such as Joshua Tree National Park, Mojave National Preserve and Death Valley National Park and in military bases were not included in the investigation scope. The multiple lines of evidence described regarding spring-aquifer connectivity include field parameters for water, such as temperature, pH, and conductivity, as well as geochemical characteristics of water, such as stable isotope and radiocarbon analyses. While other information about the setting, such as spring-site geology, are important in evaluating flow-path characteristics, simple field reconnaissance of these springs may be inconclusive as to provenance, and they are ultimately of lesser importance than the actual water characteristics in identifying spring provenance and potential hydraulic linkage to basin-fill aquifer systems that are, or may in the future, be utilized for regional groundwater development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".