Drought, Tree Rings and Water Resource Management in Colorado
Bibliographic record
Abstract
Bridging the gap between scientific knowledge and the application of this knowledge to resource management is a challenging task. Recently, a major drought in the western United States provided a "window of opportunity" to address the application of paleohydrologic data to water resource management. This drought played an important role in reminding water providers and resource managers that droughts are a natural part of the climate and that understanding the range of natural drought variability is critical for long-term planning. In Colorado, the water year 2002 was of particular concern to water providers as flows were the lowest on record at many gauges. The frequency of occurrence of this extreme event could not be assessed with the length-limited gauge records. This motivated water managers to examine the longer records of hydroclimatic variability provided by proxy data from tree rings. The interest in the information derived from tree-ring records provided an impetus for collaborations with a number of water providers, both municipal and rural. The partnerships with two of these providers are described. At Denver Water, tree-ring reconstructions are being used directly as input to a water system model to assess the reliability of water supply under a broader range of conditions than afforded by the gauge record alone. For the Rio Grande Water Conservation District, the extended record of streamflow variability has provided more qualitative information relevant for placing 20th century hydroclimatic variability into a multi-century context.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".