Bibliographic record
Abstract
Abstract. Water is essential for all ecosystem services, yet a comprehensive assessment of total (overall) water contributions to ecosystem services production has never been attempted. Quantification of the many ecosystem services impacted by water demands integrated hydrological simulations that implicitly characterize subsurface and surface water exchange. In this study, we use a fully integrated hydrological model—HydroGeoSphere (HGS)—to capture changes in subsurface water, surface water, and evapotranspiration (green water) combined with the economic valuation approach to assess ecosystem services over an 18-year period (2000–2017) in a mixed-use but predominantly agricultural watershed in eastern Ontario, Canada. Using the green water volumes and ecosystem services values as inputs, we calculate the marginal productivity of water, which is $0.45 per m3 (in 2022 Canadian dollars). The valuation results show that maximum green water is used during the dry years, with a value of $1.16 billion during a severe drought that struck in 2012. The average product of water for ecosystem services declines during the dry years. Because subsurface water is a major contributor to the green water supply, it plays a critical role in sustaining ecosystem services during drought conditions. For instance, during the 2012 drought, the subsurface water contribution to green water was estimated at $743 million, making up 72 % of the total value of green water used in that year. Conversely, the surface water contributions in green water provision over the modeling period are comparatively miniscule. This study informs watershed management on the sustainable use of subsurface water during droughts and provides an improved methodology for watershed-based integrated management of ecosystem services.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.283 | 0.169 |
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".