Landscape Influences on Hydrological Transit Times in Precambrian Shield Catchments
Bibliographic record
Abstract
The estimation of mean transit times (MTTs) is regarded as a powerful descriptor of catchment systems since it provides broad information about hydrological mixing and storage processes in a single encompassing measurement. In this study, convolution lumped modeling was incorporated into the R programming language. Approximately 3.5 years of precipitation and streamflow water isotope signatures were used to estimate transit times across six Precambrian Shield catchments in the Muskoka-Haliburton region of south-central Ontario. The main objective of this study was to explore the main physical controls governing catchment-scale transit times by investigating relationships with light detection and ranging (LiDAR)-based landscape metrics. The MTTs were best correlated to topographic metrics such as flow path gradient and the ratio of flow path length to gradient (i.e. L/G index). These findings support the notion that within shallow-soil catchments in the Precambrian Shield region, it is topography, especially gradient-driven metrics, which is most meaningful in dictating catchment-scale water storage and movement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".