A comparison of fibre‐optic distributed temperature sensing to traditional methods of evaluating groundwater inflow to streams
Bibliographic record
Abstract
Abstract There are several methods for determining the spatial distribution and magnitude of groundwater inputs to streams. We compared the results of conventional methods [dye dilution gauging, acoustic Doppler velocimeter (ADV) differential gauging, and geochemical end‐member mixing] to distributed temperature sensing (DTS) using a fibre‐optic cable installed along 900 m of Ninemile Creek in Syracuse, New York, USA, during low‐flow conditions (discharge of 1·4 m 3 s −1 ). With the exception of differential gauging, all methods identified a focused, contaminated groundwater inflow and produced similar groundwater discharge estimates for that point, with a mean of 66·8 l s −1 between all methods although the precision of these estimates varied. ADV discharge measurement accuracy was reduced by non‐ideal conditions and failed to identify, much less quantify, the modest groundwater input, which was only 5% of total stream flow. These results indicate ambient tracers, such as heat and geochemical mixing, can yield spatially and quantitatively refined estimates of relatively modest groundwater inflow even in large rivers. DTS heat tracing, in particular, provided the finest spatial characterization of groundwater inflow, and may be more universally applicable than geochemical methods, for which a distinct and consistent groundwater end member may be more difficult to identify. Copyright © 2011 John Wiley & Sons, Ltd.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".