An Application of the T‐TEL Assessment Method to Evaluate Connectivity in a Lake‐Dominated Watershed after Drought
Bibliographic record
Abstract
Abstract Lakes are landscape features that influence connectivity of mass and energy by being foci for the reception, mixing, and provision of water and material. Where lake fractions are high, they influence hydrological connectivity. This behavior was exemplified in the Baker Creek watershed in Canada's Northwest Territories during a two‐year drought in which many lake levels declined below outlet elevations. This study evaluated how lakes controlled surface runoff connectivity reestablishment following the drought using a new assessment method, T‐TEL (time scales — thresholds, excesses, losses). Analysis of daily data showed that during a summer period following the drought, connectivity occurred between 0% and 41% of the time. The size of run‐of‐the‐river lakes relative to their upstream watershed area, and the upstream lake fraction, are two factors for connectivity. These terms represent a lake's ability to control the size of storage deficits relative to rainfall, and evaporation and storage losses along pathways. The connectivity magnitude–duration curve only aligned with the watershed flow duration curve during high‐water conditions, implying lakes functioned as individuals rather than as part of a perennial watercourse during much of the study. The T‐TEL method can be used to quantify consistent metrics of hydrologic connectivity that can be used for regionalization exercises and understanding hydrologic controls on material transport.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".