The Fate of Stationary Tools for Environmental Flow Determination in a Context of Climate Change
Bibliographic record
Abstract
Environmental flows (eflows) refer to the amount of water required to sustain aquatic ecosystems. In its formal definition, three flow characteristics need to be minimally maintained: quantity, timing and quality. This overview paper highlights the challenges of some of the current methods used for eflow determination in the context of an evolving climate. As hydrological methods remain popular, they are first analyzed by describing some of the potential caveats associated with their usage when flow time series are non-stationarity. The timing of low-flow events will likely change within a season but will also likely shift in seasonality in some regions. Flow quality is a multi-faceted concept. It is proposed that a first simple step to partly incorporate flow quality in future analyses is to include the water temperature as a covariate. Finally, holistic approaches are also critically revisited, and simple modifications to the Ecological Limits of Flow Alteration (ELOHA) framework are proposed.
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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 | 0.060 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".