Cold-regions Hydrological Indicators of Change (CHIC) for ecological flow needs assessment
Bibliographic record
Abstract
Ecological flow needs (EFN) frameworks incorporate a range of ecologically-relevant hydrological variables based on prior knowledge of river regime characteristics. However, when applied in cold regions, these approaches have largely ignored the influence of winter ice cover and the spring freshet on hydrological regimes: key components of river systems in cold regions with important direct effects on water quality, aquatic habitat and ecology. Here, we combine a review of the published literature on cold-regions hydrology and hydro-ecology with available hydrometric information for sites across Canada, a major cold-region country, to explore phenomena unique to these systems. We identify several ecologically-relevant hydrological measures (i.e. annual ice on/off dates, ice-cover duration, spring freshet initiation, peak water level during river ice break-up), pairing these with established metrics for incorporation into an enhanced suite of indicators specifically designed for cold regions. This paper presents the Cold-regions Hydrological Indicators of Change (CHIC), which can provide the basis for the assessment of EFN and climate change assessments in cold-region river ecosystems. Editor Z.W. Kundzewicz; Guest editor M. AcremanCitation Peters, D.L., Monk, W.A., and Baird, D.J., 2014. Cold-regions Hydrological Indicators of Change (CHIC) for ecological flow needs assessment. Hydrological Sciences Journal, 59 (3–4), 502–516.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".