Comparative assessment of river flow regime alteration in diverse environment: cases from pan-Arctic and arid semi-arid regions
Bibliographic record
Abstract
River flow regimes are significantly altered by anthropogenic regulation activities, such as dam reservoir and hydropower. Such activities modify river flow regime, integrating three primary attributes (magnitude, timing, and monthly variability). However, these impacts are different in diverse environments according to the climate and land use. This study aims to investigate such impacts in cold climate sub-Arctic and arid semi-arid examples. First, the post and pre impact periods are set based on the changing point resulted from Pettitt test. Then the long-term monthly average of flow in the two pre and post impact periods will be assessed for each station to illustrate the form of influences in monthly hydrographs. After that, the River Impact index (RI) is employed to investigate the level of flow regime alteration and address those flow attributes that are impacted differently in different cases. The RI index is quantified by developing the respective impact factors MIF (Magnitude Impact Factor), TIF (Timing Impact Factor) and VIF (Variation Impact Factor) where RI=MIF×(TIF+VIF). The preliminary results show that in arid and semi-arid cases with intensive agriculture and hydrosystem development (such as Karkheh and Sefidrud in Iran), magnitude has altered more than the other attributes, while, in sub-Arctic cases such as (Ob, Yukon, Mackenzie), river regulation mainly impacts the timing and variability. This can highlight the role of mid-basin tributaries which naturally regulate the magnitude of flow in the sub arctic watersheds where land use change is not significant, in contrast with the other cases in arid and semi-arid regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".