Flow regime variation in Arctic rivers
Bibliographic record
Abstract
Arctic rivers’ flow regime has changed under climate change and its consequences on melting glaciers, thawing permafrost, and precipitation patterns. Reservoirs, hydro-power sites, and water diversions have also changed flow regimes in the Arctic. The flow regime alteration in the Arctic rivers has a strong influence on the conservation and sustainability of the native biodiversity of the riverine ecosystem. The main objective of this paper is to evaluate changes in the (1) magnitude of monthly stream flows, (2) magnitude and duration of annual maxima and minima flows, (3) timing of annual maxima and minima, (4) frequency and duration of high and low pulses, and (5) rate and frequency of daily flows in seven major Arctic Rivers. The analyses provide an important basis to characterize and understand the influence of climate change and anthropogenic activities on the flow regimes in the Arctic. Streamflow observations were obtained from the outlet of the Lena, Yenisei, Kolyma, Ob (Russia), Yukon (USA and Canada), Mackenzie (Canada), and Tana (Norway and Finland) rivers in this study. These rivers are main freshwater suppliers for Arctic Ocean. Of these, five have been regulated and two are considered pristine rivers. In addition, the impact of 16 reservoirs on flow regime in the headwaters and tributaries of Lena, Yenisei, Mackenzie, and Kolyma were evaluated. The annual flow showed an increasing trend in all rivers and with a statistically significant level in Yenisei, Lena, and Mackenzie. Our results also indicated that changes in the observed flow regimes at the outlet stations vary from low to incipient level. Out of 16 reservoirs that were analyzed for flow regimes changes, construction of Krasnoyarsk and Shushenskaya dams on the Yenisei River showed the highest impact on flow regime and flow regime alteration was classified as severe in this river.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".