Study of the glacial flow of rivers in the Grønfjord bay basin (Western Svalbard)
Bibliographic record
Abstract
The melting of glaciers in the Arctic archipelagoes provides glacier runoff, which must be considered when assessing river flow. Glacial runoff, as a component of the total river flow, directly affects the thermal and ice conditions of the bays, the productivity and species composition of the marine biota. Also, this process is broadly related to the livelihoods of the Arctic villages. Furthermore, it acts as the important factor for the development of transport and tourism. Melted water of Svalbard glaciers is a significant contribution in total runoff of the local rivers. The issue of this study is to assess this contribution. Grønfjord Bay was selected for assessment because of the fact of regular measurements of water discharge in the river estuaries from June to October. Among 7 rivers flowing into the bay, 6 have glacial runoff. Despite the fact that only small mountain glaciers are common in this area, its melting largely determines the water regime of the rivers. The value of the glacier runoff in Grønfjord Bay was calculated based on an estimation of the glaciers melting by average summer air temperature in 2017 and 2018. Comparison of the amount of glacial and total flow was carried out on the rivers flowing into the Grønfjord Bay.The comparative analysis of the glacial and total river flows size along the 6 rivers of the Grønfjord Bay: Grøndalselva, Grønfjorddalselva, Bretjørna, Aldegonda, Brydebekken, Vasstakelva showed significant differences in the glacier runoff content depending on the area of glaciation in the catchment of rivers. The content of glacial runoff in the total freshwater discharge by rivers into the bay in 2017 and 2018 vary from 20 to 90 %. The largest glacier runoff is observed on the Bretjørna river — 90 %, the Grøndalselva, Brydebekken and Vasstakelva have the smallest glacier runoff — 20 %. The average total glacial runoff of the rivers varied from 41 to 53 % of the total discharge of the rivers of the gulf.
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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 | 0.001 |
| 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) | 0.001 | 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".