Factors affecting river turbidity in a degrading permafrost environment: the Tasiapik River, Umiujaq (Nunavik)
Bibliographic record
Abstract
This study focuses on spatiotemporal changes in water turbidity in relation to permafrost to document the impact of meteorological conditions and water flow on hydro-sedimentary processes in northern regions. Starting in June of 2019, water turbidity data were collected at six sites along the Tasiapik River (Nunavik). A statistical analysis was completed based on records of water turbidity, precipitation, water flow, and air temperature. Our results show a significant correlation between air temperatures and turbidity, with a correlation of up to r = 0.59. These correlations depend on the location of the site along the river and the time of the study period (June–October 2019). The flow rate was the primary factor that caused variations in the turbidity of the Tasiapik River. Our results showed that following an increase in flow rate, there was an almost simultaneous increase in turbidity due to erosion of the banks. The duration and intensity of precipitation events are also important factors affecting the process of sediment transport. Even though meteorological conditions play an important role in turbidity variation, other characteristics of the site such as the topography and the existence of thermokarst lakes are additional factors that influence the dynamics of sediment transport in the Tasiapik River.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".