Comparing Thermal Regime Stages along a Small Yakutian Fluvial Valley with Point Scale Measurements, Thermal Modeling, and Near Surface Geophysics
Bibliographic record
Abstract
Arctic regions are highly impacted by the global temperature rising and its consequences and influences on the thermo-hydro processes and their feedbacks. Theses processes are especially not very well understood in the context of river–permafrost interactions and permafrost degradation. This paper focuses on the thermal characterization of a river–valley system in a continuous permafrost area (Syrdakh, Yakutia, Eastern Siberia) that is subject to intense thawing, with major consequences on water resources and quality. We investigated this Yakutian area through two transects crossing the river using classical tools such as in–situ temperature measurements, direct active layer thickness estimations, unscrewed aerial vehicle (UAV) imagery, heat transfer numerical experiments, Ground-Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). Of these two transects, one was closely investigated with a long-term temperature time series from 2012 to 2018, while both of them were surveyed by geophysical and UAV data acquisition in 2017 and 2018. Thermodynamical numerical simulations were run based on the long-term temperature series and are in agreement with river thermal influence on permafrost and active layer extensions retrieved from GPR and ERT profiles. An electrical resistivity-temperature relationship highlights the predominant role of water in such a complicated system and paves the way to coupled thermo-hydro-geophysical modeling for understanding permafrost–river system evolution.
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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 | 0.000 | 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".