The effects of water flow and temperature on thermal regime around a culvert built on permafrost
Bibliographic record
Abstract
Temperature and water flow through a culvert beneath the Alaska Highway near Beaver Creek,Yukon,were measured at hourly intervals between June and October 2013.These data were used to simulate the effect of the culvert on the thermal regime of the road embankment and subjacent permafrost.A 2-D thermal model of the embankment and permafrost was developed with TEMP/W and calibrated using field observations.Empirical relations were obtained between water temperatures at the entrance to the culvert,flow into the culvert,and water temperatures inside the structure.Water temperatures at the entrance and inside the culvert had a linear relation,while water temperatures inside the culvert and water flow were associated by a logarithmic relation.A multiple linear regression was used to summarize these relations.From this relationship,changes in the flow rate and water temperatures at the entrance of the culvert were simulated to obtain predicted water temperatures in the culvert.The temperatures in the culvert were used in the thermal model to determine their effects on the ground thermal regime near the culvert.Variation of ±10% in water flow rate had no impact on the thermal regime underneath the culvert.Variation of water temperature at the entrance of the culvert had a noticeable influence on the thermal regime.A final simulation was conducted without insulation beneath the culvert.The thaw depth was 30 cm with insulation,and 120 cm without insulation,illustrating the importance of insulation to the ground thermal regime.
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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 | 0.000 |
| 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".