Evaluation of the Frozen Ground for Developing Construction Technology of Pipelines in Cold Regions
Bibliographic record
Abstract
Abstract Cold regions, such as Alaska, Russia and Canada, get attention from many countries due to the tremendous amount of natural resources which are buried there. An accurate evaluation of the frozen ground is very important because the behavior of the active layer is greatly affected by the soil characteristics and water content in the active layer. It is necessary for developing a construction technology for pipelines in cold regions. This study has two objectives: 1) First one is to evaluate the characteristics of a newly-produced insulated aggregate and 2) the other one is to check the applicability of insulated aggregate. A series of laboratory experiments (specific gravity test, sieve analysis test, direct shear test, test for abrasion of coarse and aggregates by use of the Los Angeles machine) were performed to estimate the characteristics of the newly-produced insulated aggregate. In addition, the laboratory chamber tests were carried out to evaluate the applicability of frozen soil behavior using the newly-produced insulated aggregate. The chamber tests were conducted to check the laboratory model surrounded by soil mixing the insulated aggregate and ordinary soil in order to prevent the damage of structures such as pipelines due to the ground being frozen. For the laboratory chamber tests, the extreme cold engineering laboratory was built within the Yeon Cheon SOC Demonstration Research Center, of the Korea Institute of Construction Technology. The performance of the frozen ground which was installed with the insulated aggregate using vinyl was evaluated through monitoring the time-dependent distribution of temperature and earth-pressure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".