Analysis on physical and chemical properties of water in thermokarst lakes along Qinghai-Tibet engineering corridor
Bibliographic record
Abstract
Formation and development of thermokarst lakes have great influence on the cold region environment,and its lateral heat erosion can cause the embankment instability in permafrost regions. However,lake water with different physical and chemical composition has obviously different impact on cold region environment and permafrost engineering. In order to understand the chemical properties of thermokarst lakes and find out the relationship between chemical properties and their distribution along the Qinghai-Tibet Engineering Corridor,a corridor from Chumaerhe high plateau to Fenghuoshan mountain passing along the Qinghai-Tibet Highway was selected. Our study area included three subregions,the Chumaerhe high plateau,Hoh Xil hill region,and Beilu River basin. Nineteen thermokarst lakes along the north-south direction were observed separately for its depth,area,and shape and lake water was sampled and major cations and anions were analyzed at the State Key Laboratory of Frozen Soil Engineering,Chinese Academy of Sciences. The relationship between the physical and chemical properties of the 19 thermokarst lakes and the regional environment and thermokarst lakes distribution were studied. The results showed an obvious difference in chemical properties of the thermokarst lakes at these three sub-regions. For example,salinity of the thermokarst lakes in Chumaerhe high plateau gradually increases from north to south and the lake water belongs to saline or hypersaline water. This may be due to the lakes' spatial configuration with the big area and shallow depth,the cold and windy weather,and the high evaporation. In contrast,the salinity of lakes in Hoh Xil hill region and Beilu River basin is lower than those in Chumaerhe high plateau,and the lake water is fresh or slightly saline. The lakes at these two sub-regions are deep and the terrain is hills or basin,which reduces their evaporation. The study provided guidance for the disease control of permafrost embankment and the future engineering planning and design along this corridor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".