Lysimetric Experiment on the Riverbed in the Lower Reaches of Heihe River
Bibliographic record
Abstract
Located in the central part of the Hexi Corridor, the Heihe River rises in the Qilian Mountain and flows through Qinghai Province, Gansu Province and Inner Mongolia Autonomous Region, it is one of the large continental rivers in northwest China, and its length and catchment area are 821 km and 1.3×105 km2 respectively. Because of the impacts of natural factors and human activities, the vegetation is degenerated, the Ejina Oasis is shrinking, and the ecological environment is degenerated due to the drawdown of groundwater level caused by the reduction of the stream flow to the lower reaches. All these have been paid a great attention to. Considering from the shortage of the local surface water resources, it is suggested to reduce the seepage along the river channel in the lower reaches of Heihe River by some engineering measures. However, the vulnerable ecology relies on the limited stream water and groundwater for its existence. In this paper, the lysimetric experiment on the riverbed in the lower reaches of Heihe River is carried out. After measuring the seepage along the riverbed in the lower reaches of Heihe River by using the Model 2800 Guelph lysimeters, it is found that the penetration power of the riverbed is very strong, which is necessary for recharging groundwater from stream flow and also the necessary conditions for maintaining the vegetation in the lower reaches and for the existence of Ejina Oasis. Therefore, it is considered that the groundwater recharge will be reduced, the vegetation in the lower reaches will be further degenerated, and the safety of Ejina Oasis will be threatened if the engineering measures are applied for reducing the seepage along the river channel in the lower reaches of Heihe 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".