Changes of preferential flow path on different altitudinal zones in the Three Gorges Reservoir Area, China
Bibliographic record
Abstract
Liu, M., Du, W. and Zhang, H. 2014. Changes of preferential flow path on different altitudinal zones in the Three Gorges Reservoir Area, China. Can. J. Soil Sci. 94: 177–188. Preferential flow in soil macropores plays an important role in runoff control and soil and water conservation. The aim of this study was to investigate the distribution of preferential flow paths in the soil profile of various altitudinal belts, analyze its variation among different soil horizons, and define the cause of soil macropores. A dye tracer method combined with photographic analysis was conducted for four hillslope sites in the Three Gorges Reservoir Area of China (TGRA). The results show that stained area proportion, as well as its vertical distribution in soil sections, presented varied patterns due to changes of forest vegetation and soil type with altitude. Stained area ratio of soil profiles increased, while stained depth decreased with increasing altitude. For soil sections in the subalpine belt, mid-mountain belt, and low-mountain belt of TGRA, stained area ratios were 62, 42, and 45%, and stained depths were 52.4, 56.4, and 69.5 cm, respectively. For brown earth covered with subalpine temperate deciduous broad-leaved forest, stained area ratios were the largest, but dyed patches were concentrated in the humus horizon. For yellow earth covered with low-mountain warm coniferous forest, stained depth reached 69.5 cm, and stained patches existed in the total soil profile. Compared with forest soil, stained depth and stained area ratio of abandoned farmland in low-mountain belt were lower, and the depth of dye infiltration was even shallower.
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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 | 0.000 | 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".