Hydraulic Characteristics of Arable Fields in Korea and Applicability of Pedotransfer Functions
Bibliographic record
Abstract
Relationships between saturated conductivity (Ks) and separate contents were evaluated from 44 soil series of arable lands: 18 for paddy fields and 26 for upland crop fields.Saturated hydraulic conductivities of A, B, and C horizons were determined with tension infiltrometer and Guelph permeameter in situ.Sand, silt, clay, and organic matter content of each horizon were analyzed.Based on correlation analysis, sand separate had a positive relationship with Ks for both paddy (r=0.27,p=0.017) and upland fields (r=0.24.p=0.030).Clay content had a negative relationship with Ks for paddy soils (r=-0.32,p=0.005) while significant correlation between them was not found for upland crop fields (r=-0.20,p=0.07).Organic matter content showed a positive relationship with Ks only for upland crop fields (r=0.33,p=0.002).Due to low correlation coefficients between separate contents and Ks, performance of pedotransfer functions was not enough to estimate Ks.It implies that hydraulic properties of arable lands were affected by other factors rather than particle characteristics.Platy structure and plow pan were suggested to limit Ks of paddy fields.Soil compaction and diversity of parent materials were proposed to influence Ks of upland crop fields.It suggests that genetic processes and artificial managements should be included in pedotransfer functions to estimate hydraulic properties appropriately.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".