ANALYSIS OF POND SEEPAGE FOR APPLICATION IN FISHERIES AND AQUACULTURE
Bibliographic record
Abstract
ABSTRACT: The purpose of this research was to examine through modeling and experimentation if seepage out of a pond through stratified soil can be predicted, and effectively collected and managed to augment streamflow during a low precipitation period extending three months or more. The 55 m2 experimental pond with sandy/loamy banks was excavated to hardpan, and its bottom was approximately 0.7 above the water table. Output from a mathematical model containing both bottom and bank seepage elements agreed with experimental data, and showed that as compared to bottom seepage, the bank seepage contributed approximately 25 percent of the total seepage. Seepage collection (as measured from a circumscribing ditch) linearly varied with stage (r2 < 0.99). There was an 8 to 22 percent over-collection at the lower pond stages, and a 9 to 45 percent under-collection at the highest stage. As an example of its utility, the model was applied to estimate the pond size and shape needed to supply a hypothetical stream and maintain fish stocks during a three-month low-precipitation period. Future work will focus on nutrient transport and removal.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".