Assessing the Role of a Limestone Quarry as Sediment Source in a Developing Tropical Catchment
Bibliographic record
Abstract
Abstract Impact assessments on river systems of the combined effect of bed and suspended sediment loads from quarries are difficult to find. In this study, bed and suspended loads were measured to determine the impact of a 20‐ha limestone quarry on the river system of its 5,000‐ha steep, diverse land use/land cover but mostly forested catchment. A network of hydrologic and sediment monitoring instruments was deployed over the catchment during two separate study periods when sediment loadings were measured from captured storms. Results showed that the quarry stood to make a disproportionately large contribution to the catchment's estimated 2·1 Mg ha−1 yr−1 suspended sediment load. Large storm events contributed most of the loadings with five events supplying 92% of total loadings at the outlet. A paired method approach to compare suspended sediment loads between two subcatchments showed that during eight storm events, the quarry yielded between 2 and 49·2 Mg ha−1 per event, whereas the forest never yielded more than 0·1 Mg ha−1. Furthermore, the contribution of sediments from the quarry to bed load was more than 75% at a section located 1·2 km downstream. Future management activities to reduce sediment and bed loads, not only from this catchment but also from all others with similar land use/land covers, should focus on improving quarry operations. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".