Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments
Bibliographic record
Abstract
Abstract. This study reports the use of high-resolution water quality monitoring to assess the influence of changes in land use management on total phosphorus (TP) transfers in two 5 km2 agricultural sub-catchments. Specifically, the work investigates the issue of agricultural soil P management and subsequent diffuse transfers at high river flows over a 5-year timescale. The work also investigates the phenomenon of low flow P pollution from septic tank systems (STSs) and mitigation efforts – a key concern for catchment management. Results showed an inconsistent response to soil P management over 5 years with one catchment showing a convergence to optimum P concentrations and the other an overall increase. Both catchments indicated an overall increase in P concentration in defined high flow ranges. Low flow P concentration showed little change or higher P concentrations in defined low flow ranges despite replacement of defective systems and this is possibly due to a number of confounding reasons including increased housing densities due to new-builds. The work indicates fractured responses to catchment management advice and mitigation and that the short to medium term may be an insufficient time to expect the full implementation of policies (here defined as convergence to optimum soil P concentration and mitigation of STSs) and also to gauge their effectiveness.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".