Scaling up the phosphorus signal from soil hillslopes to headwater catchments
Bibliographic record
Abstract
Summary 1. Phosphorus (P) transfer from agricultural land to freshwater systems has been studied across many scales and environmental compartments that range from understanding biogeochemical processes in soils and fields, to assessment of localised in‐stream biotic and ecological impacts. 2. This study tackles the challenges of scale when moving from soil hillslope to headwater catchment scale. The focus is on ‘process rules’ derived from reductionist approaches at the relatively fine scale, and exploring the signal and evidence thereof at the headwater catchment scale. 3. The methodology uses new data of P dynamics in agricultural grassland headwater catchments in south‐west England. 4. We found the following: (i) it was not possible to disaggregate an influence of soil (Olsen) P concentration on P export at the larger scale; (ii) there was no clear temporally dynamic relationship between P additions of fertiliser and recycled manure and the resulting P transferred to the headwater scale; however, (iii) ploughing, digging of stream channel and leakage from farm storage all affected the temporal concentration dynamics; and (iv) overall P loss was influenced by higher long‐term history of P inputs, livestock and the domination of hydrologic processes. 5. It is concluded that process rules derived at the finer soil or plot scale cannot always produce a clearly discernable signal when studied at the larger headwater catchment scale.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".