Phosphorus runoff from Canadian agricultural land: A dataset for 30 experimental fields
Bibliographic record
Abstract
Phosphorus (P) runoff from agricultural land plays a critical role in downstream water quality. This article summarizes P and sediment runoff data for both snowmelt and rainfall runoff from 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario. The data were collected from 216 site-years of field experiments, with climates ranging from semi-arid to humid and a wide range of field management practices. In the article, mean annual and seasonal (in terms of snowmelt and rain) precipitation inputs, runoff depths, and P and sediment concentrations and loads are presented, along with ranges of yearly values. In addition, information of field management and soil characteristics (e.g. soil type and soil Olsen P) is also presented for each field. The data have potential to be reused for national and international cross-region comparisons of P and sediment losses, constructing and validating decision-support models and tools for assessing and managing P losses in both snowmelt and rainfall runoff, and informing beneficial management practices to improve agricultural water quality. Interpretation of the data is found in "Phosphorus runoff from Canadian agricultural land: A cross-region synthesis of edge-of-field results" [1].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".