P‐FLUX: A phosphorus budget dataset spanning diverse agricultural production systems in the United States and Canada
Bibliographic record
Abstract
Quantifying spatial and temporal fluxes of phosphorus (P) within and among agricultural production systems is critical for sustaining agricultural production while minimizing environmental impacts. To better understand P fluxes in agricultural landscapes, P-FLUX, a detailed and harmonized dataset of P inputs, outputs, and budgets, as well as estimated uncertainties for each P flux and budget, was developed. Data were collected from 24 research sites and 61 production systems through the Long-term Agroecosystem Research (LTAR) network and partner organizations spanning 22 U.S. states and 2 Canadian provinces. The objectives of this paper are to (a) present and provide a description of the P-FLUX dataset, (b) provide summary analyses of the agricultural production systems included in the dataset and the variability in P inputs and outputs across systems, and (c) provide details for accessing the dataset, dataset limitations, and an example of future use. P-FLUX includes information on select site characteristics (area, soil series), crop rotation, P inputs (P application rate, source, timing, placement, P in irrigation water, atmospheric deposition), P outputs (crop removal, hydrologic losses), P budgets (agronomic budget, overall budget), uncertainties associated with each flux and budget, and data sources. Phosphorus fluxes and budgets vary across agricultural production systems and are useful resources to improve P use efficiency and develop management strategies to mitigate environmental impacts of agricultural systems. P-FLUX is available for download through the USDA Ag Data Commons (https://doi.org/10.15482/USDA.ADC/1523365).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".