Identifying environmental phosphorus risk classes at the scale of Prince Edward Island, Canada
Bibliographic record
Abstract
Phosphorus (P) loss from agricultural land poses a major risk to the environment. The main objectives of this study were (i) to adapt a simple P saturation indicator using 141 soils that had contrasting P levels and to deduct critical environmental P values, and (ii) to identify environmental risk classes and their spatial and temporal distribution at the scale of Prince Edward Island, Canada. The P saturation index (PSI) was greatly influenced by the soil acidity, and two critical P saturation indices were identified (i) a PSI (P/Al)M-III of 19.2% for very to extremely acidic soils (pH < 5.5), and (ii) a PSI of 14.2% (corresponding to 200 mg PM-III kg−1) for slightly to moderately acidic soils (pH > 5.5). Above these critical values, P fertilization should be limited to crop requirements. Six environmental P risk classes from very low to extremely high were identified. Spatial distribution of the identified classes was performed using georeferenced soil data collected between 2003 and 2015. The moderate risk class (P/Al ratio from 7% to 14% for soil pH above 5.5) was the predominant class, covering approximately 70% of the total area. Hot spots in the very high to extremely high range were found in about 10% of the total area, and mitigation strategies are needed to reduce P inputs to control P-related eutrophication risks in surrounding waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".