Environmental phosphorus risk classes for silage corn in the Fraser Valley, Canada
Bibliographic record
Abstract
High soil phosphorus (P) concentrations accelerate P losses from intensively managed farmlands and must be reduced to mitigate eutrophication and water quality concerns, without reducing crop yields. Using 140 soil samples from silage corn fields within the Fraser Valley, British Columbia, Canada, water-extractable P (Pw) was positively related to the P saturation index (PSI) by linear regression (R 2 = 0.89). We established critical values for PSI 10.8 % and Pw 4.1 mg kg –1 for P loss risks and identified four environmental risk classes. Soils in the low risk class (PSI = 0–6.6 %, Pw = 0–2.2 mg kg –1 ), had the lowest Mehlich 3-P (P M3 ) concentrations, but yield was 13.7 Mg ha –1 , below the optimum provincial range (20–25 Mg ha –1 ). In the moderate risk class (PSI = 6.6–10.8 %, Pw = 2.2–4.1 mg kg –1 ), soil P was sufficient to achieve optimum silage corn yield (22.2 Mg ha –1 ). Conversely, in the high (PSI = 10.8–24.3 %; Pw = 4.1–10.3 mg kg –1 ) and very high (PSI > 24.3 % and Pw > 10.3 mg kg –1 ) risk classes, soils had excessive P concentrations (P M3 > 200 mg kg –1 ), but corn yield did not increase. Soil P must be reduced in the high and very high risk classes to reduce runoff loss risk, which will not affect crop yields. Our study shows that P fertilization could improve yields in the low risk class, but must be done carefully to minimize the likelihood of P loss risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".