Potential phosphorus mobilization from above-soil winter vegetation assessed from laboratory water extractions following freeze–thaw cycles
Bibliographic record
Abstract
Maintaining crop residue or cover crops on fields during winter is a recommended beneficial management practice (BMP). However, losses of dissolved reactive phosphorus (DRP) to runoff have been attributed to vegetation following freeze–thaw cycles (FTC). Using a factorial design in the laboratory, this study investigated the potential influence of four FTC types at −4 to +4°C (frozen, frozen and thawed 1×, frozen and thawed 5×, frozen and thawed 5× with extraction after each thaw) and one control (never frozen) on DRP loss from the residue of Triticum aestivum (winter wheat) and from two cover crops, Trifolium pretense (red clover) and Avena sativa (oat). DRP losses were measured using three different water extraction techniques: a traditional laboratory determination of water extractable P (WEP), a modified water extraction intended to simulate a rainfall event, and a modified water extraction intended to simulate surface ponding. Both cover crops released more DRP than winter wheat residue did under all treatments, suggesting that winter wheat residues pose little risk for DRP release during the non-growing season (NGS). Of the two cover crops studied, oat was more sensitive to FTC and may therefore pose a greater risk of late autumn/winter DRP loss in comparison to red clover, which is often terminated in early fall. Water extraction technique was also found to be important, as simulated surface ponding extracted more DRP than simulated rainfall did for all three plant types. These results suggest that both cover crop species and placement in the landscape can be optimized to reduce P release from above-ground vegetation in winter. The use of cover crops in sections of fields that are prone to flooding following large events such as snowmelt should be avoided. Field studies are needed to further examine the rates and timing of potential and actual losses of DRP from above-ground vegetation in winter.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".