Leaching of Nitrogen and Phenolics from Wood Waste and Co‐composts Used for Road Rehabilitation
Bibliographic record
Abstract
Rehabilitation and reforestation of disused forest roads and landings can be facilitated by the incorporation of organic matter. The British Columbia forest industry creates residual woody materials, but they are nutrient poor and may leach phenolic compounds. We assessed the potential for wood wastes (chipped cedar wood waste, sort-yard waste, hogfuel) and co-composts with shellfish waste or municipal biosolids to provide inorganic N and release phenolics and condensed tannins, compared with natural forest floor and mineral soil. Initial concentrations of tannins and phenolics were low, and 13C cross-polarization and magic-angle spinning nuclear magnetic resonance spectroscopy showed that composts were still dominated by wood. During a 426-d laboratory leaching experiment, release of phenolics from woody amendments (other than cedar wood) was lower than from native forest floor. The pH levels of woody amendments and their leachates were also within the range of native forest floor and soil (except cedar wood, which was the most acidic material). Co-composts had higher total N and available P, greatly reduced tannins and phenolics, and negligible leaching of polyphenols. Uncomposted materials released very little N during the incubation. Hogfuel-biosolids compost released a large amount of nitrate, but only during the first 100 d. Shrimp-wood compost released moderate amounts of ammonium and nitrate throughout the incubation, had high available P and low tannin content, and released less polyphenols than did native forest floors. Our results indicate that appropriate use of these amendments does not pose an environmental risk with regard to the parameters measured in this study.
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 | 0.000 |
| 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 | 0.001 | 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".