Dynamics of mineral nitrogen released from feathermosses after dehydration or handling stress
Bibliographic record
Abstract
The capacity of feathermosses to release mineral N to water and eventually re-capture it back from the solution was periodically measured in several 64-hour tests. Mosses were collected from 13 locations in western Alberta, Canada, and given several pre-treatments in the days leading up to the submersion of mosses in aerated distilled water. In a factorial experiment, the pre-extraction conditions were fertilized or left as controls and kept moist or allowed to dehydrate. The concentration of mineral N in the solution was monitored by withdrawing small samples of the solution for colorimetric analysis at pre-determined time intervals. To assess the effects of microflora and handling damage to the moss tissues on the rate of N exchange between moss and solutions, the test was repeated firstly using a solution of antibiotics instead of water and secondly using mosses that were not given time to recover from handling. No perceptible leakage of N was recorded from fully hydrated moss tissues. Dehydrated mosses lost as much as 8% of their total N content to the solution within two hours after re-hydration, but had recovered two thirds of it within the next 16 hours. Moss tested immediately after normal handling released 0.7% of their total N and recovered it at the same rate as the desiccation-damaged mosses. Application of antibiotics affected neither leakage nor re-absorption rate. During the gradual drying of moss, N apparently shifted from NO3− to NH4 . The strong ability of mosses to quickly re-absorb released N from surrounding solutions suggests that leakage of N from dried moss after rewetting, as a source of N to the ecosystem, is not as large as suggested by previous literature.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".