Controls on nutrient availability in black spruce forests of northwestern Quebec
Bibliographic record
Abstract
The presence of mosses in black spruce forests is known to have an important impact on the availability and abundance of nutrients in this ecosystem. Mosses contribute to long-term accumulation of organic matter and storage of nutrients as well as to short-term nutrient release. In the boreal forest of northwestern Quebec, the effect of mosses on nutrient cycling was examined within the framework of a chronosequence ranging from 25 to 300 years of age. Laboratory and buried bag incubations, total nutrient digests, respirometric and root abundance measurements as well as moisture and temperature measurements were done to characterize the nutritional status of the organic matter profiles. In general, no change in moss accumulation, or nutrient storage or availability across the chronosequence could be detected. There were, however, differences between feather mosses and Sphagnum mosses, the latter having significantly lower levels of nutrients than the former in terms of mineralizable nitrogen and total carbon, nitrogen, phosphorus, magnesium and potassium content. The nutritional profile of moss cores showed different horizons, reflecting differences in organic matter quality down the organic layer. More specifically, nitrogen availability on a concentration basis as well as root abundance decreased with depth. Forest floor temperature seemed to confine the most biologically active horizon, referred to as the active layer, to a shallow depth, however neither temperature nor moisture seemed to explain the nutritional differences between feather mosses and Sphagnum mosses. These results may lead to practical consequences in that they show a clear distinction between the effects of feather mosses and Sphagnum mosses in nutrient cycling, suggesting that moss cover, might be useful as an indicator of site nutritional status. The results also show that accumulation of nutrients in organic surface horizons, as has been observed elsewhere, does not appear to occur in blac
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".