Sequestration of soil nitrogen as tannin–protein complexes may improve the competitive ability of sheep laurel (<i>Kalmia angustifolia</i>) relative to black spruce (<i>Picea mariana</i>)
Bibliographic record
Abstract
The role of litter tannins in controlling soil nitrogen (N) cycling may explain the competitive ability of Kalmia relative to black spruce (Picea mariana), although this has not been demonstrated experimentally. Here, the protein-precipitation capacities of purified tannins and leaf extracts from Kalmia and black spruce were compared. The resistance to degradation of tannin-protein precipitates from both species were compared by monitoring carbon (C) and N dynamics in humus amended with protein, purified tannins or protein-tannin precipitates. The purity of the precipitates was verified using solid-state (13)C nuclear magnetic resonance (NMR) spectra. The ability of mycorrhizal fungi associated with both species to grow on media amended with tannin-protein complexes as the principal N source was also compared. The protein precipitation capacity of Kalmia tannins was superior to those of black spruce. Humus amended with protein increased both mineral and microbial N, whereas humus amended with tannin-protein precipitates increased dissolved organic N. Mycorrhizal fungi associated with Kalmia showed better growth than those associated with black spruce when N was provided as tannin-protein precipitates. These data suggest that Kalmia litter increases the amount of soil N sequestered as tannin-protein complexes, which may improve the competitive ability of Kalmia relative to black spruce by favouring N uptake by mycorrhizas associated with the former.
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".