Nitrogen and Water Availabilities and Competitiveness of Bluejoint: Spruce Growth and Foliar Carbon‐13 and Nitrogen‐15 Abundance
Bibliographic record
Abstract
How resource availabilities affect the competitiveness of Canada bluejoint grass [ Calamagrostis canadensis (Michx.) P. Beauv., hereafter referred to as bluejoint ] is poorly understood. Bluejoint is a widespread grass species in boreal forests and competes with tree species such as white spruce [ Picea glauca (Moench) Voss] for belowground resources (e.g., soil N and water) when their supply is limited. In this greenhouse‐based study, we tested the following hypotheses: (i) bluejoint competition reduces white spruce growth when belowground resource availabilities are limited; (ii) greater N and water availabilities may increase bluejoint competition and its adverse effects on white spruce growth; and (iii) white spruce foliar δ 13 C and δ 15 N are affected by soil N and water availabilities and bluejoint competition. A 2 × 2 × 2 (competition × N availability × water availability) factorial experiment was conducted using pots of planted white spruce seedlings with or without bluejoint. Bluejoint competition reduced the volume index (diameter 2 × height) of white spruce by 50%. The competitiveness of bluejoint appeared to be independent of resource availabilities, but bluejoint had greater growth response to increased N availability than white spruce. Bluejoint competition depleted white spruce foliar δ 13 C and δ 15 N by 1.2 and 1.2‰, respectively, even under adequate water supply, indicating that N deficiency caused by bluejoint competition had a dominant effect (increasing 13 C discrimination during photosynthesis) compared with the potential effect of drought stress on foliar δ 13 C, and that strong NH 4 uptake by bluejoint may have prevented significant soil N losses and 15 N enrichment through nitrification and subsequent denitrification.
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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 | 0.001 | 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".