Site history affects soil and plant <sup>15</sup>N natural abundances (<i>δ</i><sup>15</sup>N) in forests of northern Vancouver Island, British Columbia
Bibliographic record
Abstract
Abstract 1. About 10 years after establishment, plantations of Western Redcedar (Thuja plicata Donn ex D. Don) on northern Vancouver Island, British Columbia become nutrient deficient and chlorotic, grow slowly, and are susceptible to invasion by the ericaceous shrub Salal (Gaultheria shallon Pursh.). 2. To test the hypothesis that δ15N can be related to site histories (site disturbance, soil N dynamics and plant development), we measured soil and foliar δ15N in the summer of 1992 in 3‐year‐old (nutrient‐sufficient) and 10‐year‐old (nutrient‐deficient) plantations and in old‐growth stands. The foliar and soil δ15N values of the plantations and old‐growth forests were different and closely reflected site histories. Salal invasion and nutrient deficiency interacted to depress the growth of Redcedar in 10‐year‐old plantations. 3. Site preparation destroyed the top soil organic layers (fresh and decaying litter) and forced Salal (ecto‐ and ericoid mycorrhizal) into the humus layer, where it was in direct competition with Redcedar, thereby disadvantaging arbuscular mycorrhizal/non‐mycorrhizal Redcedar in its nutrient acquisition during a period when N and P are severely limited. 4. There was a large seasonal range of foliar δ15N (5·5 and 4·3‰ for 10‐year‐old Redcedar and Salal, respectively), and there was no relationship between foliar δ15N and measured rooting depth, demonstrating that rooting depths cannot be used to explain foliar δ15N variation among coexisting woody taxa. 5. Foliar and soil δ15N declined with site age and with a presumed change from ‘open’ to ‘closed’ N cycling; the 15N‐depleting effects of mycorrhizal N transformations contributed to the observed δ15N decline.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".