Soil resources and the growth and nutrition of tree seedlings near harvest gap – forest edges in interior cedar–hemlock forests of British Columbia
Bibliographic record
Abstract
Observations of tree seedlings with chlorotic foliage and stunted growth near harvest gap – forest edges in interior cedar–hemlock forests inspired a study addressing the following questions: (1) Do seedling foliar chemistry, foliar nitrogen (N) versus growth relationships, and fertilizer responses suggest N-limited seedling growth? (2) Are patterns in soil characteristics consistent with N limitation, and can interrelationships among these characteristics infer causality? Our results suggest that seedling growth near gap–forest edges was colimited by N and light availability. Soil mineral N and dissolved organic N (DON) concentrations, in situ net N mineralization, and water generally increased from forest to gap, whereas N mineralization from a laboratory incubation and total N and carbon did not vary with gap–forest position. Interrelations among variables and path analysis suggest that soil water and total soil N positively affect DON concentration and N mineralization, and proximity to mature gap–forest edge trees negatively impacts mineral N concentration and water. Collectively, our results suggest that soil N levels which limit seedling growth near gap edges can be partially explained by the direct negative impacts of gap–forest edge trees on mineral N concentrations and their indirect impacts on N cycling via soil water, and not via effects on substrate chemistry.
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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".