Vegetation management and site preparation effects on <sup>13</sup>C isotopic composition in planted white spruce
Bibliographic record
Abstract
Moisture availability is the factor that most commonly influences the discrimination against 13C fixation (Δ) by C3 plants. Therefore, by changing the availability of moisture by way of controlling competing vegetation, Δ in white spruce (Picea glauca (Moench) Voss) seedlings should be affected. The objective of this study was to determine the influence of manual brushing on Δ in white spruce seedlings planted in disc-trenched and control (i.e., no site preparation) microsites. The effects of site preparation and vegetation management on soil moisture, photosynthetically active radiation (PAR), and Δ in white spruce seedlings were evaluated over three growing seasons. Vegetation management increased the amount of PAR reaching seedlings in the control and disc-trenched treatments by removing the shading by native vegetation around each seedling. It appears that the increase in PAR reaching seedlings decreased Δ by increasing the photosynthetic consumption of CO2. Differences in soil available moisture (up to 22%) between control and disc-trenched treatments were not reflected in Δ values, contrary to our initial hypothesis. This may indicate that the site was not moisture limiting. Also, these results underline the complexity and difficulty of determining the controlling mechanisms by which Δ is affected.
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 | 0.000 | 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".