Physiological responses of black spruce layers and planted seedlings to nutrient addition
Bibliographic record
Abstract
We investigated effects of nutrient addition on several physiological characteristics of 60-cm-tall black spruce (Picea mariana Mill. B.S.P.) layers (i.e., rooted branches of overstory trees) and 20-cm-tall planted seedlings on a clear-cut, N-limited boreal site. After two growing seasons, current-year and one-year-old needles of fertilized trees (layers and seedlings combined) had higher net photosynthetic rates (A(n)) and maximum capacity of Rubisco for CO(2) fixation (V(max)) than unfertilized trees. One-year-old needles of fertilized trees had higher stomatal conductance (g(s)), higher water-use efficiency, and lower intercellular to ambient CO(2) ratio than unfertilized trees. Additionally, fertilized trees had higher predawn and midday shoot water potentials than unfertilized trees. Stomatal conductance of 1-year-old needles was 23% higher in seedlings than in layers, but there were no significant differences in g(s) of current-year needles between the regeneration types. For both needle age-classes, A(n) and V(max) of layers were 25 and 40% higher, respectively, than the corresponding values for seedlings. The higher values of A(n), V(max) and foliar N concentration of layers compared with seedlings after two growing seasons may be associated with the larger root systems of the layers compared with the transplanted seedlings.
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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) | 0.001 | 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".