Effects of variable nitrogen fertilization on growth, gas exchange, and biomass partitioning in black spruce and tamarack seedlings
Bibliographic record
Abstract
To compare the ability of black spruce ( Picea mariana (Mill.) BSP) and tamarack ( Larix laricina (Du Roi) K. Koch) to adjust to variable edaphic conditions, as found in natural peatlands, we varied N fertilization of seedlings in a growth chamber experiment over two growing seasons and examined growth, biomass partitioning, and gas exchange. Seedlings from both species received either high N (100 µg·L–1) or low N (10 µg·L–1) in consecutive growing seasons as follows: (i) low N and low N (LL), (ii) low N and high-N (LH), (iii) high N and low N (HL), and (iv) high N and high N (HH). Both species had greater shoot and total dry weight after 1 year in the high-N treatment as compared with seedlings grown for 1 year under low-N. For tamarack, these differences were larger and they also exhibited a positive effect of fertilization on net assimilation and water use efficiency. Only black spruce exhibited a positive growth response following the move to higher N fertilization in the second year (LL versus LH), whereas only tamarack exhibited a negative growth response following the move to lower N fertilization (HH versus HL). Still, tamarack had greater total biomass at the end of 2 years than did black spruce, irrespective of fertilizer treatment. Both species had greater total biomass in the HL treatment than in the LH treatment. Tamarack seems able to take advantage of favorable nutrient conditions, but it also experiences more dramatic growth declines under poor or deteriorating conditions. While black spruce grows more slowly than tamarack, it is somewhat buffered from declines in growth under poor or deteriorating conditions. Each species appears to be adapted in its own way to the edaphic heterogeneity that exists in natural boreal peatlands.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".