High nutrient supply and interspecific belowground competition enhance the relative performance of Picea mariana (Mill). B.S.P seedlings over Picea glauca [Moench] Voss. under elevated CO2
Bibliographic record
Abstract
We tested how nutrient supply and interspecific belowground competition affect ecophysiological and morphological responses to elevated CO 2 in black ( Picea mariana (Mill). B.S.P) and white spruce ( Picea glauca [Moench] Voss.). It is found that belowground competition and high nutrient greatly enhanced the relative performance of black spruce over white spruce at elevated CO 2 . We have previously found that interspecific belowground competition reduce growth, whole seedling photosynthesis, and biomass allocation to leaf and that belowground competition and nutrient supply affect responses to elevated CO2 in the above two species, but we did not examine the physiological and morphological mechanisms of the responses. To examine the interactive effects of belowground competition, nutrient supply and elevated CO2 on root morphology, photosynthetic rate, and biochemical and photochemical capacity of photosynthesis in black spruce (Sb, Picea mariana [Mill.] B.S.P.) and white spruce (Sw, Picea glauca [Moench] Voss.). Seedlings were grown in individual containers (no belowground competition) or in a common container (belowground competition) under 380 vs. 720 µmol mol−1 CO2 and high vs. low nutrient supply in the greenhouse for one growing season. Elevated CO2 stimulated photosynthesis and nutrient use efficiency to a much greater degree in black than white spruce when they were grown in the same container, particularly under high nutrient supply. The ability to produce a greater length of roots per unit volume of soil was associated with the response of black spruce. The synergistic effects of elevated CO2 and belowground competition on the physiology and root morphology of black spruce suggest that elevated CO2 will likely increase the relative competitiveness of black spruce over white spruce.
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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 | 0.000 | 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 | 0.001 |
| 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".