Effects of elevated CO<sub>2</sub> on chloroplast pigments of spruce (<em>Picea abies</em>) and beech (<em>Fagus sylvatica</em>) in model ecosystems as modified by provenance, soil type, and nitrogen supply
Bibliographic record
Abstract
Young beech (Fagus sylvatica L.) and spruce (Picea abies [L.] Karst.) trees, each from two different provenances, were grown in competition in model ecosystems in open-top chambers for four years. The treatments consisted of elevated concentrations of CO2 (370 μL L-1 versus 590 μL L-1), increased wet deposition of nitrogen (7 versus 70 kg N ha-1 y-1) and two different forest soils (acidic versus calcareous). Chloroplast pigments in dark- and light-adapted leaf material sampled in the last year of the experiment were analysed. Differences in pigment composition between provenances were observed only in beech trees. Soil type significantly affected the pigment composition in both species. Trees grown under calcareous conditions had higher contents of chlorophylls, whereas acidic soil conditions caused significantly enhanced levels of ß-carotene and xanthophylls as well as increased values of the xanthophyll de-epoxidation status. For both tree species light-adapted samples had higher carotenoid concentrations and de-epoxidation state values than dark-adapted foliage, whereas neither C02 nor N-treatment affected these parameters. Elevated CO2 application induced decreased concentrations of total chlorophyll contents in both species. Nitrogen deposition had no effects on pigment composition neither for spruce nor for beech trees. Interactions between CO2 and nitrogen application were not observed for both tree species.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".