Atmospheric carbon dioxide enrichment reduces carbohydrate and nitrogen reserves in overwintering<i>Picea mariana</i>
Bibliographic record
Abstract
Elevated levels of atmospheric carbon dioxide (CO2) can directly affect the cold hardening process in evergreens through their effect on the accumulation of carbon and nitrogen reserves. This study investigated the biochemical responses of black spruce [Picea mariana (Mill.) B.S.P.] seedlings to CO2 enrichment during growth, cold hardening and dehardening. Seedlings were grown under 350 (ambient) or 710 (elevated) ppm of CO2 for 12 months in eight mini-greenhouses. Photoperiod and temperature were gradually lowered in autumn to induce cold hardening, and the conditions were reversed in spring to promote dehardening. At regular intervals, cold tolerance was assessed and sugars, starch and amino acid concentrations were measured. The freezing tolerance differed between the two treatments only in early autumn, with seedlings growing under high CO2 being more tolerant. The northern ecotype was more cold tolerant with concomitant higher concentrations of sucrose, fructose, pinitol, glucose and total soluble sugars. The concentration of soluble sugars increased in needles and roots of black spruce along with cold hardening, and the concentrations of the cryoprotective sugars sucrose and raffinose were lower under elevated CO2. Amino acid concentrations were also lower under elevated than under ambient CO2. The lower level of reserve did not translate into a lower level of freezing tolerance.
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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 | 0.000 |
| 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".