Determination of the site of CO2 sensing in poplar: is the area-based N content and anatomy of new leaves determined by their immediate CO2 environment or by the CO2 environment of mature leaves?
Bibliographic record
Abstract
Exposure to an elevated CO(2) concentration ([CO(2)]) generally decreases leaf N content per unit area (N(area)) and stomatal density, and increases leaf thickness. Mature leaves can 'sense' elevated [CO(2)] and this regulates stomatal development of expanding leaves (systemic regulation). It is unclear if systemic regulation is involved in determination of leaf thickness and N(area)-traits that are significantly correlated with photosynthetic capacity. A cuvette system was used whereby [CO(2)] around mature leaves was controlled separately from that around expanding leaves. Expanding leaves of poplar (Populus trichocarpa×P. deltoides) seedlings were exposed to elevated [CO(2)] (720 μmol mol(-1)) while the remaining mature leaves inside the cuvette were under ambient [CO(2)] of 360 μmol mol(-1). Reverse treatments were performed. Exposure of newly developing leaves to elevated [CO(2)] increased their thickness, but when mature leaves were exposed to elevated [CO(2)] the increase in thickness of new leaves was less pronounced. The largest response to [CO(2)] was reflected in the palisade tissue thickness (as opposed to the spongy tissue) of new leaves. The N(area) of new leaves was unaffected by the local [CO(2)] where the new leaves developed, but decreased following the exposure of mature leaves to elevated [CO(2)]. The volume fraction of mesophyll cells compared with total leaf and the mesophyll cell density changed in a manner similar to the response of N(area). These results suggest that N(area) is controlled independently of the leaf thickness, and suggest that N(area) is under systemic regulation by [CO(2)] signals from mature leaves that control mesophyll cell division.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".