Dynamic photo‐inhibition and carbon gain in a C<sub>4</sub>and a C<sub>3</sub>grass native to high latitudes
Bibliographic record
Abstract
ABSTRACT C4plants are rare in the cool climates characteristic of high latitudes and altitudes, perhaps because of an enhanced susceptibility to photo‐inhibition at low temperatures relative to C3species. In the present study we tested the hypothesis that low‐temperature photo‐inhibition is more detrimental to carbon gain in the C4grassMuhlenbergia glomeratathan the C3speciesCalamogrostis Canadensis. These grasses occur together in boreal fens in northern Canada. Plants were grown under cool (14/10 °C day/night) and warm (26/22 °C) temperatures before measurement of the light responses of photosynthesis and chlorophyll fluorescence at different temperatures. Cool growth temperatures led to reduced rates of photosynthesis inM. glomerataat all measurement temperatures, but had a smaller effect on the C3species. In both species the amount of xanthophyll cycle pigments increased when plants were grown at 14/10 °C, and inM. glomeratathe xanthophyll epoxidation state was greatly reduced. The detrimental effect of low growth temperature on photosynthesis inM. glomeratawas almost completely reversed by a 24‐h exposure to the warm‐temperature regime. These data indicate that reversible dynamic photo‐inhibition is a strategy by which C4species may tolerate cool climates and overcome the Rubisco limitation that is prevalent at low temperatures in C4plants.
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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) | 0.000 | 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".