Are Wild Blueberries a Crop with Low Photosynthetic Capacity? Chamber-Size Effects in Measuring Photosynthesis
Bibliographic record
Abstract
Wild lowbush blueberries, an important fruit crop native to North America, contribute significantly to the economy of Maine, USA, Atlantic Canada, and Quebec. However, its photosynthetic capacity has not been well-quantified, with only a few studies showing its low photosynthetic rates. Its small leaves make accurate leaf-level photosynthetic measurements difficult and introduce potential uncertainties in using large leaf chambers. Here, we determined the photosynthetic rate for five different wild blueberry genotypes using a big leaf chamber enclosing multiple leaves and a small leaf chamber with a single leaf to test whether using big leaf chambers (branch-level measurements) underestimates the photosynthetic capacity. Photosynthetic rates of wild blueberries were significantly (35–47%) lower when using the big leaf chamber, and they are not a crop with low photosynthetic capacity, which can be as high as 16 μmol m−2 s−1. Additionally, wild blueberry leaves enclosed in the big chamber at different positions of a branch did not differ in chlorophyll content and photosynthetic rate, suggesting that the difference was not caused by variation among leaves but probably due to leaf orientations and self-shading in the big chamber. A significant linear relationship between the photosynthetic rate measured by the small and big leaf chambers suggests that the underestimation in leaf photosynthetic capacity could be corrected. Therefore, chamber-size effects need to be considered in quantifying photosynthetic capacity for small-leaf crops, and our study provided important guidelines for future photosynthesis research. We also established the relationship between the Electron Transport Rate (ETR) and photosynthetic CO2 assimilation for wild blueberries. ETR provides an alternative to quantify photosynthesis, but the correlation coefficient of the relationship (R2 = 0.65) suggests that caution is needed in this case.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".