Development of a standardized methodology for quantifying total chlorophyll and carotenoids from foliage of hardwood and conifer tree species
Bibliographic record
Abstract
Despite the availability of several protocols for the extraction of chlorophylls and carotenoids from foliage of forest trees, information regarding their respective extraction efficiencies is scarce. We compared the efficiencies of acetone, ethanol, dimethyl sulfoxide (DMSO), and N, N-dimethylformamide (DMF) over a range of incubation times for the extraction of chlorophylls and carotenoids using small amounts of unmacerated tissue. Of the 11 species studied, comparable amounts of chlorophyll were extracted by all four solvents from three species and by ethanol and DMF from nine species. In four species, acetone, ethanol, and DMF extracted comparable chlorophyll amounts, while in another two species comparable amounts were extracted by ethanol, DMSO, and DMF. In one species, ethanol extracted significantly greater amounts of chlorophyll compared with all other solvents. The brown coloration of DMSO extracts for some species compromised the calculations of chlorophylls and carotenoids, making DMSO a poor choice. Overall, extraction efficiencies of ethanol and DMF were comparable for analyzing chlorophyll concentrations. However, because DMF is more toxic than ethanol, we recommend ethanol as the better option of these two for chlorophyll extractions. On the other hand, DMF is the most efficient solvent among the four tested for the extraction of carotenoids from these species. The results presented will facilitate the design of multispecies local- and regional-scale ecological studies to evaluate forest health. Additionally, they will enable reliable comparisons of results from multiple laboratories and (or) studies that used different solvents and help validate chlorophyll estimates obtained by remote sensing.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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 | 0.001 |
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".