Climate change and the thermal performance of a high protein food source: <i>Wolffia globosa</i>
Bibliographic record
Abstract
Abstract Wolffia globosa is a tropical duckweed native to Southeast Asia, where it is harvested for food. This aquatic plant has a fast growth rate, a high protein content, and is also a source of important nutrients. Therefore, it could play an important role in food security under climate change and population growth. We provide the first thermal performance curve for W. globosa, and use this data to understand how climate change impacts on temperature in Southeast Asia may affect production. We find that the maximum relative growth rate occurs at constant temperatures of ∼32C. We find no significant difference between growth at current mean conditions and temperatures predicted in the next 40 years according to the high emissions scenario (SSP5-8.5 scenario) in Thailand, Laos and Myanmar when temperatures are held constant. However, the thermal performance curve is best described as asymmetric, with growth rates that fall rapidly at temperatures above this optimum. As a result, when temperatures are allowed to fluctuate about the mean in a pattern similar to recent heatwave conditions in Thailand, we find significantly lower growth rates at the optimum than at current mean temperatures. This decrease is driven by a significant increase in frond death at higher temperatures. However, given the fast growth rate of this species relative to other food crops, and the mitigating impact of water on the magnitude of temperature fluctuations, it seems likely that W. globosa may more rapidly recover from extreme heat events than other crop species. Therefore, it is likely to be a suitable candidate for adapting to climate change impacts.
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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".