Responses of carbon acquisition traits to irradiance and light quality in <i>Mercurialis annua</i> (Euphorbiaceae): evidence for weak integration of plastic responses
Bibliographic record
Abstract
It is often suggested that traits will be integrated, either because of pleiotropy or because natural selection may favor suites of integrated traits. Plant responses to different environments can provide evidence of such integration. We grew Mercurialis annua plants in high-density stands in high irradiance, in neutral shade, and in high red to far-red (R:FR) shade, resulting in environments of high irradiance, low R:FR; low irradiance, low R:FR; and low irradiance, high R:FR. We measured gas exchange, leaf morphology, stem elongation, and biomass traits and tested the prediction that traits within each functional group would show higher trait integration, as evidenced by high correlations among traits within environments, higher correlations of trait plasticity, and lower plasticity of trait correlations. Overall, we found evidence of only moderate integration for some groups of traits. Functionally related groups of traits, or pairs of traits, could be strongly integrated by one criterion but weakly integrated by another of the criteria. Stem elongation traits, though often observed to be strongly integrated in other taxa, showed little evidence of integration. Internode traits exhibited a novel pattern of responses to low R:FR, with increased elongation of the hypocotyl, decreased elongation of the first internode, and no change in the second internode. We propose that these responses to light are more likely to be the result of natural selection than the consequence of constraints imposed by pleiotropy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".