Genotype and time of day shape the <i>Populus</i> drought response
Bibliographic record
Abstract
As exposure to episodic drought can impinge significantly on forest health and the establishment of productive tree plantations, there is great interest in understanding the mechanisms of drought response in trees. The ecologically dominant and economically important genus Populus, with its sequenced genome, provides an ideal opportunity to examine transcriptome level changes in trees in response to a drought stimulus. The transcriptome level drought response of two commercially important Populus clones (P. deltoides x P. nigra, DN34, and P. nigra x P. maximowiczii, NM6) was characterized over a diurnal period using a 4 x 2 x 2 complete randomized factorial anova experimental design (four time points, two genotypes and two treatment conditions), using Affymetrix Poplar GeneChip microarrays. Notably, the specific genes that exhibited changes in transcript abundance in response to drought differed between the genotypes and/or the time of day that they exhibited their greatest differences. This study emphasizes the fact that it is not possible to draw simple, generalized conclusions about the drought response of the genus Populus on the basis of one species, nor on the basis of results collected at a single time point. The data derived from our studies provide insights into the variety of genetic mechanisms underpinning the Populus drought response, and provide candidates for future experiments aimed at understanding this response across this economically and ecologically important genus.
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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 | 0.001 |
| 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".