Making the most of drought and salinity transcriptomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More than 100 different studies of plant transcriptomic responses to salinity or drought-related stress have now been published. Most of these use microarrays or related high-throughput profiling technologies. This compels us to ask three questions in review: (1) what has transcriptomics contributed to our understanding of stress physiology; (2) what limits the ability of transcriptomics to contribute to increases in stress tolerance; and (3) given these limits, what are the most appropriate uses of transcriptomics? We conclude that although microarrays are now a mature technology that accurately describes the transcriptome, the consistently low correlation between transcript abundance and other measures of gene expression imposes an inherent limitation that cannot be ignored. Further limitations on the relevance of transcriptomics arise in some cases from experimental practices related to the treatment regimen and the selection of tissue or germplasm. Nevertheless, there is good evidence to support the continued use of transcriptomics, especially emerging techniques such as RNA-Seq, as a screening tool for candidate gene discovery. Microarrays can also be valuable in analysing the transcriptome per se (e.g. when describing the phenotype of a transcription factor mutant or discovering non-coding RNA species), and when integrated with other types of data including metabolomic analyses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it