Drought Tolerance Compared Between Two Eutrema salsugineum Ecotypes and Their Recombinant Inbred Lines
Bibliographic record
Abstract
Despite drought accounting for over 80% of agricultural losses, little progress has been made towards improving drought tolerance in crops. My approach to identifying traits underlying drought tolerance involved a comparison between two accessions of the crucifer, Eutrema salsugineum, that display differential tolerance to water deficits. The accessions, originating from the semi-arid Yukon, Canada, and a monsoonal region of Shandong, China, were subjected to a two-step, water deficit and recovery protocol to identify physiological characteristics that discern their drought-responsive behaviour. Traits that discriminate between the ecotypes were used to screen recombinant inbred lines (RILs) that were generated by crossing Yukon and Shandong parent plants. Selected physiological measurements were: anthocyanin accumulation, cut rosette water loss (CRWL), solute potential, relative water content (RWC), static leaf water content (SLWC), specific leaf area (SLA), and OJIP fluorescence emission. Of the measurements taken, CRWL measurements and anthocyanin content distinguished the Yukon ecotype from the Shandong ecotype during the first drought exposure whereas SLA and fluorescence responses differentiated these accessions better after plants that experienced the first drought were rewatered and recovering or undergoing a second drought treatment. Sixty-eight RILs were screened using SLA and OJIP fluorescence emission. SLA and OJIP measurements varied among the recombinant inbred lines (RILs) with many lines showing responses to water deficit intermediate to those of the parental lines. Evidence of heritability in SLA and/or OJIP responses to water deficits would make them useful phenotypic markers for identifying quantitative trait loci (QTLs) associated with drought tolerance in future work.
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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".