Yield Comparisons between Cotton Variety Xin Nong Mian 1 and Its Transgenic ScALDH21 Lines under Different Water Deficiencies in a Desert-Oasis Ecotone
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Bibliographic record
Abstract
Water scarcity is the major limiting factor for oasis-desert agricultural production of cotton. It is necessary to improve cotton for drought tolerance and minimize drought-related crop losses, and the transgenic approach is efficient for cotton improvement. In order to evaluate the value of ScALDH21 transgenic cotton (G. hirsutum L.), it was tested in the main cotton region of south Xinjiang, in an environment of extreme drought around the desert. Transgenic cotton, overexpressing aldehyde dehydrogenase gene (ScALDH21) from the desiccation-tolerant moss Syntrichia caninervis in cotton variety Xin Nong Mian 1, was field-tested under six treatments based on three irrigation schedules and two irrigation levels (full (FI) and deficit (DI) irrigation) as follows: root zone model-simulated forecast irrigation (F) (FFI and FDI), soil moisture sensor-based irrigation (S) (SFI and SDI), and flood irrigation based on experience estimates (E) (EFI and EDI) to evaluate growth and yield performances. The results revealed that plant height and leaf area increased significantly in ScALDH21-transgenic cotton genotypes under all treatments. Physiological parameters such as chlorophyll content, net photosynthesis rate, and instantaneous water use efficiency were not significantly highly in transgenic lines compared to non-transgenic plants (NT). However, transgenic lines showed significantly improved yield and superior fiber quality than NT plants regardless of irrigation. The results demonstrate that ScALDH21-transgenic lines were excellent compared to NT plants under different water deficiency conditions. The study also provides guidelines for optimal irrigation protocol and minimum water requirements for the use of the ScALDH21-transgenic cotton lines in arid zones.
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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) | 0.000 | 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 it