Non-invasive Physiological Approaches for Plant Phenotyping: Rice Responses to Heat Stress
Bibliographic record
Abstract
Rice (Oryza sativa L.) can be negatively impacted by supraoptimum temperatures (above 33 °C) during initial reproductive phase (R3-R5); development and adoption of approaches via non-invasive physiological phenotyping can lead to help build new plant types to face the current extreme climatic events such as future forecasts. For this purpose, screening process was designed to progressively decrease the genotypes number via non-invasive phenotyping approaches; beyond to allow the increase of phenotyping dimensionality degree across tiers. In a first-tier (in 2015-2016 growth season), phenotyping procedures involved measurements of dossel temperatures via thermography imaging in a set of 182 accessions of subspecies Indica, Japonica and Indica/Japonica cross from Embrapa’s Rice Breeding, which were cultivated in two sowing dates. About 30% (55) of the initial genotypes number which showed the lower canopy temperatures were selected based on results of multivariate analyses. In a second-tier (2016-2017 crop season), a second field trial was conducted, using polythene shelters structures aiming ensure the heat stress imposing during the critical phases of plant development; during this period, an effective photochemical quantum yield of photosystem II (YII) performance was monitored across set of genotypes. Data obtained are highlighted and discussed allowing suggest appointments about the usability/bottlenecks of thermography as suitable tool for phenotyping in a large scale manner; beside highlight the importance of some physiological responses as part of the basis of rice heat tolerance. Concluding, the LTB 14031 and BRS Pampa genotypes outperformed the set of evaluated genotypes across sowing dates and years relative to their physiological and grain yield components variables; these genotypes are integrating cross-breeding aiming to construct new plants ideotype which can associate higher grain yield performance when grown under non-stressed conditions and capable to maintain great yield stability under hard environments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".