Ultrastructural and Histochemical Changes in Glyphosate-Tolerant Soybean Leaves Exposed to Glyphosate
Bibliographic record
Abstract
Is it transgenic soy, resistant to glyphosate, does not suffer any injury or stress in contact with this herbicide? Anatomic studies of plant tissue are necessary to answer this question. This study investigated the influence of glyphosate in glyphosate-resistant soybean plants by analysis of leaf ultrastructure and histochemistry in a morphophysiological context. The experiment was carried out in a greenhouse, using RR soybean seeds (Glycine max (L.) Merrill, cultivar BRS Valiosa) in pots containing vermiculite and washed sand (1:1). Between the phenological stages V2 and V4, two treatments with glyphosate [N-(fosfonometil) glicina] were sprayed once a week: recommended dose (5.0 mg ae plant-1) and control (0.0 mg ae plant-1), with four repetitions each. Samples of midrib and internervural area of the leaves were fixed, dehydrated in ethyl series and blocks were sectioned at a 5-10 μm thickness. The material was stained with toluidine blue 0.05% and blades mounted on “Entellan”. Glyphosate decreased the thickness of the adaxial epidermis, palisade parenchyma, spongy parenchyma and total thickness of the leaf. Although, the diameter of companion cell was decreased with herbicide treatment, the diameter of the vase element increased, also increasing the size of the vascular bundle. Ultrastructural and histochemical changes caused by glyphosate can extend dysfunctions in the metabolic apparatus and plant relationship with the environment, given the inter-relation between tissue structure and its functions.
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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".