Glyphosate-based herbicide contributes to nutrient variability in forest plants
Bibliographic record
Abstract
In forestry, glyphosate-based herbicide (GBH) is applied to some logged areas to remove deciduous and herbaceous vegetation competing with commercial coniferous trees. Glyphosate has the potential to form insoluble complexes with metal ions, altering the bioavailability of metals in soil and water. The aim of this study was to determine if GBH influenced the concentrations of available nutrients in forest plants. We sampled willow ( Salix sp.), red osier dogwood ( Cornus sericea ), and fireweed ( Chamaenerion angustifolium ) from forests up to 12 years after GBH applications. Across the three focal species, only Mn consistently increased with GBH treatment. Significant positive correlations between nutrient and glyphosate concentrations within plant tissues were found for Mn and Zn, while negative correlations were found for Ba, Ca, Mg, and Ni. B, Ca, Mn, and species were significant predictors of GBH treatment. A greater number of significant differences between controls and treated samples were noted in nutrients of fireweed and red osier dogwood at year one post-treatment, while willow showed greater differences between controls and treated samples at year 12 post-treatment. The analyses conducted indicate a relationship between GBH treatment and resulting nutrient regimes after GBH applications, yet it is complex and variable. It is clear that there are species specific trends within the dataset. Due to this complexity, more research should be conducted, as many questions remain unanswered. Further research is needed to disentangle both short and long-term impacts of widespread use of GBH products in modern silviculture.
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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 | 0.000 | 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 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".