Soil Nematode Trophic Group Composition and Influence on Growth of Amaranthus palmeri and Parthenium hysterophorus
Bibliographic record
Abstract
Weeds have a significant impact on agricultural systems. They not only cause a loss in crop yield by competing with them for resources, but they can also serve hosts for several pests and parasties such as plant parasitic nematodes casusing additional crop loss. The aim of this study was to analyze plant-nematode feedback in two major weeds, Amaranthus palmeri S Watson and Parthenium hysterophorus L. First, a field survey was conducted to determine the rhizosphere nematode trophic groups associated with these two plants in the summer of 2020 and 2021. Then a 6-week greenhouse study was conducted where the two weed species were treated with nematode communities extracted from their respective rhizospheres. Results from this study show that both weeds harbored a high number of herbivore nematodes, followed by fungivore and bacterivore nematodes. Total number of these nematodes were highly influenced by total sol carbon, pH and salinity. Under greenhouse conditions, the nematode treatment did not have any impact on the growth of P. hysterophorus but A. palmeri plants treated with nematodes had significantly higher above ground biomass. In conclusion, plant-nematode relationships are complex. Given the extent of direct damage caused by these weeds and plant parasitic nematodes in global crop production, the weeds-nematode feedback warrants further detailed studies.
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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".