Valuing life in our soils: Effects of microbial activity in vermicompost tea on sunflower fitness
Bibliographic record
Abstract
Vermicompost tea (VCT) is a concentrated solution of microbes and nutrients that has been shown to increase plant fitness. This study investigated the effects of microbes and nutrients in VCT on the fitness of sunflower ( Helianthus annuus ) under ideal-water and drought-simulated conditions. Three treatments (water, VCT without microbes, and VCT) were applied to groups of twenty greenhouse-grown sunflowers under ideal-water and drought-simulated conditions. Growth rates and plant biomass were measured as proxies for fitness. Bacterial plates and carbon dioxide respiration tests measured soil microbial activity. We found that VCT increased plant growth rate and biomass under drought-simulated conditions and decreased plant growth rate and biomass under ideal-water conditions. VCT without microbes decreased plant fitness under both water regimes. Given that bacterial abundance was highest in soils with VCT added, the differing effects of VCT under ideal-water and drought-simulated conditions may have been due to the presence of different microbial communities. For example, certain microbes can increase drought-tolerance of plants by solubilizing limiting nutrients, while others can harm plants when water is in excess due to anaerobic processes. Plant-microbe symbiotic relationships, nutrient availability and hydrological factors need to be considered when evaluating the potential benefits of VCT application to agricultural crops.
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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".