Influence of moisture and freeze–thaw on leaf microbial community dynamics
Bibliographic record
Abstract
In forest ecosystems, litterfall that collects in trapping devices, to quantify organic matter and nutrient inputs, is exposed to periods of wetting, drying, freezing, and thawing. These fluctuating environmental conditions may influence the microbial community structure inhabiting the leaves and may result in the loss of mobile nutrients, leading to an underestimation of actual organic matter and nutrient inputs. The objectives of this study were to evaluate the influence of (i) different quantities of moisture (LOW = 30 mm, MED = 60 mm, HI = 100 mm) and (ii) freeze–thaw (FT) on leaf (sugar maple ( Acer saccharum Marsh.), American basswood ( Tilia americana L.), and American beech ( Fagus grandifolia Ehrh.)) microbial activity and community structure. There was a significantly greater (p < 0.05) CO2production rate in LOW and FT treatments for sugar maple and beech, and in HI and FT treatments for basswood. A similar trend occurred for leaf nitrogen concentration but not for carbon (C). Utilization of C substrates was up to 10% greater in the FT treatments. Principal components analysis on the activity of C source utilization showed a distinct clustering between leaf species and between treatments following a pattern similar to that of microbial respiration. Results from this study suggested that the collection of litter should take place more frequently during seasons when frost is imminent.
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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".