Assessing the effects of mowing intensity on the overwintering stem‐dwelling insect community of <i>Solidago altissima</i> L. (Asterales: Asteraceae)
Bibliographic record
Abstract
Abstract Mowing is a commonly used and necessary practice in the management of urban meadowscapes. However, mowing is also a source of mortality for insects in these meadowscapes. In this study, we examined how changes in mowing intensity for mows performed in late fall affect overwintering stem‐dwelling insects. We define mowing intensity as the size of thatch produced by the selected mowing equipment and blade positioning. We also generate more information on the spatial structure of the stem‐dwelling insect community in these urban meadowscapes, both within individual stems and within the broader habitat. We artificially simulated different levels of mowing intensity on the stem‐dwelling insects of Solidago altissima L. (Asterales: Asteraceae) by cutting stems to different lengths and recorded their survival and mortality outcomes. We found that a low intensity mowing treatment yielded lower mortality rates than a no‐mow control and a high intensity mowing treatment. We also found that stem‐dwelling insects are distributed in a non‐random arrangement vertically within stems of Solidago altissima and in the broader urban meadowscape. These findings highlight the importance of understanding the effects of changing mowing parameters on the insect community when designing management practices for urban meadowscapes. We also identify some new gaps in our understanding of stem‐dwelling insects and how they may interact with disturbances.
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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".