Dispersal patterns of insects after a major disturbance event is applied to a disturbance gradient that conformed to the intermediate disturbance hypothesis in an urban ecosystem
Bibliographic record
Abstract
Insects have been losing their habitats with the rapid expansion of many urban cities such as Toronto. This study aims at understanding the dispersal patterns insects take once they are placed under a heavy amount of disturbance. The use of power line corridors allows us to have a disturbance gradient within an urban city that allows us to observe the normal distribution of insects across the gradient and compare it to how their distribution patterns change once a disturbance has been applied. It is important in understanding insect dispersal patterns, because only then can we accurately create conservation strategies, and plan urban development to minimize the amount of damage done to insect populations. In this study, sweep nets, pan traps, and point counts were used to sample five areas of a disturbance gradient along a power line corridor. The experiment was conducted over three weeks and it was found that the gradient studied followed a quadratic distribution pattern predicted by the intermediate disturbance hypothesis, although with the application of disturbance, the insects diapered into an exponential type pattern in which the greatest abundance of insects were found in the area of least disturbance. The findings of this study have important implications in understanding the effects of disturbance on insects in urban ecosystems and are a step in the direction of forming a unified theory of insect/disturbance dynamics.
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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) | 0.000 | 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 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".