Human Foot Traffic and the Associated Spread of Invasive Species
Bibliographic record
Abstract
The spread of invasive species has become an important topic in ecological literature in recent years as it threatens the natural biodiversity essential for ecosystems to function and thrive. Invasive species are damaging to ecosystems because they outcompete native species for invaluable resources. Human activities have long been suspected of being the main vector for the transportation of invasive species due to the attachment of seeds to objects such as clothing, shoes, livestock, and vehicles. Gaining an understanding of the interactions between human activity and invasive species spread is crucial to diminish the negative impact of invasive species on local ecosystems. In our research endeavor, we investigated human foot traffic in urban parks and conservation areas and surveyed the surrounding area for invasive plant species. In several different parks, we set up plots with varying levels of human foot traffic and designated each as low, medium, and high foot traffic. We measured each of the plants within the plots, denoting each as invasive or non-invasive. We also measured observed foot traffic to confirm the accuracy of the designated foot traffic levels and to create a traffic gradient. We found a general correlation between high foot traffic and abundant invasive species, which supports our hypothesis. It is important to note that there remains many other factors that influence invasive species' spread that should be investigated. Further research should be conducted on the mechanisms behind this spread and how it can be reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".