Perfoliate Leaf-Mimicking Plant Clips: A One Health Strategy to Address the Effects of Urbanization on Insects
Bibliographic record
Abstract
The declining abundance of safe water sources for insects in urban areas is a wicked problem requiring urgent attention. Urbanization has transformed natural settings into environments marked by concrete and asphalt, leading to increased heat production, ecosystem degradation, and a reduction of natural water sources including perfoliate plant species whose leaves act as reservoirs. While the proposed initiative's primary aim is to address the challenge of water access for urban insects, a critical component of its scope includes an analysis of biophobia as a systemic contributing factor. Urbanization has been shown to disconnect humans from natural environments and non-human animals, creating feelings of disgust which is a common symptom of biophobia. In turn, humans often distance themselves further from natural stimuli, reducing their awareness of insects' needs. The proposed initiative involves the installation of perfoliate leaf-mimicking plant clips in the gardens of various households in Parkdale, Toronto. These novel clips would contain a shallow reservoir able to collect rain or garden water for consumption by insects. Insects present within these gardens would engage with the clip, bringing them closer to pollination targets. Providing safe and accessible water sources for insects in this manner would contribute to the well-being of humans, non-human animals, and the environment. This would be made possible by facilitating efficient pollen transfer and subsequent plant reproduction while simultaneously attempting to reduce biophobia, exposing humans to the beneficial roles of insects in their gardens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".