Moth walls: shedding light on moth biodiversity
Bibliographic record
Abstract
Lepidoptera (butterflies and moths) is one of the most diverse insect orders on Earth. Its members contribute to important ecosystem services such as pollination and herbivory while also serving as principal food for many other animals. Yet in this age of rapid climate change and declining biodiversity, the current distribution of most moth species remains largely undocumented. Here, we describe a novel and low-cost method of bridging this gap, which takes advantage of the fact that many nocturnal insects are attracted to artificial light. A robust network of “moth walls” periodically surveyed by community members serves two purposes: (1) help document moth species diversity and distribution and (2) help stakeholders engage the public about the importance of moths and other nocturnal insects. We contend that moth walls are of relevance to stakeholders interested in biodiversity data, invasive species detection, occurrence data for ranked species, and the ecology of insects attracted to light. The addition of automation and machine learning algorithms could further contribute to the capture and processing of detections across our growing network. Moth walls have already proven fruitful for monitoring and public engagement, yielding new jurisdictional records in Canada while providing local engagement opportunities for agencies and communities.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".