Bibliographic record
Abstract
Throughout both the United States and Canada, a single invasive species is wreaking havoc on the ecosystem. This species is a beetle known as the Emerald Ash Borer (EAB). In the midwest alone, millions of ash trees have already died, and in Canada studies showed that up to 99% of all ash trees were killed within 10 years of EAB infesting the first tree in an area. The Illinois government has tried to combat the insect by cutting down all trees as soon as they become infested, as well as by passing a law which requires people to have a permit in order to import and export firewood. However, this only slowed the spread of EAB, and in just 10 years it had spread throughout the country. To help gain awareness of the effect similar situations could have on our own IMSA students, we would plan to host a GA that talked about the importance of trees in an individual's everyday life. At this GA, we could bring up topics of what trees produce for us that we use in our everyday life, and how students can help combat the loss of trees themselves, possibly by attending a tree planting event. In addition, at these events we also plan to discuss how IMSA students can help to decrease the EAB population through environmentally friendly means.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".