Foreign agents: Imported for weed control
Bibliographic record
Abstract
You enter through a thick, metal door.When it closes, only a small window provides light in the room.Next, you go through another door into a room with another small window.The goal: to lure potential escape artists toward a trap in the window rather than allow them to sneak outside.Through a third door, you enter a network of laboratories and greenhouses that hold beneficial insects from foreign lands.These insects, scientists and landowners hope, may help control some of the United States' worst weed invaderslike leafy spurge, saltcedar, and melaleuca."Invasive species, including weeds, cost U.S. consumers and producers billions of dollars each year," says Ernest S. Delfosse, the Agricultural Research Service's national program leader for weed sciences in Beltsville, Maryland."Natural enemies from the weeds' homelands may be our most effective and economical tools for long-term control."When beneficial insects arrive from overseas, they are carefully sorted, screened for parasites, and reared in quarantine facilities like the one just described, which is located at ARS' Western Regional Research Center in Albany, California.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".