Bibliographic record
Abstract
My name is Scott Church. I am a Seattle based IT professional and landscape photographer who has a great interest in environmental issues. I am writing to you today to express my concern regarding the so-called "lynxgate " controversy. As you are probably aware, it was discovered last fall that several field technicians participating in the National Canada Lynx Survey on behalf of the US Forest Service and the Washington Dept. of Fish and Wildlife were caught submitting hair samples from a captive lynx and a bobcat and reporting them as being from the Wenatchee and Gifford Pinchot National Forests. Since then, this issue has become a lightning rod for critics of the Endangered Species Act and large predator conservation measures throughout the country. I too am concerned about this incident and am glad that it is being investigated at a Congressional level. There is NEVER any excuse for falsifying data in a scientific study. However, based on numerous popular press accounts, newsgroup discussions and personal contacts with policy makers, I have become deeply concerned about both the accuracy of reporting about it and the objectivity of the subsequent investigations. Given the gravity of the issue and your position to defend the ESA and NCLS in Congress, I would like to take a few moments to share what I know about this incident and why I feel that programs like these should be protected in spite of it. The National Canada Lynx Survey (NCLS) In 1999 the US Forest Service, in cooperation with several other federal and state agencies, initiated the National
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.116 | 0.053 |
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".