Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Welcome to this inaugural issue of Human-Wildlife Confl icts.Much has changed during my lifetime.I can remember when there were no deer on my family's farm in Illinois.I can remember in 1979 seeing Canada geese feeding in a golf course in New Haven, Connecticut, and thinking they were a lost family of geese from the Arctic.I can remember going to a wildlife conference and seeing a presentation in the program, titled "Turkey problems in Wisconsin," and wondering if the turkeys they were referring to had wings or wore overalls.I can remember the editor of The Journal of Wildlife Management refusing even to consider publishing my fi rst manuscript on humanwildlife confl icts because animal damage control (as it was called in those days) was outside the purview of the journal and not a part of the fi eld of wildlife management.Things have defi nitely changed in the last few decades.Today, you cannot grow soybeans on my family's farm because deer eat all the plants.Now, geese are so numerous in New Haven that the locals call them "those #$@%### geese."Today, we have urban geese, urban foxes, urban coyotes, urban deer, urban elk, and you heard it here fi rst-urban buff alo (see page 3).We have zoonotics diseases, such as avian infl uenza, West Nile disease, and Lyme disease that were unknown a few decades ago.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it