Bibliographic record
Abstract
These datafiles are used in analyses contained in the manuscript Tanner, A.L., and Leroux, S.J. [submitted]. Effect of Roadside Vegetation Cutting on Moose Browsing. These data include information on the amount of moose browse on vegetation in roadside areas on the Avalon Peninsula and central Newfoundland, Canada as well as biophysical features associated with the areas. The file also includes information on the proportion of plants browsed by moose in order to determine what species were considered preferred or high quality by moose in Newfoundland. This file also contains the R code that we used to conduct the analysis as well as for the creation of figures for the manuscript. We have included comments throughout the code in order to understand the operations being performed. When importing a new dataset (either: vegdataperplantspp.csv, vegdataplantswilcox.csv, vegdatacorr.csv, or vegdataplants.csv) while running the R code, be sure to set the Please read Metadata file carefully before using any of this data.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".