Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"
Bibliographic record
Abstract
Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”. The file that produces the Supporting Code, and presents the analyses in human-readable format, is “StoichiometryOfHomeRanges.Rmd”. For correct compilation of the .Rmd files, please organize the files in this repository as follows: - all .Rmd and .R files in a ../Code/ folder- all .csv files in a ../Data folder- all .tiff files in a ../Data/StDMs_Rasters/Ratios folder- unzip the SamplePoints.zip file in a ../Data/GridPoints folder- al .rds files in a ../Results folder Where the Code, Data, and Results folder all share the same root. Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding authors (M. Rizzuto) for any question. See README.md for details on the repository’s contents.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.312 | 0.201 |
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".