Bibliographic record
Abstract
The dataset Squirrel_Calls is a collection of vocal records (defined as primary literature that numerically describes the vocalization of at least 1 squirrel species) where each row corresponds to a single call type of one species. The details of the row include a summary of the literature metadata, categorical descriptions of the call and the caller as well as numerical values of the call frequencies. The dataset Squirrel_Ecological_Traits is a corresponding set of ecological traits for all the species listed in the Squirrel_Calls dataset. The traits listed (mass, time partitioning, gliding capabilities, habitat, and sociality) reflect hypotheses and predictions explored in the associated article. At the end of this document, there is a complete list of the literature references used to assemble these datasets. Squirrel_Script is the R script used to produce the statistics and models used in the corresponding paper. Squirrel_Tree is a nexus file compiling the data of 1000 trees downloaded from VertLife.org which were subsetted from their published mammalian supertree. The nexus file was used in the R script.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.026 |
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".