Brown bear trophic interactions database
Bibliographic record
Abstract
This dataset is a unique, highly detailed, spatially-explicit database of trophic interactions of brown bear across Europe and Türkiye. It contains six files: Supplementary Tables 1-4 (Trophic Database); Supplementary Table 5 which shows a list of species in the diet; Supplementary Table 27, summarizing the relative energy contribution of each food item (rEDEC) for each species by subpopulation. FILES INCLUDED Files name Description Supplementary Table 1 List of brown bear diet studies included in our database Supplementary Table 2 Database of diet of brown bear Supplementary Table 3 Coefficient factors (Cf1 and Cf2) associated to each category of diet used to convert rV to rEDEC Supplementary Table 4 This table is a subset of Supplementary Table 2 presenting only the infromation for the selected studies to account for current biotic intercations (See Supplementay Table 1) Supplementary Table 5 List of all species in the diet of brown bear considered with GBIF data of number of occurrences, species key, cleaned occurrences Supplementary Table 27 Matrix showing the summarized data of rEDEC for each species by subpopulation.
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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.035 |
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".