Diet estimation by faeces analysis: sampling optimisation for the European hare
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
We investigated how the sampling process of microhistological faeces analysis could be optimised for an accurate estimation. Spring diet composition of European hare (Lepus europaeus Pallas, 1778) was determined in a juniper shrubland at Bugac, Hungary. Both inter and intraobserver reliability was high permitting us to separate the components of variance due to the methodological steps in the faeces analysis. Estimates varied depending on the number of independent droppings, pellets/individual, subsamples/pellet and epidermis/subsample. The variance was much higher among than within the independent pellet groups. The cumulative frequency estimate stabilised at around 100 epidermis fragments per pellet. We conclude that the most critical steps of the sampling procedure are the collection of independent droppings and the identification of a sufficient number of epidermis fragments. We propose to collect at least 10 independent droppings, one pellet/individual, and analyse 100 epidermis fragments as an optimum for estimating the relative frequency of forage classes reliably. The importance of the individual variability in the diet should be emphasised.
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 | 0.000 |
| 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) | 0.002 | 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