Assessment of diet composition of free-ranging addax antelopes (<i>Addax</i> <i>nasomaculatus</i>) by the combination of microhistological procedures and <i>n</i>-alkanes and long-chain alcohols as fecal markers
Bibliographic record
Abstract
Understanding herbivore foraging behaviour is crucial for adequate wildlife management decisions. This study assessed the diet composition of free-ranging addax antelope (Addax nasomaculatus (de Blainville, 1816)). Samples collected during the spring season were analyzed using microhistological procedures along with n-alkanes and long-chain alcohols as fecal markers. Twelve animals were monitored and fresh fecal samples were collected daily during a 7-day period. Diet composition estimates obtained by both methods were similar and indicate that Stipagrostis pungens (Desf.) De Winter was the main diet component followed by Fagonia glutinosa Delile, Helianthemum kahiricum Delile, and Hammada schmittiana (Pomel.) Botsch. Data showed that addax have mixed diets composed of perennial woody and herbaceous plant species depending on their availability. The four species most selected by the animals were also the most abundant species in terms of plant cover. In contrast, species that accounted for less than 5% of the plant cover were not present in the fecal samples. Results of this study represent a single grazing season, and for that reason, further studies are required to fully understand how the grazing behaviour of addax varies with seasonal changes in vegetation cover.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".