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Morphological criteria to identify faecal pellets of sympatric ungulates in West African savanna and estimates of associated error

2008· article· en· W2031070453 on OpenAlexfundno aff
Fabrice Hibert, Hervé Fritz, Pierre Poilecot, Hama Noma Abdou, Dominique Dulieu

Bibliographic record

VenueAfrican Journal of Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsSympatric speciationUngulatePelletsBiologyEcologyPelletHabitat

Abstract

fetched live from OpenAlex

Abstract Indirect surveys may prove to be useful tools in complementing classical direct counts when monitoring ungulate populations and may also promote better understanding of the precise structure and functioning of the rich ungulate communities of African savannas. However, the identification of faecal pellets can be difficult where several sympatric species occur. This study develops simple field criteria for distinguishing between pellets among ten sympatric West African ungulates. A discriminant analysis was performed, using the mean of measurements of pellet groups from different species to pinpoint and characterize the most useful morphological criteria for separation between them. The mean diameter of pellets within each pellet group proved to be the most valuable variable for species segregation, whilst the second axis separated species by mean indent depth. The pellet groups of six of the ten designated species could be identified with a minimum misclassification error. However, no simple morphological variables emerged to permit discrimination between hartebeest and topi, or between bushbuck and Bohor reedbuck pellets. Once pellet groups have been identified, their density and spatial distribution may provide useful information on the use of space and habitat of sympatric species, over given periods.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.278
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2008
Admission routes1
Has abstractyes

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