<p><strong>Acridid (Orthoptera: Caelifera) diversity in agriculture ecosystems at three </strong><strong>locations in the Mzab valley, Septentrional Sahara, Algeria</strong></p>
Bibliographic record
Abstract
Caelifera is one of the largest and most diverse group of insects, and they are the dominant Orthoptera in agriculture ecosystems. We present here an inventory of Acridid fauna of the agricultural ecosystems in the Mzab valley (Septentrional Sahara, Algeria). Grasshoppers were sampled with quadrats in Béni Isguen, Ghardaïa and El-Atteuf, and we have expressed the species richness, sampling effort and relative abundance. The method used is that of quadrats sampling. The results revealed the presence of 27 species, divided into three families, Acrididae, Pyrgomorphidae and Tetrigidae. Among these families, we found that Acrididae are most diverse with six subfamilies. The subfamily Oedipodinae with 10 species was the most abundant, while the subfamily Tetriginae was the least abundant (represented by a one species). The value of the diversity index showed that the cultivated area of Béni Isguen is the most favorable for the development of many Caelifera. The abundance of grasses and low intensity of agricultural activity in this environment favors the presence of Acridid. We found that the Acridid community depends mainly on the nature and richness of vegetation cover, the intensity of agricultural activity and the type of irrigation. Keywords: Grasshopper, agricultural ecosystem, biodiversity, North Africa, Sahara
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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 | 0.001 |
| 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".