Analyse de la variabilité morphologique chez huit populations spontanées de<i>Pistacia atlantica</i>en Algérie
Bibliographic record
Abstract
To study the morphological variability within and among Pistacia atlantica Desf. populations, a comparative analysis was undertaken in eight wild populations grown under different climatic conditions in Algeria. This study addresses the morpho-biometrical aspect of the leaves and the fruits, as well as the micromorphological aspect of leaves. ANOVA as well as mean comparison of the morphological traits revealed a significant diversity within and among the populations. Furthermore, in the multivariate analysis, the populations were separated into four different groups through the discriminating variables: leaf and leaflet dimensions, number of pairs of leaflets, leaf wings, leaf smell and colour, terminal leaflet size and apex, and finally fruit colour and shape. These results showed the originality of the Algerian populations as compared with those commonly described for this species in the literature, in particular by the occurrence of wax on the leaves. The morphological variability exhibited by the Algerian populations of P. atlantica may be interpreted as relevant to the ecological plasticity and the physiological mechanisms discussed in this article.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".