Post-fire dynamics of the forest formations in the mounts of Tlemcen (Western of Algeria): Case of the Forest of Zarifet
Bibliographic record
Abstract
A study on the post-fire dynamics of plant species in the Zarifet forest (National Park of Tlemcen, north-western Algeria) was conducted after a violent fire that destroyed more than 200 hectares in the month of October 2016.Floristic records were done at a control site and the burned area during the phenological period, which extends from 2017 to 2019.The analysis of the floristic succession indicated a continuum of population dynamics over the three years after the fire.The results showed that the number of species found in the burnt sites reached 52 species (39%) after 8 months from the initial fire and 121 of them (91%) at three years afterward.The natural regrowth of the vegetation in the Tlemcen Mountains is typical of the "tiger bush".The competitivity between the different species has been highlighted in the present study.The most competitive species in the post-fire occupation of the soil are stump-rejecting species and geophytes one, such as Quercus ilex, Chamaerops humilis, Calicotome intermedia, Asparagus acutifolius, Ulex boivinii, Drimia maritima, Cistus sp., Stipa tenacissima and Ampelodesmos mauritanicus.The analysis of the frequency indices (F.I.) seem to be in favor of an expanding tendency of these taxa.
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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 | 0.001 | 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".