Vegetation Diversity and Soil Physico-chemical Properties Under Traditional Management of Rangeland in Eastern Ethiopia
Bibliographic record
Abstract
The changes of herbaceous vegetation (composition, diversity, richness, evenness, and biomass production) and soil physico-chemical attributes of Harshin rangelands, eastern Ethiopia were examined under continuously open grazed areas and three ages of enclosures group arranged along chronological sequence into: < 5 years (young), 5-10 years (mid) and > 10 years (old). The herbaceous species composition showed a clear pattern of difference between the enclosure and open access grazed areas with desirable species being more prevalent in the enclosures and the undesirable ones being dominant in the open access grazed areas. The principal component analyses (PCA) of herbaceous species composition were accounted distinct across the open grazing young, mid and old enclosure management types. The diversity, richness and biomass production of herbaceous species were significantly different in the study area. The principal component analysis (PCA) of soil physico-chemical attributes showed distinct separation in relation to open grazing and three age enclosure areas. Overall, we found that the increased biomass production in the old enclosures may threat the herbaceous species diversity by dominating by only few species and hence affected the plants which are susceptible for species inter-competition.
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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 | 0.000 | 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) | 0.000 | 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".