Impact of flooding frequency on the diversity and structures of riparian forests in Southwestern, Nigeria
Bibliographic record
Abstract
Riparian zones have been found to be a complex ecosystem subjected to flooding events of varying intensity and frequency. It is well established that flooding influences the vegetation dynamics of riparian forests but there is paucity of information on the impact of flooding frequency on the diversity, structure and composition in riparian forests in Nigeria. The Height Above Nearest Drainage (HAND) model was adopted in this study to generate flooding frequency across the host watershed/sub-basins where the study sites are located. The HAND algorithm was implemented in ArcGIS environment using SPOT DEM as input layer. Multiple regression and Canonical Correspondence analysis were employed to assess the impact of flooding frequency on structure and diversity of the study sites. The mean flooding frequency (years) across the study sites, ranged from 0.18 to 7.80, with Ifetedo site being the highest and Ilesha site the lowest. Flooding frequency impacted on the species richness and soil annual volume loss/gained across study sites. The study established that flooding frequency are the natural driver influencing species density, alluvial deposition and regeneration dynamics. The annual and total volume of soil gained/loss impacted on the diversity indices, species dominance and evenness of riparian forests in Southwestern Nigeria.
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 | 0.000 | 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) | 0.001 | 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".