Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria
Bibliographic record
Abstract
Many climate impact applications are sensitive to local differentials in the climate system. This study investigates how eco-geographic factors influence the local climate and propagate eco-climatic complexes that vary spatio-temporally. Local geography data including elevation, slope, aspect, rainfall, temperature, vegetation, population density, and soil potential for agriculture were integrated and analyzed using geographic information system and principal component analysis. The result was profiled for local climate drivers and associated spatial structures in present and future climate (2046-2065) scenarios. The results suggest a local climate system driven by the coupling between terrain, rainfall and temperature in all seasons. In the present climate, this coupling creates eco-climatic complexes that extend from the southeast to northwest corridor in all seasons except June-July-August (JJA) when it is shifted to the northeast axis. This pattern is projected to continue in the future climate scenario, but its spatial influence and intensity would weaken around the northwest axis and rainfall will become less significant in the system in JJA. The clustering of rural settlements these complexes suggests the climate-positives produced by the system significantly support rural livelihoods. Thus, these eco-climatic complexes represent climate sensitive natural resource systems that should be targeted as a fulcrum for climate change mitigation and adaptation in the wooded savannah.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".