Global Sustainable Development Agenda: An Implication for Conservation Challenges in Cross River State, Nigeria
Bibliographic record
Abstract
The pristine rainforest of Cross River State of Nigeria is ecologically a region of species endemism and one of the 25 biodiversity hotspots in the world. Globally, there has been a drive for sustainability of these valuable ecosystems. The world Commission on Environment and Development articulates this drive and offers clarifications on the instruments to achieve the goals of sustainable development. However, in many countries such as Nigeria and other African countries, the challenges of achieving the global goal of sustainable development are enormous, given the urge for economic and infrastructural development, and the challenging needs of a burgeoning human population. Natural resources conservation in the above circumstances remain an uphill task. In the developed world, advancement in technology and industrial development also poses a serious problem to the global sustainability agenda. From the Nigerian perspective, with Cross River State harboring more than 50 percent of the remaining pristine rainforest, the challenges to sustainable development include inter alia ineffective implementation of international environmental treaties, high rates of deforestation, biodiversity loss, weak institutions, non-resettlement of enclave communities of parks, and lack of commitment on the part of stakeholders. This paper therefore recommends that forest ecological restoration, biodiversity conservation in parks and protected areas, and industrial strengthening should be pursued as core strategies of sustainable development in Nigeria.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".