Butterfly Diversity: An Indicator for Environmental Health within Tarkwa Gold Mine, Ghana
Bibliographic record
Abstract
Many human societies, rely on proceeds from mining of metals and fossil fuels for income generation as well as resource supplies from biodiversity. However, these mining activities are in conflict with and threaten the sustainable utilisation and conservation of biodiversity. We used butterflies, a known bioindicator species as surrogates to measure the level of change in vegetation within Tarkwa Gold Mines (TGM) in Ghana. Butterfly assemblages were sampled within the concession and characterised in terms of diversity indices and vegetation type. Transect counts, aerial netting and charaxes trapping were employed to assess butterfly assemblages. Results showed that although there were high records of degraded forest and savanna indicator species, the fairly high presence of deep forested species also shows that some forested areas within the concession are still in good condition. Recommendations are made to maintain forest blocks to serve as refugia for butterflies and urge management to institute measures to restore old and abandoned mined sites. We also recommend policy makers to enact laws that will ensure that, mining organisations lay down plans for sustainable mine operations and biodiversity conservation before being granted licenses to operate in Ghana.
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 | 0.001 | 0.001 |
| 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".