Threat analysis of modelled potential migratory routes for<i>Miniopterus natalensis</i>in South Africa
Bibliographic record
Abstract
Abstract Migrant cave‐dwelling insectivores that rely on specific caves for maternity and hibernation, like the Natal long‐fingered batsMiniopterus natalensisin South Africa, may be at particular risk of population decline in an urbanising world. As a step towards the conservation of caves and cave‐dwelling bats in South Africa, this study aimed to (i) broadly identify the number of caves used by bats (any species) and specificallyM. natalensis, throughout South Africa, (ii) investigate the number of maternity and hibernacula roosts currently known forM. natalensis, (iii) assess the number of caves located in formal protected areas, (iv) determine potential migration paths/corridors between hibernacula and maternity sites and (v) evaluate the potential threats (like onshore wind facilities) along potential migratory routes. A meta‐analysis of scientific literature and websites was conducted to identify caves throughout South Africa and locations of maternity and hibernacula roosts forM. natalensis. Roosts were assessed to determine whether (i) they were located in protected areas, (ii) they were used for eco‐tourism and (iii) the distance to primary roads and onshore wind energy facilities. Next, likely migratory paths were modelled between maternity and hibernacula sites using least‐cost path analysis and the threats along potential routes were investigated. A total of 92 caves were identified, 50 were reported to contain bats.M. natalensiswere recorded in 37 caves, and of those, only 9% are currently located inside protected areas. A total of 12 least‐cost paths were modelled, and various paths intersected potential threat risk elements. Our analysis provides the first description of the potential migration corridors forM. natalensisin South Africa, as well as the current conservation status of bat‐inhabited caves. For a developing country set to experience increased urbanisation pressures, this study highlights the need for conservation measures for South African caves and the dependent bats.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".