COVID‐19 and biodiversity: The paradox of cleaner rivers and elevated extinction risk to iconic fish species
Bibliographic record
Abstract
Notwithstanding the human suffering caused by COVID-19, the response (e.g. shelter-in-place orders) has yielded some tangible environmental benefits such as substantial improvements in air and water quality (Corlett et al., 2020). In India, this has manifested as heavily polluted rivers now running clear for the first time in decades with, for example, reports suggesting that the quality of the River Ganges has improved sufficiently to support safe bathing. Hidden beneath these brighter stories however, COVID-19 is also intensifying pressure on India's aquatic wildlife. In an already poverty-stricken country, an additional ~12 million are predicted to face extreme poverty as a result of COVID-19 (World Bank, 2020). Lacking social security, 90% of India's workforce are entirely dependent on daily wages, and are heavily reliant on food supply chains (Reardon et al., 2019) that have been severely disrupted across rural India. With fish (farmed as well as marine-sourced) and meat forming a primary source of protein for many, its sudden unavailability has resulted in local communities exploiting wild populations, especially freshwater fish. As most newly recruited fishers lack knowledge on responsible and regulated capture techniques, illegal, indiscriminate and destructive methods are being used that have impacts on all aquatic fauna (e.g. dynamite, poisons). This also includes harvesting species of high extinction risk, exemplified by the endemic hump-backed mahseer (Tor remadevii, Figure 1), an iconic and critically endangered member of the freshwater megafauna (Pinder, Raghavan, & Britton, in press) symbolic of India's extraordinarily diverse aquatic life. There is increasing evidence that their last remaining giant specimens are being removed from South India's River Cauvery by illegal fishers using a variety of capture methods (Deccan Herald, 2020), pushing them a step closer to extinction. This demonstrates that to understand fully the longer-term environmental impacts of COVID-19, there is always a need to look beneath the surface.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".