A modelling approach to assess the impact of land mining on marine biodiversity: Assessment in coastal catchments experiencing catastrophic events (SW Brazil)
Bibliographic record
Abstract
Analysis that link hydrological processes with oceanographic dispersion offer a promising approach for assessing impacts of land-based activities on marine ecosystems. However, such an analysis has not yet been customised to quantify specific pressures from mining activities on marine biodiversity including those from spillages resulting from tailing dam failure. Here, using a Brazilian catchment in which a tailing dam collapsed (Doce river) as a case study, we provide a modelling approach to assess the impacts on key ecosystems and marine protected areas subjected to two exposure regimes: (i) a pulse disturbance event for the period 2015-2016, following the immediate release of sediments after dam burst, which witnessed an average increase of 88% in sediment exports; and (ii) a press disturbance phase for the period 2017-2029, when impacts are sustained over time by sediments along the river’s course. We integrated four components into impact assessments: hydrological modelling, coastal-circulation modelling, ecosystem mapping, and biological sensitivities. The results showed that pulse disturbance causes sharp increases in the amount of sediments entering the coastal area, exposing key sensitive ecosystems to pollution (e.g. rhodolith beds), highlighting an urgent need for developing restoration strategies for these areas. The intensity of impacts will diminish over time but the total area of sensitive ecosystems at risk are predicted to be enlarged. We determined monitoring and restoration priorities by evaluating and comparing the extent to which sensitive ecosystems within marine protected areas were exposed to disturbances. The information obtained in this study will allow the optimization of recovery efforts in the marine area affected, and valuation of ecosystem services lost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".