Benthic quality evaluation of immersion zones of sediments dredging
Bibliographic record
Abstract
Dredging operations pose the problem of sediment deposits to be evacuated by taking into account economic incidences and environmental aspects related to environmental protection. Coastal sediments are high biodiversity zones, where a multitude of living organisms have a special relationship with their substrate. An addition of exogenous materials has an impact on the natural equilibrium of the environment, notably during the transfer of contaminants on immersion zones, affecting species and the ecosystem. The objective of environmental assessment process is to estimate the potential risk of exposure upon ecosystems by integrating the benthic communities assessment. The evaluation of biological impacts in immersion zones helps to apprehend better the risk of using operational tools of appreciation. The aim of this paper is to present an expertise approach based upon index for assessing coastal endofauna. This index makes it possible to know sediment biological quality by making more transparent the environmental assessment process potentially influencing the deposits method and the monitoring of dredging operations in a sustainable development context. Study results show that sediments immersion effect is low and has a short duration on immersion zones having strong currents allowing rapid sediments resuspension. Version traduite : Evaluation de la qualité benthique des zones d’immersion des sédiments de dragage
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".