Bibliographic record
Abstract
Abstract This article reviews information recently available from existing marine and coastal mining for responses to environmental issues affecting marine mining at different depths. It is particularly but not exclusively concerned with those issues affecting seabed biodiversity impact and recovery. Much information has been gathered in the past 10 years from shallow mining operations for construction aggregate, diamonds, and gold, from coastal mines discharging tailings to shallow and deep water, and from experimental deep mining tests. The responses to issues identified are summarized in a series of eight tables intended to facilitate site-specific consideration. Since impacts can spread widely in the surface mixing layer SML, and can affect the biologically productive euphotic zone, the main issues considered arise from the depth of mining relative to the SML of the sea. Where mining is below the SML, the issue is whether it is environmentally better to bring the extraction products to the surface vessel for processing (and waste discharge), or to process the extraction products as much as possible on the seabed. Responses to the issues need to be site-specific, and dependent on adequate preoperational environmental impact and recovery prediction. For deep tailings disposal from a surface vessel, there are four important environmental unknowns: (1) the possible growth of “marine snow” (bacterial flocs) utilizing the enormous quantities of fine tailings particles (hundreds or thousands of metric tons per day) as nuclei for growth, (2) the possibility that local keystone plankton and nekton species may migrate diurnally down to and beyond the depth of deep discharge and hence be subjected to tailings impact at depth, (3) the burrow-up capability of deep benthos and their ability to survive high rates of tailings deposition, and (4) the pattern and rate of dispersion of a tailings density current through the deep water column from discharge point to seabed. Actions to obtain relevant information in general and site-specifically are suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 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 teacher head, 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".