Mining the Deep Seabed: Is Malaysia Ready?
Bibliographic record
Abstract
The mineral industry is expanding fast coinciding with the increase in demands. The depleting land-based minerals has led to the seeking of mineral resources especially from deep sea. Deep seabed minerals have high concentration of precious metal in comparison to land-based minerals. A lot of countries have started to explore and exploit the deep seabed minerals. Thus, it is necessary for Malaysia to commence on deep seabed mining exploration to reap its economic and technological benefits. Similarly, like land mining, the marine environment will face environmental degradation from deep seabed mining exploration. Being a State-party to the United Nations Convention on the Law of the Sea 1982 (UNCLOS 1982) and Convention of Biological Diversity 1994 (CBD), Malaysia has yet to develop a set of policies, regulations and laws related to deep seabed mining to mitigate its effect to the marine environment coinciding to the requirements of these conventions. The purpose of this study is to explore the preparedness of Malaysia to embark on exploration of deep seabed mining in areas beyond the national jurisdiction while observing the effects of deep seabed mining to the marine environment. The challenges in exploring the deep seabed mining as well as the relevant international and national laws related to deep seabed mining will also be observed in this study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".