The Origin And Evolution Of Ocean Noise Regulation Under The U.S. Marine Mammal Protection Act
Bibliographic record
Abstract
The Marine Mammal Protection Act (MMPA) was signed into law by President Richard Nixon on December 21, 1972. The Act established a federal responsibility, shared by the Departments of the Interior and Commerce, for the conservation and management of marine mammals. Created primarily to protect marine mammals from the threat posed by tuna fishing, it also encompassed other threats, including polar bear hunting in Alaska, the harvest of harp seals in Canada, and commercial whaling. One threat, however, not envisioned at the time was noise. When the MMPA was first enacted, there was no recognition that sounds associated with anthropogenic activities such as oil exploration, shipping, and military exercises could adversely affect marine mammals or other biota. Over the years, noise has been acknowledged as a potential threat to marine mammals and entire marine ecosystems. An increasing number of studies have shown that noise may pose a risk to marine mammals because they rely on their own echolocation and communication skills for survival. Recently, noise has been linked to other physiological and behavioral effects that could injure or kill marine mammals. Scientific evidence also shows that noise could influence fish, crustaceans, and other marine life. These threats have gained attention in recent years in response to several dramatic events linking anthropogenic, or man-made, noise, with the strandings and deaths of a number of marine mammals. Consequently, there has been growing demand by environmental groups and nongovernmental organizations to regulate and control anthropogenic sources of noise in the sea. However, a great deal of scientific uncertainty still exists over the effects of noise on the ocean ecosystem and on marine mammals in particular. Considerable research is currently being carried out to determine exactly what these effects are, but the nature of this acoustic research often requires the issuance of permits under the MMPA. Determining who must apply for these permits, who receives them, and how noise-creating activities can be regulated has resulted in lawsuits, injunctions, and controversy at the highest levels of government. This paper attempts to identify the problems with the present state of noise regulation under the MMPA, discover the origins of those problems, and recommend changes that will result in regulation that more closely matches the scientific understanding of ocean noise, is more aligned with the original intent of the MMPA, and thus, has the potential to better protect marine mammals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".