Deep-sea sound system: Scientific listening, ocean heat, colonial power
Bibliographic record
Abstract
Since the 1970s, oceanographers have used underwater sound to measure ocean heat by means of a scientific technique called acoustic tomography (AT). This article historicizes AT, arguing that both the technique itself and the climatic knowledge it produces propagate colonial, military and capitalist pursuits that are to blame for oceanic warming in the first place. The argument plays out in four parts. Part one situates AT in relation to the discovery of the deep sound channel and Cold War acoustics research. Parts two and three analyse two pivotal AT experiments, namely the Heard Island Feasibility Test (1991) and the Acoustic Thermometry of Ocean Climate experiment (1996–2006). Both experiments were premised on scientific understandings of the deep ocean as ‘nearly transparent to low-frequency sound’, as one oceanographer put it. We term this simplified image of the depthsoceanus nullius, after the nineteenth-century legal doctrineterra nullius, which has long been deployed by settler colonists to justify violently expropriating land. We propose instead that the deep ocean should be conceptualized as a loud and sonically dense space – anoceanus maximus– resonating not only with the sounds of ships’ propellers, air-guns and sonar pings, but also with the sonorous tones, clicks, buzzes, grunts and howls of manifold undersea creatures. The article concludes with a discussion of sound’s relation to ambiguity and violence in oceanographic knowledge production.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".