Bibliographic record
Abstract
In the past two decades, underwater ocean noise has emerged as an issue of global ecological concern. Commercial shipping is responsible for approximately 80 per cent of the industrial ocean soundscape. These high levels of acoustic emissions, and their impact on marine life, present reputational and regulatory risks to the shipping and logistics industry, threatening to place limits on vessel movement and the imperatives of just-in-time circulation. In this context, underwater noise management is becoming a significant site of state and industry-led greening efforts. This article examines the use of smart ocean systems in mitigating the sonic impacts of just-in-time shipping, focusing on the Port of Vancouver’s Enhancing Cetacean Habitation and Observation (ECHO) programme. Launched in 2014, ECHO develops initiatives to reduce sonic threats to marine mammals, particularly the endangered Southern Resident Killer Whale (SRKW), whose habitat overlaps with shipping lanes surrounding Canada’s largest port. As a smart ocean programme, ECHO aims to employ real-time management to synchronize the spatio-temporal rhythms of transiting whales and ships. How do logistical media and the temporal dynamics of just-in-time circulation intersect with environmental management and smart ocean governance? The article addresses this question by analysing the media and logics of real-time monitoring and response in environmental management in relation to just-in-time as a production philosophy that has underwritten logistical capitalism. By bringing work on smart oceans and marine governance into conversation with studies of logistics and logistical media, it considers how real-time sensing infrastructures are shaping management practices at the nexus of logistics and environmental governance. In doing so, it aims to shed light on the ways that the increasing use of digital sensing technologies are shaping management practices at the nexus of logistics and environmental governance. While underwater noise cast as pollution threatens to impede commodity flows, this article traces how certain actors in the industry are leveraging real-time sensing networks to coordinate circulation and maintain profitability in the emerging green shipping economy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".