Industrial ecology for the oceans
Bibliographic record
Abstract
Industrial ecology for the oceansHuman interaction with ocean resources has historically been challenging due to the difficulties that arise when a terrestrial species aims at becoming successful in a marine environment.Shipwrecks, for instance, have doomed coastal communities for centuries, and even today fishing is one of the deadliest sectors in the labor force.Similarly, human-induced marine environmental catastrophes, such as oil spills for instance (Trevors & Saier, 2010), have commonly been laborious to clean up due to the inherent difficulty of humans performing beyond terrestrial ecosystems.Continued human population and economic growth since the beginning of the Industrial Revolution have exacerbated the need of human societies for mineral ores, fossil fuels, and other sources of energy, water, and food.This has led to the occupation of vast areas of terrestrial land, to the extent that humans now have a noticeable footprint in all the world's terrestrial biomes.In the world's oceans this same pattern has occurred at a slower pace throughout the decades, with fishing activities becoming more efficient with the arrival of steam vessels in the 1880s, diesel in the 20th century (Engelhard, 2008), and the incorporation of sophisticated detection systems turning ancestral coastal fishing activities into highly industrialized systems that land millions of metric tons of fish and other marine species annually (Fornshell & Tesei, 2013).Similarly, oil rigs spread quickly in the world's ocean to provide additional fossil fuel supplies for thirsty growing economies (Nyman, 2015), marine fright soared with the process of globalization (Mersin et al., 2019) with thousands of cargo vessels swarming the seas and, more recently, seabed mining has appeared in the public and private agenda as an alternative and lucrative sector to maintain the supply of metal ores in the technosphere (Levin et al., 2020).This increased pressure of human activities on the ocean and its resources has translated into a series of environmental impacts that have affected marine conservation (Knowlton, 2021) and degraded vast areas of the ocean.However, it must be noted that not all environmental impacts affecting the ocean are located in the ocean itself, but rather are created by terrestrial activities.In this sense, nutrient loading linked to wastewater treatment plants, agriculture, and cattle ranching are responsible for vast dead zones generated in multiple coastal zones across the globe (Diaz & Rosenberg, 2008), and it is also mainly terrestrial activities that are responsible for the accumulation of plastic waste in the world's oceans (Beaumont et al., 2019).Interestingly, many of these environmental impacts have only been analyzed in detail in recent years.For instance, marine plastic accumulation due to anthropogenic activities and its impacts on ecosystems and human health have only become a relevant field of research in the past decade after the Call for Action "Our Ocean, Our Future" of the Ocean Conference, organized by the United Nations in New York on June 5-9, 2017 (Sonnemann & Valdivia, 2017).In this context, although the focus of oceans-based research has traditionally been narrowly focused, researchers are recognizing the value of a wider, systems-based perspective with the aim of linking industrial uses with the environmental and resource impacts they engender.We argue that the field of industrial ecology is well-suited to fill that gap, as it is interdisciplinary in nature, rapidly growing, and has systems analysis at its core.The current special issue of the Journal of Industrial Ecology, entitled "Industrial Ecology for the Oceans," explores all of the above-mentioned issues with the ultimate objective of catalyzing and compiling novel research regarding the use of industrial ecology in the world's oceans.A total of 24 articles were accepted for publication in the current special issue.These can be divided into five main topics: (i) fishing and aquaculture; (ii) shipping; (iii) ocean acidification; (iv) marine plastics; (v) nutrient flows; and (vi) seabed mining, and are described below. FISHING AND AQUACULTUREFishing and aquaculture constitute the biggest section of papers that are published in this special issue.Out of the 12 studies, 8 of them focus on applying a variety of life cycle methods to different case studies linked to the marine environment, 3 are linked to modeling fishing gear, and one final study analyzes the implications of including a circular economy perspective in the aquaculture sector.In terms of life cycle assessment (LCA) studies, Ziegler et al. (2022) analyzed the greenhouse gas (GHG) emissions of a wide range of seafood products in Norway.Their results indicate that the fuel use intensity of most products has increased over the past decade in terms of fisheries, and
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".