Bibliographic record
Abstract
Asian States and the Arctic Ocean Hide Sakaguchi According to solar physics data, the earth receives 7,400 quadrillion kilojoules (kJ) of energy from the sun each day. From this energy hitting the earth, it is estimated that roughly 80 trillion kJ is available to humans through the food chain from plants and animals. The amount of energy from food that each individual human needs each day is approximately 10 thousand kJ. Thus, solar energy can feed at most 8 billion people through the food chain from plants and animals. According to World Population Prospects 2022, the global population reached 8 billion in November 2022. The populations of China and India are each more than 1.4 billion, and other Asian countries such as Indonesia, Pakistan, Bangladesh, Japan, the Philippines, Vietnam, and Thailand are also among the top twenty largest countries by population. The sum of the populations in these nine countries alone is 3.9 billion. Thus, we should be aware that world population has already reached earth's capacity, and 50% of these people are concentrated in Asia. And yet, world population is projected to continue growing at a high rate for several decades, so we must intensify food production. However, doing so requires huge amounts of energy—in fact, 4% of global electric energy currently consumed is to produce nitrogen fertilizer in support of food production. Consequently, population, food, resources, energy, and the environment are all interrelated global problems, and we are now at a critical point. However, there exists a buffer to alleviate some of the tension of these problems—the oceans. The oceans can work to produce food and energy. At the same time, science has proven that the oceans can support carbon and nitrogen fixation, enabling these elements to support plant life and be used in fertilizers. The Arctic Ocean, especially, has great potential for those purposes. The Arctic Ocean is considered a large polar sea surrounded by the following five coastal states: Canada, Denmark (via Greenland), Norway (via Svalbard), Russia, and the United States (via Alaska). In terms of economic exclusive zones (EEZs), the largest area of Arctic Ocean by country belongs to Canada, 5.3 million square kilometers, while the second-largest belongs to Russia at 4.3 million square kilometers. Therefore, to address the global problems mentioned above, collaboration and cooperation between Asian states, which are largely driving global population growth, and the Arctic states, especially Canada with the largest share of the Arctic Ocean within its EEZ, are essential. International cooperation must be carried out through the development and conservation of the Arctic Ocean. In this context, projects to introduce the [End Page 3] ocean and its development potential to Asian states to attract investment, scientific research, and technological development are of the highest importance. This ocean is truly the last frontier and may be key to our last stand to save the earth. [End Page 4] Hide Sakaguchi President Ocean Policy Research Institute of the Sasakawa Peace Foundation Copyright © 2023 The National Bureau of Asian Research, Seattle, Washington
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".