Mapping diversity of species in global aquaculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aquaculture is the world's most diverse farming practice in terms of number of species, farming methods and environments used. While various organizations and institutions have promoted species diversification, overall species diversity within the aquaculture industry is likely not promoted nor sufficiently well quantified. Using the most extensive dataset available (FAO‐statistics) and an approach based on the Shannon Diversity index, this paper provides a method for quantifying and mapping global aquaculture species diversity. Although preliminary analyses showed that a large part of the species forming production is still qualified as undetermined species (i.e. ‘not elsewhere included’); results indicate that usually high species diversity for a country is associated with a higher production but there are considerable differences between countries. Nine of the top 10 countries ranked highest by Shannon Diversity index in 2017 are from Asia with China producing the most diverse collection of species. Since species diversity is not the only level of diversity in production, other types of diversity are also briefly discussed. Diversifying aquatic farmed species can be of importance for long‐term performance and viability of the sector with respect to sustaining food production under (sometimes abrupt) changing conditions. This can be true both at the global and regional level. In contrast, selection and focus on only a limited number of species can lead to rapid improvements in terms of production (towards sustainability or not) and profitability. Therefore, benefits and shortcomings of diversity are discussed from both economical and social‐ecological perspectives that concurrently are shaping the expanding aquaculture industry.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it