Ensuring equitable access to ocean and coastal information to advance knowledge and inform decision-making: The global Aquatic Sciences and Fisheries Abstracts
Bibliographic record
Abstract
While the oceans are vitally important for human life and the global economy, they have been under extraordinary pressure in recent decades due to human interactions and climate change. Launched in 1971, the Aquatic Sciences and Fisheries Abstracts (ASFA) database of over four million records serves as an invaluable information source about the oceans and other aquatic environments for use in research and decision-making, particularly due to its inclusion of both primary and grey literature. Since information production and use practices are rapidly changing, an assessment of use of the database and information needs of stakeholders was conducted, guided by the following questions: 1) What do individuals (researchers, practitioners, students, and decision makers) seeking aquatic sciences and fisheries information expect the global ASFA database will provide? 2) Have evolving information production practices and delivery technologies affected expectations of users of a database like ASFA? and 3) What action should the ASFA Secretariat take to ensure the development of the database continues to fulfil its mandate as a highly relevant and informative resource for all types of users worldwide? By means of surveys, interviews, and analysis of longitudinal usage data, users' views of ASFA's strengths, weaknesses, and potentially beneficial enhancements were identified. Based on the results of the use assessment, the ASFA Secretariat has implemented several substantial changes, including creating an open information platform containing grey literature, as well as restructuring its international affiliate partnership arrangement to facilitate involvement of new members in ongoing additions to the database. These changes, designed to make the information more easily found, accessible, and interoperable, will enable aquatic and fisheries scientists, managers, students, and decision makers to use relevant primary and grey literature from around the world and assist with ocean and coastal research efforts, mitigation of the effects of climate change, and reaching global sustainability goals.
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 | 0.088 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".