Pacific-wide marine metadata discovery, management and delivery: The PICES Metadata Federation
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The member countries of the North Pacific Marine Science Organization (PICES) separately maintain vast quantities of marine ecosystem data. To support detection and prediction of ecosystem change in the North Pacific Ocean, it is beneficial to discover data holdings with a single search, rather than having to access each country’s records, perhaps stored in different languages and formats. We report on the creation of a PICES “metadata federation” of member countries (Canada, People’s Republic of China, Japan, Republic of Korea, Russian Federation, and the United States of America). Through (1) English-language coding of metadata using the Federal Geographic Data Committee standard; (2) acquisition, installation and configuration of ANSI Z39.50-1995 (ISO 10163-1995) open-source communications software on a public-access server; and (3) registration with a clearinghouse, it is possible for any metadata-serving agency to become part of the PICES Metadata Federation. The federation enables an Internet user to search the collected metadata holdings of any or all members, thus providing access to information across national holdings in a single search. To date, metadata collections from Japan, the Russian Federation, Republic of Korea, USA and China are federated. This activity supports PICES’ goals to promote and co-ordinate marine scientific research in the northern North Pacific and adjacent marginal seas; to advance scientific knowledge about the ocean environment, global weather and climate change, living resources and their ecosystems, and the impact of human activities on them; and to promote the collection and rapid exchange of scientific information on these issues.
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 | 0.047 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".