The Development of the Data System and Growth in Data Sharing
Bibliographic record
Abstract
A great wealth of ocean data exists, for a wide range of disciplines, derived from in-situ and remote sensing observing platforms, in real-time, near-real-time and delayed mode. These data are acquired as part of routine monitoring activities and as part of scientific surveys by a few thousand institutes and agencies all around the world. Both the means to acquire these data and the way in which they are used have changed greatly in the past ten years. Over the last decade, information technology has progressed a great deal. It presently allows the exchange of gigabytes of data and more via the Internet in developed countries. In the late nineties, it was considered high technology to provide data on CDROM rather than on magnetic tapes and only small datasets were distributed via the Internet. Nowadays CDROMs are considered as a backup delivery system especially for countries with poor Internet connections. The explosion in use of the Internet has provided new communications capabilities, new tools, and a new way of using computers. The nature of requirements from government agencies have changed: they want to know or estimate what the future of the earth will look like and what will be the impact on their territories due to climate change issues, ocean health monitoring and fisheries assessment, but they can't pay the full bill for the data acquisition. Therefore, they are pushing, and nowadays more often imposing, a change in data policy and a move towards increased data sharing, in which data acquired with public funds should be freely available to the community. Moreover, the nature of science itself has changed. Investigators and research funding agencies are looking for context, impacts, and synthesis, rather than just focusing on individual, well-defined processes. Most scientists need the data collected by others as well as their own. They cannot do their work using only data they have collected themselves. Finally, the growth of operational oceanographic services, based on downscaling of global model results, is really important. These are in demand by users especially for real/near real-time data just as operational meteorology has been doing for a long time.
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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.040 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".