Task Team to Establish and Maintain Co-operation Between IODE and Research Programmes
Bibliographic record
Abstract
Over the last two decades, the number and complexity of international cooperative programmes has increased. We are just completing WOCE, and JGOFS, but there is the IGBP, CLIVAR, GOOS, GCOS and GTOS among others. Many of these programmes originated in the research community. Using WOCE as an example, the programme organized 13 Data Assembly Centres (DACs) whose jobs were to assemble, quality control, and prepare the data for the final archive. IODE data centres participate in 4 of these DACs while another 4 DACs (non-IODE) handle data that are also managed by many IODE data centres, thus duplicating the effort. The remainder of the DACs dealt with data outside of traditional IODE data centre responsibility. \nThe TOGA/TAO centre at PMEL is an example of a facility that started up in the research community and exists outside of an IODE data centre but which performs many elements that an IODE centre does. TOGA/TAO is a complete program carrying out scientific research, design of an observation programme, building and deploying instrumentation, archiving and disseminating data. It is often cited as a model of what the research community thinks of how a data collection and archive facility should run. \nIf IODE data centres were doing their jobs, the 4 new DACs built to handle oceanographic data in WOCE would not have been created. Likewise, the data management functions now performed by the TOGA/TAO centre would have been sited in an IODE centre. \nBy and large, IODE centres operate independently of each other. The result is that each data centre designs, builds and operates data processing routines that carry out fundamentally the same functions as done by others. Because of variations in available resources, each of these processing systems has variations, some of which enhance the quality of the data from the archives, and some that do not perform so well. The result is that what a user sees from an archive depends on which archive provided the results. Of greater concern is that the data derived from the same source but in two different archives may be different. \nThe working relationship between data centres and researchers tends to be distant. All data centres receive and process data from researchers, but few have a day-to-day working relationship with them. Data centres need scientific advice to ensure that data and information are handled by appropriate procedures. The purpose of data centres is not just to archive the data, but to be sure the data are available to users. Data value increases when additional information about the data collection is also available. Research programs are in the forefront of important uses and requirements of data. Collaboration gives data centres a clientele that is demanding and useful in recommending what needs to be done.
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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 | 0.079 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.102 |
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".