ESA USE CASES IN LONG TERM DATA PRESERVATION: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
Long Term Data Preservation (LTDP) aims at ensuring the intelligibility of digital information at any given time in the near or distant future.LTDP has to address changes that inevitably occur in hardware or software, in the organisational or legal environment, as well as in the designated community, i.e. the people that will use the preserved information.A preservation data manages communication from the past while communicating with the future.Information generated in the past is sent into the future by the current preservation data.European Space Agency (ESA) has a crucial and unique role in this mission, because it maintains in its archives long time series of Earth Observation (EO) data.In order to ensure to future generations data use and accessibility of this cultural heritage is needed to define a systematic approach, accompanied by different use cases.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.998 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".