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Record W2981505700 · doi:10.4095/219713

Data in Jeopardy

2000· report· en· W2981505700 on OpenAlexaffabout
K Fadaie, T Milne, H P Varma, J Harding, R Macnab, P Gareau, D O'Brien

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsDouble jeopardyComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

During the last 30 years, there has been a serious neglect of the information base stored within our data holdings. In the 1800's data was stored in media such as books, which had encapsulated indexes, meta information, versioning and data. The media, paper, was stable and could be opened up a hundred years later and the information could still be retrieved. Due in large part to rapid technology shifts and the lack of appropriate international standards, this is no longer the case. For the past 30 years, technology shifts have created serious problems in large data repositories. The constant change of storage media from punched cards, to paper tape to 800 BPI magnetic tapes, to cartridges, to magnetic disks, to optical media etc. has resulted in major problems for very large data archives. The hardware and software required to read the old media may not be available in a hundred years, leaving the entire information base in a crisis state. The technology providers also constantly change application software to store and read data. These changes are sometimes implemented within the space of six months, such as in GIS and Wordprocessing software applications. The software is not always backward compatible due to the encapsulation of proprietary file architectures, algorithms and smart compression. These file architectures, algorithms, compression or otherwise, may not be available if the company should ever go out of business or radically change the application domain. There is an urgent need to stabilize the storage data structures to some open international standard for long term archival. If not, the large data holdings will be in jeopardy - not in a hundred years but in less then ten years. A generic storage mechanism, SDS (Self Defining Structure), has been proposed to the ISOTC211 WG1 under the guidance of the Image and Gridded Data Working Group. This paper addresses problems encountered in the state of the data sets used for Canada's Law of the Sea project and possible solutions using SDS (Self Defining Structures) and distributed archives.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.167
Threshold uncertainty score0.559

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0070.007
Scholarly communication0.0180.021
Open science0.0040.014
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.1670.081

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.

Opus teacher head0.346
GPT teacher head0.375
Teacher spread0.029 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes2
Has abstractyes

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