Data rescue archive weather (DRAW)
Bibliographic record
Abstract
Purpose To rescue at-risk historical scientific data stored at the McGill Observatory, the objectives of the Data Rescue Archive Weather (DRAW) project are: to build a repository; to develop a protocol to preserve the data in weather registers; and to make the data available to research communities and the public. The paper aims to discuss these issues. Design/methodology/approach The DRAW project adopts an open archive information system compliant model as a conceptual framework for building a digital repository. The model consists of data collection, conversion, data capture, transcription, arrangement, description, data extraction, database design and repository setup. Findings A climate data repository, as the final product, is set up for digital images of registers and a database is designed for data storage. The repository provides dissemination of and access to the data for researchers, information professionals and the public. Research limitations/implications Doing a quality check is the most important aspect of rescuing historical scientific data to ensure the accuracy, reliability and consistency of data. Practical implications The DRAW project shows how the use of historical scientific data has become a key element in research analysis on scientific fields, such as climatology and environmental protection. Originality/value The historical climate data set of the McGill Observatory is by nature unique and complex for preservation and research purposes. The management of historical scientific data is a challenge to rescue and describe as a result of its heterogeneous and non-standardized form.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.018 |
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".