Why the Provenance of Data Matters: Assessing Fitness for Purpose for Environmental Data
Bibliographic record
Abstract
While fitness for purpose is the principle universally accepted among scientists as the correct approach to obtaining data of appropriate quality, many scientists or end-users of data are not in a position to specify exactly what quality of data are required for a specific analysis. Agencies that collect environmental observations provide data as is offering no guarantee or warranty concerning the accuracy of information contained in the data, in particular, no warranty either expressed or implied is made regarding the condition of the product or its fitness for any particular purpose. While the increasing implementation of ISO 9002 will benefit users in the future, the reality is that many of the existing databases generally contain data that were not gathered with present standards and protocols, or the same methods over the period of record. Usually, long-term records will contain observations that have been made with several different observation techniques, sometimes several locations, and frequently a progression of quality assurance and workup techniques, and these changes may not be well documented. While it is important that hydrometric and climate services focus on capturing data that are fit for their intended purpose, the burden for assessing the actual suitability for use lies entirely with the user. Some general principles for assessing fitness for purpose are proposed.
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 | 0.222 | 0.643 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.019 | 0.052 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".