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Record W1982436693 · doi:10.4296/cwrj3701866

Why the Provenance of Data Matters: Assessing Fitness for Purpose for Environmental Data

2012· article· en· W1982436693 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsSimon Fraser UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsWarrantyComputer scienceData qualityQuality (philosophy)Data scienceQuality assuranceRisk analysis (engineering)Product (mathematics)EngineeringOperations managementBusiness

Abstract

fetched live from OpenAlex

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.Re sume : Bien que le principe de l' aptitude a `l'emploi soit universellement accepte parmi les scientifiques en tant qu'approche ade quate pour l'obtention de donne es d'une qualite approprie e, de nombreux scientifiques ou utilisateurs finaux de donne es ne sont pas en mesure de pre ciser avec exactitude quelle qualite de donne es s'ave `re ne cessaire pour une analyse spe cifique.Les organismes qui recueillent des observations environnementales fournissent les donne es telles quelles sans aucune garantie quant a `l'exactitude de l'information qu'elles renferment.En particulier, aucune garantie, explicite ou tacite, n'est offerte quant a `l'e tat du produit ou quant a `son aptitude a `un emploi particulier.Me me si la mise en aeuvre croissante de la norme ISO 9002 avantagera les utilisateurs a l'avenir, le fait est que bon nombre des bases de donne es existantes contiennent en ge ne ral des donne es

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0080.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.200
GPT teacher head0.346
Teacher spread0.146 · 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