A Method to Classify Data Quality for Decision Making Under Uncertainty
Bibliographic record
Abstract
Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".