Criteria-based ranking (CBR): A comprehensive process for selecting and prioritizing monitoring indicators
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.
Bibliographic record
Abstract
Resources allocated to natural resource management often fluctuate, requiring the types and numbers of parameters used in monitoring programs (e.g., indicators of ecosystem health) to be frequently reassessed. Conventional approaches to selecting monitoring indicators are often biased and non-inclusive. A new Criteria-based Ranking (CBR) process for selecting and/or prioritizing indicators was tested in the Muskoka River Watershed (Ontario, Canada). The CBR process is based on two environmental assessment tools, Simple Weighted and Leopold matrices. It incorporates environmental components and criteria for assessing each indicator, which generate a score per indicator. The process tested in this study was concluded to be an effective way to prioritize and/or select environmental monitoring indicators. A different set of indicators emerged when a common set of criteria was used to assess monitoring indicators. Benefits of the CBR process include: •Standardization of indicator selection process with less bias and lower cost (e.g., time and human resources).•Indicators that are representative of the community and more relevant for decision-making (e.g., more resilient to socio-political change).•Adaptability: (1) to other goals, e.g., selecting from a list of Valued Ecosystem Components (VECs), and (2) to any context through localized scoring criteria. Easily integrated into existing practice.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it