Conceptual framework for the water use benefit index in the Great Barrier Reef region
Bibliographic record
Abstract
In this paper, an initial conceptual framework for the development of a performance indication tool for water use in the Great Barrier Reef region is described.The tool is envisaged as a simple, cost-effective and informative tool for indication of the temporal and regional performance.It is intended for use by non-technical target groups, such as the general public and the local government, and is proposed to be presented as an internet-based index.The index is proposed to cover objectives in the areas of physical-chemical, biological and socio-economic primary and secondary aspects of water use and benefits derived from its use.Each aspect is proposed to consist of not more than five indicators.It is clear that this number of indicators cannot provide a full and comprehensive picture of water use and benefits to the region, but rather an overview of the performance trends.To reinforce this understanding, the index results are proposed to be presented as letters rather than absolute numbers, which represent ordinal 'scores'.This approach would also allow for comparison across a variety of indicators and the units they are measured in.The paper also presents a brief overview of the types of indicators currently used for the assessment of water-related attributes and the methods of their aggregation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".