Chapter 8: Development of a national river bioassessment system (AUSRIVAS) in Australia
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
Book Abstract: This book presents an up-to-date account of developments in predictive bioassessment systems for classifying and monitoring fresh waters, based on macroinvertebrates. It describes in considerable detail developments with the River Invertebrate Prediction and Classification System (RIVPACS) of the UK, the AUSRIVAS programme in Australia, and the BEAST in Canada. Multimetric methods from North America, bioassessment approaches in The Netherlands, Sweden and Spain, and the application of artificial intelligence techniques are all included. The book is based on an international workshop of 59 invited scientists from 23 countries that took place at Jesus College, Oxford in 1997. For all those who are professional scientists involved in aquatic bioassessment methods or the management of natural and impacted fresh waters, this book is a necessary reference text. Similarly for students wishing to learn more about the use of macroinvertebrates for assessing biological quality of fresh waters, it is an invaluable source of information. - - - - - Chapter Abstract: The history and development of the new Australian national river bioassessment system is described. The AUStralian RIVer Assessment Scheme (AUSRIVAS), contains a river bioassessment system largely based on the British RIVPACS. It has been developed in a cooperative effort between federal and state government agencies, and a variety of researchers. Despite the maintenance of state and territory boundaries in developing a national river bioassessment framework, uniformity of sampling, modelling and reporting was sought. The encapsulation of the reference condition within a bioassessment system was also seen as important. The RIVPACS framework was selected for adaptation to Australian conditions under the nationally-managed Monitoring River Health Initiative (MRHI), and over 1500 reference sites were sampled over a 2-year period for invertebrates and environmental variables, under a common protocol. The political, managerial and environmental context of Australia markedly shaped the manner in which RIVPACS was adopted. The 48 RIVPACS-type models developed under the MRHI are run behind a common software platform, accessible over the internet. Differences between RIVPACS and AUSRIVAS are described. AUSRIVAS is being used to conduct the first national assessment of river health, and is becoming integrated into a variety of policy and regulatory mechanisms. Problems associated with developing and maintaining integrated, evolving systems like RIVPACS at a national level are described, including the recent wave of changes to the public sector.
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 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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it