Bibliographic record
Abstract
With over a quarter million linear kilometres of rivers in South Africa, this is an incredibly challenging resource to look after and police, comments Mark Graham, director at Ground Truth Water, Wetlands and Environmental Engineering Consultants. Ground Truth works in conjunction with the Wildlife and Environment Society of South Africa (WESSA), the Water Research Commission (WRC), the Department of Water and Sanitation (DWS), the Department of Science and Technology (DST), the Department of Environmental Affairs (DEA), and other government institutions, communities and schools to apply the groundbreaking concept of in order to overcome the monumental challenges posed by national river care. According to a definition by the United Nations Environment Programme, In citizen science, people who are not professional scientists take part in one or more aspects of science - systematic collection and analysis of data, development of technology, testing of natural phenomena and dissemination of the results of activities. They mainly participate on a voluntary basis.
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.
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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.038 |
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".