Efficient disparity estimation using region based segmentation and multistage feedback
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
--In recent years, implementation of antiretroviral therapy in developing countries with a high prevalence of HIV-1 has been recognised as a public health priority. Consequently, the availability ofantiretroviral combination therapy for people with HIV is increasing rapidly in sub-Saharan Africa. --HIV treatment programmes are implemented according to the standardised, simplified public health guidelines developed by the World Health Organization (WHO). --However, the implementation of treatment programmes in Africa is hindered by several factors, including the lack of adequate immunological and virological laboratory monitoring, insufficient support for adherence to therapy, vulnerable health care systems and the use of suboptimal drug combinations. --These suboptimal treatment conditions increase the risk that resistant virus strains will emerge that are less susceptible to standard first-line combination therapy, thus threatening the long-term success of the treatment programmes. --The WHO has initiated HIVResNet, an international expert advisory board that has developed a global strategy for surveillance and prevention of antiretroviral drug resistance. --The Dutch initiative known as 'PharmAccess African studies to evaluate resistance' (PASER) is contributing to this strategy by creating a surveillance network in sub-Saharan Africa.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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