A small deep infarct revealing leukoencephalopathy, calcifications and cysts in an adult patient
Bibliographic record
Abstract
<h3>Aim of special session</h3> Successful examples of collaborations and WHWB projects for building occupational health and safety capacity will be illustrated, noting how better interactions with other national and international organisations could increase impact. Discussion will focus on how to increase these collaborations and how WHWB can expand its footprint globally, to improve its current offerings in terms of delivering training, mentoring, development and translation of guidance materials, and technical assistance to build knowledge and capacity in occupational health and hygiene, particularly for under-served workforces in both developed and developing countries. <b>Presenters:</b><sup>1</sup>Ms Claudina MCA Nogueira, <sup>2</sup>Dr Kevin Hedges, <sup>3</sup>Dr David F Goldsmith, <sup>4</sup>Dr Steve M Thygerson <sup>1</sup>University of Pretoria, Faculty of Health Sciences, Pretoria, South Africa <sup>2</sup>Occupational Health Clinics for Ontario Workers (OHCOW) Inc., Toronto, Canada <sup>3</sup>George Washington University, Washington DC, USA <sup>4</sup>Department of Health Science, Brigham Young University, Provo, Utah, USA
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".