Исследование возможности сорбционной очистки при ликвидации нефтяных загрязнений
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
Syndromic surveillance systems can enhance early disease warning, endemic disease monitoring, or help to accumulate proof of disease freedom. In order to provide immediate feedback to achieve these goals, the health data sources scanned should be acquired continuously, in an automated fashion, and should be stored electronically. Recognizing that data from diagnostic test requests often meet these requirements, two systems designed to automatically extract surveillance information from animal laboratory databases have been developed and are described in this paper. These systems are designed to contribute to early disease detection, as well as the timely management of epidemiological information, in a province of Canada and in Sweden, the areas served by the diagnostic laboratories concerned. Classifying in-coming requests into syndromes, the first step, was the most time-consuming and the least portable step between the two systems. The remaining steps were more easily adjusted from one system to implementation in the other. These steps included: retrospective evaluation of data to create baseline profiles following the removal of excessive noise and aberrations; the identification of temporal effects; prospective evaluation of detection algorithms; and finally real-time monitoring and implementation. Building upon the institutions' existing data management software, all steps to use those data for the purposes of syndromic surveillance were set up using open source software; as a result this approach could be readily adopted by other institutions. Relatively straight-forward development and maintenance is expected to lead to the incorporation of these systems into each institution's surveillance processes, becoming an indispensable tool for diagnosticians and epidemiologists, as well as stimulating further technical development of such systems.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.041 |
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