Bibliographic record
Abstract
© Her Majesty the Queen as represented by the Minister of National Defence, 2007 DRDC Valcartier TM 2006-140 i By the end of the 90s, Defence Research and Development Canada (DRDC) initiated the investigation of a novel LIDAR concept which open the possibility of collecting all at once the detailed spectral information contained in the return signals. This 3-year project called SINBAHD (Stand-off INtegrated Bioaerosol Active Hyperspectral Detection) aimed at evaluating the capability of using UV LIF with intensified range-gated spectrometry to detect and characterize bioaerosol from stand-off position. Essentially, the LIDAR system monitors atmospheric volumes in which specific spectrally wide fluorescence signal can be generated from inelastic interactions with complex molecules forming the building blocks of most bioaerosols. This LIF signal is collected by the combination of a dispersive element and a range-gated ICCD that limits the spectral information within the selected volume. This technique has showed an important potential of detecting and discriminating different bioaerosol agent simulants in real time. Through the Standoff Biodetection Working Group
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.794 | 0.696 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".