Bibliographic record
Abstract
1. Aquatic volatile metabolomics - using trace gases to examine ecological processes Michael Steinke 2. Next generation approaches to rapid monitoring Bio-aerosol and the link between human health and environmental microbiology Robert Michael William Ferguson 3. NGB in Canadian wetlands Donald Baird 4. Monitoring the biodiversity and functioning of terrestrial systems via high resolution trace gas fluxes Kelly Robert Redeker 5. Computational approaches to gathering biomonitoring data from social media platforms: a superior solution to next generation biomonitoring challenges? Jon Chamberlain 6. What more can the eDNA-NGS revolution bring to biomonitoring? - the untapped potential of molecular methods Alex J. Dumbrell 7. Bioinformatics for Biomonitoring: Species Detection and Diversity Estimates across Platforms and Tools Joanne E. Littlefair 8. Derocles et al. Statistics from networks or other Biomonitoring - what are the statistics of measuring and evaluating change? Athen Ma 9. CELLDEX/global monitoring of functional responses Scott Tiegs 10. Citizen Science and Biomonitoring Michael Pocock
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.078 |
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".