PANDORA database: A compilation of indoor air pollutant emissions
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
Modeling indoor air quality (IAQ) in real buildings still remains difficult because of the limited data regarding the pollutant outdoor concentrations and indoor sources. The characterization of indoor sources is currently problematic, as most of studies have focused solely on measuring indoor concentration levels instead of determining the source emission rates that are required to model the indoor concentration changes with time. The present work aims at compiling the available data regarding the emission rates of both gaseous and particulate pollutants in a systematic way into a unique database called PANDORA (a comPilAtioN of inDOor aiR pollutAnt emissions) to provide useful information for IAQ modelers. In addition to the presentation of PANDORA, the elaboration of a target volatile organic compounds (VOC) list based on the emission rates implemented in the database is also described. Results show that the obtained target VOC list is similar to those based on actual indoor VOC concentration measurements, demonstrating that PANDORA alone can be used to produce such a list and that, considering the data integrated in the database, formaldehyde, acetaldehyde, and benzene are the three VOC that should be carefully accounted for in IAQ analysis.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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