Impacts of Plant Presence on Formaldehyde Levels in an Office
Bibliographic record
Abstract
The toxin formaldehyde has been associated with muscle weakness, itchy eyes and skin and is considered a carcinogen.The toxin poses a risk to the occupants of a space when found at high concentrations.Plant life has been long associated with cleaning air and improving indoor air quality levels, so this study will look at the impact of plant life on formaldehyde levels within an existing office in the United Arab Emirates.Through the selection of a plant that has been proven to reduce formaldehyde levels (i.e.Corn Cane), the study will determine the extent of the ability of the plant to reduce formaldehyde levels in an existing office setting.Three spaces within an office were selected; labelled as space A, B and C. The plants were included within these spaces, each with a different space/plant ratio.The plants remained within the space for one month before a secondary round of testing was conducted.Results showed that plants were able to reduce the highest amount of formaldehyde levels when introduced at the highest plant/space ratio of 3.75m2/plant (Space B).This allowed the plants to be more effective within their space.When equated, there was a total drop of 4% in formaldehyde levels within space B.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".