Empirical measure of absorption and scattering properties of living wall plants and systems and predictive modeling of room acoustic benefits
Bibliographic record
Abstract
This series of research projects investigate the acoustical characteristics of interior living walls and predicts how they can be used to positively benefit room acoustics. Scaled and full scale evaluations were executed in a reverberation chamber to validate test methods for absorption and scattering coefficients of soil/substrate and plant species (characterized by height, stem diameter, mass, leaf geometries and dimensions, and leaf area index (LAI)). Wall systems were evaluated over a gradient of plant coverage with monoculture and community planting. Evaluation indicates that evenly distributed pumice can act as the baseline on the scattering turn-table in a method to evaluate scattering coefficients of plant-specific foliage. Findings indicate that percentage of plant coverage is related to absorption coefficients (0.16–1.1) as averaged across all evaluated species. Only at low plant coverage do specific plant characteristics affect the absorption coefficients. The percentage of plant coverage is related to scattering coefficients (0.05–0.51) at 500 Hz and higher. LAI x Mass predicts absorption and scattering coefficients at mid-frequency (200–2500 Hz). Comparison of prediction and field studies identify that use of scattering coefficients improves the prediction of the beneficial use of living walls in room acoustics.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".