Issue Status: For Distribution to the BC EAO and Working Group Prepared by: Melanie Austin, Jasco Applied Sciences on behalf of NaiKun
Bibliographic record
Abstract
Purpose: The following information has been prepared in response to comments raised by Health Canada in their submission (July 17, 2009) and at the working group meetings held July 28/29, 2009. The following document is intended to provide supplemental information regarding the application of the INPM noise model for environmental assessments of industrial noise sources. The INPM noise model applied in the NaiKun environmental assessment was developed by JASCO Research, originally for use by the Canadian Department of National Defense (DND) as an environmental impact assessment and forecasting tool. The model is used in planning military training activities to avoid annoyance in surrounding communities. INPM has been in regular use at Canadian Forces Base Gagetown to predict potential community annoyance in response to changing weather conditions for day to day planning of training operations. DND has also used INPM to assess potential disturbance of nearby wildlife. Extensive model validation was carried out during development of the model, both in terms of verifying the accuracy of the underlying parabolic-equation-based algorithm against accepted numerical benchmark cases and in actual comparisons of field measured and modelled received levels in various environments. An example of such model-to-data comparison is presented in Figure 1 for sound from an explosive charge detonated in a pit as recorded at 1.6 km range across a varied terrain. This figure presents measured and modelled sound energy levels in one-octave frequency bands, and demonstrates a very good degree of agreement when one considers that both the source levels from the blast and the sound propagation were modelled numerically.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.850 | 0.816 |
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".