Bibliographic record
Abstract
There are various method in the world to sample and analyze odour. No matter what method or technique that is used, it should be accordingly to the standard. For the new researcher or people involved in order management, they mightlack in knowledge on how to use a proper or a suitable technique to assess odour. In Malaysia, there is no specific method of handling the odour problem. Currently in this country is following the European standard, which using the Olfactometer to analyze odour. Since the Olfactometer is expensive for the first time of installation, a cost effective Odour Threshold Test has been developed from Japan was trying to introduce. A new method from Canada called SM100 Olfactometer was also available in the laboratory. Comparisons between those methods are studied and suitability for use are presented. For odour sampling, there are three types of source that need to be considered; point, area and volume. Proper techniques should be done in order to sample at various sources. This paper would guide on sampling method, test procedure and data analysis of some method. This would make sense as the newer can choose their technique based on available instrument and environment condition.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".