Bibliographic record
Abstract
Non-targeted methods have been gaining ground in recent decades, especially with the tremendous development of high-resolution mass spectrometry. The challenges for non-targeted methods are becoming increasingly necessary as there are limited guidelines to regulate and harmonize the methods worldwide. AOAC-Europe and have been signed a Memorandum of understanding for this sector. Two years ago, a series of Webinars on "Trends & Challenges for Non-Targeted Methods" have been started. As a result of this webinars a joint working group was set up with the aim of providing guidelines for the harmonization of non-targeted methods, mainly based on Mass Spectrometry. So far, the starting working group was now split into five subgroups dealing with food, authenticity, environmental/water, packaging and metabolomic testing. As an initial step the five groups are summarizing definitions and workflows for non-target testing. Based on the theses results a harmonized wording and where possible a harmonized workflow incl. validation should be created. The aim of this presentation is to present especially the result of the packing (food contact material area). The current status of initiation of common parameters as well harmonized criteria for reporting Screening (NIAS) results is presented. The focus of this harmonisation activity is on descriptive physico-chemical parameter, which allow the reader of such Screening reports to find out easily, if it worthwhile to compare results or are the test results achieved based on different assumptions. Currently the focus is only on the results but not on comparability, which lead to massive problems in understanding.
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 | 0.191 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".