Corrigendum to “Phytochemicals and bioactive constituents in food packaging - A systematic review” [Heliyon 9(11) (November 2023) e21196]
Bibliographic record
Abstract
In the original published version of this article, there is a mistake in Fig. 1 since one of the fields is empty (the right green box), without the corresponding information. The authors provided correct version for Fig. 1 [The correct version of Fig. 1 can be found below and in the Amended figure folder] and they apologize for the errors. Both the HTML and PDF versions of the article have been updated to correct the errors. Fig. 1. All the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Phytochemicals and bioactive constituents in food packaging - A systematic reviewSiddiqui et al.HeliyonOctober 24, 2023In BriefDesigning and manufacturing functional bioactive ingredients and pharmaceuticals have grown worldwide. Consumers demand for safe ingredients and concerns over harmful synthetic additives have prompted food manufacturers to seek safer and sustainable alternative solutions. In recent years the preference by consumers to natural bioactive agents over synthetic compounds increased exponentially, and consequently, naturally derived phytochemicals and bioactive compounds, with antimicrobial and antioxidant properties, becoming essential in food packaging field. Full-Text PDF Open Access
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 | 0.006 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.389 | 0.083 |
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".