Innovative drugs, chemicals, and enzymes within the animal production chain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The alarming number of recently reported human illnesses with bacterial infections resistant to multiple antibacterial agents has become a serious concern in recent years. This phenomenon is a core challenge for both the medical and animal health communities, since the use of antibiotics has formed the cornerstone of modern medicine for treating bacterial infections. The empirical benefits of using antibiotics to address animal health issues in animal agriculture (using therapeutic doses) and increasing the overall productivity of animals (using sub-therapeutic doses) are well established. The use of antibiotics to enhance profitability margins in the animal production industry is still practiced worldwide. Although many technical and economic reasons gave rise to these practices, the continued emergence of antimicrobial resistant bacteria is furthering the need to reduce the use of medically important antibiotics. This will require improving on-farm management and biosecurity practices, and the development of effective antibiotic alternatives that will reduce the dependence on antibiotics within the animal industry in the foreseeable future. A number of approaches are being closely scrutinized and optimized to achieve this goal, including the development of promising antibiotic alternatives to control bacterial virulence through quorum-sensing disruption, the use of synthetic polymers and nanoparticles, the exploitation of recombinant enzymes/proteins (such as glucose oxidases, alkaline phosphatases and proteases), and the use of phytochemicals. This review explores the most recent approaches within this context and provides a summary of practical mitigation strategies for the extensive use of antibiotics within the animal production chain in addition to several future challenges that need to be addressed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it