Bibliographic record
Abstract
Antimicrobial resistance has emerged in the public eye as a serious threat for humanity and livestock agriculture. Based on World Health Organization reports, antimicrobial resistance is estimated to cost our global medical sector over 100 trillion dollars and cause 10 million human fatalities per year by 2050. These numbers become even more ominous when you add livestock production to the equation—a production sector that has traditionally relied on the use of antibiotics without clearly understanding the impact on humans or the environment. It is clear that antibiotics have been misused in livestock agriculture globally and already some countries, such as Denmark and the Netherlands, have banned their use for specific applications in livestock agriculture, such as growth promotion. Multinational companies are also now marketing antibiotic-free animal products. All of these factors compound the pressure on our international livestock sectors to reduce antibiotic use and discover alternatives. Included in this issue of Animal Frontiers are six articles showcasing different approaches to reducing antimicrobial use in livestock production. The first review, by “Dr. David Speksnijder” from Utrecht University (Netherlands), is entitled “Reducing antimicrobial use in farm animals: How to support behavioral change of veterinarians and farmers.” Using a social science lens, this article describes the underlying social mechanisms that motivate farmers and veterinarians to change often long-standing practices—an essential component in reducing the use of antibiotics on the farm level. From this unique social science perspective, we then move to the latest revolution in livestock production animal research: the search for antibiotic alternatives. Since our livestock production sector has relied on antibiotic use in routine management protocols, it would be short-sighted to think they can be removed without replacing them with sound alternatives. This search for such sound alternatives has opened the floodgates over the past decade, with public, academic, and industry researchers all investing heavily in alternative approaches. To make sense of the huge amount of data being generated, “Dr. Tim McAllister” from Agriculture Canada summarizes the latest developments in antibiotic alternatives in his article, “Challenges of a one-health approach to the development of alternatives to antibiotics.” Several novel approaches to antibiotic alternatives are gaining some momentum, including antimicrobial peptides, bacteriophages, and immunized products—all of which are reviewed by “Dr. Li” from Zhejiang University (China) and “Dr. Marquardt” from the University of Manitoba (Canada). The last article in this issue, “Bacterial resistance to antibiotic alternatives: A wolf in sheep’s clothing?”, by “Dr. Ben Willing” from the University of Alberta (Canada), provides an interesting perspective on how these alternatives to antibiotics may also cause bacterial resistance. The overall goal of this issue of Animal Frontiers is to provide insight into emerging concerns around antimicrobial resistance and offer viewpoints from some of the leading researchers in the field on how to reduce antibiotic use in livestock agriculture. It is clear that the pressure to reduce antibiotic use will only increase in the future, so the need to critically evaluate new strategies at the farm level is imperative. The initial research findings showcased in this issue are encouraging and suggest that it is possible to reduce use through the implementation of new approaches and technologies. Finding solutions to antimicrobial resistance in livestock agriculture will not come from one technology, but an integrated approach involving many different levels of farm management, public policy, and industry commitment. Are you ready for the challenge? Michael Steele is an assistant professor at the University of Alberta, NSERC Industrial Research Chair in Dairy Cattle Nutrition and President of the Canadian Society of Animal Science (CSAS). He completed his Ph.D. at the University of Guelph and worked for Nutreco Canada Agresearch for 2 years prior to returning to academia at the University of Alberta as an NSERC Industrial Research Chair. He was recently awarded the CSAS Young Scientist Award and the Lallemand Award for Excellence in Dairy Nutrition Research. His current research focuses on the mechanisms that control gastrointestinal health and development in cattle.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.261 | 0.202 |
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".