How Should We Be Determining Background and Baseline Antibiotic Resistance Levels in Agroecosystem Research?
Bibliographic record
Abstract
Although historically, antibiotic resistance has occurred naturally in environmental bacteria, many questions remain regarding the specifics of how humans and animals contribute to the development and spread of antibiotic resistance in agroecosystems. Additional research is necessary to completely understand the potential risks to human, animal, and ecological health in systems altered by antibiotic-resistance-related contamination. At present, analyzing and interpreting the effects of human and animal inputs on antibiotic resistance in agroecosystems is difficult, since standard research terminology and protocols do not exist for studying background and baseline levels of resistance in the environment. To improve the state of science in antibiotic-resistance-related research in agroecosystems, researchers are encouraged to incorporate baseline data within the study system and background data from outside the study system to normalize the study data and determine the potential impact of antibiotic-resistance-related determinants on a specific agroecosystem. Therefore, the aims of this review were to (i) present standard definitions for commonly used terms in environmental antibiotic resistance research and (ii) illustrate the need for research standards (normalization) within and between studies of antibiotic resistance in agroecosystems. To foster synergy among antibiotic resistance researchers, a new surveillance and decision-making tool is proposed to assist researchers in determining the most relevant and important antibiotic-resistance-related targets to focus on in their given agroecosystems. Incorporation of these components within antibiotic-resistance-related studies should allow for a more comprehensive and accurate picture of the current and future states of antibiotic resistance in the environment.
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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 | 0.448 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.022 | 0.048 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".