Setting priorities for existing conservation needs of crayfish and mink
Bibliographic record
Abstract
Antimicrobial resistance (AMR) evolution and onward transmission of resistance genes is impacted by interrelated biological and social drivers, with evidence and impacts observed across human, animal and environmental One Health domains.Systems-based research examining how food production impacts on AMR in complex agrifood systems is lacking, with little written on management approaches in the UK that might prevent and respond to this challenge.One approach is the creation of a transdisciplinary network to enhance capacity, capability and collaboration between agrifood-focused disciplines and stakeholders.This co-creation platform for network-wide systems-based activities would reduce inefficiencies in AMR-related activities around agrifood, providing a cross-cutting, cohesive community to deliver transformational guidance on relevant, practical agrifood solutions that add value by reducing AMR, antimicrobial usage and associated costs, and decreasing resultant environmental contamination by prioritising challenges, sharing knowledge and best practice, and cocreating practical solutions with key stakeholders.An online survey determined prospective network focus, structure and priorities, with responses analysed using mixed methods.Survey results suggested respondents have interests in synthesising data using systems-approaches and using certain disciplines such as 'social sciences' within network activities.There were disconnects in how and whom to work with on this, with generalised use of 'social science/scientists' but lack of disciplinary understanding (e. g., anthropology, sociology) suggesting disciplinary differences awareness-training is useful.A similar generalisation is seen for mathematics/statistics.There are strong interests in working with food system practitioners (e. g., farmers/vets), providing opportunities for farm/field visits/knowledge exchange, and human health, reflecting the need for farm-to-fork understanding of impacts.There were notable mentions of policy/governance, emphasising translational research desires to create meaningful change.Disciplines/fields did not always
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".