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Record W4410823923 · doi:10.1016/j.agsy.2025.104371

Analyzing the factors driving the adaptability and robustness of mixed ruminant herds in grassland systems

2025· article· en· W4410823923 on OpenAlex
Thomas Puech, Fabien Stark, Rodolphe Sabatier

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgricultural Systems · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsASTER
FundersInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementInstitut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture
KeywordsAdaptabilityRuminantGrasslandRobustness (evolution)HerdHerd behaviorBiologyEcologyGeographyHerdingPastureForestry

Abstract

fetched live from OpenAlex

CONTEXT Diversified systems that work to agroecology principles are a pathway worth exploring. However, the complexity of these systems and their high dependency on environmental conditions creates issues around the methods needed to assess them and the proxies needed to design them. OBJECTIVE The aim of this article is to characterize the robustness and adaptability of mixed herds in the face of environmental hazards. It also aims to identify the structural drivers (herd size and composition) of these two properties. METHODS Here we used the viability theory modelling approach calibrated on data from a long-term experiment to investigate the adaptability and robustness of mixed ruminant herds to meteorological and economic hazards, and their structural drivers. We applied our model to grass-based dairy-cattle and suckler-sheep herds. RESULTS AND CONCLUSIONS Results show that expected economic constraint is a determinant factor in the shape and composition of viable herds. Herd size and proportion of adult cattle in the herd are drivers of robustness in situations of uncertainty. The results also show that mixed herds are particularly valuable in situations with low economic requirements, especially in terms of herd adaptability to environmental hazards. SIGNIFICANCE Our results are consistent with existing mixed systems in western Europe but call for a change in the scale of analysis to include farm-level dynamics, associated management practices (land-use trade-offs, forage management, etc.) and uncertainties. This work questions the specialization of livestock farms and public policies to support agroecological transition and emergency aid for farmers.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.195
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it