Building the GLENCOE Platform -Grasslands LENding eConomic and ecOsystems sErvices
Bibliographic record
Abstract
To feed the rising population whilst also preserving ecosystem functions, creative solutions are needed for the ecological intensification of natural grassland-based livestock systems. In Uruguay, natural grasslands are the main nutritional resource for livestock production. In these ecosystems, cattle and sheep graze together all the year round, and grasslands are frequently heavily grazed. Considerable research has been generated concerning grassland management, but there is still no knowledge about the impact of decision rules that supports management actions on long-term ecosystem functioning, at the system level. To meet this deficit, a participatory working group of farmers, researchers, and consultants have developed the GLENCOE platform. This platform is a large-scale facility, supported by INIA-Uruguay, designed to answer the following question: How to intensify the grazing management to improve the sustainability of livestock systems based on natural grasslands? To build the platform three steps were followed: (I) definition of the research problem using a problem tree analysis; (ii) conceptualization of the platform and the design of the grazing systems to be evaluated; and, (iii) spatial allocation of the grazing systems according to the variability of soil, slopes, and seasonal dynamic of vegetation indexes. These criteria were considered across farmlets that were equivalent in the initial stage, allowing causal inferences for the systems trajectories on productive and environmental traits. The platform is composed of three independent farmlets of 50 ha each, where multiparous Hereford cows and Merinos wethers co-graze under three grazing management systems. Each farmlet is managed according to different spatio-temporal decisions of the specific management of vegetation communities, grazing methods, and the stockpile of forage that is allowed by the number of the existing paddocks. Farmlet-1; comprises less decisions (2 paddocks), Farmlet-2; intermediate (8 paddocks), and Farmlet-3; high level of decisions (32 paddocks). This innovative platform will be used as a participatory and interdisciplinary space for research and co-learning of management on processes that can only be observed in long-term evaluations, and at farmlet scale. We expect that this new approach will contribute to the developement and implemention of sustainable grazing management systems in Uruguay.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".