428 Platform Speaker: Nutrients and plant secondary compounds in pasturelands and their ecological services.
Bibliographic record
Abstract
Grazing-based livestock-production systems are between a rock and a hard place— where they experience increasing societal pressures to reduce environmental impacts in a world that demands increased and sustained productivity. Recent advances in understanding the nutritional ecology of herbivores may contribute to alleviate these seemingly contradictory endeavors. Forages are nutrition centers and pharmacies with vast arrays of primary (nutrients) and secondary (pharmaceuticals, nutraceuticals) compounds (PSC), which can provide multiple services vital for agroecosystems. Legumes with different types and concentrations of cell walls and with high concentrations of cell contents (e.g., birdsfoot trefoil, sainfoin, cicer milkvetch), coupled with different types and concentrations of PSC (e.g., hydrolizable and condensed tannins, terpenes) create a diverse foodscape with potential to enhance livestock nutrition, health and welfare relative to grasses, other legumes or pasture monocultures. In the process, livestock learn to forage these PSC-containing plants and their combinations, leading to reductions in methane and nitrogen emissions and to improvements in meat quality. Condensed tannins from sainfoin and saponins from alfalfa and from manure of cattle consuming these forages also reduce nitrogen mobilization in soils, reducing leaching and increasing plant-available nitrogen stores for future use. The challenge for future grazing-based livestock-production systems entails the provision of the “ideal” chemically diverse forages for a specific ecoregion in optimal temporal and spatial scales and sequences such that sustainability is achieved without compromising the ability to meet the production levels and ecological services described above.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.219 | 0.050 |
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".