Bibliographic record
Abstract
Grassland species are normally seeded in mixtures rather than monocultures. In theory, seeding rates for mixtures are simply a sum of the amount of pure live seed (PLS) of each seed lot in the mix, an amount sufficient to ensure establishment and survival of each species. Mixtures can be complex because of the number of species used (especially in conservation and reclamation programs) and variations in seed purity and seed size. Soil limitations and seeding equipment settings need to be considered and in Canada, a metric conversion may be required. All these conditions make by-hand calculations of mixtures containing more than 3 species tedious and complicated. Thus, in practice, agronomists and growers use simple rules to set rates. The easiest rule is to estimate the mixture’s components as a percentage by weight of a standardized total weight of the seed required (e.g. 10% of 10 kg/ha). The resulting errors can be observed in the predominance of thin stands, the unexpected dominance of small seeded species and the added costs of interseeding to compete with weeds and fertilizer to increase yield. The objective of this project was to develop a decision support tool, a seed mixture calculator to simplify conversion and improve the estimates of seed required for individual seeding projects.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".