Replication Data for: Final report: Year one: G19-04A – Greens height soil surfactant study – Calcareous sand rootzone G19-04B – Greens height soil surfactant study – Siliceous sand rootzone
Bibliographic record
Abstract
This project compares three products from the Aqua-Aid family of soil surfactants and soil enhancers that Ontario Seed Company currently has in their commercially available turf products line-up. These products were compared with an industry standard wetting agent, Revolution, from Aquatrols as a positive control in the study. There was also an untreated control included in the study. The projects were conducted on two newly established putting greens. Project A) was conducted on a putting green constructed from calcareous USGA sand at the new Guelph Turfgrass Institute site. Project B) was completed on a putting green constructed from siliceous sand conforming to the United States Golf Association (USGA) construction method. 2019 results provide a preliminary look at the function/efficacy of soil surfactants, with the goal of examining their long-term effects on rootzone and turfgrass properties over subsequent seasons. Current and future results may also be used by the sponsor for marketing of these products in the Canadian market.
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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.062 |
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".