Field evaluation of wetting agent efficacy against localized dry spot, soil moisture, and root system growth on creeping bentgrass putting green turf - Norac
Bibliographic record
Abstract
The objective of this project was to determine efficacy of wetting agents on creeping bentgrass putting green turf on a high sand rootzone. The experiment was located on the alternative construction putting green at the GTI, which is a typical industry standard green of this type, with 30 cm of USGA specification rootzone mixture on a graded subsoil with tile drains but no gravel drainage layer. The green has developed hydrophobic layers in previous seasons. Environmental data collected included daily max/min air temperature, irrigation records (turfgrass site manager), rainfall and an initial soil analysis: macro and micronutrients, soil type, particle size, % organic matter. Turf health and uniformity were assessed with canopy reflectance measurements. Significant treatment effects on volumetric water content in the plots were also limited. The canopy reflectance data, which is indicative of photosynthetic activity and plant health, showed some clearer patterns. On the dates when the pattern was at its strongest, the order of NDVI values was: Respond 3 curative > NCF-091 2x preventative > NCF-091 1x preventative, NCF-091 1/2x curative > NCF-091 1/2x preventative, NCF-091 2x curative > Respond 3 preventative > NCF-091 1x curative > Control. Treatment effects on hydrophobicity of the soil as measured by the water drop penetration tests indicated some treatment effects, particularly at the higher rates of NCF-091 and shallower levels in the rootzone, but the background hydrophobicity of the rootzone remains very high and patchy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".