Specific Kieselguhr Increased Hop Yield Over Several Years
Bibliographic record
Abstract
The yields of farm crops are strongly affected by rainfall. Cultivation methods that can alleviate rainfall shortage have been long awaited to ensure stable and sustainable crop production. Porous materials, such as kieselguhr and perlite, were applied during hop cultivation, and their contribution to yield and beer quality was analyzed. An increase of over 20% in yield was confirmed following the application of burnt coarse kieselguhr. This increase was sustained for at least 3 years from the time of burnt coarse kieselguhr application in the first year. Water content around the hop root was maintained at a high level after burnt coarse kieselguhr application. Therefore, it appeared that burnt coarse kieselguhr absorbs water in its porous space during rainfall and releases water to the plants thereafter, including during drought conditions. However, the quality of the hops was not changed following burnt coarse kieselguhr application. Burnt coarse kieselguhr application around the plant root is expected to alleviate the effect of climate change and contribute to the stable and sustainable production of various crops that need a constant water supply.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".