Bibliographic record
Abstract
In Northern Finland (65º40'N), haulm killing is used in seed potato production primarily to regulate tuber size. The most common haulm killing method is mechanical-chemical. We studied the effects of mechanical and mechanical-chemical haulm killing methods on seed potato quality, comparing to natural haulm senescence (control). The timing of haulm killing (when no more than 5% of the crop tubers were over 50 mm in size) and the time between destruction and harvest (21–26 days) were similar to practices followed in seed-potato production. Matilda was the cultivar used. In this study, haulm killing clearly increased plant disease pressure, as black scurf (Rhizoctonia solani) could be seen in tubers whose haulm had been destroyed. Black scurf was also observed in mechanical haulm killing. The yield from naturally senesced haulms had less black scurf than the other treatments. In addition, when haulm senescence occurred naturally, crop yield and starch content were highest compared to other treatments.
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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) | 0.001 | 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".