Efficiency of night interruption treatments with red and far-red light-emitting diodes (LEDs) in preventing bud set in Norway spruce seedlings
Bibliographic record
Abstract
Terminal bud set can be prevented by interrupting night with short pulses of light when the natural photoperiod is too short to maintain growth. Norway spruce (Picea abies Karst.) seedlings originating from 61°N and 64°N were grown in growth chambers under conditions that mimic growth conditions in a heated greenhouse in early spring in Finland (experiment 1) or under constant growth conditions (experiment 2). The seedlings were exposed to the following night interruption (NI) treatments using light-emitting diodes (LEDs) that generated red (R, peak at 660 nm) and far-red (FR, peak at 735 nm) wavelengths in 20 s pulses at 15 min intervals: (i) red light alone (R); (ii) R combined with FR (R + FR); and (iii) control (no NI treatment). The R + FR treatment was more effective in preventing terminal bud set than the R treatment. Seedling responses depended on the provenance and growth conditions. The R treatment reduced the proportion of seedlings with terminal buds in the 61°N seedlings and delayed bud set in the 64°N seedlings. The fluctuating growth conditions or longer dark period between the photoperiod and NI treatments reduced the efficiency of the R + FR treatment. A combination of R and FR LEDs with adequate light intensity and duration is suitable for intermittent NI treatment in Norway spruce seedlings.
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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 | 0.001 |
| 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".