Mechanical Shaking and Baling of Balsam Fir Trees Influence Postharvest Needle Senescence and Abscission
Bibliographic record
Abstract
This study investigated the influence of both shaking duration and number of trees per bale on postharvest needle characteristics such as percentage needle loss, needle retention duration and explored the physiological roles of endogenous ethylene and volatile terpene compounds (VTCs). To accomplish these objectives, 25 six-year-old trees were detached and exposed to a range of shaking durations (0 to 60 sec.), and 30 six-year-old detached trees were exposed to baling treatments from 0 to 5 trees. Response variables measured were percent needle loss, needle retention duration, average water use, ethylene and volatile terpene compound evolution. Trees shaken for 60 seconds lost 16% less needle compared to control, which was consistent with the decrease in percent needle loss with increasing shaking duration. Baled trees lost 13% more needles compared to control, but percent needle loss was observed to decrease with increasing number of trees in a bale. These trends corresponded with increasing ethylene and VTC evolutions, where the longer the shaking duration or larger number of trees in a bale, the higher the ethylene and VTC evolutions. One can therefore draw inference that mechanical perturbation as a result of shaking and baling induce biosynthesis and regulation of ethylene and VTC in balsam fir trees in an effort to regulate postharvest needle abscission.
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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".