Implications of an exceptional autumn bud flush on subsequent cold tolerance of Garry oak (<i>Quercus garryana</i>)
Bibliographic record
Abstract
In the fall of 2016, an unusual phenological event occurred in Quercus garryana Douglas ex Hook. in Victoria, British Columbia. After normal autumn leaf drop, some trees burst bud and leafed out prematurely in late October. This allowed a comparison of the cold hardiness of the prematurely flushed and non-flushed trees over the following year. Cold hardiness of five tree pairs (premature fall flush and non-flush) in three locations in Victoria was assessed bi-weekly over the dehardening period in January–March 2017 and again over the hardening period in September–December 2017. Cold hardiness of 10 non-flushed trees from the most northerly population of Q. garryana was also assessed twice in spring 2017. Between January and March, all trees dehardened, but cold hardiness was greatest in non-flushed trees on the first sampling date, and thereafter, the non-flushed trees dehardened more rapidly than the prematurely flushed trees. Index of injury was consistently 10% greater in Victoria than in northern trees. In fall 2017, trees that had flushed prematurely in fall 2016 had the same cold hardiness as non-flushed trees. Hardiness of all trees decreased from mid-September to the end of October, followed by rapid hardening in November and December of 2017.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".