Integrating the Rate of Moisture Loss into Needle Retention Testing to Improve the Selection of Balsam Fir (Abies balsamea) for Use as Christmas Trees
Bibliographic record
Abstract
A study was conducted to identify balsam fir (Abies balsamea) for use in Christmas tree breeding programs in Northeastern North America. A total of sixty-four phenotypically good trees were identified from a provincial clonal seed orchard in New Brunswick, a multi-aged natural seed production area in Nova Scotia, and an operational plantation in Quebec, Canada. Trees were selected for good growth and form, numbers, and vigour of internodal branches, acceptable crown taper, needle colour, age class, branch angle, needle configuration, and freedom from insects and diseases or deformities, all traits deemed desirable in a Christmas tree. Three branches from each tree were collected at two harvest dates each year, one early and one late during the normal harvesting season in 2020, 2021, and 2022. Qualitative assessments of colour and branch pliability were performed weekly in conjunction with needle loss rub tests. A quantitative assessment of moisture loss was conducted weekly by measuring branch weights. The combination of qualitative and quantitative assessments was used to identify 7 of the 64 trees for which one or more tested branches consistently remained green and pliable, retained the majority of their needles, and lost moisture at a significantly reduced rate in all six testing periods. Grafts of the seven ‘Select’ trees are currently being integrated into seed orchards in the three participating provinces. These second-generation seed orchards are part of breeding programs designed to develop Christmas trees that will perform well under warm fall conditions, an outcome of climate change. The results from this study combined with the progeny testing of the ‘Select’ trees will be used to test for the heritability of needle retention, a key trait for the next generation of Christmas trees in the Northeast.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".