Optimal white spruce breeding zones for Ontario under current and future climates
Bibliographic record
Abstract
Optimal breeding zones were developed for white spruce ( Picea glauca (Moench) Voss) in Ontario under present and future climate conditions to examine potential shifts due to climate change. These zones were developed by (i) determining a set of candidate breeding zones based on the relationship between measured performance variables and climate and (ii) employing a decision support model to select subsets of breeding zones that maximize geographic coverage subject to a constraint on the maximum number of zones. Current optimal breeding zones were based on 1961–1990 climate normals, and future breeding zones were based on three general circulation model (CGCM2, HADCM3, and CSIRO) predictions of 2041–2070 climate. Based on a maximum adaptive distance of 2.0 least significant difference values between sites within zones, 14 zones were required to cover the Ontario range of white spruce for the 1961–1990 data. Compared with breeding zones of other boreal conifers, current optimal breeding zones for white spruce were quite large, spanning up to 3° latitude and 10°–12° longitude and indicating large distances of effective seed transfer. Of the three general circulation models used to simulate future climate, HADCM3 B2 and CGCM2 B2 predicted 2041–2070 breeding zones that largely coincide with 1961–1990 zones. In contrast, CSIRO B2 indicated much narrower 2041–2070 breeding zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".