Comparison of canonical correlation and regression based focal point seed zones of white spruce
Bibliographic record
Abstract
The focal point seed zone methodology determines spatially explicit areas of adaptive similarity for any selected geographic point and is used to match seed sources and planting sites. A total of 127 seed sources (provenances) of white spruce (Picea glauca (Moench) Voss) from Ontario and western Quebec were established at a greenhouse and in six field trials throughout Ontario. Growth and phenological variables were measured over three growing seasons. Two focal point seed zone methodologies were employed: (i) using models derived from principal components analysis (PCA) of biological response variables followed by multiple linear regression against climate variables and (ii) using models derived from canonical correlation analysis (CANCOR). While both approaches use climate data to model adaptive variation, CANCOR reduces the number of steps in the analysis by simultaneously finding the relationships of biological and climatic variables that maximize the covariance between the two data sets. Although more of the variation in adaptive biological traits was actually described by climate variables using the PCAregression approach, this method produced intuitively less realistic patterns. Both methods showed similar overall geographic trends, but the CANCOR method had a finer resolution, especially in southern Ontario, presumably due to statistical efficiency; growth was modeled by all climate variables.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".