Relationships between anatomical and densitometric characteristics of black spruce and summer temperature at tree line in northern Quebec
Bibliographic record
Abstract
Tracheid cell number, cell diameter, and cell-wall thickness of black spruce (Picea mariana (Mill.) BSP) tree rings at the tree line (northern Quebec, Canada) were measured using image analysis. The densitometric data from the same samples were used to evaluate the features of image analysis. The anatomical data were correlated with summer temperature variables, including: means of pentad temperature (five consecutive days), growing season (May- September), sum of degree-days, and number of frost-free days. Our results suggest that the main cause of the pale appearance of light rings is thinner latewood cell-wall thickness. Some latewood variables are strongly correlated with corresponding indices for ring cell number and diameter, and ring cell wall thickness. Anatomical ring cell number and the sum of cell diameters (ring widths) were correlated to tree-ring width parameters derived from densitometry. Ring cell number and annual sum of cell diameter also showed very similar trends with both chronologies, suggesting that ring-width length may depend on the number of cells within a ring. Ratio diagram of double cell-wall thickness to cell radial diameter showed similar trend to wood density profile. There is a statistically significant correlation between maximum density and the highest annual ratio between cell wall thickness and lumen diameter. Cell-wall thickness was significantly correlated to maximum density, and both were significantly correlated with summer temperature variables. Our results suggest that wood anatomy may be used as a substitute to densitometry for climate reconstruction as densitometric data require expensive equipments. Also the anatomical method allows the recording of intra-annual information for dendroecological purposes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".