Growth Response of Eight Hardwood Species to Current and Past Climatic Variations Using Regression Models
Bibliographic record
Abstract
The impact of climatic variations on basal area growth of basswood (BA) (Tilia americana L.), American beech (BE) (Fagus grandi/olia Enrh.), bitternut hickory (BH) (Caria cordi/ormis (Wang.) K. Koch), largetooth aspen (LA) (Populus grandidentata Michx.), red maple (RM) (Acer rubrum L.), red oak (RO) (Quercus rubra L.), sugar maple (SM) (Acer saccharum Marsh.), and white ash (WA) (Fraxinus american aL.) was studied in a southern province of Quebec, Canada (45025 ' N, 73 0 57 ' W). In total, forty-eight climatic variations of precipitation (P) (13 variables), temperature (T) (13 variables), heat index (H), (11 variables), and evapotranspiration (11 variables) from the current (C) and past three years (PI, P2, & P3) were tested in regression models to find the best model of the relationship between those independent variables and the last ten years (1985-1994) of basal area growth of the species. Simple individual linear and second degree, mixed, and combination of multiple regression models were used to develop the best regression model for each tree species, separately. The best models explained 79% , 80% , 99% , 91% , 71%, 99% , 49% , and 98% of the total variance of the growth in BA, BE, BH, LA, RM, RO, SM and WA, respectively. The growth in BH, LA, RM, 1(0, SM, and WA were more associated with the previous year's climatic variations rather than the current year's. Bitternut hickory, LA, RM, SM, and WA growth were more related to the first year rather than the second or third preceding year variables. The June heat index of the third previous year of variables explained only 7% of the growth of white ash. It was concluded that the impact of climatic variables on tree growth may vary and may depend on the species and other unknown variables. Also, the results suggested that the first and second previous climatic variables have an important role on the growth of some species. American beech, BH, RO, and WA seem to be a good species to use for the study in dendrochronological and dendroclimatological studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".