Revisiting the work of Fred von Althen – an update on the growth and yield of a mixed hardwood plantation in Southern Ontario
Bibliographic record
Abstract
Dr. Fred W. von Althen, formerly of the Canadian Forest Service, established hundreds of research plantations with a focus on identifying tree species and silvicultural techniques conducive to successful hardwood afforestation in southern Ontario, Canada. Here we provide an update, using 30-year remeasurement data, on the growth and yield of one of his most productive, and compositionally unique, mixed hardwood plantations. At age 30, the plantation exhibited the following characteristics on average: 1) density of 790 stems/ha—reduced from 2222 stems/ha at time of planting through natural mortality; 2) height of 14.4 meters; 3) quadratic mean diameter of 20.1 cm; and 4) gross stand volume of 181 m3/ha. With a mean annual increment (MAI) of 6.1 m3/ha/year, this plantation exhibits one of the highest published growth rates for mixed hardwoods in temperate North America. There was considerable variation in growth and yield between the 10 hardwood species making up the stand—silver maple, white ash, and black walnut had the highest growth rates, and red and white oak the lowest. Several Carolinian species, such as catalpa and sycamore, showed good growth rates, despite the study site being located north of their published range limits. This data set provides rare information on the growth and yield of mixed hardwood plantations in Canada. Key words: growth and yield, southern Ontario, afforestation, mixed hardwoods, Carolinian species
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".