ection News / Nouvelles des sections
Bibliographic record
Abstract
IF/IFC "Tree of Life" Awards are nationally recognized awards to individuals who have made superior, dedicated or particularly effective contributions to sustainable resource management, forest renewal and sustained yield, or integrated management of forests and their intrinsic values.In a Section meeting in late October, in the presence of Executive Director John Pineau, Tree of Life Awards were presented to CIF/IFC members Len Suomu and Don Dickson.The following brief bios outline their outstanding careers.Len Suomu graduated from the Lakehead Technical Institute Forest Technician program in 1960 and went on to obtain a degree in Forest Management in 1963 from the University of New Brunswick.After working one year in Marathon for American-Can, he attended Teacher's College in 1965 to become a qualified teacher.However, teaching was not to be Len's career and he was hired by the Dryden Paper Co. in 1967 to be the company's first Silviculture Forester.Len particularly enjoyed this challenge and was one of the first foresters to successfully implement largescale scarification for natural and aerial seeding.He oversaw the early development of the Trout Forest and was instrumental in increasing the amount of land reforested by plantations.By many, Len is considered one of the pioneers of silviculture in Northwestern Ontario and most of the silviculture practices used today are a result of the successes of his silviculture program.Employed by the Dryden Paper Co. (and successive ownerships) throughout his career, Len held the positions of Operations Planner, Divisional Manager, Chief Forester and upon retirement in 1996, Resource Development Manager.Len continued to be active in forestry by doing consulting work on the Trout Forest and as a member of the Local Citizens Advisory Committee in Dryden.The CIF/IFC was important to Len and he served in the Lake of the Woods
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.266 | 0.154 |
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".