Trends in forest depletion, seed supply, and reforestation in Canada during the past four decades
Bibliographic record
Abstract
Progress in reforestation is reviewed and the results of a 1999 survey of seed collection and utilization are presented. The review includes forest depletion, regeneration and seed supply for Canada for the 1960–99 period. Depletion of stocked, timber-productive land has increased from less than 2 million ha annually to just under 2.5 million ha, primarily due to an increase in harvesting from about 800 000 ha to slightly over 1 million ha in recent years, and also due to larger fires during the last decade. The depleted area amounts to 1% of the total commercial forest. Natural regeneration consistently covered more than 60% of the depleted area. Artificial regeneration (seeding and planting) has increased dramatically from 86 000 ha per year in 1965 to 513 000 ha in 1990, and has since levelled off at about 460 000 ha. The seed supply situation has also improved greatly, primarily due to tree improvement that resulted in the establishment of seed orchards, which were expanded from 364 ha in 1981 to 3008 ha in 1995. New analytical biochemical methods suggest that in orchard seed, genetic variation and diversity are maintained.The seed survey, including 36 conifer and 29 broadleaf species, indicated that 2.5 billion seeds were collected in 1999 (which was not a good seed year) and 3.9 billion seeds were sown. These 3.9 billion seeds appear to be sufficient to again restock approximately 460 000 ha per year, i.e., the same area as regenerated annually in the 1995–99 period. Improved seed treatment and sowing methods appear to have made seedling production substantially more efficient during the last 20 years. The major species sown in 1999 were: Picea mariana (35%), Picea glauca (22%), Pinus contorta (13%), and Pinus banksiana (12%). For Canada as a whole, 25% of the seed came from seed orchards, but for the Maritime Provinces, Québec, and Manitoba this percentage ranged from 60 to 90%. Orchard production is still growing: British Columbia and Québec will produce 80% of their planting stock from it during the next decade, and New Brunswick and British Columbia are already harvesting seed from second-generation orchards. It is expected that the high quality of seed obtained from seed orchards will contribute significantly to the efficiency of the reforestation program and increase the value of future forests. Key words: forest depletion, reforestation, seed orchards, seed supply
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".