Unravelling the past to manage Newfoundland’s forests for the future
Bibliographic record
Abstract
The forests of Newfoundland represent a unique type of boreal ecosystem with diverse environmental gradients that exercise strong control over disturbances and vegetation. We have assembled and analyzed a comprehensive database on disturbance history in Newfoundland. Defoliating insects, led by the eastern spruce budworm (Choristoneura fumiferana Clemens) and the hemlock looper (Lambdina fiscellaria Guenée), have the largest disturbance footprint on the island. Infrequent wildfires (fire cycle = 769 years) had a decisive role in driving forest succession, particularly in the Central Newfoundland Forest and Maritime Barrens ecoregions. We hypothesize that the historical disturbance regime in Newfoundland would not have enabled steady-state conditions, although the amount of old-growth forests and deadwood would likely have been greater than it is today. We argue that the implementation of the natural range of variation (NRV) concept in forest management for such non-equilibrium systems will be challenging in Newfoundland and in other regions of Canada. We propose guiding principles to adapt the NRV concept using ecological knowledge. If a sciencebased approach is desired, assumptions about NRV should be tested using a rigorous experimental design. We encourage the establishment of large-scale experiments in at least a portion of forestry operations to enable an ecosystem sciencebased approach.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".