The economic impact of the 1998 ice storm on eastern Ontario woodlots: Case studies of red pine and white cedar
Bibliographic record
Abstract
This paper reports estimates of the economic costs of the 1998 ice storm at the enterprise and regional levels for owners of red pine (Pinus resinosa Ait.) and white cedar (Thuja occidentalis L.) woodlots. The results are based on alternative management regimes and response strategies and illustrates the broader issues currently discussed in forestry such as intensive silviculture and harvest practices. A partial capital budget approach was used to estimate representative per hectare losses for red pine and white cedar. Stochastic simulations and sensitivity analyses were used to examine the robustness of the estimates of economic damages. Per hectare losses for red pine ranged from $560 per hectare for minimal damage for a 25-year-old stand being managed under a target harvest regime to $13,236 per hectare for a 55-year-old stand subjected to severe damage and being managed under a Faustmann harvest regime. Total economic loss for red pine plantations is estimated to be between $21.2 and $32.5 million (1999 constant dollars) at the regional level. This estimate varies with the harvest regime being used. Per hectare losses for white cedar ranged from $307 per hectare for a 70-year-old stand suffering minimal damage and being harvested under a mean annual increment rule on site index 12 land to $1721 per hectare for a 70-year-old stand suffering severe damage and being managed under a mean annual increment rule on site index 10 land. The range of estimated aggregate losses for white cedar is larger than the range for red pine, extending from $3.56 million to $39.6 million with a mean estimate of $22 million (1999 constant dollars) for the mean annual increment harvest regime. Key words: partial capital budget, stochastic simulation, sensitivity analysis, natural disaster policy
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".