Analysis of the Interaction Between Timber Markets and the Forest Resources of Maine
Bibliographic record
Abstract
Abstract The abundant timber resources of Maine are critical to the State's timber economy; thus, when the 1995 forest inventory indicated a 20% decline in softwood growing stock, there was great concern by industry and government. Furthermore, declining near-term softwood growing stock levels were forecast. To better understand what was occurring in Maine's forest, we examined changes in composition and evaluated the relative impacts of harvesting versus growth and mortality. Much of the decline in spruce-fir inventory can be attributed to the budworm infestation of the 1970s and 1980s, although continued high utilization contributed to the decline. The high rate of softwood utilization was facilitated by low softwood timber prices due to increased supply from salvage cutting and high prices for softwood dimension lumber. The high price of dimension lumber also allowed the adoption of sawmill technology in Canada and Maine that used small-diameter logs, formerly consumed by the pulp industry, for lumber production. The increased demand for spruce-fir roundwood occurred during a period when changes in paper demand and pulping technology increased the demand for hardwood pulpwood. Unlike spruce-fir and hemlock, hardwood growing-stock volumes have increased steadily due to low utilization, high growth, and low mortality. Ample inventories of hardwoods have allowed increased volumes of these species to be used in the manufacture of pulp and engineered wood products. A recent partial forest survey of Maine indicated that spruce-fir growing stock inventory has stabilized as a result of regeneration of these species that began after the last spruce budworm infestation. North. J. Appl. For. 21(3):135–143.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".