Bibliographic record
Abstract
The mixedwood forest is a broad transitional area of intermingled deciduous and ever-green coniferous forest found along the southern extent of the boreal forest in western Canada. Extensive timber harvesting is a recent development in the mixedwood region. This thesis contributes to the scientific foundation of sustainable forest management in the mixedwood by describing the natural dynamics of a 73,000 km2 study area in northeastern Alberta. Large scale patterns in species composition and in the age and size structures of forest stand populations show that the forest in not an equilibrial, or shifting mosaic steady state, landscape. The age structure of the forest and the relative abundances of aspen and white spruce are not constant over time. Trends in fire size distributions and in rates of fire extinguishment, and a re-gression of monthly area burned on monthly weather, imply that fire suppression has reduced the mean proportional area burned per annum (r) by a factor of three since the late 1970s, but had negligible effect before 1960. Statistical models of fire size and composition, developed from mapped fires, predict the total size and composition of all known fires from 1940-1993. Over this interval, and corrected for fire suppression, r — 0.004, or one fifth the previously accepted rate. I conclude that the forest is older than generally believed. Extreme fire years and "background" years are of approx-imately equal importance, in terms of total area burned, but fire years more strongly influence large scale landscape pattern. The rate r varies with forest cover type, from 0.0027 for aspen to 0.007 for some coniferous types. I conclude that fire hazard increases along some successional pathways, which may be the mechanism by which landscape disequilibrium is maintained. Accumulated adjacent stem mortality forms small canopy gaps in young aspen stands. The gaps exhibit elevated recruitment of aspen and other shade-intolerant hardwoods. The age and size structures of older aspen stands, and an analysis of extensive forest inventory data, show that uneven-aged aspen stands are abundant, a finding consistent with gap dynamics. Simulation studies, and the statistics of some age-verification data, show that unexpectedly old aspen stands probably exist, which is consistent with the analysis of fire history. In these stands, the mean or maximum age of canopy trees are negatively biased estimators of the time that has elapsed since a stand-initiating fire.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".