Spatial analysis in a successional perspective : a boreal mixedwood landscape in northeastern British Columbia
Bibliographic record
Abstract
Digital elevation models (DEMs), multitemporal Landsat TM images and forest inventory maps were use to study the spatial patterns of boreal mixedwoods in the Boreal White and Black Spruce zone of British Columbia. A Bayesian classification method was developed to produce a high resolution map of posterior probabilities of forest cover classes. The procedure employed the forest inventory maps as prior probabilities, while evidence was provided by the results of a maximum likelihood classifier applied to the satellite images. Topographic parameters, derived from the DEMs by fitting a conic surface equation, were used in a fuzzy-logic based classification to produce maps of terrain classes. A filtering technique, expressly developed to mitigate the systematic errors present, was applied to the DEMs before the classification. The resulting maps were employed to investigate the relations between terrain and vegetation using an appropriately modified version of Ivlev's electivity index. The analysis suggests that topography plays a role in the distribution of the different types of boreal mixedwood forest stands. As expected, black spruce (Picea mariana (Mill.) B.S.P.) and tamarack (Larix laricina (Du Roi) K. Koch) dominated stands were strongly associated with flat areas. Hardwood dominated stands were found significantly associated with convex slopes and ridges, white spruce (Picea glauca (Moench) Voss) dominated stands positively were associated with concave slopes and channels, and mixed stands were associated with neutral slopes. The forest cover maps were used to study the distribution of mixedwood and hardwood stands in relation to white spruce stands. The analysis employed a logistic regression model where topographic variables and stand age were used as covariates. The likelihood of a site being occupied by hardwoods instead of mixedwoods was found to be positively correlated with increasing distance from white spruce stands. This result suggests that the rates or the outcomes of succession in the mixedwood forest are influenced by the spatial arrangement of different stand types at the landscape level. The most likely explanation is the control of seed dispersal on white spruce seed availability in the landscape, although a direct causation cannot be inferred from this correlation analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".