Natural revegetation of mining disturbances in the Klondike area, Yukon Territory
Bibliographic record
Abstract
Placer mining has taken place continuously since 1896 in the Klondike area of the Yukon Territory. Excavation of lower slopes and creek bottoms for mining has resulted in extensive areas of disturbed land. The purpose of this study is to identify and describe the spatial and temporal factors which influence successional trends in the natural revegetation of these areas. The study concentrates on identifying these factors and determining the degree to which each of them influences both total vegetation cover and dominant tree species. Plant communities ranging from 2 to 80 years in age are described on 67 sites disturbed by placer mining. Principal Components Analysis, a gradient analysis technique, is used to transform site environmental variables into single component scores. A series of regression analyses are then used to isolate the factors influencing vegetation patterns. Predicted and residual scores represent the influence of site age, site conditions, solar radiation, and other unidentified factors on vegetation abundance. Results show that the influence of environmental conditions at a site accounts for 48.8% of the variation in total vegetation cover. Local climate, represented by solar radiation values, explains another 9.8%. Only 9.2% of the variation is explained by site age, leaving 32.2% of the variation to be accounted for by unidentified factors which might include seed supply from adjacent vegetated areas, soil instability due to erosion, sampling error, and chance. Once the effects of site age and the residual factors are accounted for, vegetation cover and site conditions are significantly correlated. Soil drainage, soil macropore space and slope angle comprise the major environmental factors influencing vegetation development on disturbed land. This information is used to help define conditions in which present mining areas should be left, in order to promote optimal natural revegetation.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".