Natural jack pine forest edges are not an earlier warning system than interior forests to detect impacts of atmospheric deposition in the Athabasca Oil Sands Region
Bibliographic record
Abstract
The intense industrial development in the Athabasca Oil Sands Region (AOSR) in northern Alberta, Canada, emits gases and particles containing nutrients and trace elements that are deposited to the surrounding landscape (forest, lakes, fens, bogs). Long-term monitoring (at six-year intervals) of jack pine (Pinus banksiana Lamb.) stands within the AOSR, including interior and edge monitoring, aims to detect changes to biological, physical, and chemical indicators in response to this deposition. In this study, we compared edge and interior jack pine sites, assessing their potential roles as early warning systems to detect effects of deposition on jack pine forest in the AOSR, including whether differences might be detected at the edge prior to the interior (i.e., earlier warning system). We quantified foliage chemistry for current-year foliar growth, tree properties (height, diameter at breast height, and live crown ratio), and plant community composition, and the influences of distances from two sources of emissions (upgrader stacks and surface mining) and wind directionality. Understory vegetation was surveyed within established plots. Our results did not show a consistent pattern of edges detecting deposition effects earlier than interior sites. Instead, variables such as proximity to emission sources and wind directionality were more influential in capturing deposition impacts. While atmospheric deposition appears to affect forest sites in the AOSR, monitoring efforts to understand and quantify such effects may be better focused on these broader spatial and temporal factors rather than emphasizing forest edges.
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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 | 0.000 |
| 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) | 0.001 | 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".