Introduction to the virtual special issue monitoring ecological responses to air quality and atmospheric deposition in the Athabasca Oil Sands region the wood Buffalo environmental Association's Forest health monitoring program
Bibliographic record
Abstract
The expansion of oil sands resource development in the Athabasca Oil Sands Region in the early 1990's led to concerns regarding the potential ecological and health effects of increased emissions and deposition of acidic substances. Conditions attached to a 1994 approval for an oil sands facility expansion led to the creation of the Wood Buffalo Environmental Association, and its Terrestrial Environmental Effects Monitoring committee. This multi-stakeholder body was tasked with development and operation of an environmental (forest health) monitoring program for the detection of ecological responses to atmospheric emissions and deposition. Initially focused on acid deposition monitoring, jack pine forest, growing on sandy soils with limited acid buffering capacity, was selected as the receptor system. An initial set of 10 monitoring locations was established using the Canadian Acid Rain Network Early Warning System methodology (since increased to 27, with three lost to development). Ecological monitoring is on a 6-year cycle, with concurrent measures of soil, needle and lichen chemistry, and tree and understory condition, together with ongoing measurements of air quality and atmospheric deposition. Because jack pine forest edges facing the emissions sources were expected to be more exposed to acidic emissions, evaluation of stand edge monitoring locations began in 2008. Monitoring of a targeted suite of indicators began in 2012 at 25 jack pine stand edge monitoring sites. This special issue presents the results derived from biophysical sampling campaigns (1998 to 2013), coupled with ongoing ambient atmospheric, deposition and epiphytic lichen monitoring (data through 2017) and source apportionment studies, as well as papers contributed by others engaged in regional research and monitoring programs. The Forest Health Monitoring Program provides data supportive of regulatory and stakeholder evaluations of environmental quality, and is adaptive to new needs, extreme environmental events and technological development while providing continuity of monitoring.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".