MétaCan
Menu
Back to cohort

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

2019· article· en· W2945861758 on OpenAlexafffundabout
Kenneth R. Foster, Carla Davidson, Rajiv N. Tanna, David C. Spink

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of CalgaryCumulative Environmental Management Association
FundersWood Buffalo Environmental AssociationAcademia Oamenilor de Știință din România
KeywordsOil sandsEnvironmental scienceAir quality indexDeposition (geology)Forest healthEnvironmental monitoringEnvironmental protectionEnvironmental engineeringGeographyAgroforestryMeteorologyGeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.267
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueThe Science of The Total EnvironmentSame topicHeavy metals in environmentFrench-language works237,207