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Use of an epiphytic lichen and a novel geostatistical approach to evaluate spatial and temporal changes in atmospheric deposition in the Athabasca Oil Sands Region, Alberta, Canada

2019· article· en· W2958687959 on OpenAlexfundaboutno aff
Matthew S. Landis, Shanti Berryman, Emily M. White, Joseph R. Graney, Eric S. Edgerton, William B. Studabaker

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
FundersWood Buffalo Environmental Association
KeywordsOil sandsLichenEnvironmental scienceDeposition (geology)Spatial variabilityHydrology (agriculture)TaigaAsphaltAtmospheric sciencesPhysical geographyGeologyEcologyForestryGeographyGeomorphologySediment

Abstract

fetched live from OpenAlex

Temporal and spatial atmospheric deposition trends of elements to the boreal forest surrounding bitumen production operations in the Athabasca Oil Sands Region (AOSR), Alberta, Canada were investigated as part of a long-term lichen bioindicator study. The study focused on eight elements (sulfur, nitrogen, aluminum, calcium, iron, nickel, strontium, vanadium) that were previously identified as tracers for the major oil sand production sources. Samples of the in situ epiphytic lichen Hypogymnia physodes were collected in 2002, 2004, 2008, 2011, 2014, and 2017 within a ~150 km radius from the center of surface oil sand production operations in the AOSR. Site-specific time series analysis conducted at eight jack pine upland sites that were repeatedly sampled generally showed significant trends of increasing lichen concentrations for fugitive dust linked elements, particularly at near-field (<25 km from a major oil sands production operation) sample locations. Multiple regional scale geostatistical models were developed and evaluated to characterize broad-scale changes in atmospheric deposition based on changes in H. physodes elemental concentrations between 2008 and 2014. Empirical Bayesian kriging and cokriging lichen element concentrations with oil sands mining, bitumen upgrading, coke materials handling, and limestone quarry/crushing influence variables produced spatial interpolation estimates with the lowest validation errors. Gridded zonal mean lichen element concentrations were calculated for the two comprehensive sampling years (2008, 2014) and evaluated for spatial and temporal change. Lichen sulfur concentrations significantly increased in every grid cell within the domain with the largest increases (44-88%) in the central valley in close proximity to the major surface oil sand production operations, while a minor nitrogen concentration decrease (-20%) in a single grid cell was observed. The areal extent of fugitive dust element deposition generally increased with significantly higher deposition to lichens restricted to the outer grids of the enhanced deposition field, reflecting new and expanding surface mining activity.

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)0.000
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 score0.764
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.191
Teacher spread0.175 · 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

Citations24
Published2019
Admission routes2
Has abstractyes

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