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Record W2268218112 · doi:10.14288/1.0074574

Baseline soil composition data for the Delta Nature Reserve

2015· article· en· W2268218112 on OpenAlexaboutno aff
Emma Gosselin, Lauren Johnson, Sam MacKay, Megan A Reich

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsBaseline (sea)DeltaComposition (language)Nature reserveEnvironmental scienceForestryGeographyGeologyEngineeringArchaeology

Abstract

fetched live from OpenAlex

Coal trains are scheduled to pass through the Delta Nature Reserve in fall of 2015. The Burns Bog Conservation Society is concerned about the possible effects of coal contamination on the bog ecosystem. Due to this, baseline data on soil chemistry has been collected for the Delta Nature Reserve in Delta, British Columbia to study the current soil conditions. Soil samples were taken at 34 different sites around the reserve. The metal concentrations in the samples were determined using ICP-­‐MS analysis, and were compared to soil standards and concentrations found in coal. Carbon and Nitrogen isotopic values were determined using isotope ratio mass spectrometry. A spatial analysis was also conducted on the Delta Nature Reserve. The results showed that soil samples exceeded the standard concentrations for lead, arsenic, copper, zinc, selenium and cobalt in some locations, based on comparison to the Canadian Council of Ministers of the Environment soil standards for agriculture and residential areas. Results did not indicate current contamination from coal. From our analysis, it can be seen that arsenic, copper, and zinc concentrations are higher closer to the train track running along the northeastern side of the reserve. The baseline data and analysis will allow the Burns Bog Conservation Society to conduct future studies on soil contamination in the reserve. This baseline data is essential because it will allow for comparison between current and future soil content as development occurs around the bog.

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)0.001
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 score0.880
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.024
GPT teacher head0.214
Teacher spread0.190 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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