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Record W7163526237 · doi:10.26108/dn27-zt83

Mercury in soil horizons from southwestern Nova Scotia: relationships with vegetative bioindicators and mineralogy

2025· other· en· W7163526237 on OpenAlexaboutno aff
Hayley Newell

Bibliographic record

VenueAcadiaU-DEV · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)TransectSoil waterBioindicatorSoil testBedrock

Abstract

fetched live from OpenAlex

Mercury (Hg) is a highly toxic mobile element which has organometallic forms (e.g. methyl mercury - MeHg) which bioaccumulate and biomagnify in food webs. Southwestern Nova Scotia is a known hotspot for Hg bioaccumulation. There is extensive research on mercury in birds, fish, invertebrates, and water; however, there is less data on Hg in soils and plants. This study examines total mercury (THg) and mineralogy of soils as well as previously obtained THg in lichens. Samples were taken from 16 sites along a transect through southwestern Nova Scotia to examine relationships between THg in soils, soil mineralogy, and THg in lichens. Soil samples from the O, A, B, and E (if present) horizons were dried, sieved to a silt/clay fraction and analyzed for THg and loss on ignition (LOI) using thermal pyrolysis atomic absorption spectroscopy. Soil mineralogy of the A, B, and E horizons from seven sites with different bedrock were analyzed using scanning electron microscopy. The soil THg data was examined using statistical analyses to test for significant differences between horizons and for correlations with soil mineralogy, and lichen THg. Preliminary results show that THg in soil ranges between 2.2 ppb and 323.9 ppb and appeared to be broadly correlative with THg in lichen. This work will help to clarify the relationship between soil mineralogy and THg soil and lichen, and relationships with bioindicators in SW Nova Scotia.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.006

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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.

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
Published2025
Admission routes1
Has abstractyes

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