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Record W3159734978 · doi:10.1139/cjb-2020-0158

Offsite effects of mining on the frequency and abundance of five understorey plant species in western Québec (Canada)

2021· article· en· W3159734978 on OpenAlexaffvenueabout
Rémi Boisvert, Xiangbo Yin, Nicole J. Fenton

Bibliographic record

VenueBotany · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsAbundance (ecology)UnderstoryEcologyBiologyEcosystemEcological nicheGeographyHabitatCanopy

Abstract

fetched live from OpenAlex

Although the mining industry provides minerals and metals to the global market and represents important economic opportunities, it also constitutes a major anthropogenic disturbance in the ecosystems where it takes place. However, little is known about its offsite impacts on plant communities. We assessed the frequency and the abundance of five common boreal understorey plant species (Coptis trifolia (L.) Salisb., Cornus canadensis L., Linnaea borealis L., Lysimachia borealis (Raf.) U.Manns & Anderb., and Maianthemum canadense Desf.) at different distances around six mines in Abitibi-Témiscamingue and Nord-du-Québec (Québec). Frequency and (or) abundance of Coptis trifolia and M. canadense decreased near mines, suggesting that they might be sensitive to the loss of forest cover, particle deposition, and soil contamination induced by mining. Conversely, the frequency and abundance of Cornus canadensis increased near active mines, which indicates its resilience to the stress caused by mines and its ability to exploit the niche space left by more sensitive species. No effects on Linnaea borealis and Lysimachia borealis were observed. Our study suggests the possible presence of offsite impacts of mines on plant communities. Further studies considering enigmatic impacts should be conducted in order to develop our understanding of the broader environmental footprint of 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.906

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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