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Record W2735393125 · doi:10.1002/ecs2.1886

Impacts of late‐Holocene climate variability and watershed‐lake interactions on diatom communities in Lac Brûlé, Québec

2017· article· en· W2735393125 on OpenAlexafffundabout
Karen Neil, Konrad Gajewski

Bibliographic record

VenueEcosphere · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiatomHoloceneSubfossilPaleolimnologyEcologyWatershedVarveEcological successionClimate changeEnvironmental scienceSedimentPhysical geographyGeologyOceanographyGeographyBiologyPaleontology

Abstract

fetched live from OpenAlex

Abstract A high‐resolution diatom analysis of a varved sediment sequence from Lac Brûlé, southwestern Québec, was used to study temporal patterns of environmental change in the late Holocene. Key periods of interest in the record included the Medieval Warm Period (~800–1300 CE ), the Little Ice Age (~1450–1850 CE ), and post‐European settlement (~1850–present). Subfossil diatom assemblages were compared to previously published pollen, cladocera, and sediment records from Lac Brûlé, revealing complex dynamics between terrestrial vegetation succession, nutrient fluxes, and trophic interactions. Generalized additive models showed a response to long‐term climate variability in the diatom record, although it was not the most influential driver of community changes at Lac Brûlé. Catchment‐mediated processes instead played the largest role in governing the structure of diatom assemblages in the lake. For example, nutrient loading following a local fire in the watershed at 1345 CE led to an abrupt and significant increase in Fragilaria spp. Human activity associated with deforestation and the Wallingford‐Back Mine (1924–1972 CE ) also had strong impacts on the landscape, which triggered further responses in the aquatic communities of Lac Brûlé.

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 categoriesInsufficient 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: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.996

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations6
Published2017
Admission routes3
Has abstractyes

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