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Record W3157492426 · doi:10.1017/qua.2021.22

Ecohydrological controls on apparent rates of peat carbon accumulation in a boreal bog record from the Hudson Bay Lowlands, northern Ontario, Canada

2021· article· en· W3157492426 on OpenAlexaffabout
Marissa A. Davies, Jerome Blewett, B. David A. Naafs, Sarah A. Finkelstein

Bibliographic record

VenueQuaternary Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMacrofossilPeatBogTestate amoebaeHoloceneGeologyWetlandOmbrotrophicBorealPhysical geographyTemperature recordBayOceanographyEnvironmental scienceEcologyPaleontologyGeography

Abstract

fetched live from OpenAlex

Abstract A multiproxy Holocene record from a bog in the Hudson Bay Lowlands, northern Ontario, Canada, was used to evaluate how ecohydrology relates to carbon accumulation. The study site is located at a somewhat higher elevation and on coarser grained deposits than the surrounding peatlands. This promotes better drainage and thus a slower rate of carbon accumulation relative to sites with similar initiation age. The rate of peat vertical accretion was initially low as the site transitioned from a marsh to a rich fen. These lower rates took place during the warmer temperatures of the Holocene thermal maximum, confirming the importance of hydrological controls limiting peat accretion at the local scale. Testate amoebae, pollen, and plant macrofossils indicate a transition to a poor fen and then a bog during the late Holocene, as the carbon accumulation rate and reconstructed water table depth increased. The bacterial membrane lipid biomarker indices used to infer paleotemperature show a summer temperature bias and appear sensitive to changes in peat type. The bacterial membrane lipid biomarker pH proxy indicates a rich to a poor fen and a subsequent fen to bog transition, which are supported by pollen, macrofossil, and testate amoeba records.

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 score0.034
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.325
Teacher spread0.237 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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