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Record W2033557946 · doi:10.1139/b03-036

Rates of peat accumulation during the postglacial period in 32 sites from Alaska to Newfoundland, with special emphasis on northern Minnesota

2003· article· en· W2033557946 on OpenAlexvenueaboutno aff
Eville Gorham, Joannes A. Janssens, Paul H. Glaser

Bibliographic record

VenueCanadian Journal of Botany · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsPeatTransectBogPhysical geographyPeriod (music)MireRange (aeronautics)BorealHydrology (agriculture)GeologyEnvironmental scienceGeographyOceanographyArchaeology

Abstract

fetched live from OpenAlex

We examined long-term rates of dry peat accumulation in 32 14 C-dated cores from poor fens in Alaska, to bogs and fens in midcontinental North Dakota and Minnesota, to oceanic bogs in Maine and the Atlantic Provinces of Canada. Sites along this belt transect exhibit mostly linear relationships between cumulative mass and age. Long-term rates of peat accumulation range from 16 to 80 g·m –2 ·year –1 , with a median rate of 47 g·m –2 ·year –1 and a mean rate of 50 g·m –2 ·year –1 . Rate of accumulation is inversely correlated with mean annual precipitation, but is not correlated with the area of the peat basin, basal age, or mean annual temperature. Four of the five highest rates are from relatively dry midcontinental locations in North Dakota and Minnesota; the other is for a coastal site in Newfoundland. The two lowest rates are from extremely rainy sites on Pleasant Island in the Alaskan panhandle. Individual accumulation rates between adjacent dates are quite variable within the peat cores, and across the transect, they do not correlate significantly with immediately previous rates. The same is true of the four sites with the greatest numbers of dates. There is a small but significant negative correlation within the Red Lake Peatland.Key words: bog, fen, mire, North America, peatland.

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 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 score0.865
Threshold uncertainty score1.000

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)0.0010.000

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations101
Published2003
Admission routes2
Has abstractyes

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