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Record W1877414403 · doi:10.1657/aaar0015-002

Recent Rates of Carbon Accumulation in Montane Fens of Yosemite National Park, California, U.S.A.

2015· article· en· W1877414403 on OpenAlexaboutno aff
Judith Z. Drexler, Christopher C. Fuller, James L. Orlando, Peggy E. Moore

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersNational Park ServiceU.S. Geological Survey
KeywordsPeatNational parkMontane ecologyBorealEnvironmental scienceBogTemperate climateCarbon fibersPhysical geographyGeographyForestryEcologyArchaeology

Abstract

fetched live from OpenAlex

Little is known about recent rates of carbon storage in montane peatlands, particularly in the western United States. Here we report on recent rates of carbon accumulation (past 50 to 100 years) in montane groundwater-fed peatlands (fens) of Yosemite National Park in central California, U.S.A. Peat cores were collected at three sites ranging in elevation from 2070 to 2500 m. Core sections were analyzed for bulk density, % organic carbon, and 210Pb activities for dating purposes. Organic carbon densities ranged from 0.026 to 0.065 g C cm-3. Mean vertical accretion rates estimated using 210Pb over the 50-year period from ∼1960 to 2011 and the 100-year period from ∼1910 to 2011 were 0.28 (standard deviation = ±0.09) and 0.18 (±-0.04) cm yr-1, respectively. Mean carbon accumulation rates over the 50- and 100-year periods were 95.4 (±25.4) and 74.7 (±17.2) g C m-2 yr-1, respectively. Such rates are similar to recent rates of carbon accumulation in rich fens in western Canada, but more studies are needed to definitively establish both the similarities and differences in peat formation between boreal and temperate montane fens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.220
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.118
GPT teacher head0.375
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes1
Has abstractyes

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