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Record W3138199239 · doi:10.1139/cjb-2020-0196

Diatoms and other siliceous indicators track the ontogeny of a “bofedal” (wetland) ecosystem in the Peruvian Andes

2021· article· en· W3138199239 on OpenAlexafffundvenue
Connor King, Neal Michelutti, Carsten Meyer‐Jacob, Richard Bindler, Pedro M. Tapia, Christopher Grooms, John P. Smol

Bibliographic record

VenueBotany · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeatSubfossilWetlandDiatomEcosystemEcologyClimate changeRadiocarbon datingBioindicatorPhysical geographyEnvironmental scienceHoloceneGeologyBiologyGeographyOceanographyPaleontology

Abstract

fetched live from OpenAlex

Recent warming in the Andes is affecting the region’s water resources including glaciers and lakes, which supply water to tens of millions of people downstream. High-elevation wetlands, known locally as “bofedales”, are an understudied Andean ecosystem despite their key role in carbon sequestration, maintenance of biodiversity, and regulation of water flow. Here, we analyze subfossil diatom assemblages and other siliceous bioindicators preserved in a peat core collected from a bofedal in Peru’s Cordillera Vilcanota. Basal radiocarbon ages show the bofedal likely formed during a wet period of the Little Ice Age (1520–1680 CE), as inferred from nearby ice core data. The subfossil diatom record is marked by several dynamic assemblage shifts documenting a hydrosere succession from an open-water system to mature peatland. The diatoms appear to be responding largely to changes in hydrology that occur within the natural development of the bofedal, but also to pH and possibly nutrient enrichment from grazing animals. The rapid peat accretion recorded post-1950 at this site is consistent with recent peat growth rates elsewhere in the Andes. Given the many threats to Peruvian bofedales including climate change, overgrazing, peat extraction, and mining, these baseline data will be critical to assessing future change in these important ecosystems.

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 score0.019
Threshold uncertainty score0.038

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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