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Record W2917597463 · doi:10.1017/qua.2018.143

Ecology and paleoenvironmental application of testate amoebae in peatlands of the high-elevation Colombian páramo

2019· article· en· W2917597463 on OpenAlexfundno aff
Bing Liu, Robert K. Booth, Jaime Escobar, Zhiqiang Wei, Broxton W. Bird, Andrés Pardo, Jason H. Curtis, Jun Ouyang

Bibliographic record

VenueQuaternary Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersSouth China Normal UniversityFondations communautaires du CanadaInternational Development Research CentreLehigh UniversityNational Science Foundation
KeywordsTestate amoebaePeatHoloceneEcologyPaleoecologyBiologyGeologyPaleontology

Abstract

fetched live from OpenAlex

Abstract We investigated the ecology and paleoecology of testate amoebae in peatlands of the Colombian páramo to assess the use of testate amoebae as paleoenvironmental indicators. Objectives were to (1) identify environmental controls on testate amoebae, (2) develop transfer functions for paleoenvironmental inference, and (3) examine testate amoebae in a Holocene peat core and compare our findings with other proxy records. Results from 96 modern samples indicate that testate amoebae are sensitive to pH and surface moisture, and cross-validation of transfer functions indicates potential for paleoenvironmental applications. Testate amoebae from the Triunfo Peatland in the Central Cordillera provided a proxy record of pH and water-table depth for the late Holocene, and inferred changes were correlated with peat C/N measurements during most of the record. Comparison with a lake-level reconstruction suggests that at least the major testate amoeba–inferred changes were driven by climate. Our work indicates that testate amoebae are useful paleoenvironmental indicators in high-elevation tropical peatlands.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.400

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 studies0.0000.001
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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