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Record W4409704359 · doi:10.1080/20442041.2025.2497249

Long-term ecological stability in high mountain lakes of Costa Rica

2025· article· en· W4409704359 on OpenAlexafffund
Neal Michelutti, Carsten Meyer‐Jacob, Christopher Grooms, Gerardo Umaña‐Villalobos, Sally P. Horn, John P. Smol

Bibliographic record

VenueInland Waters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEcologyTerm (time)Environmental scienceEcosystemGeographyHydrology (agriculture)GeologyBiology

Abstract

fetched live from OpenAlex

Chirripó National Park (Costa Rica) contains an important mountain lake district that supplies water to downstream populations. Sediment records document remarkable stability in biotic assemblages over millennia, but the limnological response to the most recent decades of climate change is unknown. We assess ecological change in three lakes on the Chirripó massif by analyzing diatom assemblages in sediment cores spanning the past ∼100 years. Also, water chemistry samples and hourly depth-temperature profiles over a one-year period were compared to data from earlier studies. For all three study lakes, we recorded circumneutral, dilute, and oligotrophic conditions with near-continuous mixing water columns, matching earlier characterizations of the lakes from ∼50 years ago. The diatom profiles spanning the past century showed either stable assemblages or minor variations among the same dominant taxa. The lack of marked limnological and ecological change in the study lakes, compared to those documented in tropical mountain lakes globally, is explained by nearby meteorological records that show no significant warming trend over the past two decades and limited catchment disturbances from anthropogenic activities. These data qualify the study sites as “heritage lakes,” a concept developed to identify and protect rare aquatic ecosystems that are as close to pristine as possible.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.221

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)0.0000.000

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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