MétaCan
Menu
Back to cohort
Record W2063843171 · doi:10.1021/es100147j

Melting Alpine Glaciers Enrich High-Elevation Lakes with Reactive Nitrogen

2010· article· en· W2063843171 on OpenAlexaff
Jasmine E. Saros, Kevin C. Rose, David W. Clow, Verlin C. Stephens, Andrea B. Nurse, Heather A. Arnett, Jeffery R. Stone, Craig E. Williamson, Alexander P. Wolfe

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGlacial periodGlacierEnvironmental scienceBiogeochemical cycleDiatomLake ecosystemSpecies richnessSnowpackBiogeochemistryPhysical geographyEcosystemMeltwaterEcologyHydrology (agriculture)GeologySnowGeographyBiologyGeomorphology

Abstract

fetched live from OpenAlex

Alpine glaciers have receded substantially over the last century in many regions of the world. Resulting changes in glacial runoff not only affect the hydrological cycle, but can also alter the physical (i.e., turbidity from glacial flour) and biogeochemical properties of downstream ecosystems. Here we compare nutrient concentrations, transparency gradients, algal biomass, and fossil diatom species richness in two sets of high-elevation lakes: those fed by snowpack melt alone (SF lakes) and those fed by both glacial and snowpack meltwaters (GSF lakes). We found that nitrate (NO(3)(-)) concentrations in the GSF lakes were 1-2 orders of magnitude higher than in SF lakes. Although nitrogen (N) limitation is common in alpine lakes, algal biomass was lower in highly N-enriched GSF lakes than in the N-poor SF lakes. Contrary to expectations, GSF lakes were more transparent than SF lakes to ultraviolet and equally transparent to photosynthetically active radiation. Sediment diatom assemblages had lower taxonomic richness in the GSF lakes, a feature that has persisted over the last century. Our results demonstrate that the presence of glaciers on alpine watersheds more strongly influences NO(3)(-)concentrations in high-elevation lake ecosystems than any other geomorphic or biogeographic characteristic.

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 score0.054
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations133
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicCryospheric studies and observationsFrench-language works237,207