MétaCan
Menu
Back to cohort
Record W1998655018 · doi:10.1073/pnas.0806481105

Recent climate extremes alter alpine lake ecosystems

2008· article· en· W1998655018 on OpenAlexaffabout
Brian Parker, Rolf D. Vinebrooke, David W. Schindler

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Alberta
Fundersnot available
KeywordsEnvironmental scienceEcosystemPhytoplanktonBiomass (ecology)Lake ecosystemSnowmeltEcologyBiodiversityAquatic ecosystemClimate changeLimnologyOceanographyNutrientBiologySurface runoffGeology

Abstract

fetched live from OpenAlex

Here, we show that alpine lake ecosystems are responsive to interannual variation in climate, based on long-term limnological and meteorological data from the Canadian Rockies. In the 2000s, in years with colder winter temperatures, higher winter snowfall, later snowmelt, shorter ice-free seasons, and dryer summers, relative to the 1990s, alpine lakes became clearer, warmer, and mixed to deeper depths. Further, lakes became more dilute and nutrient-poor, the latter leading to significant declines in total phytoplankton biomass. However, increased concentrations of dissolved organic carbon in lake water stimulated the appearance of small mixotrophic algal species, partially offsetting the decline in autotrophic phytoplankton biomass and increasing algal species richness. The climate regime in the 2000s altered the physical, chemical, and biological character and the function of high-elevation aquatic ecosystems. Forecasts of increased climatic variability in the future pose serious ramifications for both the biodiversity and ecosystem function of high-elevation lakes.

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 score0.082
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.266
Teacher spread0.228 · 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

Citations164
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207