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TRACKING LONG‐TERM CHANGES IN CLIMATE USING ALGAL INDICATORS IN LAKE SEDIMENTS

2000· article· en· W1988614095 on OpenAlexaff
John P. Smol, Brian F. Cumming

Bibliographic record

VenueJournal of Phycology · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsClimate changeProxy (statistics)Environmental sciencePhysical geographyEcologyPaleoclimatologyHindcastArcticClimatologyBiologyGeologyGeography

Abstract

fetched live from OpenAlex

Interest in climate change research has taken on new relevance with the realization that human activities, such as the accelerated release of the so‐called greenhouse gases, may be altering the thermal properties of our atmosphere. Important social, economic, and scientific questions include the following. Is climate changing? If so, can these changes be related to human activities? Are episodes of extreme weather, such as droughts or hurricanes, increasing in frequency? Long‐term meteorological data, on broad spatial and temporal scales, are needed to answer these questions. Unfortunately, such data were never gathered; therefore, indirect proxy methods must be used to infer past climatic trends. A relatively untapped source of paleoclimate data is based on hindcasting past climatic trends using the environmental optima and tolerances of algae (especially diatoms) preserved in lake sediment profiles. Paleophycologists have used two approaches. Although still controversial, attempts have been made to directly infer climatic variables, such as temperature, from past algal assemblages. The main assumption with these types of analyses is that species composition is either directly related to temperature or that algal assemblages are related to some variable linearly related to temperature. The second more commonly used approach is to infer a limnological variable (e.g. water chemistry, lake ice cover, etc.) that is related to climate. Although paleolimnological approaches are broadly similar across climatic regions, the environmental gradients that paleophycologists track can be very different. For example, climatic inferences in polar regions have focused on past lake ice conditions, whereas in lakes near arctic treeline ecotones, paleophycologists have developed methods to infer past lakewater‐dissolved organic carbon, because this variable has been linked to the density of coniferous trees in a drainage basin. In closed‐basin lakes in arid and semiarid regions, past lakewater salinity, which can be robustly reconstructed from fossil algal assemblages, is closely tied to the balance of evaporation and precipitation (i.e. drought frequency). Some recent examples of paleophycolgical work include the documentation of striking environmental changes in high arctic environments in the 19th century believed to be related to climate warming. Meanwhile, diatom‐based reconstructions of salinity (e.g. the Great Plains of North America and Africa) have revealed prolonged periods of droughts over the last few millennia that have greatly exceeded those recorded during recent times. Marked climatic variability that is outside the range captured by the instrumental record has a strong bearing on sustainability of human societies. Only with a long‐term perspective can we understand natural climatic variability and the potential influences of human activities on climate and thereby increase our ability to understand future climate.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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

Citations321
Published2000
Admission routes1
Has abstractyes

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