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Record W1974213093 · doi:10.4319/lo.2001.46.7.1784

Climatic influences on algal populations of boreal forest lakes in the Experimental Lakes Area

2001· article· en· W1974213093 on OpenAlexafffundabout
D. L. Findlay, S. E. M. Kasian, M. P. Stainton, K. G. Beaty, M. Lyng

Bibliographic record

VenueLimnology and Oceanography · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsFisheries and Oceans Canada
FundersÚstav analytické chemie, Akademie Věd České RepublikyFisheries and Oceans Canada
KeywordsPhytoplanktonPhotic zoneEnvironmental scienceWater columnNutrientTaigaBiomass (ecology)EcologyOceanographyBorealStratification (seeds)Nutrient cyclePrecipitationAutotrophBiologyAgronomyGeographyGeology

Abstract

fetched live from OpenAlex

We examined long‐term phytoplankton data records for four oligotrophic boreal lakes situated in the Experimental Lakes Area (ELA), western Ontario, for responses to climatic change. ELA experienced a cyclical wet‐dry‐wet pattern from 1968 to 1998, with the early 1970s and 1990s having above‐average precipitation and the 1980s being a period of drought with a 2°C increase in air temperature. During this drought, the length of ice‐free season, duration of stratification, depth of the euphotic zone, and light extinction increased while precipitation and nutrient inputs to the lakes decreased. Phytoplankton assemblages of four study lakes were temporally coherent. During the drought, phytoplankton biomass and the number of phytoplankton species increased despite decreased nutrient inputs. There was a noticeable shift in species composition to greater abundances of dinoflagellates and large chrysophytes—mixotrophic species capable of cycling through the deeper, lower light, high‐nutrient waters, presumably to consume bacteria as an alternative to autotrophic production. These species have slow turnover times; therefore, suspended nutrients were held in the water column for a longer period of time. Phytoplankton photosynthesis was less responsive.

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 score0.008
Threshold uncertainty score0.258

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 head0.019
GPT teacher head0.249
Teacher spread0.231 · 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

Citations107
Published2001
Admission routes3
Has abstractyes

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