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Record W2098325788 · doi:10.4319/lo.2003.48.4.1633

Biotic, chemical, and morphometric factors contributing to winter anoxia in prairie lakes

2003· article· en· W2098325788 on OpenAlexafffundabout
Marianne E. Meding, Leland J. Jackson

Bibliographic record

VenueLimnology and Oceanography · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMacrophyteLittoral zoneEnvironmental scienceAnoxic watersPhytoplanktonBiomass (ecology)Hydrology (agriculture)PhosphorusChlorophyll aWatershedOceanographyWaves and shallow waterPlanktonAquatic plantHypoxia (environmental)ProductivityOxygenEcologyNutrientGeologyBiologyChemistry

Abstract

fetched live from OpenAlex

To assess how features of lakes and their watersheds influence winter oxygen decay rates and the frequency of anoxia in shallow prairie lakes, we measured lake and watershed characteristics for 21 south‐central Alberta lakes and related these to measured oxygen decay rates during 1998–2000. Oxygen decay rates were functions of macrophyte biomass, percentage littoral area, and total phosphorus and ranged from 0.006 to 0.216 mg O 2 m −3 d −1 . Oxygen decay rates were ~4 times higher in shallow polymictic lakes compared to deep, stratified lakes. Within shallow lakes, those classified as turbid had decay rates ~1.5 times higher than those classified as clear. Chlorophyll a was not a predictor of the oxygen decay rate in shallow lakes; however, macrophyte‐derived carbon averaged ~150 times more than phytoplankton‐derived carbon in the shallow lakes we examined. Reasons that lakes frequently or never become anoxic are related to productivity and morphometry; however, processes explaining occasional anoxia appear not to be related to factors we measured.

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 score0.009
Threshold uncertainty score0.444

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 studies0.0000.000
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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations56
Published2003
Admission routes3
Has abstractyes

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