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Record W2764247344 · doi:10.1002/lol2.10050

Magnitude and regulation of zooplankton community production across boreal lakes

2017· article· en· W2764247344 on OpenAlexafffund
Nicolas Fortin St‐Gelais, Akash R. Sastri, Paul A. del Giorgio, Beatrix E. Beisner

Bibliographic record

VenueLimnology and Oceanography Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaGroupe de recherche interuniversitaire en limnologieUniversité du Québec à Montréal
KeywordsZooplanktonPlanktonBiomass (ecology)EcologyBorealEnvironmental scienceCommunity structurePopulationGeographyFisheryOceanographyBiologyDemographyGeology

Abstract

fetched live from OpenAlex

Abstract A major outstanding question in plankton ecology is whether the regulation of zooplankton production at the community level follows the same patterns that have been observed for individual populations. We used a novel biochemical approach to estimate in situ rates of crustacean zooplankton community production in 83 boreal lakes, with the objective of identifying the main drivers of zooplankton production at the community level across the boreal landscape. Our results show that the relationship of zooplankton community production to average community body size, total biomass, and temperature is comparable to what has been observed for individual populations. At the community level, however, there are additional drivers, including lake morphometry, and catchment properties, which further influence zooplankton production and which cannot be inferred from population level patterns.

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)0.000
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

Explore more

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