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Record W2108104385 · doi:10.1093/plankt/fbs104

Evaluation of chitobiase-based estimates of biomass and production rates for developing freshwater crustacean zooplankton communities

2013· article· en· W2108104385 on OpenAlexaff
Akash R. Sastri, Philippe Juneau, Beatrix E. Beisner

Bibliographic record

VenueJournal of Plankton Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsBiomass (ecology)ZooplanktonProductivityPlanktonCrustaceanWater columnEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Increasing attention has been devoted to the development of alternative (often biochemical) methods for measuring crustacean zooplankton productivity because conventional methods are not globally applicable and rarely practical when community-level rates are required. Here we evaluate the chitobiase method as a rapid, routine and instantaneous method for measuring the productivity of freshwater crustacean zooplankton communities. Chitobiase, a moulting enzyme, is liberated into water following moulting and production rates are calculated by measuring its turnover rate in the water column. First, using literature-based instar- and stage-specific individual body mass values, we found a common relationship between post-moult body size (and individual chitobiase activity) and the biomass produced between successive moults for common freshwater groups. Secondly, using a time-series of weekly measurements in a North-Temperate lake, we found a good correspondence between the standing activity of chitobiase in the water column (CBANAT) and the biomass sampled by a plankton net and laser optical plankton counter (LOPC). Overall, however, CBANAT-based biomass more closely corresponded to LOPC-based biomass estimates. Lastly, depth-specific biomass production rates and daily production to biomass estimates varied positively with temperature. Daily production to biomass ratios also varied closely with predictions of a taxon-specific temperature-dependent model for freshwater zooplankton.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.125
GPT teacher head0.386
Teacher spread0.261 · 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 designBench or experimental
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

Citations18
Published2013
Admission routes1
Has abstractyes

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