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Record W3157634314 · doi:10.1139/cjfas-2020-0427

The response of <i>Daphnia</i> to nutrient additions and kokanee abundance in Dworshak Reservoir, Idaho

2021· article· en· W3157634314 on OpenAlexvenueno aff
Sean M. Wilson, Matthew P. Corsi, Darren H. Brandt, Eric J. Stark

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersBonneville Power Administration
KeywordsDaphniaAbundance (ecology)PredationBiomass (ecology)NutrientBiologyZooplanktonLake ecosystemEcologyBranchiopodaFisheryBiomanipulationCladoceraPhosphorusEcosystemPhytoplanktonChemistry

Abstract

fetched live from OpenAlex

Daphnia are a keystone species in lentic systems worldwide and an important prey source for planktivorous fishes, such as kokanee (lacustrine Oncorhynchus nerka). Managers have added nutrients, nitrogen (N) and (or) phosphorus (P), in an attempt to improve the growth or carrying capacity for fish by increasing available prey, such as Daphnia. To be successful, this strategy requires that fish growth be prey limited, and prey availability be nutrient limited. In this study, kokanee in Dworshak Reservoir, Idaho fed preferentially on Daphnia ≥ 0.8 mm TL. The biomass of consumable Daphnia decreased with increasing kokanee abundance, indicating top-down control. However, the biomass of consumable Daphnia was greater at any given level of kokanee abundance when N was added to the reservoir, indicating bottom-up control. These results provide an example where Daphnia biomass was simultaneously controlled by both bottom-up and top-down forces, and that N addition alone can be an effective tool for increasing Daphnia biomass.

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)0.000
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 score0.076
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicAquatic Ecosystems and Phytoplankton Dynamics→French-language works237,207→