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Record W2789683932 · doi:10.1080/15230430.2017.1414472

Changes in food web dynamics of low Arctic ponds with varying content of dissolved organic carbon

2018· article· en· W2789683932 on OpenAlexafffund
Heather Mariash, Matteo Cazzanelli, Milla Rautio, Ladislav Hamerlík, Matthew J. Wooller, Kirsten Christoffersen

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à ChicoutimiEnvironment and Climate Change Canada
FundersCanada Research Chairs
KeywordsDissolved organic carbonFood webEnvironmental scienceBenthic zoneAquatic ecosystemEcosystemArcticEcologyPelagic zonePrimary productionBiology

Abstract

fetched live from OpenAlex

An influx of terrestrial dissolved organic carbon (DOC) into freshwater habitats can regulate a range of ecosystem characteristics, from water clarity to productivity. To understand the extent to which DOC can regulate ecosystem functioning, we conducted a survey to determine the source of DOC in low Arctic ponds close to the Arctic Circle (Kangerlussuaq, Greenland), including its role in food web dynamics. We used a multiple element (carbon, nitrogen, and hydrogen) stable isotope approach to examine the proportional contribution of different sources to aquatic consumers in nine arctic ponds that spanned a broad gradient of DOC (6.6–60.1 mgL-1). Our results show that benthic and pelagic primary production decreased along a gradient of increasing DOC content. Additionally, the changes in the organic matter pool with increasing DOC translated into changes in consumer resource use. We found significant differences in resource use between species. All consumers relied on benthic autotrophic material when DOC was low; but when DOC was high some consumers changed their diet. Collectively, our findings demonstrate how the concentration of DOC influences aquatic production and our study can be used as a baseline to predict how the aquatic food web may respond to regionally changing DOC concentrations.

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)0.001
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 score0.187
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.250
Teacher spread0.214 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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