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Record W7028939280

Influence of lake volume on trophic position, carbon use, and resource partitioning in fish across a narrow range of ecosystem size

2023· dissertation· en· W7028939280 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelLittoral zoneFood webLake ecosystemTrophic state indexEcosystemRange (aeronautics)Species richnessCoregonus clupeaformisHabitat
DOInot available

Abstract

fetched live from OpenAlex

Lake size is an important factor governing seasonal variation in limnological phenomena, origin of nutrient sources, species interactions, cross-habitat linkages, and trophic pathways, all having complex influences on food web structure and function. Lake size effects are most clearly demonstrated across very wide gradients in surface area or volume. This approach incorporates several complicating and collinear elements such as changing fish assemblages and species richness, and therefore, incorporates additional but unaccounted shifts in food web structure and function. A comparison across a finer lake size gradient where fish assemblages and species richness change little or not at all is needed in order to understand the direct influence of lake size. Here, we quantified spring (May) and summer (August) food web metrics (trophic position, littoral carbon use, and resource partitioning) in six fish species across six lakes (volume = 8.7 x106 m3 to 814.5x106 m3) located in Algonquin Provincial Park (Ontario, Canada) using carbon (d13C), nitrogen (d15N), and sulfur stable isotopes (d34S) . Lake volume was the most important factor describing trophic position and littoral carbon use for all species across all six lakes, except cisco (Coregonus artedi) d34S. Relationships between food web metrics and lake size were not as strong as previous studies that looked at a wider range of lake sizes, and trophic position and littoral carbon use exhibited negative and positive relationships with lake size, respectively, contradictory to previous studies. Even though lake size was still the best predictor of feeding in these species, other lake characteristics, including amount of habitat (littoral:limnetic volume ratio) and nutrients (total phosphorus), were also significant; therefore, comparisons of similar sized lakes should use established relationships for a wider range of lake size with caution.

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 score0.180
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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