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Impact of sediment and nutrient inputs on growth and survival of tadpoles of the Western Toad

2008· article· en· W2117842003 on OpenAlexaff
Sylvia Wood, John S. Richardson

Bibliographic record

VenueFreshwater Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMesocosmMetamorphosisPeriphytonTadpole (physics)NutrientBiologySedimentExclosureLarvaEcologyAnimal scienceGrazing

Abstract

fetched live from OpenAlex

Summary 1. Sediment and nutrient loading in freshwater systems are leading causes of aquatic habitat degradation globally. We investigated the impacts of fine‐sediment and nutrient additions on the growth and survival of western toad ( Bufo boreas ) tadpoles and emergent metamorphs in mesocosm and exclosure experiments. 2. Mesocosm tanks received weekly pulses of fine sediments to create initial concentrations of 0, 130 and 260 mg L −1 of suspended sediment and either bi‐weekly additions of nutrients (N = 160 μg L −1 , P = 10 μg L −1 ) or no additions in a factorial design. Within mesocosms, tadpole exclosures allowed for quantification of tadpole grazing pressure on periphyton biomass, chlorophyll‐ a and sediment deposition. 3. Tadpoles receiving sediment additions experienced slower growth rates and reduced survival to metamorphosis, although no effects of treatment were detected on size at metamorphosis or time to metamorphosis. Nutrient additions also lowered survival, but had no impact on other measured parameters of tadpole fitness. Dissections and gut content analysis revealed that tadpoles ingested sediment in large quantities altering the proportion of the organic content of ingested food. 4. Together these results suggest that although sediment was readily consumed by tadpoles, its presence in the larval environment had an overall negative effect on tadpole growth and survival, although not as severe as predicted.

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)0.000
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 score0.058
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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