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Record W2031608275 · doi:10.1111/fwb.12470

Modelling nutrient transport and transformation by pool‐breeding amphibians in forested landscapes using a 21‐year dataset

2014· article· en· W2031608275 on OpenAlexaboutno aff
Krista A. Capps, Keith A. Berven, Scott D. Tiegs

Bibliographic record

VenueFreshwater Biology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsLithobatesEcologyHabitatNutrientPopulationBiologyEphemeral keyEnvironmental scienceWetlandHydrobiologyAquatic ecosystemAmphibian

Abstract

fetched live from OpenAlex

Summary Migrations of animals can transfer energy and nutrients through and among terrestrial and aquatic habitats. Pool‐breeding amphibians, such as the wood frog ( Lithobates sylvaticus ), make annual breeding migrations to ephemeral wetlands in forest habitats in the eastern and midwestern United States and Canada. To model the influence of wood frogs on nutrient transport and transformation through time, we coupled long‐term population monitoring data (1985–2005) from a wood frog population with estimates of the elemental composition of wood frog egg masses and emerging juveniles. Over the 21‐year study period, 8.8 kg carbon (C), 2.0 kg nitrogen (N) and 0.20 kg phosphorus (P) were transported from the terrestrial to the aquatic habitat and approximately 21 kg C, 5.5 kg N and 1.2 kg P were exported to the surrounding terrestrial habitat by wood frogs. During the study period, the average net flux of C, N and P was from aquatic to terrestrial habitats, but the magnitude and direction of the net flux was element dependent. Thus, the net flux of C, N and P did not always flow in the same direction. Predicting long‐term trends in nutrient and energy flux by organisms with biphasic life cycles should rely on long‐term population data to account for temporal variability. This is especially true for organisms that are sensitive to long‐term shifts in temperature and precipitation patterns, such as amphibians that breed in ephemeral pools.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.781

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 studies0.0000.000
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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Citations77
Published2014
Admission routes1
Has abstractyes

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