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Record W2921377069 · doi:10.1127/fal/2019/1187

Consumer responses to resource patch size and architecture: leaf packs in streams

2019· article· en· W2921377069 on OpenAlexaff
John S. Richardson, Éric Chauvet

Bibliographic record

VenueFundamental and Applied Limnology / Archiv für Hydrobiologie · 2019
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSTREAMSResource (disambiguation)ArchitectureEcologyEnvironmental scienceBiologyComputer scienceGeographyComputer network

Abstract

fetched live from OpenAlex

Accumulations of leaf litter in freshwaters can vary from individual leaves to large leaf packs. Past studies have demonstrated that decomposition rates decrease as leaf pack size increases. We considered a set of hypotheses that lower breakdown in larger leaf packs occurs as a consequence of diffusion gradients of oxygen and nutrients, or lower accessibility to larger detritivores to leaf tissue in the middle of these leaf packs. We manipulated leaf pack size in a stream to quantify decomposition rates and the abundances of consumers relative to the amount of detrital mass available. Mass loss rates of beech leaves were lower as leaf pack size increased. We found no differences in fungal biomass across our treatment gradients, or when comparing leaves in the middle of the leaf pack versus those on the outside. The lower decomposition of larger leaf packs would thus not result from a lower quality of the resource based on fungal biomass. Invertebrates per unit mass of leaf packs declined exponentially with size of leaf pack. Smaller invertebrates were less abundant per unit of resource as leaf pack size increased, but abundances of larger invertebrates declined even more dramatically than that of smaller ones. The results are consistent with accessibility within leaf packs decreasing as leaf pack size increases, a factor that is important for the estimation of consumer-resource functions for this patchy resource.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score1.000

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 science0.0010.002
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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