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Record W1977998003 · doi:10.1002/rra.1283

Resource subsidies across the land–freshwater interface and responses in recipient communities

2009· article· en· W1977998003 on OpenAlexaff
John S. Richardson, Yixin Zhang, Laurie B. Marczak

Bibliographic record

VenueRiver Research and Applications · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRiparian zoneSTREAMSSubsidyResource (disambiguation)Plant litterEcologyEcosystemAquatic ecosystemInvertebrateEnvironmental scienceLitterInterceptionPopulationFreshwater ecosystemBiologyComputer scienceEconomicsHabitat

Abstract

fetched live from OpenAlex

Abstract Fluxes of resource subsidies, such as terrestrial leaf litter to streams and adult aquatic insects to riparian predators, are examples of important links between adjacent ecosystems. The importance of these cross‐ecosystem resource flows from donor systems to recipient consumers is increasingly recognized. Streams, especially small streams with their high edge ratio with the terrestrial system, provide excellent models for the study of subsidies and a large portion of this literature has been produced by aquatic scientists. Field experiments manipulating flows between small streams and their riparian areas (e.g. leaf litter, terrestrial invertebrates, and adult aquatic insects to riparian areas) have indicated that consumers in streams and riparian areas are highly dependent upon such subsidies and the value of the subsidies are further modified by patterns of retention and pathways of use. Experiments typically indicate rapid growth or demographic responses by consumers, indicating these populations are resource limited or at levels of incipient population limitation, and can capitalize on short‐term resource pulses. More press manipulations are still necessary to determine the dynamical consequences of subsidies for recipient communities. The nature of the subsidy (e.g. species of litter or invertebrates) and its timing are also important details that need further study. Finally, there are opportunities to consider the evolution of life cycle timing (modelling), interception strategies by recipient populations and short‐term and long‐term responses of communities. Copyright © 2009 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.048
GPT teacher head0.348
Teacher spread0.300 · 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

Citations218
Published2009
Admission routes1
Has abstractyes

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