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Record W2332087377 · doi:10.4081/jlimnol.2016.1373

Combining taxonomy and function in the study of stream macroinvertebrates

2016· article· en· W2332087377 on OpenAlexaboutno aff
Kenneth W. Cummins

Bibliographic record

VenueJournal of Limnology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeriphytonInvertebrateEcologyPredationBiologyHerbivoreTaxonomy (biology)HydrobiologyRiparian zoneHabitatAquatic environmentBiomass (ecology)

Abstract

fetched live from OpenAlex

Over the last fifty years, research on freshwater macroinvertebrates has been driven largely by the state of the taxonomy of these animals. In the great majority of studies conducted during the 2000s macroinvertebrates have been operationally defined by investigators as invertebrates retained by a 250 μ mesh in field sampling devices. Significant advances have been and continue to be made in developing ever more refined keys to macroinvertebrate groups. The analysis by function is a viable alternative when advances in macroinvertebrate ecological research is restricted by the level of detail in identifications. Focus on function, namely adaptations of macroinvertebrates to habitats and the utilization of food resources, has facilitated ecological evaluation of freshwater ecosystems (Functional feeding groups; FFG). As the great stream ecologist Noel Hynes observed, aquatic insects around the world exhibit similar morphologies and behaviors, even though they are in very different taxonomic groups. This is the basis for the FFG analysis that was initially developed in the early 1970s. FFG analysis applies taxonomy only to the level of detail that allows assignment to one of six FFG categories: scrapers adapted to feed on periphyton, detrital shredders adapted to feed on coarse (CPOM) riparian-derived plant litter that has been colonized by microbes, herbivore shredders that feed on live, rooted aquatic vascular plants, filtering collectors adapted to remove fine particle detritus (FPOM) from the water column, gathering collectors adapted to feed on FPOM where it is deposited on surfaces or in crevices in the sediments, and predators that capture live prey. The interacting roles of these FFGs in stream ecosystems were originally depicted in a conceptual model. Thus, there are a limited number of adaptations exhibited by stream macroinvertebrates that exploit these habitats and food resources. This accounts for the wide range of macroinvertebrate taxa in freshwater ecosystems found in different geographical settings that are represented by a much smaller number of FFGs. An example of the generality of the functional group concept is the presence of detrital shredders that are dependent upon riparian plant litter inputs being found in essentially all forested streams world-wide (e.g., across the USA and Canada, Chile, Brazil, West Africa, New Zealand, Australia, Japan, Thailand; Cummins, unpublished). Freshwater macroinvertebrate taxonomic determinations, especially at the species level, may be the best basis for developing specific indices of pollution (tolerance values in ecological tables). However, the FFG method appears to provide better indicators of overall freshwater ecosystem condition.

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)0.009
metaresearch head score (Gemma)0.015
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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.199
Teacher spread0.178 · 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

Citations67
Published2016
Admission routes1
Has abstractyes

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