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Record W2011935053 · doi:10.1139/x03-030

Benthic microbial utilization of differential dissolved organic matter sources in a forest headwater stream

2003· article· en· W2011935053 on OpenAlexfundvenueno aff
David P. Kreutzweiser, Scott S. Capell

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
FundersCanadian Forest Service
KeywordsMesocosmBenthic zoneDissolved organic carbonEnvironmental scienceMicrobial population biologyOrganic matterEcologyTerrestrial ecosystemRespirationSoil waterSoil respirationAquatic ecosystemCarbon cycleEcosystemEnvironmental chemistryBiologyChemistryBotany

Abstract

fetched live from OpenAlex

Streamside mesocosm experiments were conducted in a low-order forest watershed to directly examine responses by microbial communities on standardized substrates to different terrestrial and aquatic sources of dissolved organic matter (DOM). Community respiration (oxygen uptake), microbial density (colony-forming units on agar plates), leaf decomposition, and community metabolic profiles (metabolism patterns in sole carbon source utilization assays) were measured. Stream benthic microbial communities responded immediately and positively to increases in terrestrially derived DOM. Respiration activity and density estimates increased significantly, but there was no significant change in community metabolic profile. Responses were greater to DOM extracted from upper soil horizons than from deeper soils. Community respiration and bacterial abundance also increased in response to an aquatic DOM source, but were accompanied by a significant change in community metabolic profiles. Results provide direct experimental evidence that benthic microbial communities of forest headwater streams are able to rapidly utilize terrestrial DOM.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

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 head0.031
GPT teacher head0.247
Teacher spread0.216 · 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

Citations29
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicFreshwater macroinvertebrate diversity and ecologyFrench-language works237,207