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Record W1973833646 · doi:10.1139/x08-191

Stream temperature responses to partial-harvest logging in riparian buffers of boreal mixedwood forest watersheds

2009· article· en· W1973833646 on OpenAlexaffvenue
David P. Kreutzweiser, Scott S. Capell, Stephen B. Holmes

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsRiparian zoneLoggingEnvironmental scienceBorealCanopyTaigaClearcuttingSTREAMSRiparian forestHydrology (agriculture)Basal areaDisturbance (geology)ForestryEcologyGeologyGeographyHabitatBiology

Abstract

fetched live from OpenAlex

As part of a larger study to examine the operational feasibility, ecological benefits, and environmental impacts of partial-harvest logging in riparian buffers along boreal mixedwood forest streams, we determined the effects on summer stream temperatures. Three logged study reaches were compared with three reference reaches over two prelogging and two postlogging summers. Partial-harvest logging resulted in an average removal of 10%, 20%, and 28% of the basal area from riparian buffers at the three logged sites. At the two more intensively logged sites, there were small (<10%) reductions in canopy cover (P = 0.024) and no significant changes in light at stream surfaces (P > 0.18). There were no measurable impacts on stream temperatures at two of the three logged sites. At the most intensively logged site, daily maximum temperatures were significantly higher (∼4 °C) for about 6 weeks in the first summer after logging than in prelogging years or at the reference sites (P < 0.001). Temperature increases were attributed to a logging-induced temporary disruption of cool water inputs from ground disturbance in a lateral-input seep area. Our results indicate that partial-harvest logging in riparian buffers of boreal mixedwood forest streams can sustain effective canopy cover and mitigate logging-induced water temperature increases.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.287
Teacher spread0.264 · 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

Citations48
Published2009
Admission routes2
Has abstractyes

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