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Record W4246451860 · doi:10.1139/f00-109

Stream temperature responses to forest harvest and debris flows in western Cascades, Oregon

2000· article· en· W4246451860 on OpenAlexvenueno aff
Sherri L. Johnson, Julia Jones

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceRiparian zoneHydrology (agriculture)BiotaAbiotic componentRiparian forestDebrisBiogeochemical cycleStream bedSTREAMSVegetation (pathology)SnagEcologyGeologyOceanographySedimentBiologyGeomorphology

Abstract

fetched live from OpenAlex

Stream temperature controls the rates of many biotic and abiotic processes and is influenced by changes in streamside land use practices. We compiled historic stream temperature data and reestablished study sites in three small basins in the H.J. Andrews Experimental Forest in the western Cascades, Oregon, to reexamine the effects on and recovery of stream temperatures following removal of riparian vegetation. Maximum stream temperatures increased 7°C and occurred earlier in the summer after clear-cutting and burning in one basin and after debris flows and patch-cutting in another. Diurnal fluctuations in June increased from approximately 2 to 8°C. Stream temperatures in both basins gradually returned to preharvest levels after 15 years. The influence of the primary factor controlling stream temperatures, shortwave solar radiation, was amplified following removal of riparian vegetation, and conduction between stream water and nearby soils or substrates also appeared to be an important factor. Shifts in the timing of summer maxima and greater increases in early summer stream temperatures could impact sensitive stages of aquatic biota.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations254
Published2000
Admission routes1
Has abstractyes

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