MétaCan
Menu
Back to cohort
Record W341061320

The importance of temporal changes in gravel-stored fine sediment on habitat conditions in a salmon spawning stream

2006· article· en· W341061320 on OpenAlexaffabout
Ellen L. Petticrew, John F. Rex

Bibliographic record

VenueIAHS-AISH publication · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSedimentSpawn (biology)Environmental scienceOrganic matterInfiltration (HVAC)SettlingHydrology (agriculture)HabitatFisheryEcologyGeologyBiologyGeographyGeomorphologyEnvironmental engineeringGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Sediment (<2 mm and <75 μm) was collected in a productive sockeye spawning stream in northern British Columbia, Canada, using infiltration gravel bags from the pre-spawn through to the post-spawning period of 2002. As much of the gravel-stored fine sediment (<75 μm) exists as larger, aggregated particles composed of inorganic and organic matter, their quantity, structure, composition and settling behaviour were assessed. The goal was to evaluate the temporal changes in the gravel-stored fine sediment in the context of: (a) fish activity (i.e. active spawning and die-off) and (b) inter-gravel oxygen concentrations which reflect the habitat quality. Infiltration rates of <2 mm sediment increased with stream discharge and fish redd construction. The finer (<75 μm) sediment exhibited lower infiltration rates during the peak of fish spawning activity indicating successful reduction of this sediment fraction. Inter-gravel oxygen concentration decreased 18% over the period of active spawning and salmon die-off, but recovery occurred later. Aggregate particle size and density changes were explained by the physical action of spawning fish and the inter-gravel microbial activity associated with increased high quality organic matter (fish decay products) which reduced inter-gravel oxygen concentrations.

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 score0.162
Threshold uncertainty score0.852

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 studies0.0000.000
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 head0.010
GPT teacher head0.231
Teacher spread0.222 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueIAHS-AISH publicationSame topicFish Ecology and Management StudiesFrench-language works237,207