MétaCan
Menu
Back to cohort
Record W2116973070 · doi:10.1139/f00-038

Spatial variability of stream bed scour and fill: a comparison of scour depth in chum salmon (<i>Oncorhynchus keta</i>) redds and adjacent bed

2000· article· en· W2116973070 on OpenAlexfundvenueno aff
Colin D. Rennie, Robert G. Millar

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyNatural Sciences and Engineering Research Council of Canada
KeywordsOncorhynchusFlood mythRiver bedHydrology (agriculture)GeologyCurrent (fluid)FisheryEnvironmental scienceFish <Actinopterygii>OceanographyGeotechnical engineeringGeographyBiology

Abstract

fetched live from OpenAlex

Scour depth in egg pockets of chum salmon (Oncorhynchus keta) egg nests (redds) in a short gravel-bed spawning reach (45 × 20 m) of Kanaka Creek, British Columbia, was not significantly different from that in the adjacent bed during 1997-1998 winter flood events, whereas the scour depth in tailspills of redds was greater. Over the course of the incubation period, none of the egg pocket locations (zero of four), all of the tailspills (three of three), and 17% of the immediately adjacent bed locations (three of 18) scoured to the assumed egg burial depth of 15 cm below the initial postspawning surface elevation. Egg pocket scour depth has not previously been monitored, and the reliance of earlier studies on tailspill monitoring as an indication of redd scour may have led to faulty assessment of embryo loss. Only one flood event, which exceeded bankfull, caused widespread and deep scour and fill. Despite implementation of the most spatially intensive array of wiffle-ball scour monitors to date, scour was so variable that there was no spatial autocorrelation of scour depths.

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)0.001
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 score0.097
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.014
GPT teacher head0.229
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 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
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207