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Record W2383112992 · doi:10.1139/cjfas-2015-0546

Long-term monitoring reveals the success of salmonid habitat restoration

2016· article· en· W2383112992 on OpenAlexvenueno aff
Pauliina Louhi, Teppo Vehanen, Ari Huusko, Aki Mäki‐Petäys, Timo Muotka

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersOulun Yliopisto
KeywordsSalmoBrown troutTroutHabitatLarge woody debrisStream restorationSTREAMSEcologyOncorhynchusJuvenileFisheryDebrisRestoration ecologySalmonidaeEnvironmental scienceFish <Actinopterygii>GeographyBiology

Abstract

fetched live from OpenAlex

The growing concern on declining salmonid populations has resulted in numerous restoration projects with variable responses worldwide. In this spatially replicated multiyear study, we assessed the long-term (12 years postrestoration) effects of in-stream habitat restoration (i.e., addition of boulders or large woody debris (LWD) together with boulders) on densities of three age-classes of juvenile brown trout (Salmo trutta) in six forest streams in northern Finland. LWD combined with boulders was more beneficial, particularly for the larger trout (age-2 and older), than were boulder structures alone, indicating that the more diverse habitat created by LWD may have provided a safeguard against drought for the larger fish. Density of age-0+ trout showed a significant long-term increase in boulder-restored sections, providing evidence that log structures may need to be complemented by stony enhancement structures to guarantee the availability of suitable stream habitat for all trout age-classes. As trout densities are known to exhibit inherently wide interannual variability that tracks climatically induced hydrological variation, long-term postrestoration monitoring that encompasses extreme hydrological events is critical for evaluating the success of restoration projects.

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 score0.012
Threshold uncertainty score0.745

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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

Citations52
Published2016
Admission routes1
Has abstractyes

Explore more

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