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Record W1998040846 · doi:10.1139/f08-129

Validation of annual growth rings in freshwater mussel shells using cross dating

2008· article· en· W1998040846 on OpenAlexvenueno aff
Andrew L. Rypel, Wendell R. Haag, Robert H. Findlay

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersU.S. Army Corps of Engineers
KeywordsMusselStreamflowAnnual growth %DendrochronologyEnvironmental scienceEcologyBiologyGeographyPaleontologyDrainage basinCartography

Abstract

fetched live from OpenAlex

We examined the usefulness of dendrochronological cross-dating methods for studying long-term, interannual growth patterns in freshwater mussels, including validation of annual shell ring formation. Using 13 species from three rivers, we measured increment widths between putative annual rings on shell thin sections and then removed age-related variation by standardizing measurement time series using cubic splines. Initially, cross dating was a valuable quality control technique allowing us to correct interpretive and measurement errors in 16% of specimens. For all species, growth varied among years but was highly synchronous among individuals. Standardized measurement time series of 94% of individuals were significantly correlated with species master chronologies, and mean interseries correlations ranged from 0.37 to 0.96. Growth was also synchronous among species, even from different rivers, and growth was negatively correlated with mean annual streamflow for most species except Quadrula pustulosa from a regulated dam tailrace. Highly synchronous growth and the strong relationship to streamflow showed that large-scale environmental signals generated non-age-related variation in mussel growth giving strong support for annual formation of the growth increments we measured. Cross dating can be a valuable technique for studying freshwater mussel growth providing quality control, validation of annual rings, and reconstruction of long-term growth histories.

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 score0.040
Threshold uncertainty score0.993

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)0.0010.000

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 head0.021
GPT teacher head0.227
Teacher spread0.206 · 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

Citations66
Published2008
Admission routes1
Has abstractyes

Explore more

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