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Record W2078813200 · doi:10.1139/f05-161

Effects of crystal structure on the uptake of metals by lake trout (<i>Salvelinus namaycush</i>) otoliths

2005· article· en· W2078813200 on OpenAlexvenueno aff
Sonia Melancon, Brian J. Fryer, Stuart A. Ludsin, Joel E. Gagnon, Zhaoping Yang

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsVateriteAragoniteTroutChemistrySalvelinusOtolithCalciteCrystallographyAnalytical Chemistry (journal)MineralogyFisheryEnvironmental chemistryBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

This is the first study to report spectroscopic and elemental analysis of aragonite and vaterite growing simultaneously and separately in both the core and the edges of the same otolith. Our investigations focused on understanding differential trace metal uptake, including the influence of the metal itself (i.e., ionic radii), the crystalline structure, and the development state of the fish. Chemistry and crystal structure of sagittal otoliths from lake trout (Salvelinus namaycush) were studied using laser ablation combined with inductively coupled plasma mass spectrometry (LA-ICP-MS) and Raman spectroscopy, respectively. Analyses of the composition of vaterite and aragonite growing in the same growth ring show that smaller cations like Mg (0.86 Å) (1 Å = 0.1 nm) and Mn (0.81 Å) were more abundant in the vaterite hexagonal crystal structure, whereas larger cations such as Sr (1.32 Å) and Ba (1.49 Å) were preferentially incorporated in aragonite (orthorhombic). Similarly, the coprecipitation of aragonite and vaterite in cores and edges allowed us to demonstrate that the uptake rates (as determined by element-specific partition coefficients) for Sr and Ba were greater in aragonite than vaterite, whereas those of Mg and Mn were higher in vaterite than in aragonite.

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 score0.190
Threshold uncertainty score0.755

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 head0.216
Teacher spread0.205 · 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

Citations96
Published2005
Admission routes1
Has abstractyes

Explore more

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