MétaCan
Menu
Back to cohort
Record W7065104292

Detecting marine nutrient and organic matter inputs into multiple trophic levels in streams of Atlantic Canada and France

2009· other· en· W7065104292 on OpenAlexfundaboutno aff

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2009
Typeother
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersCanada Research Chairs
KeywordsSpawn (biology)Fish migrationTrophic levelSalmoBenthic zoneEstuarySmeltHerringSTREAMS
DOInot available

Abstract

fetched live from OpenAlex

We used stable isotope analysis in an attempt to detect marine subsidies from anadromous fish to freshwater benthos in four river systems draining to the Atlantic Ocean. Benthic invertebrates in the West River, Nova Scotia, Canada, had elevated d13C, d15N, and d34S values in a downstream reach that suggested consumption of marine-derived organic matter from spawning blueback herring Alosa aestivalis. In Doctor's Brook, Nova Scotia, the arrival of rainbow smelt Osmerus mordax to spawn led to rapid increases in the d13C and d15N of a predatory stonefly (Perlidae), but lower trophic levels (mayflies and biofilm) showed inconsistent responses. Sculpin Cottus sp. showed no evidence of predation on Atlantic salmon Salmo salar eggs in Catamaran Brook, New Brunswick, Canada or the Scorff River, Brittany, France. These analyses suggest that marine organic matter subsidies, in the form of direct consumption of eggs and/or carcasses, are important in streams with concentrated spawning activity such as by alosid and osmerid species, whereas carbon and nitrogen contributions from more sparse spawning species such as by Atlantic salmon may be minimal.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueGriffith Research Online (Griffith University, Queensland, Australia)Same topicParticle Detector Development and PerformanceFrench-language works237,207