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Record W2066030502 · doi:10.3354/ab00171

Degradation of sea urchin feces in a rocky subtidal ecosystem: implications for nutrient cycling and energy flow

2009· article· en· W2066030502 on OpenAlexafffund
LK Sauchyn, RE Scheibling

Bibliographic record

VenueAquatic Biology · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSea urchinBiologyStrongylocentrotus droebachiensisFecesFood webNutrient cycleNutrientEcosystemEcologySettlingInvertebrateCyclingOceanographyEnvironmental scienceGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

During destructive grazing events in the Northwest Atlantic, sea urchins Strongylocentrotus droebachiensis consume large amounts of kelp biomass, transforming this material into feces. To determine the role of urchin fecal material in nutrient cycling and energy flow in the shallow rocky subtidal zone, we monitored the physical, chemical, and microbial degradation of urchin feces at 6, 9, 12, and 16 m depth over 19 d at a wave-exposed site on the Atlantic coast of Nova Scotia by quantifying changes in fecal biochemical composition, pellet size, and settling velocity. We observed an exponential loss of fecal material and rapid degradation of total and labile organic matter fractions; this suggests that urchin fecal production plays an important role in local nutrient cycling and energy flow via the microbial food web. As the feces were further degraded there was a relative increase in organic carbon, nitrogen, lipid, and available energy content, and a decrease in the C:N ratio, suggesting that degraded urchin feces are an important food source for suspension-and deposit-feeding invertebrates. The settling velocity of the feces also decreased over time, likely due to a decrease in fecal pellet density. Older, less dense feces with a relatively high energy content are more likely to be suspended and transported horizontally, providing a mechanism for the export of kelp primary production to deeper, less productive waters.

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)0.000
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 score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations46
Published2009
Admission routes2
Has abstractyes

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