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Record W4285114824 · doi:10.1139/facets-2021-0125

Distinct realized physiologies in green sea urchin (<i>Strongylocentrotus droebachiensis</i>) populations from barren and kelp habitats

2022· article· en· W4285114824 on OpenAlexaffvenue
Jasmin M. Schuster, A. Kurt Gamperl, Patrick Gagnon, Amanda E. Bates

Bibliographic record

VenueFACETS · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsKelpStrongylocentrotus droebachiensisKelp forestSea urchinHabitatBiologyEcologyFisheryReefPopulationOceanography

Abstract

fetched live from OpenAlex

Overgrazing of habitat-forming kelps by sea urchins is reshaping reef seascapes in many temperate regions. Loss of kelp, in particular as a food source, may alter individual consumer physiology, which in turn may impair their ability to respond to climate warming. Here, we measured the temperature dependence of absolute and mass-independent oxygen consumption ([Formula: see text]) using two different exposure protocols (acute exposure and temperature “ramping”), as proxies of realized physiology, between green sea urchin ( Strongylocentrotus droebachiensis) populations from neighbouring barren and kelp habitats. Sea urchins from kelp habitats consumed 8%–78% more oxygen than sea urchins from barrens (across the range of temperatures tested (4–32 °C)) and had higher maximum [Formula: see text] values (by 26%). This was in part because kelp urchins typically had greater body masses. However, higher mass-independent [Formula: see text] values of kelp urchins suggest metabolic plasticity in response to habitat per se. In addition, the [Formula: see text] of sea urchins from kelp habitats was less sensitive to increases in temperature. We conclude that sea urchins from barren and kelp habitats of comparable body mass represent different energetic units. This highlights that habitat type can drive population-level variation that may shape urchins activities and environmental impact. Such variation should be integrated into energy-based models.

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 score0.014
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.023
GPT teacher head0.237
Teacher spread0.215 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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