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Record W4353085212 · doi:10.3389/fmars.2023.1136748

Performance of the long-snouted seahorse, Hippocampus guttulatus, under warming conditions

2023· article· en· W4353085212 on OpenAlexaff
Ana B. Costa, Miguel Correia, Gonçalo Silva, Ana M. Faria

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaEuropean Commission
KeywordsSeahorseBiologyPopulationFisheryEcologyZoology

Abstract

fetched live from OpenAlex

Temperature is a determinant cue for several behavioral, physiological, and metabolic processes in fish, which occur within a range set to optimize species fitness. Understanding how ocean warming will impact species, at individual and population levels, is, therefore, of utmost relevance for management and conservation purposes. This knowledge assumes particular relevance when it comes to species with unique life history traits that experience multiple threats, such as seahorses. This study aimed to assess the effects of warming on growth, feed intake, and behavioral patterns of the long-snouted seahorse, Hippocampus guttulatus . Fish were collected, before the breeding season, in the Sado estuary, Portugal, and subsequently exposed for an 8-week period to three different temperatures: 17°C, 20°C, and 24°C. Three times a week, behavioral observations were performed. Feed intake was measured every day and individuals were weighed once a week. Results indicate differences in behavioral responses of seahorses exposed to increasingly warming conditions. Under extreme temperature conditions (24°C), fish were more active, and fed more, but this increment of energy through feeding did not translate into increased growth in weight. Altogether, these results indicate that Sado’s population of H. guttulatus may become under thermal stress when exposed, for a long term, to warming conditions that are expected to occur by the end of the century.

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 score0.038
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.227
Teacher spread0.210 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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