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DataSheet_1_Quantifying the effects of extreme events and oceanographic variability on the spatiotemporal biomass and distribution of two key euphausiid prey species.docx

2023· dataset· en· W6965027672 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBiomass (ecology)FishingMarine ecosystemEcosystemSpring bloomSea surface temperatureClimate changeKrill

Abstract

fetched live from OpenAlex

<p>Euphausiids form a critical component of oceanic food chains and individual species vary in their responses to perturbation events. We aimed to establish if patterns of fine scale oceanographic variability and larger-scale climate events such as marine heatwaves (MHWs) could be linked with spring variability in the biomass of two key forage species in the northeast Pacific Ocean, Euphausia pacifica and Thysanoessa spinifera. To achieve this, we used long-term datasets from the west coast of Vancouver Island Canada, an important commercial fishing area, to first quantify interannual signals of variability in fine-scale oceanographic conditions using multivariate analysis. We then used geostatistical spatiotemporal modelling to quantify the effects on species-specific euphausiid biomass. Oceanographic data showed that the effects of warm events are not always observable, and effects vary across small spatial scales. Warming due to the 2014-2016 MHW was relatively mild on the continental shelf during spring (<1°C above climatology). Spring biomass of euphausiids, particularly E. pacifica, peaked in 2015, and all euphausiid groups analysed (E. pacifica, T. spinifera and total euphausiids) exhibited significant correlations with positive phases of the Pacific Decadal Oscillation. These results have implications for marine predators as euphausiids may act as system stabilisers in the northeast Pacific, thereby potentially increasing ecosystem resilience during extreme events.</p>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.799
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0070.000

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 head0.071
GPT teacher head0.255
Teacher spread0.184 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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