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Record W6967246938 · doi:10.5061/dryad.4p9v048

Data from: Drivers of temporal beta diversity of a benthic community in a seasonally hypoxic ocean fjord

2018· dataset· en· W6967246938 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsMemorial University of NewfoundlandUniversity of Victoria
Fundersnot available
KeywordsBenthic zoneBeta diversityHabitatFjordInterspecific competitionMarine protected areaClimate changeMarine habitatsEcological successionEnvironmental change

Abstract

fetched live from OpenAlex

Global expansion of oxygen deficient (hypoxia) waters will have detrimental effects on marine life in the Northeast Pacific Ocean (NEP) where some of the largest proportional losses in aerobic habitat are predicted to occur. However, few studies have accounted for the high environmental variability in this region while including natural community-assembly dynamics. Here, we present results from a 14-month deployment of a benthic camera platform tethered to the VENUS cabled observatory in the seasonally hypoxic Saanich Inlet. Our time series continuously-recorded, natural cycles of deoxygenation and reoxygenation that allowed us to test whether a community from the NEP showed hysteresis in its recovery compared to hypoxia-induced decline and to address the processes driving temporal beta diversity under variable states of hypoxia. Using high-frequency ecological time series we reveal (1) differences in the response and recovery of the epibenthic community are rate-limited by recovery of the sessile species assemblage, (2) both environmental and biological processes influence community assembly patterns at multiple time-scales, and (3) interspecific processes can drive temporal beta diversity in seasonal hypoxia. Ultimately, our results illustrate how different time scale dependent drivers can influence the response and recovery of a marine habitat under increasing stress from environmental change.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.475
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0200.057
Research integrity0.0000.002
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 head0.060
GPT teacher head0.285
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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