MétaCan
Menu
Back to cohort
Record W4393062876 · doi:10.47611/jsrhs.v12i4.5896

The Effects of Climate Change on Hawaii’s Coral Reef Ecosystem Over the Past 20 Years

2023· article· en· W4393062876 on OpenAlexaff
Aaron Joshi, Diana Ribeiro Tosato, Magaly Koch

Bibliographic record

VenueJournal of Student Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsBrock University
Fundersnot available
KeywordsEnvironmental issues with coral reefsReefCoral bleachingCoral reefPoritesResilience of coral reefsFisheryAquaculture of coralMarine ecosystemEcosystemEcologyCoralCoral reef protectionOceanographyBiodiversityBiologyGeology

Abstract

fetched live from OpenAlex

The Hawaiian islands are known for their rich marine biodiversity in coral reef ecosystems. However, in the last 20 years, changes in sea surface temperatures, sea levels, carbon dioxide levels, and ocean pH have severely impacted these reef ecosystems. Hawaiian corals can experience heat stress with temperatures as little as 1-2 °C above the average and thermal stress events were prevalent from 2014-2017, causing bleaching and increasing mortality. The increase in sea level in Hawaiian marine ecosystems has caused reefs to “drown” from the lack of sufficient sunlight and thus suffer from bleaching. Coral disease is linked with high temperatures, seen through the common reef-building coral disease white syndrome. Invasive species outbreaks, with Acanthaster planci as an example, are also correlated with changes in the climate and reef communities. With the loss of coral, there have been consequences on marine biodiversity in Hawaiian reef ecosystems. Specifically, the three most native coral genera, porites, montipora, and pocillopora, which are the most important species to the reefs, have been declining in population. As a result, higher threat statuses have been observed among turtles, reef fish, and marine mammals that are reliant on their coral reef ecosystems for protection and food sources.

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)0.003
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 score0.032
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.083
GPT teacher head0.384
Teacher spread0.301 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Student ResearchSame topicTurtle Biology and ConservationFrench-language works237,207