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Overview of Decadal Ecosystem Changes in the Western Arabian Sea and the Occurrence of Algal Blooms

2019· article· en· W2910529147 on OpenAlexaff
Paul J. Harrison, Sergey Piontkovski, Khalid Al-Hashmi

Bibliographic record

VenueJournal of Agricultural and Marine Sciences [JAMS] · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
FundersUnited Arab Emirates University
KeywordsOceanographyPhotic zoneEnvironmental scienceShoaling and schoolingRed tideEcosystemAlgal bloomRegime shiftDiatomEcologyPhytoplanktonBiologyNutrientGeology

Abstract

fetched live from OpenAlex

Gradual decadal changes have taken place in the Western Arabian Sea over the last 50 years. These changes have affected wind speeds, atmospheric and sea surface temperature, thermohaline stratification, shoaling of the oxycline, and dust/iron inputs. A decrease in nitrate supply of the photic layer have caused an increase in annual frequency of harmful algal blooms and fish kills. Along with that, a decrease in diatom biomass and a shift from red Noctiluca to green Noctiluca during the northeast monsoon was observed during the last two decades, Even though these are the same species they have very different nutritional modes. The red one is a heterotroph with a preference for grazing diatoms, while the green one has a symbiont and thus it is a mixotroph. Recent results suggest that this shift may be caused by the shoaling oxycline since the green one grows better under low oxygen because the symbiont produces oxygen for its host. The western Arabian Sea is temporally and spatially complex. With the recent advances in remote sensing of the ocean, a further understanding of these temporal and spatial changes can be gained through analyzing frequent images with opportunistic ground-truthing.

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)0.001
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 score0.026
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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