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Record W2789589646 · doi:10.1038/sdata.2018.18

A database of chlorophyll a in Australian waters

2018· article· en· W2789589646 on OpenAlexaff
Claire H. Davies, Penelope Ajani, Linda Armbrecht, Natalia Atkins, Mark E. Baird, Jason M. Beard, Pru Bonham, Michele A. Burford, Lesley Clementson, Peter Coad, Christine Crawford, Jocelyn Dela‐Cruz, Martina A. Doblin, Steven Edgar, Ruth Eriksen, Jason D. Everett, Miles Furnas, Daniel P. Harrison, Christel Hassler, Natasha Henschke, Xavier Hoenner, Tim Ingleton, Ian Jameson, John K. Keesing, Sophie C. Leterme, James McLaughlin, Margaret Miller, D. Moffatt, A. Moss, Sasi Nayar, Nicole L. Patten, Renee Patten, Sarah A. Pausina, Roger Proctor, Eric J. Raes, Malcolm Robb, Peter C. Rothlisberg, Emily Saeck, Peter Scanes, Iain M. Suthers, Kerrie M. Swadling, Samantha Talbot, Peter A. Thompson, Paul Thomson, Julian Uribe‐Palomino, Paul D. van Ruth, Anya M. Waite, Simon W. Wright, Anthony J. Richardson

Bibliographic record

VenueScientific Data · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
FundersDepartment of Science, Information Technology and Innovation, Queensland GovernmentAustralian Antarctic DivisionDirectorate for Biological SciencesSchool of Civil, Environmental and Mining Engineering, University of AdelaideSouth Australian Research and Development InstituteGriffith UniversityFlinders UniversityAntarctic Climate and Ecosystems Cooperative Research CentreUniversity of New South WalesUniversity of Technology SydneyMacquarie UniversityUniversity of TasmaniaUniversität BremenAustralian GovernmentCommonwealth Scientific and Industrial Research OrganisationGreat Barrier Reef Marine Park AuthorityUniversity of QueenslandNSW Office of Environment and HeritageUniversité de GenèveAustralian Institute of Marine Science
KeywordsPhytoplanktonEnvironmental scienceBiomass (ecology)ZooplanktonTrophic levelChlorophyll aAbundance (ecology)OceanographyChlorophyllEcosystemFood webFisheryEcologyBiologyNutrientBotany

Abstract

fetched live from OpenAlex

Chlorophyll a is the most commonly used indicator of phytoplankton biomass in the marine environment. It is relatively simple and cost effective to measure when compared to phytoplankton abundance and is thus routinely included in many surveys. Here we collate 173, 333 records of chlorophyll a collected since 1965 from Australian waters gathered from researchers on regular coastal monitoring surveys and ocean voyages into a single repository. This dataset includes the chlorophyll a values as measured from samples analysed using spectrophotometry, fluorometry and high performance liquid chromatography (HPLC). The Australian Chlorophyll a database is freely available through the Australian Ocean Data Network portal (https://portal.aodn.org.au/). These data can be used in isolation as an index of phytoplankton biomass or in combination with other data to provide insight into water quality, ecosystem state, and relationships with other trophic levels such as zooplankton or fish.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.253
Teacher spread0.203 · 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
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

Citations27
Published2018
Admission routes1
Has abstractyes

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