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Macronutrient, temperature and salinity measurements made around the island of South Georgia and the wider Scotia Sea, the Antarctic Peninsula, and in the Bellingshausen Sea between 1980 and 2009

2022· dataset· en· W6912851410 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Environmental Data Service · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaSubmarine pipelineSalinityEcosystemMarine researchSeawaterMarine ecosystem

Abstract

fetched live from OpenAlex

Between 1980 and 2009, marine macronutrient concentrations (silicate, Si(OH)4-Si; phosphate, PO4-P; nitrate, NO3-N; ammonium, NH4-N; and nitrite, NO2-N) and concurrent temperature and salinity were measured by British Antarctic Survey researchers as part of an integrated ecosystem investigation. Areas sampled included South Georgia and the wider Scotia Sea, around the Antarctic Peninsula, and in the Bellingshausen Sea. The data were collected from aboard the RRS John Biscoe or the RRS James Clark Ross during all months of the year with the exceptions of May and June. Samples were collected from CTD water bottles (vertical profiles) to maximum depth of 5400 m, and by monitoring continuously the ship's non-toxic seawater supply (intake at 6 - 7 m) while the vessel was transecting. Analyses were performed immediately aboard ship and logged to computer while full data analysis was performed post-cruise using custom written software programmes. The data collection was enabled through Natural Environment Research Council National Capability funding to the British Antarctic Survey. This was organised through a series of BAS programmes including the Offshore Biological Research programme, the DYNAMOE programme and the ECOSYSTEMS programme. Data creation was facilitated through a combination of NERC funding for Antarctic Logistics and Infrastructure (ALI) Science and the NERC Science Multi-Centre Round 2 (NCSM2) programme BIOPOLE (NE/W004933/1).

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0030.005
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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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