MétaCan
Menu
Back to cohort
Record W1987823356 · doi:10.5194/bg-9-4835-2012

Phytoplankton distribution in unusually low sea ice cover over the Pacific Arctic

2012· article· en· W1987823356 on OpenAlexaboutno aff
P. Coupel, Haiyan Jin, Marianna Joo, Rita A. Horner, H. A. Bouvet, V. Garçon, Marie‐Alexandrine Sicre, J.-C. Gascard, J. F. Chen, Diana Ruiz‐Pino

Bibliographic record

VenueBiogeosciences · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersState Oceanic AdministrationNational Natural Science Foundation of ChinaCentre National de la Recherche ScientifiqueEuropean Commission
KeywordsOceanographyPhytoplanktonSea iceArctic ice packArcticCanada BasinMelt pondGeologyEnvironmental scienceStructural basinAntarctic sea iceEcologyNutrientBiologyPaleontology

Abstract

fetched live from OpenAlex

Abstract. A large part of the Pacific Arctic basin experiences ice-free conditions in summer as a result of sea ice cover steadily decreasing over the last decades. To evaluate the impact of sea ice retreat on the marine ecosystem, phytoplankton in situ observations were acquired over the Chukchi shelf and the Canadian basin in 2008, a year of high melting. Pigment analyses and taxonomy enumerations were used to characterise the distribution of main phytoplanktonic groups. Marked spatial variability of the phytoplankton distribution was observed in summer 2008. Comparison of eight phytoplankton functional groups and 3 size-classes (pico-, nano- and micro-phytoplankton) also showed significant differences in abundance, biomass and distribution between summer of low ice cover (2008) and heavy ice summer (1994). Environmental parameters such as freshening, stratification, light and nutrient availability are discussed as possible causes to explain the observed differences in phytoplankton community structure between 1994 and 2008.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations78
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueBiogeosciencesSame topicArctic and Antarctic ice dynamicsFrench-language works237,207