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Record W7005348879

Quantification of ikaite in first and multi year sea ice

2017· dissertation· en· W7005348879 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceAlkalinityDissolved organic carbonCarbonateCarbon cycleCarbon fibersSalinityTotal inorganic carbon
DOInot available

Abstract

fetched live from OpenAlex

Ikaite (CaCO₃•6H₂O) is a metastable calcium carbonate mineral that forms in all types of sea ice that may play a significant role in the sea ice driven carbon pump, particularly with the increasing abundance of seasonal sea ice in the Arctic. Due to difficulties in determining its concentration and abundance, the spatial and temporal dynamics, and therefore the significance, of ikaite are poorly understood. To improve knowledge of ikaite in sea ice, a new method of quantification using dissolved inorganic carbon (DIC) analysis was developed and tested at the Sea-ice Environmental Research Facility (SERF), at Station Nord, Greenland, and at Cambridge Bay, Nunavut. Environmental parameters, including temperature, salinity, total alkalinity (TA), and DIC were also measured at all sampling sites. Ikaite concentrations ranged from 8 to 2595 μmol kg⁻¹ and were generally highest in low temperature, high salinity sea ice with high TA:DIC ratios. Results indicate that the new method of ikaite quantification is an effective technique that can be used in the future to improve understanding of ikaite and its role in carbon dynamics in ice covered seas.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.029
GPT teacher head0.209
Teacher spread0.181 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMspace (University of Manitoba)Same topicColeoptera Taxonomy and DistributionFrench-language works237,207