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A Web GIS for Sea Ice Information and an Ice Service Archive

2011· article· en· W1581388392 on OpenAlexaffabout
Songnian Li, Chenfeng Xiong, Ziqiang Ou

Bibliographic record

VenueTransactions in GIS · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change CanadaToronto Metropolitan University
Fundersnot available
KeywordsSea iceArcticWeb serviceThe InternetComputer scienceWorld Wide WebGeographyOceanographyMeteorologyGeology

Abstract

fetched live from OpenAlex

Abstract Sea ice data has significant scientific value for climate, environmental impact and engineering studies leading to the construction of facilities in Arctic waters, as well as to support tourism and fishing planning. Large collections of such data are acquired, compiled, produced and maintained by national and international agencies such as the Canadian Ice Service (CIS). Some of these data collections have been made available online. However, current Internet‐based sea ice data dissemination practices do not foster easy access to and use of the data, especially given the amount of the archived sea ice data and the nature of their spatial changes and high temporal variations. This article reports a research effort in developing a web‐based geographical information system (GIS) that facilitates the access and use of the historical sea ice data. The system provides online access, exploration, visualization, and analysis of the archived data, mostly in the form of ice charts, within a web‐based GIS. The results from a prototype development indicate that web GIS, developed using Rich Internet Application (RIA) technologies, provides added values in serving sea ice data and suggests that such a system can better accommodate more advanced sea ice data access and analysis tools.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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 designOther design
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

Citations23
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueTransactions in GISSame topicArctic and Antarctic ice dynamicsFrench-language works237,207