MétaCan
Menu
Back to cohort
Record W1932519615

Software Design Description for the Polar Ice Prediction System (PIPS) Version 3.0

2008· article· en· W1932519615 on OpenAlexaboutno aff
Pamela G. Posey, Lucy F. Smedstad, Ruth H. Preller, E. Joseph Metzger, Suzanne N. Carroll

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceGeologyArctic ice packOceanographySubmarineArcticClimatologyPolar
DOInot available

Abstract

fetched live from OpenAlex

The Polar Ice Prediction System (PIPS) Version 3.0 is a dynamic sea-ice model that forecasts conditions in all sea-ice covered areas in the northern hemisphere (down to 30 degrees north in latitude). It has a horizontal resolution of approximately 9 km. The vertical resolution in the model has been set at 45 levels so that Arctic shelves, continental slopes, and submarine ridges are accurately represented. Currently, the domain includes the Irminger, Labrador, North, and Baltic Seas on the Atlantic side and the Bering Sea, Sea of Japan, and the Sea of Okhotsk on the Pacific. The PIPS 3.0 system is based on the Los Alamos ice model and coupled (via file transfer) to the operational, global Navy Coastal Ocean Model (gNCOM). The system forecasts daily ice thickness, concentration, and drift in the Artic Ocean. This report documents the matematical formulations, flow charts, and descriptions of the programs and subroutines.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0970.065

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 head0.027
GPT teacher head0.199
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreSoftware

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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueDefense Technical Information Center (DTIC)Same topicArctic and Antarctic ice dynamicsFrench-language works237,207