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Record W4322010428 · doi:10.5194/egusphere-egu23-9355

Role of climatological and geophysical controls on the landfast sea ice regime in the Hudson Bay region

2023· preprint· en· W4322010428 on OpenAlexaboutno aff
Kaushik Gupta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBaySea iceOceanographySnowBathymetryEnvironmental scienceClimatologyGeographyPhysical geographyGeologyMeteorology

Abstract

fetched live from OpenAlex

This study investigates how both climatological and geophysical factors impact and have changed the landfast sea ice (LFI) regime in Hudson Bay and James Bay (HJB), Canada. LFI plays an important role in coastal land use practices, traditional livelihood and the formation of wetland ecosystems. Stability and extent of LFI platform is crucial to coastal communities as they use it for travel, fishing and hunting. The most vital concern raised in the Hudson Bay Summit 2022, was regarding the unpredictability of the LFI in terms of presence and thickness, which endangers the ecosystem services and livelihood of these coastal communities. The investigation relied on three sub-objectives: 1) trends of fast ice persistence and extent across HJB from 2001-2018; 2) impact of climatological factors on the landfast ice cycle, and 3) how coastal topography impacts the fast ice cycle. For this study we utilised an array of remote sensing and reanalysis products to study variables such as the landfast ice cycle (freeze-up, break-up) and persistence (CIS Ice charts, MODIS), air temperature (ERA5 reanalysis product), snow melt on land (MOD10A2 snow cover product), coastline orientation (Landsat) and coastal bathymetry (GEBCO). In addition to notable east-west contrast of the LFI climatology in HJB, the observations reveal how coastal topography impacts ice stability and extent, and eventually influences ice persistence, and how a positive feedback is created between the LFI and local air temperature. An understanding of these interlinkages are of critical importance to improve the prediction of LFI breakup in face of rapid climate warming and increased variability. The trends revealed through this study were unique compared to other Sub-Arctic regions with seasonal ice cover. Hence, a focused investigation of the factors that works as precursors of ice freeze-up and triggers break-up is proven to be vital to the continued safe use of the LFI platform.

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)0.002
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 score0.244
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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