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Record W7065432613

Dynamics and spatial variability of Arctic and Subarctic thermokarst lakes and their biogeochemical significance

2025· dissertation· en· W7065432613 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório da Universidade de Lisboa (University of Lisbon) · 2025
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsThermokarstPermafrostSubarctic climateArcticBiogeochemical cycleSpatial variabilityBiogeochemistry
DOInot available

Abstract

fetched live from OpenAlex

The rapidly thawing permafrost in the Arctic and Subarctic regions poses a complex scientific problem, with its mechanisms defying traditional climate modeling. Unlike gradual thaw, abrupt permafrost thaw processes, such as thermokarst lake and pond formation, depend on intricate local geological and environmental interactions, rendering them extremely challenging to integrate into Earth System Models (ESM). This is due to their abundance, small size (< 10,000 m2 ), and high optical and biogeochemical variability. In this study, spanning the different permafrost zones of Canada, a deep learning Mask-Regional-based Convolutional Neural Network (Mask R-CNN) model was trained over PlanetScope-Dove (PS-D) imagery, producing the High Latitude Water (HLWATER) model. The model was then deployed to automatically delineate 335,281 water bodies, 90% smaller than 10,000 m2 . Unmanned Aerial System (UAS) data were used to validate and assess the limitations of the HLWATER-derived products. The resultant HLWATER database was used as a spatial reference for Sentinel-2 (S2) reflectance retrievals, allowing optical assessments of water bodies (HLWATER-Optical). In the continuous permafrost zone of Paulatuk, the number of ponds increased from 164 to 454 between 1975 and 2020. In the regional sector of western Nunavik, spanning the discontinuous and sporadic permafrost zones, some landscapes, although representing only 2-7% of the total area, corresponded to over one-third of the total number of small water bodies (< 10,000 m2 ). The HLWATER-Optical allowed the identification of different Arctic and Sub-Arctic lake dominated landscapes, including thermokarst areas. Water body optical properties, ranging from oligotrophic black to brown and light-brown colors, showcased complex degradation processes of palsas and lithalsas, likely reflecting varying organic and inorganic (mineral) concentrations. The workflow presented in this thesis provides a scalable framework for mapping and characterizing thermokarst landscapes in high-latitude environments. It underlines the importance of contemplating the role of small water bodies in climate change assessments. Further, it advances the understanding of permafrost dynamics, establishing a framework for similar investigations in other vulnerable ecological systems worldwide.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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.

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
Published2025
Admission routes1
Has abstractyes

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