Landforms and geoecological units of Pingo Canadian Landmark (Northwest Territories, Canada): a remote sensing-based approach
Bibliographic record
Abstract
Pingo Canadian Landmark (PCL) is a 16.6 km2 protected coastal area located in the southwest region of Tuktoyaktuk Peninsula (Northwest Territories, Canada). PCL presents great geomorphological significance, containing characteristic permafrost landforms, such as pingos and tundra polygons. The scientific value of PCL gains even more relevance in a context of climate change, which is known to have particularly strong impacts on the Arctic (Cohen et al., 2014; Davy et al., 2018; Screen & Simmonds, 2010; Stjern et al., 2019). Much scientific evidence points to the increasing magnitude of coastal erosion and permafrost degradation, linked to higher air and sea temperatures, and marine storms, which may put the unique landforms of PCL at risk (Holland et al., 2023; Irrgang et al., 2018; Karjalainen et al., 2020; Lim et al., 2020a; Lim et al., 2020b; O’Rourke, 2017; Parker, 2021; Vermaire et al., 2013). The focus of this dissertation is to present a very detailed analysis of the landscape of PCL, supported by high quality remote sensing data. This analysis primarily includes: a Geomorphological Map, based on an ultra-high resolution (10 cm) optical orthomosaic and Digital Surface Model, captured in July 2019 using Unmanned Aerial Vehicles; and a supervised classification Landcover Map, based on very-high resolution (46 cm) WorldView-2 satellite imagery from 2017. The combined interpretation of these results permitted the identification of homogeneous sectors in terms of geomorphology and vegetation within PCL (geoecological units). Additionally, historic data (archived aerial photographs from 1950 to 2004) are interpreted to identify general trends of coastal erosion in the study area, and areas with direct anthropogenic impacts on PCL’s landscape are pointed out. The selected methodologies allowed to develop an unprecedentedly detailed and multifaceted diagnosis of PCL’s landscape. Also, hopefully this study will aid in the management of PCL and the conservation of its geoheritage.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".