Quantitative Sea Ice Reconstruction for the Canadian Arctic Archipelago using the PIP25 Approach
Bibliographic record
Abstract
Sea ice is an important component of Earth’s climate system; it plays major roles in albedo, carbon dioxide exchange, ocean circulation, and is an integral component of polar ecosystems. Sea ice in the Arctic, however, is rapidly declining; a decline which is projected to maintain throughout the 21st century as the annual mean global surface temperature rises. The possibility of a nearly ice-free Arctic Ocean will have negative consequences for the Earth’s climate system, such as creating positive feedbacks that will intensify warming. Reconstructing the past observational (satellite-based) records since 1979 of Arctic sea ice and sea surface conditions provides essential context for the recently observed multi-year sea ice decline. The primary objective of this project is to analyse biomarker (IP25 and brassicasterol/dinosterol) content in surface sediments of the Canadian Arctic Archipelago. This calibration will then be used as a basis on which to reconstruct sea-ice histories over the historical past from longer marine sediment cores. By recovering IP25 and phytoplankton biomarker concentrations from marine sediment cores, PIP25 sea ice indices for the Northwest Passage can be calculated. This sea ice index can be used to reconstruct specific past sea ice conditions, such as first-year vs multi-year ice cover, in a specific region. Subsequently, these results can be mapped. In order to compare current sea-ice with the historical past, current PIP25 values need to be related to observed modern sea ice conditions, therefore providing a regionally appropriate calibration for the study of past conditions in the geological record. Discipline: Earth and Planetary Sciences Faculty Mentor: Dr. Anna Pienkowski
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".