Analysis of First Year Ice Surface Ocean Current and Ice Floe Drift Speed and Motion Offshore Newfoundland and Labrador from Satellite-Tracked Buoys During the 2015 Ice Season
Bibliographic record
Abstract
Abstract This paper presents an analysis of the dynamics of ice, current, and wind based on the data collected on land-fast ice and ice floes from the offshore environment of Newfoundland and Labrador during April-August 2015 using satellite-tracked beacons. The beacons were deployed in three sets of three as follows: fast ice beacons (FIB) 2, 3, and 5 were deployed in a triangular array on the land-fast ice offshore Makkovik; fast ice beacons (FIB) 1, 4, and 6 were deployed in a triangular array on the landfast ice offshore Nain, and ice floe beacons (IFB) 7590, 1590, and 0650 were deployed on drifting ice floes offshore Makkovik. Ten ocean drifter beacons were deployed on May 22, 2015 in open water in the vicinity of ice floe beacons 1590 and 0650 to study the characteristics of surface ocean current dynamics. The drift velocities of the two ice floes have been compared with the wind velocities measured by the two weather stations deployed on the ice floes. The buoy drift rose and exceedance probability plots have been presented to analyze the dynamical characteristics of first year ice and ice floes in the offshore Labrador ice environment.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".