Time-Series Analysis from the Scotian Slope (iAtlantic Study Region 4)
Bibliographic record
Abstract
A main objective of the iAtlantic project is to assess the stability and vulnerability of deep and open-ocean Atlantic ecosystems as well as to test for the presence of tipping points in response to environmental change. This requires the statistical analysis of complex time-series data. One of the areas where valuable temporal datasets are available is the Scotian slope (iAtlantic study region 4). The Scotian slope is located offshore Nova Scotia, Canada and is characterised by complex oceanographic conditions. The study region contains the Gully, a large submarine canyon of 65 km long and 15 km wide reaching 2,000 m of depth, protected by a marine protected area since 2004. While nowadays fishing pressures in the region are overall low, the area did experience intense fishing activities on the continental shelf and the upper continental slope between 1960 and 1993. Several long time series datasets are available from this study region, including zooplankton surveys data (1999 - 2019), demersal fish data (1982 - 2019) as well as modelled cetacean population outputs (1999 - 2019). In addition, satellite measured sea surface temperature and chlorophyll concentration will also be analysed as well as CTD measured sea surface temperature and nutrient concentrations. These environmental datasets offer the opportunity to ground truth outputs from the Viking 20X model, also available thanks to WP1. This pre-recorded presentation will summarise results obtained so far from the statistical analysis of these time-series datasets and therefore provide an update to Follow the Fellows talk of May 2021.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".