Deliverable 3.14 First implementation and data: Ocean and sea ice
Bibliographic record
Abstract
This document First implementation and operational use of the observing systems. Data delivery and report on results of the ocean and sea ice system, describes autonomous components of the Arctic observing system for ocean and sea ice measurements that were implemented during the first field season under INTAROS. Instruments and platforms described in D3.4 drift freely on the sea ice or in the water column (ice tethered platforms, ice buoys and floats), move along preprogrammed tracks (gliders) or measure autonomously at fixed locations (deep ocean moorings). An autonomous sensor package (FerryBox) and drone-based sensors are used to collect observations from the ships of opportunity. This document is intended to: −Review current status of autonomous mobile and fixed observing platforms and sensors used for collecting ocean and sea ice observations in the Arctic during the first INTAROS field season −Describe an ice tethered IAOOS-Equipex platform used for combined physical, atmospheric and sea ice measurements in the central Arctic Ocean and provided data −Describe new deep ocean BGC mooring deployed for the second INTAROS field season in the deep Nansen Basin −Describe SIMBA (Snow and Ice Mass Balance Array) platforms for sea ice measurements, deployed with INTAROS contribution and data provided by them −Describe deployment of new biogeochemical sensors for the FerryBox (pH/carbonate sensor, spectral absorption sensor and microplastic sampler) developed under INTAROS and provided data sets −Describe results from new endurance glider lines established under INTAROS in Fram Strait and north of Svalbard −Describe INTAROS contribution to an array of BGC Argo floats in the Baffin Bay observatory and data provided by the floats
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.291 | 0.376 |
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".