D7.3 Vessels' capabilities and limitations to adopt the ARICE system
Bibliographic record
Abstract
Data from the ice-covered areas of the Arctic Ocean is very scarce. To improve this situation, the ARICE project will contribute to obtaining a better picture of the ARCTIC by funding scientific cruises in the area, on board of six European and international research icebreakers. As pointed out in D7.1 “Data management plan”, ARICE will distribute datasets in model ready format and web-services interoperable to Earth Sciences platforms. It will contribute to advanced data and computing services by producing highly structured datasets compatible to the current climate and earth system modelling programmes. In order to be able to reuse data, standardization is important. This implies both standardization of the encoding/documentation, as well as the interfaces to the data. As has been summarized in the D7.2 “Report on user and stakeholder feedback on the current status of their data management and potential gaps.”, the research icebreakers involved in the ARICE project act as data creators, performing observations and sending raw data to the national data centres [...]. Primary investigators involved in ARICE surveys may also send their data and metadata to the corresponding national data centres. [...] In addition, national data centres will provide interfaces for data discovery and access, performing initial quality control, making sure that metadata are into compliance with the ISO 19115 metadata standard, transform data into interoperable formats, apply DOI and publish the complete datasets online. Such data flow has been proven by years and is considered as a robust way of transferring data from research vessels to the data storage and final users. The deliverable “D7.3 – Vessels’ capabilities and limitations to adopt the ARICE system” is the third deliverable to fulfil in the “Enhancing virtual and remote access to data” Work Package (WP7). The objective of this deliverable is to provide an analysis of the on-board infrastructure needed to implement the recommendations pointed in the D7.2 and then highlight the necessary capacities to undertake the aforementioned recommendations as well as an estimate of the limitations that may compromise them.
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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".