D16.1 – Mid-term interim report on ARIADNEplus pilots
Bibliographic record
Abstract
This deliverable describes the activities carried out during the first two years of the ARIADNEplus project within Work Package 16 (WP16) by the different partners and describes the results achieved by this work package. WP16, titled Innovative Methods and Pilots, concerns the preparation of pilots, i.e. exemplary applications of the ARIADNEplus services to be used as demonstrators of the potential of the ARIADNEplus framework. They address specific archaeological research questions and use one or more of the ARIADNEplus services operating on a selected data sample. The list of such pilots and the corresponding WP tasks is provided below. Partners involved are also listed, with the Task leader in bold. Task 16.1 – Establishing a common framework for the ARIADNEplus pilots. Partners involved: CYI, SRFG, INRAP. Task 16.2 – ARIADNEplus for Airborne-LiDAR data use and reuse. Partners involved: ZRCSAZU, CARARE, CNR-ISTI(VCL). Task 16.3 – ARIADNEplus for a Historic Environment Spatial Data Infrastructure. Partners involved: CARARE, ZRC-SAZU, other ARIADNEplus partners as required. Task 16.4 – ARIADNEplus for sharing archaeology in 3D. Partners involved: CYI, CNRISTI(VCL). Task 16.5 – ARIADNEplus for public/community archaeology. Partners involved: AU, UoYADS, DANS-KNAW, BUP-DMS, UH. Task 16.6 – ARIADNEplus for understanding ancient and present cities: scanning the heart of Rome. Partners involved: MIBACT-ICCU (ICA), CNR-ISTI(VCL). Task 16.7 – Exploiting the ARIADNEplus Data Infrastructure to understand complex phenomena of the Past. Partners involved: UB, PIN. Task 16.8 – ARIADNEplus for Preventive Archaeology. Partners involved: INRAP, UoY- ADS, USW. The report starts with a brief introduction to the WP goals and with a section dedicated to Task 16. 1, in charge of preparing a common framework for all the pilots. Then each pilot is described in a specific section, which details the archaeological issue addressed, including the necessary background; the perspective target users and the planned dissemination; the data to be used; the pilot progress so far; the deviation from the work plan, if any; and the plans for the next period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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; both teacher heads agree on what is shown here.
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".