Deliverable 5.4 Intermediary report on synergies and collaboration
Bibliographic record
Abstract
This deliverable introduces the collaborative activities, synergies, and meaningful interactions with related projects and initiatives that AGEMERA has initiated and/or sustained in the second half of the project (M19-M36) and provides further details on actors involved, the type of collaboration and activity that took place, as well as relevant outcomes. Many of these interactions happened with sister projects or simply projects that work on similar topics, across the whole raw material value chain. In this case, synergies are crucial, as projects exchange research findings, lessons and experiences, as opposed to working in silos, and therefore contribute to advancing knowledge in the field. Moreover, AGEMERA, through various partners, joined a multitude of events around the globe (and this is not an euphemism, as the list includes events in Australia, Zambia, the United States of America, Canada, and Chile), connecting with experts and industry leaders and further creating and building on these opportunities for collaboration and network building. And that’s not all: AGEMERA also initiated joint activities aiming to connect projects working on similar projects to learn from each other’s experiences – aclear example in this sense is the parallel session at the European Geosciences Union(EGU) 2025 General Assembly in Vienna. Lastly, AGEMERA’s coordinator, but also otherpartners, took every opportunity to spread the word about its mission in other projects’events, be they project meetings (for example, General Assemblies) or clustering events. Last but not least, AGEMERA’s work has been recognised several times by the European Commission, in different instances: as part of the cohort representing the EU booth atthe PDAC 2025 event, in a publication spotlighting EU Horizon Technology Success Stories, as the only Horizon Europe-funded project in a parallel session on successful rawmaterials projects at the Raw Materials Week event in Brussels. Being included in these prestigious lists – apart from being an honour and a recognition of the consortium’s hardwork - has given way to new collaborations and has increased the project’s visibility among new audiences. It is expected that these collaborations will continue past the project’s lifetime, and new projects may potentially form.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.402 | 0.228 |
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".