EMPHASIS – European Multi-environment Plant pHenotyping And Simulation InfraStructure
Bibliographic record
Abstract
The EMPHASIS proposal aim to establish a European Infrastructure within the ESFRI framework, based on national phenotyping infrastructure (Belgium, France, Germany and UK). The partnership will be extended to other European member states.EMPHASIS will develop and provide access to infrastructures addressing multi-scale phenotyping for analysing genotype performance under diverse environmental conditions and quantify the diversity of traits contributing to performance in diverse environmental scenario (i.e. plant architecture, major physiological functions and output, yield components and quality).EMPHASIS will address the technological and organizational limits. EMPHASIS will:• develop an integrated pan-European network of instrumented phenotyping platforms using current and future agro-climatic scenarios.• link data acquisition to a European-level data management system and to state-of-the art crop models to simulate plants and crops in current and future climates.• develop, evaluate and disseminate novel technologies, thereby providing new opportunities for research involved in phenotyping and precision agriculture• make these infrastructures and concepts accessible to European plant science community in academia and industryEMPHASIS infrastructures will include• platforms in (semi-)controlled conditions for high resolution and throughput phenotyping• Intensive field experimental sites• a coordinated network of field experiments with phenotyping infrastructure• Modelling platforms for testing existing or virtual combinations of alleles.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".