Nordic crop wild relative priority list
Bibliographic record
Abstract
Aim: The aim of this dataset is to provide a list of the Crop Wild Relatives (CWR) in the Nordic region that are most important for future food security, and to provide basic data on geographic distribution, gene pool affinity, invasiveness, and threat level. The dataset can serve as a basis for Nordic level, as well as national level, conservation planning and implementation.Method: A comprehensive CWR checklist for all Nordic CWR taxa was developed in 2017 (Fitzgerald et al., 2017). The taxa on this list were prioritized based on socio-economic value of the related crop(s) and potential utilization value of the CWR for breeding, resulting in the first version of the priority dataset. More information on how the prioritization was performed can be found in Fitzgerald et al. (2019). In 2021, an update of the dataset was made. Nordic scientists and plant breeders were contacted and asked if, in their opinion, there were taxa missing from the dataset. All suggestions were considered and evaluated for socio-economic value and utilization potential. The taxa deemed to fulfill the criteria were added to the list. Also, information on national threat category and national invasive category were added, and information on local names and geographic distribution were updated. Results: The result of the analysis is a list/data set of CWR prioritized based on socio-economic value of the related crop(s) and potential utilization value of the CWR. The list includes information on national occurrence (indigenous, naturalized foreign, temporary findings), to which genepool/taxon group the CWR belongs, use category (food/forage), national threat status and national invasiveness classification.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.024 |
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".