Annotating biodiversity data via the Internet
Bibliographic record
Abstract
Abstract Biological specimens in research collections provide the most important baseline information for systematic research. Traditionally, they are annotated by experts in written form, which remains directly associated with the specimens. These annotations, defined as data added at a later stage to the original data, provide an important quality control mechanism. They improve the value of herbarium specimens and are identification trails documenting the development of taxonomic concepts over time. With specimen data increasingly becoming accessible via the Internet, a general online annotation system that ensures that the traditional data sharing and documentation of specimen data is continued after the information is mobilised through digitisation, is currently missing. We lay out the prerequisites for such an annotation system including data standards, a data repository, system access, and user roles. We also introduce an exemplar solution developed in the DFG–funded AnnoSys project. AnnoSys is being implemented using the example of collection and observation data in the botanical domain as provided by the GBIF/BioCASe networks. It provides a user–friendly interface to allow researchers to produce and discover annotations. If a record has been annotated, both the annotation and the original record will be stored in a repository, linked via a persistent identifier, and will be accessible through the AnnoSys interfaces. Collection holders and scientists specifically interested in a subset of data will be informed about annotations in which they have expressed interest. We discuss AnnoSys in relation to the FilteredPush project, which pursues the same goal in facilitating and communicating online annotations, but which takes a different approach.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.180 | 0.027 |
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".