Occurrences: Data resources and Biocache-hub
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Atlas of Living Australia (ALA) [*1] framework is an open source infrastructure used to share biodiversity data through severals modules. Adding datasets in ALA is an important step that give access to occurrences. Setting of parameters needs to be accurate in order to correctly view occurrences. Biocache-hub [*2] is an interface that allows research on ingested occurrences by Biocache-store [*3]. It’s an advanced data explorer with filters. This training will be divided in two parts. First part will provide tools and techniques to add datasets, from a csv local resource to a GBIF dataset DWC file, within the administration management of the Collectory module [*4]. It will also present the important steps to link occurrences with datasets and how to update a dataset. Second part, within user view, will present the access to occurrences and options available from a Simple search to a Spatial search.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it