Bio-logging Data in Darwin Core: Use Cases
Bibliographic record
Abstract
Animal-borne sensor data, along with other types of sensor-based observations, provide a growing volume and proportion of documentation about biodiversity. These data differ from the traditional specimen, sampling and human observation records for which the Taxonomic Database Working Group (TDWG) originally designed the Darwin Core standard. The original intention of the new TDWG Machine Observations Interest Group is to facilitate a body of work combining the informatics expertise of TDWG with that of subject matter experts to document best practice guidelines for applying Darwin Core to bio-logging datasets. This session offers the opportunity to walk through some of the use cases developed so far, including a terrestrial GPS tracking and acceleration dataset from Movebank and a marine acoustic telemetry dataset from the Ocean Tracking Network using stationary as well as mobile acoustic receivers. Through these examples, we will describe the strategy and rationale for the approaches taken to the application of Darwin Core using typical animal tracking scenarios laced with some of the common complexities in bio-logging and other types of machine-based biodiversity observations.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".