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 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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".