mangal - making sense of biotic interaction data
Bibliographic record
Abstract
There are exponentially more species interactions than there are species - and this should entice us to be exponentially more careful when designing a data format to represent species interactions. The tradition in the analysis of species interaction networks was to store matrices, but this format is inefficient, does not offer access to the information in a modular and atomic way, and is poor in meta-data. To help research, faciltiate interaction with other biodiversity data representations, and offer a universal, unified format to represent biotic interactions, we designed mangal.io. At its core, the mangal data format specificies how various component of an interaction should be represented, and the hierarchy between them. In this presentation, we will (i) describe the original data format and explain how it was designed based on ecological principles, (ii) describe the revised data format and why it can function as the standard for biotic interaction data, and (iii) illustrate case studies based on the use of mangal.io, notably related to using AI/machine learning to infer missing biotic interactions and the macroecology of food webs
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".