Mangal: An open infrastructure for ecological interactions
Bibliographic record
Abstract
Interactions among species is at the heart of ecology. Despite their importance, studying ecological interactions remains difficult due to the lack of standard information and the disparity of formats in which ecological interactions are stored (Poisot et al. 2015). Historically, ecologists have used matrices to store interactions, which tend to easily decontextualize interactions from fieldwork when metadata is missing. To overcome these limitations, we designed Mangal - a global ecological interactions database - which serialize ecological interaction matrices into nodes (e.g. taxon, individuals or population) and edges. This database offers the opportunity to store information on traits, environment and homogenized taxonomy through unique taxonomic identifiers such as Encyclopedia of Life (EOL), Catalogue of Life (COL), Global Biodiversity Information Facility (GBIF) and Integrated Taxonomic Information System (ITIS). Here, we present the new release of Mangal including more than 120,000 interactions, 1,300 networks from 172 scientific publications distributed across the globe. We explore the content, illustrate case studies and present templates in order to contribute to this open infrastructure. For this purpose, we developed and maintained two packages/clients from popular scientific languages: R and Julia to facilitate data access, curation and network deposits on the database (Source code).
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".