SimpleSDMLayers.jl and GBIF.jl: A Framework for Species Distribution Modeling in Julia
Bibliographic record
Abstract
Predicting where species should be found in space is a common question in ecology and biogeography. Species distribution models (SDMs), for instance, aim to predict where environmental conditions are suitable for a given species, often on continuous geographic scales. Such analyses require the use of geo-referenced data on species distributions coupled with climate or land cover information, hence a tight integration between environmental data, species occurrence data, and spatial coordinates. Thus, it requires an efficient way to access these different data types within the same software, as well as a flexible framework on which to build various analysis workflows. Here we present SimpleSDMLayers.jl and GBIF.jl, two packages in the Julia language implementing a simple framework and type-system on which to build SDM analyses, as well as providing access to popular data sources for species occurrences and environmental conditions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 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".