Species distribution modeling of deep sea sponges in the North Pacific Ocean.
Bibliographic record
Abstract
Knowledge of deep-sea species and their ecosystems is limited due to the inaccessibility of the areas and the prohibitive cost of conducting large-scale field studies. My graduate research has used predictive modeling methods to map hexactinellid sponge habitat extent in the North Pacific, as well as climate-induced changes in oceanic dissolved oxygen levels and how this will impact sponges. Results from a MaxEnt model based on sponge presence data from the eastern Pacific, in conjunction with bathymetric terrain derivatives, closely mapped existing sponge habitats, and suggested a depth threshold around 3000 meters below which sponges are not found. Early results suggest that oxygen is another important predictor of sponge habitat, including this and a variety of other environmental predictors (e.g. based on ocean chemistry, physics and biology) and different model scales would improve model accuracy. The long-term goal of this research is to apply climate prediction data to the predictive modeling in order to assess the sensitivity of deep-sea sponge habitat to global climate changes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".