Eleven strategies for getting into graduate school in ecology & evolutionary biology
Bibliographic record
Abstract
Getting into graduate school can be tough if you have not done your homework. I outline eleven strategies for increasing your chances of successfully being accepted into an ecology or evolutionary biology lab. Try to get good grades as an undergraduate, do well on the Graduate Record Exam (if applicable), join a lab reading group or undertake an undergraduate thesis, take time to forge relationships so you can have strong reference writers, obtain relevant work experience, author a publication, read peer-reviewed literature, attend national meetings, come up with some good research ideas, develop a relationship with a potential advisor, and apply to at least ten schools. If you follow these strategies, you have a high probability of getting into graduate school in ecology and evolutionary biology.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".