Using Physiological Models to Identify the Relative Importance of Plant Adaptations to Environmental Variation, an Appreciation of Heckathorn and DeLucia (1991)
Bibliographic record
Abstract
When seeking an influential paper from a journal's archive, it's not uncommon to gravitate toward a contribution that is thought to impact an entire discipline, perhaps because it finds support for a novel hypothesis that changes how we think about a particular scientific problem, or perhaps simply because it is formative enough to be cited by everyone working on a particular topic.In thinking about the influential papers that have been published by IJPS during its 150-year history, there are many examples of papers that fall into both categories -and several of these have been commemorated in IJPS during the past year (e.g., Mason 2025; Weber and Goodwillie 2025).But there are also papers that could be highlighted for other reasons -reasons that are more relevant to the development of an individual career, rather than for the field at large.In this reflection, I've chosen to highlight one such paper because it happened to be an important influence on me at the beginning of my graduate career, when I was still learning how to be a scientist.It's a paper by Scott Heckathorn and Evan DeLucia, published in 1991, the final year that IJPS was still called by its former name, the Botanical Gazette
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".