Bibliographic record
Abstract
INTRODUCTION So far we have dealt with how plant systems work to promote, sustain, and preserve life. But what do we know about the processes leading to decline and death in plants? We tend to imagine death as a process that begins at birth and progresses to an end, sometime. “Lifespan” is the maximum length of time an organism could live if all the conditions of life were at their most favorable; the human lifespan, for example, is about 120 years, but most of us do not expect to be around that long. “Life expectancy” more closely describes the reality. The question is not “How long could I live?” but rather, “How long can I expect to live?” which is dependent on prevailing environmental, social, and cultural conditions. In some parts of the world, human life expectancy may be only 30 or 40 years, about the same as it was some 2500 years ago at the height of ancient Greek culture, whereas in others we know it to be over 80 years. Disease, starvation, predation, accident, and polluted environments are just some of the hazards faced by all living things which affect how long they survive. LIFE HISTORY STRATEGIES All species share one basic aim in life: the survival of at least some individuals to reproductive age is crucial to the passing on of genetic traits to descendants. What is important is the different strategies living things have evolved to achieve this fundamental aim.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".