Commentary Global biogeography of
Bibliographic record
Abstract
plant chemistry: filling in the blanks It would perhaps come as a surprise to many nonbiological scientists (or even some biologists) to learn that despite our ability to characterize a number of environmental variables, such as climate, along regional or continental gradients, until recently we have had almost no basis for doing so for plant and soil chemistry. New work, including a paper by Han et al. in this issue (pp. 377–385), is beginning to fill in the blanks on this otherwise empty slate. It is well known that long-term climate records exist in a relatively well-distributed network across much, but not all, of the globe. Hence, we are able to quantify the difference in climate between, for example, central Saskatchewan, Canada and central Nebraska, USA but not the differences in plant or soil nutrient concentrations or contents between these two regions. Given the importance of nitrogen (N) and phosphorus (P) to plant function, to production of agricultural and unmanaged ecosystems and to global biogeochemical cycles, including the carbon (C) cycle, one could argue that knowledge of biogeography of their biochemistry is as useful as knowledge of many other kinds, yet it has been little emphasized. Why? ‘ … global heterogeneity in leaf N and P is likely substantial enough that we will require sweepingly comprehensive data sets before we will be able to reconcile differences that may arise owing to differences in intensity of sampling in different ‘ecoregions ’ of the
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.032 | 0.041 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".