A new way to account for the effect of source-sink spatial relationships in whole plant carbon allocation models
Bibliographic record
Abstract
To improve sourcesink relationship based carbon-allocation models, the basic proportional model was extended to account for a well-known effect of individual source to sink distances: among different sinks of similar characteristics, the more distant from the source, the lower the allocation coefficient. This was achieved through multiplication of the sink strength value by a coefficient that is proportional to a decreasing, simple function of distance, f; the power form was chosen for both simplicity and theoretical reasons. The resulting model was parameterized and evaluated on the empirical allocation matrix of the ECOPHYS model, after grouping together several individual, small sinks of similar nature and close location to remove any phyllotaxy-related bias. Both goodness of fit and predictive value were significantly improved compared with the basic proportional model (f = constant). The f-extended model yielded even better results if segments of different nature or age on the source to sink pathway were assigned different weights in the expression of distance, whereas the default expression of f, with an exponent of 1 and no additive constant, was optimal with no further parameter required. Thus, only 7 parameters (3 for pathway segment weights and 4 for sink strength values) were sufficient to retrieve the original 68 independent experimental allocation coefficients with a reasonable degree of accuracy. Pathway segment weights likely reflect both intrinsic transport pathway properties and situation within the plant architecture; this is discussed in relation to the possibilities of generalization and practical use of the model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".