What is the value of agricultural census data in carbon cycle studies?
Bibliographic record
Abstract
[1] Agricultural census data have been identified as possessing the potential to provide constraints on modeled carbon uptake by croplands at the regional scale. In this study, we build on previous efforts and further assess this potential quantitatively by comparing (1) fractional cropland coverage in southwestern Ontario, Canada, derived from agricultural statistics against three different remotely sensed land cover maps and (2) carbon uptakes determined from agricultural data with simulations generated by a satellite-data-driven biospheric model. In addition, we assimilated the census-data-derived carbon uptakes with modeled estimates in a Bayesian inverse approach to determine if the crop data can provide constraint, as exhibited by uncertainty reductions, and if so, how much. Uncertainties in census-data-derived gross primary production (GPP) estimates are carefully quantified using a Monte Carlo simulation. In general, results from the fractional cropland coverage comparison indicate significant value of the agricultural census data by revealing biases in the spatial distribution of croplands, as found in all three of the satellite land cover products. However, we find that the carbon uptake values derived from crop harvested records are still subject to significant uncertainties that have been underestimated or neglected altogether in past studies. The Monte Carlo simulation suggests that the largest source of uncertainty can be traced to errors in the growth efficiency, followed by harvest production records, and then the harvest index. As a result, attention must be paid to such errors when using the agricultural census data for carbon accounting purposes or to provide constraints to simulations of crop carbon uptake.
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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 | 0.070 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".