Comparison of Three Methods for the Quantification of Sediment Organic Carbon in Salt Marshes of the Rubicon Estuary, Tasmania, Australia
Bibliographic record
Abstract
With the increasing need to accurately quantify carbon sinks of coastal wetlands, this study compares three methods in use. Sediment cores (n = 4) were collected from three salt marsh sites of the Rubicon Estuary, Tasmania Australia and analysed for organic carbon using three methods for measuring soil C concentration. The first method of elemental analysis quantified carbon using a Thermo Finnigan EA 1112 Series Flash Elemental Analyser. The second method used the loss on ignition technique at 550°C for 4 hours and applied a standard carbon conversion. The third method used loss on ignition technique at 450°C for 8 hours and applied a standard carbon conversion. Results from each method found significantly different quantities of carbon in replicate samples, however each method differentiated similar carbon trends within datasets. Each methodology investigated was found to have a useful application in carbon science, including the broad scale use of the standard carbon conversion and the accuracy of the elemental analysis method. The application of the standard carbon conversion in this study showed an underestimate of carbon stores in salt marsh sediment. Wetlands are well known to be among the most carbon rich ecosystems of the world, however this study highlights that in some cases those carbon stores may have been underestimated, and verification may further increase the value of wetlands as carbon sinks.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".