Soil and permafrost carbon and nitrogen content map of the Herschel Island based on ecological units
Bibliographic record
Abstract
Carbon and nitrogen are two of basic nutrients that control primary production and organism growth in Arctic Seas. Consequently their contents control further geochemical and biological processes including potential releases of carbon dioxide and methane. Nutrients are delivered to sea by coastal erosion and river discharge. Knowing the exact amounts of carbon and nitrogen that are available for transport is crucial for further estimates of nutrient cycling and gas fluxes. \nStudy area is Herschel Island which is situated in the northern part of Yukon Coast. Island and its coasts are highly ground ice rich and thus subjected to rapid coastal erosion and thermokarst processes. Great amounts of sediments, including carbon and nitrogen, are transported to the near-shore zone each year by coastal erosion, retrogressive thaw slump activity and fluvial action. \nFinal goal of research is to produce maps of soil organic carbon and nitrogen. Thirteen cores up to two meters depth were obtained from different ecological units on Herschel Island during the expedition in 2013. Samples are being analysed for CNS contents with combustion method. Carbon and nitrogen values will be averaged for the whole core column. Point data will be extrapolated to the whole island, based on the assumption that contents are homogenous within ecological units. Latter were produced from RapidEye multispectral imagery and training areas that were also collected during expedition in 2013.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".