CONTROLS ON ENERGY AND CARBON FLUXES FROM SELECT HIGH-LATITUDE TERRESTRIAL SURFACES
Bibliographic record
Abstract
While the physical processes driving energy fluxes in the high latitudes are universal, some of the controlling factors such as permafrost, temperature, and vegetation play a special role. Annual net radiation at Arctic treeline is larger over subarctic forest than over tundra as a result of smaller albedo during the snow-cover period. The absorbed solar radiation is notably larger in late winter. During the snow-free period, in well-watered areas, there is a hierarchy in potential evaporation from very high rates for shallow tundra lakes and ponds to low rates for well-drained upland heath terrain. With abundant moisture and warm conditions, open coniferous forests, dwarf deciduous forests, and sedge fens have similar energy and water balances. During the growing season when moisture is limiting, a sedge fen, more so than a coniferous forest, curtails its evaporation rate. Under cold conditions, however, coniferous forest has the smaller evaporation. Soil heat fluxes in summer comprise from 10% to 15% of the net radiation and are fairly uniform both temporally and spatially. The carbon budget of peatlands, which are major global repositories of carbon, responds strongly to air and soil temperatures and to the water balance. Warm and wet conditions support strong photosynthesis and a substantial methane flux. Warm and dry conditions favor strong respiration carbon losses from plants and soil. In a 2 × CO2 world, substantial changes in temperature, precipitation, and energy and water balances are anticipated and these will drive substantial changes in the high-latitude carbon budget. [Key words: energy and carbon fluxes, high latitudes.]
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".