СТРУКТУРА ГОРЮЧИХ МАТЕРИАЛОВ В СОСНЯКАХ РАЗНОГО ВОЗРАСТА КРАСНОЯРСКОЙ ЛЕСОСТЕПИ
Bibliographic record
Abstract
RUSSIAN JOURNAL OF FOREST SCIENCE, 2017, No. 5, P. 431-436, DOI: 10.7868/S0024114817060055 THE STRUCTURE OF FOREST FUELS IN VARIOUSLY AGED PINE WOODLANDS OF FOREST-STEPPE DOMAIN IN KRASNOYARSK N. M. Kovaleva, R. S. Sobachkin, D. S. Sobachkin, A. E. Petrenko Forest Institute, Siberian Branch of the Russian Academy of Sciences Academgorodok 50 bldg. 28, Krasnoyarsk, 660036, Russia Е-mail: nk-75@mail.ru Received 22 March 2016 Storages and structure of forest fuels were documented in herbs-green mosses pine forests of different age in forest-steppe domain. Storages of forest fuels have grown up to 31.95 t ha -1 (mature pine forest) and 29.43 t ha -1 (middle-aged pine forest) because the last fire took place more than 50 years ago. Forest litter contributed 83.4% (24.5 t ha -1 ) in the middle-aged and 79.3% (25.3 t ha -1 ) in the mature stand. Fall in the mature stand 4.8 t ha -1 (15.1%) increased the one in the middle-aged stand 3.2 t ha -1 (11.0%). Ground cover contributed 2.8% in the middle-aged pine forest and 2.6% in the mature pine forest to total storages of forest fuel. Wooded fall contributed 0.98 t ha -1 (3.1%) in the mature stand, and 0.84 t ha -1 (2.9%) in the middle-aged stand. We found that fire conducting fuels (forest litter, tree waste, mosses) contribute 30.8 t ha -1 in the mature forest, and 28.4 t ha -1 in the middle-aged forest. Long-term period without fire has increased the fire danger in the stands of different ages. Highly intensive and stable surface fire is probable in the mature and middle-aged pine stands. They can cause their serious injury or even death. Keywords: forest fuels, ground cover, forest litter, fall, wooded fall fuels, pine forests. REFERENCES Agroklimaticheskii spravochnik po Krasnoyarskomu krayu i Tuvinskoi avtonomnoi oblasti (Handbook of agroclimatic features in Krasnoyarsk krai and Tuva autonomous oblast), Leningrad: Gidrometeoizdat, 1961, 288 p. Bugaeva K.S., Struktura i dinamika lesnoi rastitel'nosti ''Pogorel'skogo bora'' (Krasnoyarskaya lesostep'). Avtoreferat diss. kand. biol. nauk (Structure and dynamics of forest vegetation in Pogorelskii bor, Krasnoyarsk forest-steppe. Extended abstract of candidate's biol. sci. thesis), Krasnoyarsk: IL SO RAN, 2009, 18 p. Chernykh V.A., Furyaev V.V., Lesnye pozhary v lentochnykh borakh Kulundinskoi stepi (Wildfires in ribbon pine forests in Kulunda steppe), Novosibirsk: Nauka, 2011, 175 p. Evdokimenko M.D., Dinamika lesnoi podstilki v sosnyakakh Zabaikal'ya posle nizovykh pozharov (Dynamics of forest litter after creeping fire in pine forests of Transbaikalia), Rol' podstilki v lesnykh biogeotsenozakh , Krasnoyarsk, 14-16 September 1983, Moscow: Nauka, 1983, pp. 62. 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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".