Bibliographic record
Abstract
Washington Experiment Station Puyallup, Wa shin g t o nWestern Washin g ton and western Or egon appear to be about the only sections of the Unit ed States where filb erts are grown successfully in a commercial way.Commercial plantings in the eastern states have not been profitable.The climate in Wa shin g ton and Oreg on w es t of the Cascade mountain s seems to be excellent for filb ert s.In the Northwes t there are tr ees over forty years old which are still vigorous .The filbert tree and poll en catkin s will u sually with stand zero temperatures but the pistillate or nut b earing blossoms may be injured at 12° to 15° F. This plant prefers t emp eratures below 100°F.and above 15°F.A crop failure in western Wa shington has seldom occurred.Spring frosts and winter rains have not proved harmful.To date no se ri o u s pest s have appeared.Production costs are comparatively low.Th e nut s ripen in late September and early Octo ber, w h en mo s t ot h er crop s are out of the way, and do n o t require careful handlin g, precooling o r cold storage.Mature orchards are n ow producin g profitable crops in the Vancouver, Washington di strict, and certain parts of the Willamette Valley of Ore go n .Young bearin g o rchard s are d o ing well at Sedro Woolley, No o k sack, S ea ttle, Kirkland, Puyallup and in the Chehali s secti on.Th e consumption of nuts a s a g roup , is increasing out of proporti o n to populati o n in cr ease.Th e average per capita yearly consumptio n of filbert s is one-quarter pound.F ilb ert g rowin g combines well with certain other types of farmin g.It is a good di ve r s ification crop.Filberts and berries are harve s ted at diff er ent seasons.Sour cherri es r equire abo ut th e same soil conditions and care but ripen before th e filb ert s.Filberts and poultry supplement each o th er.Not eve r y farm, h oweve r , will have suitable soil.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.631 | 0.530 |
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".